System

Abstract

To efficiently arrange cleaning on the basis of requests and schedules of customers of hotels.SOLUTION: A system according to an embodiment comprises a reception unit, an information management unit, a determination unit, and a cleaning arrangement unit. The reception unit receives customer information indicating the requests and schedules of customers of a hotel. The information management unit manages customer information received by the reception unit. The determination unit determines the timing and routes of cleaning of the hotel on the basis of customer information managed by the information management unit. The cleaning arrangement unit arranges cleaning on the basis of the result of determination by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently arrange cleaning based on the requests and schedules of hotel guests.

[0005] The system according to the embodiment aims to efficiently arrange cleaning based on the requests and schedules of hotel guests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an information management unit, a determination unit, and a cleaning arrangement unit. The reception unit receives customer information indicating requests and schedules of hotel customers. The information management unit manages the customer information received by the reception unit. The determination unit determines the timing and route of cleaning for the hotel based on the customer information managed by the information management unit. The cleaning arrangement unit arranges cleaning based on the result of the determination by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently arrange cleaning based on the requests and schedules of hotel guests. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a hotel-specific smart building. This smart building is a system that grasps the requests and schedules of customers, and grasps the congestion situation and the number of times of use by sensors installed at each location. For example, a customer inputs information such as "going out at XX o'clock," "want dinner around XX o'clock," "reservation for hot spring," and "whether or not cleaning is desired." This information is accepted by the reception unit. Next, the customer information accepted by the reception unit is managed by the information management unit. The information management unit centrally manages the requests and schedules of customers and provides necessary information to other departments. Furthermore, based on the customer information managed by the information management unit, the determination unit determines the timing and route of cleaning for the hotel. For example, a schedule is created so that cleaning is performed according to the timing when customers go out. In addition, the optimal route is determined so that the cleaning staff can move efficiently. Based on the result of the determination by the determination unit, the cleaning arrangement unit arranges cleaning. The cleaning arrangement unit issues specific instructions to the cleaning staff to smoothly proceed with the cleaning work. In addition, the reception unit also accepts sensor information output from sensor devices installed at each location in the hotel. The sensor information indicates the congestion situation and the number of times of use, and is managed by the information management unit. The determination unit further optimizes the cleaning timing and route based on the customer information and sensor information. Furthermore, the reservation arrangement unit makes reservations for facilities within the hotel based on the customer information and sensor information. For example, reservations for hot springs and restaurants can be automatically arranged. This mechanism makes it possible to arrange cleaning and make facility reservations according to customer needs, making hotel operations more efficient. It also improves customer satisfaction. As a result, the smart building system can optimize the cleaning timing and route based on customer requests and schedules, enabling efficient cleaning arrangements.

[0029] The smart building system according to the embodiment includes a reception unit, an information management unit, a determination unit, and a cleaning arrangement unit. The reception unit receives customer information indicating the customer's requests and schedule. The customer information includes, for example, a name, contact information, requests, schedule, and the like, but is not limited to such examples. The reception unit can input information such as, for example, a customer's "going out at XX o'clock", "want dinner around XX o'clock", "reservation for hot spring", and "whether or not cleaning is requested". The information management unit manages the customer information received by the reception unit. The information management unit centrally manages the customer's requests and schedules and provides necessary information to other departments. For example, the information management unit stores the customer's requests and schedules in a database so that other departments can access them. The determination unit determines the timing and route of cleaning the hotel based on the customer information managed by the information management unit. For example, the determination unit creates a schedule so that cleaning is performed according to the timing when the customer goes out. In addition, the determination unit determines the optimal route so that the cleaning staff can move efficiently. For example, the determination unit optimizes the movement of the cleaning staff by taking into account the customer's schedule and the arrangement of rooms in the hotel. The cleaning arrangement unit arranges cleaning based on the determination result by the determination unit. The cleaning arrangement unit issues specific instructions to the cleaning staff to smoothly proceed with the cleaning work. For example, the cleaning arrangement unit instructs the cleaning staff as to which rooms to clean and when. In this way, the smart building system according to the embodiment can optimize the timing and route of cleaning based on the customer's requests and schedule, enabling efficient cleaning arrangements.

[0030] The reception unit accepts customer information indicating the customer's requests and schedule. The customer information includes, but is not limited to, for example, a name, contact information, requests, schedule, and the like. The reception unit allows the customer to input information such as "going out at XX o'clock," "wanting dinner around XX o'clock," "reserving a hot spring," and "whether or not cleaning is required." Specifically, the reception unit accepts customer information through an interface such as a tablet terminal, a smartphone app, or a web portal. This allows the customer to easily input their schedule and requests. Furthermore, the reception unit allows the customer to convey requests by voice using voice recognition technology. For example, when a customer gives a voice instruction such as "I want my room cleaned at 10 a.m. tomorrow," the content is automatically converted into text and registered in the system. The reception unit also has a function of learning the customer's past usage history and preferences and automatically making suggestions the next time the customer uses the service. For example, for a customer who has frequently made hot spring reservations in the past, the option of hot spring reservations is automatically presented at the next check-in. This allows the reception unit to improve the convenience of the customer and increase customer satisfaction.

[0031] The information management department manages the customer information received by the reception department. The information management department centrally manages customer requests and schedules and provides necessary information to other departments. Specifically, the information management department stores customer information in a database and ensures that it is updated in real time. The database is secured and uses encryption technology to prevent leakage of customer information. The information management department also analyzes customer information and understands trends and patterns to help manage the hotel. For example, if there is a tendency for cleaning requests to be concentrated during a specific time period, measures such as increasing the number of cleaning staff during that time period can be taken. Furthermore, the information management department shares data through APIs to strengthen cooperation with other departments. This allows the cleaning arrangement department and decision department to quickly access the information they need. For example, the cleaning arrangement department can check the latest schedule of the customer and adjust the timing of cleaning. This allows the information management department to centrally manage customer information and efficiently share information, thereby improving the operational efficiency of the entire hotel.

[0032] The determination unit determines the timing and route of cleaning in the hotel based on the customer information managed by the information management unit. For example, the determination unit creates a schedule to perform cleaning according to the timing when the customer goes out. In addition, the determination unit determines the optimal route so that the cleaning staff can move efficiently. Specifically, the determination unit uses AI to analyze the customer's schedule and the layout of the rooms in the hotel and calculates the optimal cleaning route. The AI ​​simulates the movement of the cleaning staff based on past data and the current situation and proposes the most efficient route. For example, the AI ​​optimizes the route so that the cleaning staff can move efficiently, taking into account the time of day when the customer goes out and the relative positions of the rooms. In addition, the determination unit also takes into account the schedule and working hours of the cleaning staff and makes adjustments to prevent overwork. As a result, the determination unit can provide a flexible cleaning schedule according to the customer's requests and balance the efficiency and ease of work of the cleaning staff.

[0033] The cleaning arrangement department arranges cleaning based on the result of the decision by the decision department. The cleaning arrangement department issues specific instructions to the cleaning staff to smoothly proceed with the cleaning work. Specifically, the cleaning arrangement department instructs the cleaning staff which rooms to clean and when. The instructions are given in real time through a smartphone or tablet terminal, so that the cleaning staff can always keep up to date with the latest information. In addition, the cleaning arrangement department monitors the progress of the cleaning staff and provides support as needed. For example, if cleaning is delayed or additional cleaning is required, the cleaning arrangement department responds promptly and issues additional instructions to the cleaning staff. In addition, the cleaning arrangement department performs regular checks and feedback to ensure the quality of cleaning. For example, it randomly checks rooms that have been cleaned to confirm that the quality meets the standards. In addition, it collects feedback from customers and provides feedback to the cleaning staff to continuously improve the quality of cleaning. In this way, the cleaning arrangement department can achieve efficient and high-quality cleaning work and increase customer satisfaction.

[0034] The determination unit can determine the placement of cleaning staff in the hotel based on the customer information managed by the information management unit. The determination unit optimizes the placement of cleaning staff based on, for example, the customer's requests and schedules. For example, the determination unit places cleaning staff according to the timing when the customer goes out. The determination unit can also determine the optimal placement taking into consideration the skills of the cleaning staff and the difficulty of cleaning the room. For example, the determination unit assigns the cleaning staff to rooms with high difficulty levels according to their skills. The determination unit can also determine an efficient placement taking into consideration the working hours of the cleaning staff. For example, the determination unit adjusts the timing of cleaning according to the working hours of the cleaning staff. This optimizes the placement of cleaning staff based on customer information, enabling efficient cleaning work. Part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the placement of cleaning staff using an AI model that inputs customer information and outputs the placement of cleaning staff.

[0035] The reception unit can receive sensor information output from a sensor device installed at each location in the hotel. The sensor information includes, for example, temperature, humidity, motion detection, and sound, but is not limited to such examples. The reception unit receives, for example, sensor information output from a sensor device installed at each location in the hotel. The information management unit manages the sensor information received by the reception unit. The information management unit stores the sensor information in a database so that other departments can access it. The determination unit determines the timing and route of cleaning the hotel based on the customer information and the sensor information. The determination unit optimizes the timing and route of cleaning, for example, based on the sensor information, taking into account the congestion situation and the number of times of use. For example, the determination unit creates a schedule to avoid crowded places and perform cleaning. The determination unit can also determine a route to give priority to cleaning places that are frequently used. This makes it possible to further optimize the timing and route of cleaning by utilizing the sensor information, and to make efficient cleaning arrangements. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs sensor information and outputs the timing and route of cleaning.

[0036] The reservation arrangement unit can make reservations for facilities in the hotel based on customer information and sensor information. The reservation arrangement unit automatically arranges reservations for hot springs and restaurants based on customer information and sensor information, for example. For example, the reservation arrangement unit can input information such as "I would like to have dinner around XX o'clock" and "Hot spring reservation". The reservation arrangement unit makes reservations for the most suitable facility according to the customer's request. For example, the reservation arrangement unit makes reservations for hot springs and restaurants according to the customer's schedule. The reservation arrangement unit can also make reservations for the most suitable facility based on the sensor information, taking into account the congestion situation and number of times of use. For example, the reservation arrangement unit makes reservations by avoiding busy time periods. The reservation arrangement unit can also give priority to reservations for facilities that are used frequently. This makes it possible to automatically arrange reservations for facilities using customer information and sensor information, and provide services according to the needs of customers. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation arrangement unit can make reservations for facilities using an AI model that inputs customer information and sensor information and outputs reservations for the facilities.

[0037] The sensor device can output the congestion status and number of uses of the place where it is installed. For example, the sensor device outputs the number of people, the duration of stay, the congestion level of the flow line, etc., in order to evaluate the congestion status. The sensor device can also output the number of triggers of the sensor and the records of check-in and check-out in order to count the number of uses. For example, the sensor device is installed at each location in a hotel and outputs the congestion status and the number of uses in real time. As a result, the sensor device outputs the congestion status and the number of uses, making it easier to grasp the usage status of the facility. Part or all of the above-mentioned processing in the sensor device may be performed, for example, using AI, or may be performed without using AI. For example, the sensor device can output the congestion status and the number of uses using an AI model that inputs the congestion status and the number of uses and outputs the congestion level and the frequency of use.

[0038] The reception unit can receive customer information by an AI installed in the guest room of the customer. The reception unit, for example, receives customer information using an AI installed in the guest room of the customer. The AI ​​includes, for example, a voice recognition AI, a natural language processing AI, an image recognition AI, and the like, but is not limited to such examples. For example, the reception unit can allow the customer to input information such as "going out at XX o'clock", "want dinner around XX o'clock", "make a reservation for a hot spring", and "whether or not cleaning is desired" to the AI ​​in the guest room. The AI ​​recognizes the customer's requests and schedule, and provides the information to the reception unit. As a result, the convenience of the customer is improved by receiving customer information using the AI ​​installed in the guest room. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can receive customer information using an AI model that manages customer information, with customer information acquired from the AI ​​in the guest room as input.

[0039] The reception unit can analyze the customer's past request history and select the optimal reception method. For example, the reception unit automatically displays requests that the customer frequently input in the past as candidates. The reception unit can also preferentially suggest reception methods (such as voice and text) that the customer has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period from the customer's past request history. This improves customer convenience by selecting the optimal reception method using the customer's past request history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select a reception method using an AI model that inputs the customer's past request history and outputs the optimal reception method.

[0040] The reception unit can perform filtering based on the customer's current stay status and areas of interest when receiving a request. For example, if the customer is currently staying in a hotel, the reception unit preferentially displays services available during the customer's stay. The reception unit can also suggest related services and activities based on the customer's areas of interest. Furthermore, the reception unit can filter and display available time slots and locations according to the customer's stay status. This makes it possible to provide more appropriate services by filtering based on the customer's stay status and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the customer's stay status and areas of interest and outputs the filtering results.

[0041] When receiving a request, the reception unit can preferentially receive a highly relevant request in consideration of the geographical location information of the customer. For example, when the customer is in a specific area in the hotel, the reception unit preferentially suggests services available in that area. In addition, when the customer is outside the hotel, the reception unit can also preferentially provide information on nearby tourist attractions and restaurants. Furthermore, the reception unit can also suggest optimal routes and transportation means based on the customer's location information. As a result, by receiving a request in consideration of the geographical location information of the customer, more appropriate service can be provided. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can receive a request using an AI model that inputs the geographical location information of the customer and outputs a highly relevant request.

[0042] The reception unit can analyze the social media activity of the customer when receiving the request and receive the related request. The reception unit can, for example, suggest related services based on information shared by the customer on social media. The reception unit can also suggest activities that the customer may be interested in based on the content of the customer's social media posts. Furthermore, the reception unit can also suggest related requests based on services used by the customer's social media followers and friends. This makes it possible to provide more appropriate services by analyzing the customer's social media activity and receiving the request. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or without using AI. For example, the reception unit can receive the request using an AI model that inputs the customer's social media activity and outputs related requests.

[0043] The information management unit can select an optimal management method by referring to the customer's past behavior history during information management. For example, the information management unit preferentially displays services that the customer has frequently used in the past. The information management unit can also predict and suggest services to be used in a specific time period from the customer's past behavior history. Furthermore, the information management unit can analyze the customer's past behavior patterns and select an optimal information display method. As a result, more appropriate information management is possible by managing information by referring to the customer's past behavior history. A part or all of the above-mentioned processing in the information management unit may be performed, for example, using AI or may be performed without using AI. For example, the information management unit can manage information using an AI model that inputs the customer's past behavior history and outputs an optimal management method.

[0044] The information management unit can determine the priority of information based on the customer's current stay status during information management. For example, when a customer is staying in a hotel, the information management unit preferentially displays services available during the customer's stay. The information management unit can also filter and display available time slots and locations according to the customer's stay status. Furthermore, the information management unit can select an optimal information display method based on the customer's stay status. This enables more appropriate information management by determining the priority of information based on the customer's stay status. A part or all of the above-mentioned processing in the information management unit may be performed, for example, using AI or may be performed without using AI. For example, the information management unit can manage information using an AI model that inputs the customer's stay status and outputs the priority of information.

[0045] The information management unit can select an optimal management method in consideration of the geographical location information of the customer when managing information. For example, when the customer is in a specific area in the hotel, the information management unit preferentially displays services available in that area. In addition, when the customer is outside the hotel, the information management unit can also preferentially provide information on nearby tourist attractions and restaurants. Furthermore, the information management unit can select an optimal information display method based on the customer's location information. This allows for more appropriate information management by managing information in consideration of the customer's geographical location information. Part or all of the above-mentioned processing in the information management unit may be performed, for example, using AI, or may be performed without using AI. For example, the information management unit can manage information using an AI model that inputs the customer's geographical location information and outputs an optimal management method.

[0046] The information management unit can adjust the information management method by analyzing the social media activity of the customer during information management. For example, the information management unit can suggest related services based on information shared by the customer on social media. The information management unit can also suggest activities that the customer may be interested in based on the content of the customer's social media posts. Furthermore, the information management unit can also suggest related information based on services used by the customer's social media followers and friends. This enables more appropriate information management by analyzing the customer's social media activity and managing the information. Some or all of the above-mentioned processing in the information management unit may be performed, for example, using AI or may be performed without using AI. For example, the information management unit can manage the information using an AI model that uses the customer's social media activity as an input and adjusts the information management method.

[0047] The determination unit can select the optimal determination method by referring to the customer's past behavior history when determining the timing and route of cleaning. For example, the determination unit can suggest the optimal timing based on the cleaning timing used by the customer in the past. The determination unit can also suggest a cleaning route that avoids congestion from the customer's past behavior history. Furthermore, the determination unit can analyze the customer's past behavior history and suggest the most efficient cleaning route. As a result, more efficient cleaning is possible by determining the timing and route of cleaning by referring to the customer's past behavior history. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the customer's past behavior history and outputs the timing and route of cleaning.

[0048] The determination unit can adjust the determination method based on the customer's current stay status when determining the timing and route of cleaning. For example, if the customer is staying in the hotel, the determination unit adjusts so that cleaning is not performed during the customer's stay. The determination unit can also determine the optimal cleaning timing and route according to the customer's stay status. Furthermore, the determination unit can also optimize the movement of the cleaning staff based on the customer's stay status. This enables more appropriate cleaning by determining the timing and route of cleaning based on the customer's stay status. Part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the customer's stay status and outputs the timing and route of cleaning.

[0049] The determination unit can select an optimal determination method by taking into consideration the geographical location information of the customer when determining the timing and route of cleaning. For example, when the customer is in a specific area in the hotel, the determination unit prioritizes cleaning of that area. In addition, when the customer is outside the hotel, the determination unit can also complete cleaning before the customer returns. Furthermore, the determination unit can determine an optimal cleaning route based on the location information of the customer. This enables more efficient cleaning by determining the timing and route of cleaning in consideration of the geographical location information of the customer. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the geographical location information of the customer and outputs the timing and route of cleaning.

[0050] The determination unit can adjust the determination method by analyzing the social media activity of the customer when determining the timing and route of cleaning. For example, the determination unit prioritizes cleaning of related areas based on information shared by the customer on social media. The determination unit can also prioritize cleaning of areas that are likely to be of interest to the customer based on the content of the customer's social media posts. Furthermore, the determination unit can also prioritize cleaning of related areas based on areas used by the customer's social media followers and friends. This allows for more appropriate cleaning by determining the timing and route of cleaning by analyzing the social media activity of the customer. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the social media activity of the customer and outputs the timing and route of cleaning.

[0051] The cleaning arrangement unit can select the optimal arrangement method by referring to the customer's past behavior history when arranging cleaning. For example, the cleaning arrangement unit proposes the optimal arrangement method based on the cleaning arrangement method used by the customer in the past. The cleaning arrangement unit can also predict and propose the cleaning arrangement method to be used in a specific time period from the customer's past behavior history. Furthermore, the cleaning arrangement unit can analyze the customer's past behavior pattern and select the optimal cleaning arrangement method. As a result, more efficient cleaning arrangements can be made by making cleaning arrangements by referring to the customer's past behavior history. Part or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the customer's past behavior history and outputs the optimal arrangement method.

[0052] The cleaning arrangement unit can adjust the arrangement method based on the customer's current stay status when arranging cleaning. For example, if a customer is staying in a hotel, the cleaning arrangement unit arranges not to perform cleaning during the customer's stay. The cleaning arrangement unit can also select the optimal cleaning arrangement method according to the customer's stay status. Furthermore, the cleaning arrangement unit can also optimize the movement of the cleaning staff based on the customer's stay status. This makes it possible to make more appropriate cleaning arrangements by making cleaning arrangements based on the customer's stay status. Some or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the customer's stay status and outputs the arrangement method.

[0053] The cleaning arrangement unit can select the optimal arrangement method in consideration of the geographical location information of the customer when arranging cleaning. For example, when a customer is in a specific area in the hotel, the cleaning arrangement unit prioritizes cleaning of that area. In addition, when a customer is outside the hotel, the cleaning arrangement unit can also complete cleaning before the customer returns. Furthermore, the cleaning arrangement unit can select the optimal cleaning arrangement method based on the location information of the customer. This makes it possible to make more efficient cleaning arrangements by making cleaning arrangements in consideration of the geographical location information of the customer. Part or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the geographical location information of the customer and outputs the optimal arrangement method.

[0054] The cleaning arrangement unit can adjust the arrangement method by analyzing the social media activity of the customer when arranging cleaning. For example, the cleaning arrangement unit prioritizes cleaning of related areas based on information shared by the customer on social media. The cleaning arrangement unit can also prioritize cleaning of areas that are likely to be of interest to the customer based on the content of the customer's social media posts. Furthermore, the cleaning arrangement unit can also prioritize cleaning of related areas based on areas used by the customer's social media followers and friends. This makes it possible to make more appropriate cleaning arrangements by analyzing the customer's social media activity and making cleaning arrangements. Part or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the customer's social media activity and outputs an arrangement method.

[0055] The reservation arrangement unit can select the optimal arrangement method by referring to the customer's past behavior history when making a reservation arrangement. The reservation arrangement unit, for example, proposes the optimal arrangement method based on the reservation arrangement method used by the customer in the past. The reservation arrangement unit can also predict and propose the reservation arrangement method to be used in a specific time period from the customer's past behavior history. Furthermore, the reservation arrangement unit can analyze the customer's past behavior pattern and select the optimal reservation arrangement method. As a result, more efficient reservation arrangement is possible by making reservation arrangements by referring to the customer's past behavior history. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make reservation arrangements using an AI model that inputs the customer's past behavior history and outputs the optimal arrangement method.

[0056] The reservation arrangement unit can adjust the arrangement method based on the customer's current stay status when making a reservation arrangement. For example, when a customer is staying in a hotel, the reservation arrangement unit preferentially reserves facilities that are available during the customer's stay. The reservation arrangement unit can also select the optimal reservation arrangement method according to the customer's stay status. Furthermore, the reservation arrangement unit can also determine the priority of reservation arrangement based on the customer's stay status. This makes it possible to make more appropriate reservation arrangements by making reservation arrangements based on the customer's stay status. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make reservation arrangements using an AI model that inputs the customer's stay status and outputs an arrangement method.

[0057] The reservation arrangement unit can select the optimal arrangement method in consideration of the geographical location information of the customer when making a reservation. For example, when the customer is in a specific area of ​​the hotel, the reservation arrangement unit preferentially reserves facilities available in that area. In addition, when the customer is outside the hotel, the reservation arrangement unit can also preferentially provide information on nearby tourist attractions and restaurants. Furthermore, the reservation arrangement unit can select the optimal reservation arrangement method based on the customer's location information. This makes it possible to make a reservation arrangement more efficiently by making a reservation arrangement in consideration of the customer's geographical location information. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make a reservation arrangement using an AI model that inputs the customer's geographical location information and outputs the optimal arrangement method.

[0058] The reservation arrangement unit can adjust the arrangement method by analyzing the social media activity of the customer when making the reservation arrangement. For example, the reservation arrangement unit preferentially reserves related facilities based on information shared by the customer on social media. The reservation arrangement unit can also preferentially reserve facilities that the customer is likely to be interested in based on the content of the customer's social media posts. Furthermore, the reservation arrangement unit can also preferentially reserve related facilities based on facilities used by the customer's social media followers and friends. This makes it possible to make more appropriate reservation arrangements by analyzing the customer's social media activity and making the reservation arrangement. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make the reservation arrangement using an AI model that inputs the customer's social media activity and outputs the arrangement method.

[0059] When collecting sensor information, the sensor device can select the optimal collection method by referring to the customer's past behavior history. The sensor device, for example, proposes the optimal collection method based on sensor information used by the customer in the past. The sensor device can also predict and propose sensor information to be used in a specific time period from the customer's past behavior history. Furthermore, the sensor device can analyze the customer's past behavior pattern and select the optimal collection method for sensor information. This makes it possible to collect information more efficiently by collecting sensor information by referring to the customer's past behavior history. Some or all of the above-mentioned processing in the sensor device may be performed, for example, using AI or may be performed without using AI. For example, the sensor device can collect sensor information using an AI model that inputs the customer's past behavior history and outputs the optimal collection method.

[0060] When collecting sensor information, the sensor device can select the optimal collection method in consideration of the geographical location information of the customer. For example, when the customer is in a specific area in the hotel, the sensor device preferentially collects sensor information of that area. In addition, when the customer is outside the hotel, the sensor device can also preferentially collect nearby sensor information. Furthermore, the sensor device can select the optimal collection method of sensor information based on the location information of the customer. This allows for more efficient information collection by collecting sensor information in consideration of the geographical location information of the customer. Part or all of the above-mentioned processing in the sensor device may be performed, for example, using AI or may be performed without using AI. For example, the sensor device can collect sensor information using an AI model that inputs the geographical location information of the customer and outputs the optimal collection method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The reception unit can analyze the customer's past behavior history and select the optimal reception method. For example, requests that the customer frequently input in the past are automatically displayed as candidates. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period from the customer's past request history. This improves customer convenience by selecting the optimal reception method using the customer's past request history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select a reception method using an AI model that inputs the customer's past request history and outputs the optimal reception method.

[0063] The reception unit can perform filtering based on the customer's current stay status and areas of interest when receiving a request. For example, if the customer is currently staying in a hotel, the reception unit preferentially displays services available during the customer's stay. The reception unit can also suggest related services and activities based on the customer's areas of interest. Furthermore, the reception unit can filter and display available time slots and locations according to the customer's stay status. This makes it possible to provide more appropriate services by filtering based on the customer's stay status and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the customer's stay status and areas of interest and outputs the filtering results.

[0064] The information management unit can select the optimal management method by referring to the customer's past behavior history when managing information. For example, the information management unit preferentially displays services that the customer has frequently used in the past. The information management unit can also predict and suggest services to be used in a specific time period from the customer's past behavior history. Furthermore, the information management unit can analyze the customer's past behavior patterns and select the optimal information display method. As a result, more appropriate information management is possible by managing information by referring to the customer's past behavior history. A part or all of the above-mentioned processing in the information management unit may be performed, for example, using AI or may be performed without using AI. For example, the information management unit can manage information using an AI model that inputs the customer's past behavior history and outputs the optimal management method.

[0065] When determining the timing and route of cleaning, the determination unit can select the optimal determination method by referring to the customer's past behavior history. For example, the determination unit can suggest the optimal timing based on the cleaning timing used by the customer in the past. The determination unit can also suggest a cleaning route that avoids congestion from the customer's past behavior history. Furthermore, the determination unit can analyze the customer's past behavior history and suggest the most efficient cleaning route. As a result, more efficient cleaning is possible by determining the timing and route of cleaning by referring to the customer's past behavior history. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the customer's past behavior history and outputs the timing and route of cleaning.

[0066] The cleaning arrangement unit can adjust the arrangement method based on the customer's current stay status when arranging cleaning. For example, if the customer is staying in the hotel, it arranges not to clean during the customer's stay. The cleaning arrangement unit can also select the optimal cleaning arrangement method according to the customer's stay status. Furthermore, the cleaning arrangement unit can also optimize the movement of the cleaning staff based on the customer's stay status. This makes it possible to make more appropriate cleaning arrangements by making cleaning arrangements based on the customer's stay status. Some or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the customer's stay status and outputs the arrangement method.

[0067] The reservation arrangement unit can select the optimal arrangement method in consideration of the geographical location information of the customer when making a reservation. For example, when the customer is in a specific area in the hotel, the reservation arrangement unit can preferentially reserve facilities available in that area. In addition, when the customer is outside the hotel, the reservation arrangement unit can also preferentially provide information on nearby tourist attractions and restaurants. Furthermore, the reservation arrangement unit can select the optimal reservation arrangement method based on the customer's location information. This allows for more efficient reservation arrangement by making reservation arrangements in consideration of the customer's geographical location information. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make reservation arrangements using an AI model that inputs the customer's geographical location information and outputs the optimal arrangement method.

[0068] The process flow of the first embodiment will be briefly described below.

[0069] Step 1: The reception department accepts customer information indicating the customer's requests and schedule. Customer information includes name, contact information, requests, schedule, etc. For example, a customer can enter information such as "going out at x o'clock," "preferring dinner around x o'clock," "reserving a hot spring bath," and "whether or not cleaning is required." Step 2: The information management department manages the customer information received by the reception department. The information management department centrally manages customer requests and schedules and provides necessary information to other departments. For example, the information management department stores customer requests and schedules in a database so that other departments can access them. Step 3: The decision unit determines the timing and route for cleaning the hotel based on the customer information managed by the information management unit. For example, the decision unit creates a schedule to match cleaning with when customers will be leaving the hotel. It also determines the optimal route for cleaning staff to move efficiently. The movement of cleaning staff is optimized taking into account customer schedules and the layout of rooms in the hotel. Step 4: The cleaning arrangement unit arranges cleaning based on the result of the decision made by the decision unit. The cleaning arrangement unit issues specific instructions to the cleaning staff to ensure smooth cleaning work. For example, the cleaning staff is instructed on which rooms to clean and when to clean them.

[0070] (Example 2) The system according to the embodiment of the present invention is a hotel-specific smart building. This smart building is a system that grasps the requests and schedules of customers, and grasps the congestion situation and the number of times of use by sensors installed at each location. For example, a customer inputs information such as "going out at XX o'clock," "want dinner around XX o'clock," "reservation for hot spring," and "whether or not cleaning is desired." This information is accepted by the reception unit. Next, the customer information accepted by the reception unit is managed by the information management unit. The information management unit centrally manages the requests and schedules of customers and provides necessary information to other departments. Furthermore, based on the customer information managed by the information management unit, the determination unit determines the timing and route of cleaning for the hotel. For example, a schedule is created so that cleaning is performed according to the timing when customers go out. In addition, the optimal route is determined so that the cleaning staff can move efficiently. Based on the result of the determination by the determination unit, the cleaning arrangement unit arranges cleaning. The cleaning arrangement unit issues specific instructions to the cleaning staff to smoothly proceed with the cleaning work. In addition, the reception unit also accepts sensor information output from sensor devices installed at each location in the hotel. The sensor information indicates the congestion situation and the number of times of use, and is managed by the information management unit. The determination unit further optimizes the cleaning timing and route based on the customer information and sensor information. Furthermore, the reservation arrangement unit makes reservations for facilities within the hotel based on the customer information and sensor information. For example, reservations for hot springs and restaurants can be automatically arranged. This mechanism makes it possible to arrange cleaning and make facility reservations according to customer needs, making hotel operations more efficient. It also improves customer satisfaction. As a result, the smart building system can optimize the cleaning timing and route based on customer requests and schedules, enabling efficient cleaning arrangements.

[0071] The smart building system according to the embodiment includes a reception unit, an information management unit, a determination unit, and a cleaning arrangement unit. The reception unit receives customer information indicating the customer's requests and schedule. The customer information includes, for example, a name, contact information, requests, schedule, and the like, but is not limited to such examples. The reception unit can input information such as, for example, a customer's "going out at XX o'clock", "want dinner around XX o'clock", "reservation for hot spring", and "whether or not cleaning is requested". The information management unit manages the customer information received by the reception unit. The information management unit centrally manages the customer's requests and schedules and provides necessary information to other departments. For example, the information management unit stores the customer's requests and schedules in a database so that other departments can access them. The determination unit determines the timing and route of cleaning the hotel based on the customer information managed by the information management unit. For example, the determination unit creates a schedule so that cleaning is performed according to the timing when the customer goes out. In addition, the determination unit determines the optimal route so that the cleaning staff can move efficiently. For example, the determination unit optimizes the movement of the cleaning staff by taking into account the customer's schedule and the arrangement of rooms in the hotel. The cleaning arrangement unit arranges cleaning based on the determination result by the determination unit. The cleaning arrangement unit issues specific instructions to the cleaning staff to smoothly proceed with the cleaning work. For example, the cleaning arrangement unit instructs the cleaning staff as to which rooms to clean and when. In this way, the smart building system according to the embodiment can optimize the timing and route of cleaning based on the customer's requests and schedule, enabling efficient cleaning arrangements.

[0072] The reception unit accepts customer information indicating the customer's requests and schedule. The customer information includes, but is not limited to, for example, a name, contact information, requests, schedule, and the like. The reception unit allows the customer to input information such as "going out at XX o'clock," "wanting dinner around XX o'clock," "reserving a hot spring," and "whether or not cleaning is required." Specifically, the reception unit accepts customer information through an interface such as a tablet terminal, a smartphone app, or a web portal. This allows the customer to easily input their schedule and requests. Furthermore, the reception unit allows the customer to convey requests by voice using voice recognition technology. For example, when a customer gives a voice instruction such as "I want my room cleaned at 10 a.m. tomorrow," the content is automatically converted into text and registered in the system. The reception unit also has a function of learning the customer's past usage history and preferences and automatically making suggestions the next time the customer uses the service. For example, for a customer who has frequently made hot spring reservations in the past, the option of hot spring reservations is automatically presented at the next check-in. This allows the reception unit to improve the convenience of the customer and increase customer satisfaction.

[0073] The information management department manages the customer information received by the reception department. The information management department centrally manages customer requests and schedules and provides necessary information to other departments. Specifically, the information management department stores customer information in a database and ensures that it is updated in real time. The database is secured and uses encryption technology to prevent leakage of customer information. The information management department also analyzes customer information and understands trends and patterns to help manage the hotel. For example, if there is a tendency for cleaning requests to be concentrated during a specific time period, measures such as increasing the number of cleaning staff during that time period can be taken. Furthermore, the information management department shares data through APIs to strengthen cooperation with other departments. This allows the cleaning arrangement department and decision department to quickly access the information they need. For example, the cleaning arrangement department can check the latest schedule of the customer and adjust the timing of cleaning. This allows the information management department to centrally manage customer information and efficiently share information, thereby improving the operational efficiency of the entire hotel.

[0074] The determination unit determines the timing and route of cleaning in the hotel based on the customer information managed by the information management unit. For example, the determination unit creates a schedule to perform cleaning according to the timing when the customer goes out. In addition, the determination unit determines the optimal route so that the cleaning staff can move efficiently. Specifically, the determination unit uses AI to analyze the customer's schedule and the layout of the rooms in the hotel and calculates the optimal cleaning route. The AI ​​simulates the movement of the cleaning staff based on past data and the current situation and proposes the most efficient route. For example, the AI ​​optimizes the route so that the cleaning staff can move efficiently, taking into account the time of day when the customer goes out and the relative positions of the rooms. In addition, the determination unit also takes into account the schedule and working hours of the cleaning staff and makes adjustments to prevent overwork. As a result, the determination unit can provide a flexible cleaning schedule according to the customer's requests and balance the efficiency and ease of work of the cleaning staff.

[0075] The cleaning arrangement department arranges cleaning based on the result of the decision by the decision department. The cleaning arrangement department issues specific instructions to the cleaning staff to smoothly proceed with the cleaning work. Specifically, the cleaning arrangement department instructs the cleaning staff which rooms to clean and when. The instructions are given in real time through a smartphone or tablet terminal, so that the cleaning staff can always keep up to date with the latest information. In addition, the cleaning arrangement department monitors the progress of the cleaning staff and provides support as needed. For example, if cleaning is delayed or additional cleaning is required, the cleaning arrangement department responds promptly and issues additional instructions to the cleaning staff. In addition, the cleaning arrangement department performs regular checks and feedback to ensure the quality of cleaning. For example, it randomly checks rooms that have been cleaned to confirm that the quality meets the standards. In addition, it collects feedback from customers and provides feedback to the cleaning staff to continuously improve the quality of cleaning. In this way, the cleaning arrangement department can achieve efficient and high-quality cleaning work and increase customer satisfaction.

[0076] The determination unit can determine the placement of cleaning staff in the hotel based on the customer information managed by the information management unit. The determination unit optimizes the placement of cleaning staff based on, for example, the customer's requests and schedules. For example, the determination unit places cleaning staff according to the timing when the customer goes out. The determination unit can also determine the optimal placement taking into consideration the skills of the cleaning staff and the difficulty of cleaning the room. For example, the determination unit assigns the cleaning staff to rooms with high difficulty levels according to their skills. The determination unit can also determine an efficient placement taking into consideration the working hours of the cleaning staff. For example, the determination unit adjusts the timing of cleaning according to the working hours of the cleaning staff. This optimizes the placement of cleaning staff based on customer information, enabling efficient cleaning work. Part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the placement of cleaning staff using an AI model that inputs customer information and outputs the placement of cleaning staff.

[0077] The reception unit can receive sensor information output from a sensor device installed at each location in the hotel. The sensor information includes, for example, temperature, humidity, motion detection, and sound, but is not limited to such examples. The reception unit receives, for example, sensor information output from a sensor device installed at each location in the hotel. The information management unit manages the sensor information received by the reception unit. The information management unit stores the sensor information in a database so that other departments can access it. The determination unit determines the timing and route of cleaning the hotel based on the customer information and the sensor information. The determination unit optimizes the timing and route of cleaning, for example, based on the sensor information, taking into account the congestion situation and the number of times of use. For example, the determination unit creates a schedule to avoid crowded places and perform cleaning. The determination unit can also determine a route to give priority to cleaning places that are frequently used. This makes it possible to further optimize the timing and route of cleaning by utilizing the sensor information, and to make efficient cleaning arrangements. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs sensor information and outputs the timing and route of cleaning.

[0078] The reservation arrangement unit can make reservations for facilities in the hotel based on customer information and sensor information. The reservation arrangement unit automatically arranges reservations for hot springs and restaurants based on customer information and sensor information, for example. For example, the reservation arrangement unit can input information such as "I would like to have dinner around XX o'clock" and "Hot spring reservation". The reservation arrangement unit makes reservations for the most suitable facility according to the customer's request. For example, the reservation arrangement unit makes reservations for hot springs and restaurants according to the customer's schedule. The reservation arrangement unit can also make reservations for the most suitable facility based on the sensor information, taking into account the congestion situation and number of times of use. For example, the reservation arrangement unit makes reservations by avoiding busy time periods. The reservation arrangement unit can also give priority to reservations for facilities that are used frequently. This makes it possible to automatically arrange reservations for facilities using customer information and sensor information, and provide services according to the needs of customers. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation arrangement unit can make reservations for facilities using an AI model that inputs customer information and sensor information and outputs reservations for the facilities.

[0079] The sensor device can output the congestion status and number of uses of the place where it is installed. For example, the sensor device outputs the number of people, the duration of stay, the congestion level of the flow line, etc., in order to evaluate the congestion status. The sensor device can also output the number of triggers of the sensor and the records of check-in and check-out in order to count the number of uses. For example, the sensor device is installed at each location in a hotel and outputs the congestion status and the number of uses in real time. As a result, the sensor device outputs the congestion status and the number of uses, making it easier to grasp the usage status of the facility. Part or all of the above-mentioned processing in the sensor device may be performed, for example, using AI, or may be performed without using AI. For example, the sensor device can output the congestion status and the number of uses using an AI model that inputs the congestion status and the number of uses and outputs the congestion level and the frequency of use.

[0080] The reception unit can receive customer information by an AI installed in the guest room of the customer. The reception unit, for example, receives customer information using an AI installed in the guest room of the customer. The AI ​​includes, for example, a voice recognition AI, a natural language processing AI, an image recognition AI, and the like, but is not limited to such examples. For example, the reception unit can allow the customer to input information such as "going out at XX o'clock", "want dinner around XX o'clock", "make a reservation for a hot spring", and "whether or not cleaning is desired" to the AI ​​in the guest room. The AI ​​recognizes the customer's requests and schedule, and provides the information to the reception unit. As a result, the convenience of the customer is improved by receiving customer information using the AI ​​installed in the guest room. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can receive customer information using an AI model that manages customer information, with customer information acquired from the AI ​​in the guest room as input.

[0081] The reception unit can estimate the customer's emotion and adjust the request reception method based on the estimated customer's emotion. For example, when the customer is stressed, the reception unit provides a simple interface and minimizes input procedures. In addition, when the customer is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the customer is in a hurry, the reception unit can prioritize voice input and enable the customer to input the request quickly. This allows for more appropriate service provision by adjusting the request reception method according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input facial expression data of the customer to the generation AI and cause the generation AI to execute the estimation of the customer's emotion.

[0082] The reception unit can analyze the customer's past request history and select the optimal reception method. For example, the reception unit automatically displays requests that the customer frequently input in the past as candidates. The reception unit can also preferentially suggest reception methods (such as voice and text) that the customer has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period from the customer's past request history. This improves customer convenience by selecting the optimal reception method using the customer's past request history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select a reception method using an AI model that inputs the customer's past request history and outputs the optimal reception method.

[0083] The reception unit can perform filtering based on the customer's current stay status and areas of interest when receiving a request. For example, if the customer is currently staying in a hotel, the reception unit preferentially displays services available during the customer's stay. The reception unit can also suggest related services and activities based on the customer's areas of interest. Furthermore, the reception unit can filter and display available time slots and locations according to the customer's stay status. This makes it possible to provide more appropriate services by filtering based on the customer's stay status and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the customer's stay status and areas of interest and outputs the filtering results.

[0084] The reception unit can estimate the customer's emotions and determine the priority of the requests to be received based on the estimated customer's emotions. For example, when the customer is stressed, the reception unit preferentially proposes a service that allows the customer to relax. In addition, when the customer is in a hurry, the reception unit can also preferentially propose a service that can be quickly responded to. Furthermore, when the customer is having fun, the reception unit can also preferentially propose an entertainment-related service. This enables more appropriate service provision by determining the priority of requests according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the customer's emotion data into the generation AI and determine the priority of requests.

[0085] When receiving a request, the reception unit can preferentially receive a highly relevant request in consideration of the geographical location information of the customer. For example, when the customer is in a specific area in the hotel, the reception unit preferentially suggests services available in that area. In addition, when the customer is outside the hotel, the reception unit can also preferentially provide information on nearby tourist attractions and restaurants. Furthermore, the reception unit can also suggest optimal routes and transportation means based on the customer's location information. As a result, by receiving a request in consideration of the geographical location information of the customer, more appropriate service can be provided. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can receive a request using an AI model that inputs the geographical location information of the customer and outputs a highly relevant request.

[0086] The reception unit can analyze the social media activity of the customer when receiving the request and receive the related request. The reception unit can, for example, suggest related services based on information shared by the customer on social media. The reception unit can also suggest activities that the customer may be interested in based on the content of the customer's social media posts. Furthermore, the reception unit can also suggest related requests based on services used by the customer's social media followers and friends. This makes it possible to provide more appropriate services by analyzing the customer's social media activity and receiving the request. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or without using AI. For example, the reception unit can receive the request using an AI model that inputs the customer's social media activity and outputs related requests.

[0087] The information management unit can estimate the customer's emotions and adjust the information management method based on the estimated customer's emotions. For example, when the customer is stressed, the information management unit displays information in a simple and intuitive manner. In addition, when the customer is relaxed, the information management unit can also provide detailed information and suggest a customizable display method. Furthermore, when the customer is in a hurry, the information management unit can preferentially display important information so that it can be accessed quickly. This enables more appropriate information management by adjusting the information management method according to the customer's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the information management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information management unit can input customer emotion data into the generation AI and adjust the information management method.

[0088] The information management unit can select an optimal management method by referring to the customer's past behavior history during information management. For example, the information management unit preferentially displays services that the customer has frequently used in the past. The information management unit can also predict and suggest services to be used in a specific time period from the customer's past behavior history. Furthermore, the information management unit can analyze the customer's past behavior patterns and select an optimal information display method. As a result, more appropriate information management is possible by managing information by referring to the customer's past behavior history. A part or all of the above-mentioned processing in the information management unit may be performed, for example, using AI or may be performed without using AI. For example, the information management unit can manage information using an AI model that inputs the customer's past behavior history and outputs an optimal management method.

[0089] The information management unit can determine the priority of information based on the customer's current stay status during information management. For example, when a customer is staying in a hotel, the information management unit preferentially displays services available during the customer's stay. The information management unit can also filter and display available time slots and locations according to the customer's stay status. Furthermore, the information management unit can select an optimal information display method based on the customer's stay status. This enables more appropriate information management by determining the priority of information based on the customer's stay status. A part or all of the above-mentioned processing in the information management unit may be performed, for example, using AI or may be performed without using AI. For example, the information management unit can manage information using an AI model that inputs the customer's stay status and outputs the priority of information.

[0090] The information management unit can estimate the customer's emotions and adjust the information display method based on the estimated customer's emotions. For example, when the customer is nervous, the information management unit provides an interface with calm colors to reduce visual stress. In addition, when the customer is having fun, the information management unit can also provide an interface with bright colors to make the information display more enjoyable. Furthermore, when the customer is tired, the information management unit can also provide a simple and highly visible interface to facilitate the information display. This enables more appropriate information display by adjusting the information display method according to the customer's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the information management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information management unit can input customer emotion data into the generation AI and adjust the information display method.

[0091] The information management unit can select an optimal management method in consideration of the geographical location information of the customer when managing information. For example, when the customer is in a specific area in the hotel, the information management unit preferentially displays services available in that area. In addition, when the customer is outside the hotel, the information management unit can also preferentially provide information on nearby tourist attractions and restaurants. Furthermore, the information management unit can select an optimal information display method based on the customer's location information. This allows for more appropriate information management by managing information in consideration of the customer's geographical location information. Part or all of the above-mentioned processing in the information management unit may be performed, for example, using AI, or may be performed without using AI. For example, the information management unit can manage information using an AI model that inputs the customer's geographical location information and outputs an optimal management method.

[0092] The information management unit can adjust the information management method by analyzing the social media activity of the customer during information management. For example, the information management unit can suggest related services based on information shared by the customer on social media. The information management unit can also suggest activities that the customer may be interested in based on the content of the customer's social media posts. Furthermore, the information management unit can also suggest related information based on services used by the customer's social media followers and friends. This enables more appropriate information management by analyzing the customer's social media activity and managing the information. Some or all of the above-mentioned processing in the information management unit may be performed, for example, using AI or may be performed without using AI. For example, the information management unit can manage the information using an AI model that uses the customer's social media activity as an input and adjusts the information management method.

[0093] The determination unit can estimate the customer's emotions and determine the timing and route of cleaning based on the estimated customer's emotions. For example, if the customer is relaxed, the determination unit adjusts the timing of cleaning to match the customer's schedule. In addition, if the customer is in a hurry, the determination unit can also determine the optimal route for quickly cleaning. Furthermore, if the customer is stressed, the determination unit can adjust the timing of cleaning so that it is not performed during the customer's stay. This enables more appropriate cleaning by determining the timing and route of cleaning according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data into the generation AI to determine the timing and route of cleaning.

[0094] The determination unit can select the optimal determination method by referring to the customer's past behavior history when determining the timing and route of cleaning. For example, the determination unit can suggest the optimal timing based on the cleaning timing used by the customer in the past. The determination unit can also suggest a cleaning route that avoids congestion from the customer's past behavior history. Furthermore, the determination unit can analyze the customer's past behavior history and suggest the most efficient cleaning route. As a result, more efficient cleaning is possible by determining the timing and route of cleaning by referring to the customer's past behavior history. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the customer's past behavior history and outputs the timing and route of cleaning.

[0095] The determination unit can adjust the determination method based on the customer's current stay status when determining the timing and route of cleaning. For example, if the customer is staying in the hotel, the determination unit adjusts so that cleaning is not performed during the customer's stay. The determination unit can also determine the optimal cleaning timing and route according to the customer's stay status. Furthermore, the determination unit can also optimize the movement of the cleaning staff based on the customer's stay status. This enables more appropriate cleaning by determining the timing and route of cleaning based on the customer's stay status. Part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the customer's stay status and outputs the timing and route of cleaning.

[0096] The determination unit can estimate the customer's emotions and determine the cleaning priority based on the estimated customer's emotions. For example, when the customer is stressed, the determination unit prioritizes cleaning an environment where the customer can relax. In addition, when the customer is in a hurry, the determination unit can also prioritize cleaning an area where a quick response can be made. Furthermore, when the customer is having fun, the determination unit can also prioritize cleaning an entertainment-related area. This enables more appropriate cleaning by determining the cleaning priority according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data into the generation AI to determine the cleaning priority.

[0097] The determination unit can select an optimal determination method by taking into consideration the geographical location information of the customer when determining the timing and route of cleaning. For example, when the customer is in a specific area in the hotel, the determination unit prioritizes cleaning of that area. In addition, when the customer is outside the hotel, the determination unit can also complete cleaning before the customer returns. Furthermore, the determination unit can determine an optimal cleaning route based on the location information of the customer. This enables more efficient cleaning by determining the timing and route of cleaning in consideration of the geographical location information of the customer. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the geographical location information of the customer and outputs the timing and route of cleaning.

[0098] The determination unit can adjust the determination method by analyzing the social media activity of the customer when determining the timing and route of cleaning. For example, the determination unit prioritizes cleaning of related areas based on information shared by the customer on social media. The determination unit can also prioritize cleaning of areas that are likely to be of interest to the customer based on the content of the customer's social media posts. Furthermore, the determination unit can also prioritize cleaning of related areas based on areas used by the customer's social media followers and friends. This allows for more appropriate cleaning by determining the timing and route of cleaning by analyzing the social media activity of the customer. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the social media activity of the customer and outputs the timing and route of cleaning.

[0099] The cleaning arrangement unit can estimate the customer's emotions and adjust the cleaning arrangement method based on the estimated customer's emotions. For example, when the customer is stressed, the cleaning arrangement unit can quickly arrange cleaning and provide a relaxing environment. In addition, when the customer is relaxed, the cleaning arrangement unit can provide detailed cleaning options and propose customizable cleaning arrangements. Furthermore, when the customer is in a hurry, the cleaning arrangement unit can prioritize cleaning arrangements that can be quickly responded to. This allows for more appropriate cleaning arrangements by adjusting the cleaning arrangement method according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the cleaning arrangement unit can input customer emotion data into the generation AI and adjust the cleaning arrangement method.

[0100] The cleaning arrangement unit can select the optimal arrangement method by referring to the customer's past behavior history when arranging cleaning. For example, the cleaning arrangement unit proposes the optimal arrangement method based on the cleaning arrangement method used by the customer in the past. The cleaning arrangement unit can also predict and propose the cleaning arrangement method to be used in a specific time period from the customer's past behavior history. Furthermore, the cleaning arrangement unit can analyze the customer's past behavior pattern and select the optimal cleaning arrangement method. As a result, more efficient cleaning arrangements can be made by making cleaning arrangements by referring to the customer's past behavior history. Part or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the customer's past behavior history and outputs the optimal arrangement method.

[0101] The cleaning arrangement unit can adjust the arrangement method based on the customer's current stay status when arranging cleaning. For example, if a customer is staying in a hotel, the cleaning arrangement unit arranges not to perform cleaning during the customer's stay. The cleaning arrangement unit can also select the optimal cleaning arrangement method according to the customer's stay status. Furthermore, the cleaning arrangement unit can also optimize the movement of the cleaning staff based on the customer's stay status. This makes it possible to make more appropriate cleaning arrangements by making cleaning arrangements based on the customer's stay status. Some or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the customer's stay status and outputs the arrangement method.

[0102] The cleaning arrangement unit can estimate the customer's emotions and determine the priority of cleaning arrangements based on the estimated customer's emotions. For example, when a customer is stressed, the cleaning arrangement unit prioritizes cleaning an environment where the customer can relax. In addition, when a customer is in a hurry, the cleaning arrangement unit can also prioritize cleaning an area where a quick response can be made. Furthermore, when a customer is having fun, the cleaning arrangement unit can also prioritize cleaning an entertainment-related area. This enables more appropriate cleaning arrangements by determining the priority of cleaning arrangements according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cleaning arrangement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cleaning arrangement unit can input customer emotion data into the generation AI and determine the priority of cleaning arrangements.

[0103] The cleaning arrangement unit can select the optimal arrangement method in consideration of the geographical location information of the customer when arranging cleaning. For example, when a customer is in a specific area in the hotel, the cleaning arrangement unit prioritizes cleaning of that area. In addition, when a customer is outside the hotel, the cleaning arrangement unit can also complete cleaning before the customer returns. Furthermore, the cleaning arrangement unit can select the optimal cleaning arrangement method based on the location information of the customer. This makes it possible to make more efficient cleaning arrangements by making cleaning arrangements in consideration of the geographical location information of the customer. Part or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the geographical location information of the customer and outputs the optimal arrangement method.

[0104] The cleaning arrangement unit can adjust the arrangement method by analyzing the social media activity of the customer when arranging cleaning. For example, the cleaning arrangement unit prioritizes cleaning of related areas based on information shared by the customer on social media. The cleaning arrangement unit can also prioritize cleaning of areas that are likely to be of interest to the customer based on the content of the customer's social media posts. Furthermore, the cleaning arrangement unit can also prioritize cleaning of related areas based on areas used by the customer's social media followers and friends. This makes it possible to make more appropriate cleaning arrangements by analyzing the customer's social media activity and making cleaning arrangements. Part or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the customer's social media activity and outputs an arrangement method.

[0105] The reservation arrangement unit can estimate the customer's emotions and adjust the reservation arrangement method based on the estimated customer's emotions. For example, when the customer is stressed, the reservation arrangement unit provides a simple interface and minimizes the reservation procedure. In addition, when the customer is relaxed, the reservation arrangement unit can provide detailed reservation options and propose customizable reservation arrangements. Furthermore, when the customer is in a hurry, the reservation arrangement unit can prioritize voice input and quickly arrange reservations. This allows for more appropriate reservation arrangements by adjusting the reservation arrangement method according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reservation arrangement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reservation arrangement unit can input customer emotion data into the generation AI and adjust the reservation arrangement method.

[0106] The reservation arrangement unit can select the optimal arrangement method by referring to the customer's past behavior history when making a reservation arrangement. The reservation arrangement unit, for example, proposes the optimal arrangement method based on the reservation arrangement method used by the customer in the past. The reservation arrangement unit can also predict and propose the reservation arrangement method to be used in a specific time period from the customer's past behavior history. Furthermore, the reservation arrangement unit can analyze the customer's past behavior pattern and select the optimal reservation arrangement method. As a result, more efficient reservation arrangement is possible by making reservation arrangements by referring to the customer's past behavior history. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make reservation arrangements using an AI model that inputs the customer's past behavior history and outputs the optimal arrangement method.

[0107] The reservation arrangement unit can adjust the arrangement method based on the customer's current stay status when making a reservation arrangement. For example, when a customer is staying in a hotel, the reservation arrangement unit preferentially reserves facilities that are available during the customer's stay. The reservation arrangement unit can also select the optimal reservation arrangement method according to the customer's stay status. Furthermore, the reservation arrangement unit can also determine the priority of reservation arrangement based on the customer's stay status. This makes it possible to make more appropriate reservation arrangements by making reservation arrangements based on the customer's stay status. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make reservation arrangements using an AI model that inputs the customer's stay status and outputs an arrangement method.

[0108] The reservation arrangement unit can estimate the emotion of the customer and determine the priority of reservation arrangement based on the estimated emotion of the customer. For example, when the customer is stressed, the reservation arrangement unit preferentially reserves a facility where the customer can relax. In addition, when the customer is in a hurry, the reservation arrangement unit can also preferentially reserve a facility that can respond quickly. Furthermore, when the customer is having fun, the reservation arrangement unit can also preferentially reserve an entertainment-related facility. This enables more appropriate reservation arrangement by determining the priority of reservation arrangement according to the emotion of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reservation arrangement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reservation arrangement unit can input the emotion data of the customer into the generation AI and determine the priority of reservation arrangement.

[0109] The reservation arrangement unit can select the optimal arrangement method in consideration of the geographical location information of the customer when making a reservation. For example, when the customer is in a specific area of ​​the hotel, the reservation arrangement unit preferentially reserves facilities available in that area. In addition, when the customer is outside the hotel, the reservation arrangement unit can also preferentially provide information on nearby tourist attractions and restaurants. Furthermore, the reservation arrangement unit can select the optimal reservation arrangement method based on the customer's location information. This makes it possible to make a reservation arrangement more efficiently by making a reservation arrangement in consideration of the customer's geographical location information. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make a reservation arrangement using an AI model that inputs the customer's geographical location information and outputs the optimal arrangement method.

[0110] The reservation arrangement unit can adjust the arrangement method by analyzing the social media activity of the customer when making the reservation arrangement. For example, the reservation arrangement unit preferentially reserves related facilities based on information shared by the customer on social media. The reservation arrangement unit can also preferentially reserve facilities that the customer is likely to be interested in based on the content of the customer's social media posts. Furthermore, the reservation arrangement unit can also preferentially reserve related facilities based on facilities used by the customer's social media followers and friends. This makes it possible to make more appropriate reservation arrangements by analyzing the customer's social media activity and making the reservation arrangement. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make the reservation arrangement using an AI model that inputs the customer's social media activity and outputs the arrangement method.

[0111] The sensor device can estimate the customer's emotion and adjust the method of collecting sensor information based on the estimated customer's emotion. For example, when the customer is stressed, the sensor device preferentially collects sensor information in a relaxing environment. In addition, when the customer is relaxed, the sensor device can also collect detailed sensor information and provide customizable information. Furthermore, when the customer is in a hurry, the sensor device can also preferentially collect sensor information that can be quickly responded to. This allows more appropriate information collection by adjusting the method of collecting sensor information according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sensor device may be performed using, for example, an AI, or may be performed without using an AI. For example, the sensor device can input customer emotion data into the generation AI and adjust the method of collecting sensor information.

[0112] When collecting sensor information, the sensor device can select the optimal collection method by referring to the customer's past behavior history. The sensor device, for example, proposes the optimal collection method based on sensor information used by the customer in the past. The sensor device can also predict and propose sensor information to be used in a specific time period from the customer's past behavior history. Furthermore, the sensor device can analyze the customer's past behavior pattern and select the optimal collection method for sensor information. This makes it possible to collect information more efficiently by collecting sensor information by referring to the customer's past behavior history. Some or all of the above-mentioned processing in the sensor device may be performed, for example, using AI or may be performed without using AI. For example, the sensor device can collect sensor information using an AI model that inputs the customer's past behavior history and outputs the optimal collection method.

[0113] The sensor device can estimate the customer's emotion and determine the priority of the sensor information based on the estimated customer's emotion. For example, when the customer is stressed, the sensor device preferentially collects sensor information of a relaxing environment. In addition, when the customer is in a hurry, the sensor device can also preferentially collect sensor information that can be quickly responded to. Furthermore, when the customer is having fun, the sensor device can also preferentially collect entertainment-related sensor information. This enables more appropriate information collection by determining the priority of the sensor information according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sensor device may be performed using, for example, an AI, or may be performed without using an AI. For example, the sensor device can input customer emotion data into the generation AI and determine the priority of the sensor information.

[0114] When collecting sensor information, the sensor device can select the optimal collection method in consideration of the geographical location information of the customer. For example, when the customer is in a specific area in the hotel, the sensor device preferentially collects sensor information of that area. In addition, when the customer is outside the hotel, the sensor device can also preferentially collect nearby sensor information. Furthermore, the sensor device can select the optimal collection method of sensor information based on the location information of the customer. This allows for more efficient information collection by collecting sensor information in consideration of the geographical location information of the customer. Part or all of the above-mentioned processing in the sensor device may be performed, for example, using AI or may be performed without using AI. For example, the sensor device can collect sensor information using an AI model that inputs the geographical location information of the customer and outputs the optimal collection method.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The reception unit can analyze the customer's past behavior history and select the optimal reception method. For example, requests that the customer frequently input in the past are automatically displayed as candidates. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period from the customer's past request history. This improves customer convenience by selecting the optimal reception method using the customer's past request history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select a reception method using an AI model that inputs the customer's past request history and outputs the optimal reception method.

[0117] The determination unit can estimate the customer's emotions and determine the timing and route of cleaning based on the estimated customer's emotions. For example, if the customer is relaxed, the timing of cleaning is adjusted to match the customer's schedule. In addition, if the customer is in a hurry, the determination unit can also determine the optimal route for quickly cleaning. Furthermore, if the customer is stressed, the determination unit can adjust the timing of cleaning so that it is not performed during the customer's stay. This enables more appropriate cleaning by determining the timing and route of cleaning according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data into the generation AI to determine the timing and route of cleaning.

[0118] The information management unit can estimate the customer's emotions and adjust the information management method based on the estimated customer's emotions. For example, if the customer is stressed, the information management unit displays information in a simple and intuitive manner. In addition, if the customer is relaxed, the information management unit can provide detailed information and suggest a customizable display method. Furthermore, if the customer is in a hurry, the information management unit can display important information preferentially so that the information can be accessed quickly. This allows for more appropriate information management by adjusting the information management method according to the customer's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the information management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information management unit can input customer emotion data into the generation AI and adjust the information management method.

[0119] The cleaning arrangement unit can estimate the customer's emotions and adjust the method of cleaning arrangement based on the estimated customer's emotions. For example, if the customer is stressed, the cleaning arrangement unit can quickly arrange cleaning and provide a relaxing environment. In addition, if the customer is relaxed, the cleaning arrangement unit can provide detailed cleaning options and propose customizable cleaning arrangements. Furthermore, if the customer is in a hurry, the cleaning arrangement unit can prioritize cleaning arrangements that can be quickly responded to. This allows for more appropriate cleaning arrangements by adjusting the method of cleaning arrangements according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the cleaning arrangement unit can input customer emotion data into the generation AI and adjust the method of cleaning arrangement.

[0120] The reservation arrangement unit can estimate the customer's emotions and adjust the reservation arrangement method based on the estimated customer's emotions. For example, if the customer is stressed, a simple interface is provided to minimize the reservation procedure. In addition, if the customer is relaxed, the reservation arrangement unit can provide detailed reservation options and propose customizable reservation arrangements. Furthermore, if the customer is in a hurry, the reservation arrangement unit can prioritize voice input and quickly arrange reservations. This allows for more appropriate reservation arrangements by adjusting the reservation arrangement method according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reservation arrangement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reservation arrangement unit can input customer emotion data into the generation AI and adjust the reservation arrangement method.

[0121] The reception unit can perform filtering based on the customer's current stay status and areas of interest when receiving a request. For example, if the customer is currently staying in a hotel, the reception unit preferentially displays services available during the customer's stay. The reception unit can also suggest related services and activities based on the customer's areas of interest. Furthermore, the reception unit can filter and display available time slots and locations according to the customer's stay status. This makes it possible to provide more appropriate services by filtering based on the customer's stay status and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the customer's stay status and areas of interest and outputs the filtering results.

[0122] The information management unit can select the optimal management method by referring to the customer's past behavior history when managing information. For example, the information management unit preferentially displays services that the customer has frequently used in the past. The information management unit can also predict and suggest services to be used in a specific time period from the customer's past behavior history. Furthermore, the information management unit can analyze the customer's past behavior patterns and select the optimal information display method. As a result, more appropriate information management is possible by managing information by referring to the customer's past behavior history. A part or all of the above-mentioned processing in the information management unit may be performed, for example, using AI or may be performed without using AI. For example, the information management unit can manage information using an AI model that inputs the customer's past behavior history and outputs the optimal management method.

[0123] When determining the timing and route of cleaning, the determination unit can select the optimal determination method by referring to the customer's past behavior history. For example, the determination unit can suggest the optimal timing based on the cleaning timing used by the customer in the past. The determination unit can also suggest a cleaning route that avoids congestion from the customer's past behavior history. Furthermore, the determination unit can analyze the customer's past behavior history and suggest the most efficient cleaning route. As a result, more efficient cleaning is possible by determining the timing and route of cleaning by referring to the customer's past behavior history. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can determine the timing and route of cleaning using an AI model that inputs the customer's past behavior history and outputs the timing and route of cleaning.

[0124] The cleaning arrangement unit can adjust the arrangement method based on the customer's current stay status when arranging cleaning. For example, if the customer is staying in the hotel, it arranges not to clean during the customer's stay. The cleaning arrangement unit can also select the optimal cleaning arrangement method according to the customer's stay status. Furthermore, the cleaning arrangement unit can also optimize the movement of the cleaning staff based on the customer's stay status. This makes it possible to make more appropriate cleaning arrangements by making cleaning arrangements based on the customer's stay status. Some or all of the above-mentioned processing in the cleaning arrangement unit may be performed, for example, using AI or may be performed without using AI. For example, the cleaning arrangement unit can make cleaning arrangements using an AI model that inputs the customer's stay status and outputs the arrangement method.

[0125] The reservation arrangement unit can select the optimal arrangement method in consideration of the geographical location information of the customer when making a reservation. For example, when the customer is in a specific area in the hotel, the reservation arrangement unit can preferentially reserve facilities available in that area. In addition, when the customer is outside the hotel, the reservation arrangement unit can also preferentially provide information on nearby tourist attractions and restaurants. Furthermore, the reservation arrangement unit can select the optimal reservation arrangement method based on the customer's location information. This allows for more efficient reservation arrangement by making reservation arrangements in consideration of the customer's geographical location information. Part or all of the above-mentioned processing in the reservation arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation arrangement unit can make reservation arrangements using an AI model that inputs the customer's geographical location information and outputs the optimal arrangement method.

[0126] The process flow of the second embodiment will be briefly described below.

[0127] Step 1: The reception department accepts customer information indicating the customer's requests and schedule. Customer information includes name, contact information, requests, schedule, etc. For example, a customer can enter information such as "going out at x o'clock," "preferring dinner around x o'clock," "reserving a hot spring bath," and "whether or not cleaning is required." Step 2: The information management department manages the customer information received by the reception department. The information management department centrally manages customer requests and schedules and provides necessary information to other departments. For example, the information management department stores customer requests and schedules in a database so that other departments can access them. Step 3: The decision unit determines the timing and route for cleaning the hotel based on the customer information managed by the information management unit. For example, the decision unit creates a schedule to match cleaning with when customers will be leaving the hotel. It also determines the optimal route for cleaning staff to move efficiently. The movement of cleaning staff is optimized taking into account customer schedules and the layout of rooms in the hotel. Step 4: The cleaning arrangement unit arranges cleaning based on the result of the decision made by the decision unit. The cleaning arrangement unit issues specific instructions to the cleaning staff to ensure smooth cleaning work. For example, the cleaning staff is instructed on which rooms to clean and when to clean them.

[0128] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0130] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] For example, the reception unit receives customer information via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The information management unit is realized by the specific processing unit 290 of the data processing device 12, and manages customer information in a centralized manner and provides necessary information to other departments. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the timing and route of cleaning based on the customer information. The cleaning arrangement unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and issues specific instructions to cleaning staff. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] For example, the reception unit receives customer information via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The information management unit is realized by the specific processing unit 290 of the data processing device 12, and manages customer information in a centralized manner and provides necessary information to other departments. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the timing and route of cleaning based on the customer information. The cleaning arrangement unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and issues specific instructions to cleaning staff. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0150] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0163] For example, the reception unit receives customer information via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The information management unit is realized by the specific processing unit 290 of the data processing device 12, and manages customer information in a centralized manner and provides necessary information to other departments. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the timing and route of cleaning based on the customer information. The cleaning arrangement unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and issues specific instructions to cleaning staff. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0172] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0175] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] For example, the reception unit receives customer information via the microphone 238 of the robot 414 and the communication I / F 26 of the data processing device 12. The information management unit is realized by the specific processing unit 290 of the data processing device 12, and manages customer information in a centralized manner and provides necessary information to other departments. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the timing and route of cleaning based on the customer information. The cleaning arrangement unit is realized by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12, and issues specific instructions to cleaning staff. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0181] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0182] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0184] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0185] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0186] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0189] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0190] In addition, 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 upon request from the data processing device 12.

[0191] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0192] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0193] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0194] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0195] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0197] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0199] (Appendix 1) a reception unit for receiving customer information indicating requests and schedules of hotel customers; an information management unit that manages the customer information received by the reception unit; A determination unit that determines a timing and route for cleaning the hotel based on the customer information managed by the information management unit; A cleaning arrangement unit that arranges the cleaning based on the determination result by the determination unit. A system characterized by: (Appendix 2) The determination unit is The allocation of cleaning staff within the hotel is determined based on the customer information managed by the information management unit. 2. The system of claim 1, (Appendix 3) The reception unit is receiving sensor information output from a sensor device installed at each location in the hotel; The information management unit Manage the sensor information received by the reception unit; The determination unit is Based on the customer information and the sensor information, the timing and route of cleaning for the hotel are determined. 2. The system of claim 1, (Appendix 4) A reservation arrangement unit that makes reservations for facilities within the hotel based on the customer information and the sensor information. 2. The system of claim 1, (Appendix 5) The sensor device includes: Output the congestion status and number of times the device is used at the installed location 2. The system of claim 1, (Appendix 6) The reception unit is The customer information is received by an AI installed in the guest room of the customer. 2. The system of claim 1, (Appendix 7) The reception unit is Infer customer sentiment and adjust how requests are received based on the inferred sentiment 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the customer's past request history and select the optimal reception method 2. The system of claim 1, (Appendix 9) The reception unit is Filter requests based on the customer's current location and interests 2. The system of claim 1, (Appendix 10) The reception unit is Estimate customer sentiment and prioritize incoming requests based on the estimated sentiment 2. The system of claim 1, (Appendix 11) The reception unit is When accepting requests, consider the customer's geographic location to prioritize the most relevant requests 2. The system of claim 1, (Appendix 12) The reception unit is When receiving a request, analyze your social media activity and receive related requests 2. The system of claim 1, (Appendix 13) The information management unit Infer customer sentiment and adjust how information is managed based on the inferred customer sentiment 2. The system of claim 1, (Appendix 14) The information management unit When managing information, select the optimal management method by referring to the customer's past behavior history. 2. The system of claim 1, (Appendix 15) The information management unit When managing information, prioritize information based on the customer's current stay 2. The system of claim 1, (Appendix 16) The information management unit Infer customer sentiment and adjust how information is displayed based on the inferred customer sentiment 2. The system of claim 1, (Appendix 17) The information management unit When managing information, select the optimal management method taking into account the geographic location information of the customer. 2. The system of claim 1, (Appendix 18) The information management unit When managing information, analyze your customers' social media activity to adjust how you manage information. 2. The system of claim 1, (Appendix 19) The determination unit is Estimate customer sentiment and decide cleaning timing and routes based on the estimated customer sentiment 2. The system of claim 1, (Appendix 20) The determination unit is When determining cleaning timing and routes, the system selects the optimal method by referring to the customer's past behavioral history 2. The system of claim 1, (Appendix 21) The determination unit is Adjust cleaning timing and routes based on the customer's current location 2. The system of claim 1, (Appendix 22) The determination unit is Estimate customer sentiment and prioritize cleaning based on estimated customer sentiment 2. The system of claim 1, (Appendix 23) The determination unit is Considering customers' geographic location when determining cleaning timing and routes to optimize decisions 2. The system of claim 1, (Appendix 24) The determination unit is Analyze customer social media activity to adjust cleaning timing and routes 2. The system of claim 1, (Appendix 25) The cleaning arrangement department includes: Estimate customer sentiment and adjust cleaning arrangements based on estimated customer sentiment 2. The system of claim 1, (Appendix 26) The cleaning arrangement department includes: When arranging cleaning, the system selects the optimal arrangement method by referring to the customer's past behavior history 2. The system of claim 1, (Appendix 27) The cleaning arrangement department includes: Adjust cleaning arrangements based on the customer's current stay 2. The system of claim 1, (Appendix 28) The cleaning arrangement department includes: Estimate customer sentiment and prioritize cleaning arrangements based on estimated customer sentiment 2. The system of claim 1, (Appendix 29) The cleaning arrangement department includes: When arranging cleaning, select the optimal arrangement based on the customer's geographic location information 2. The system of claim 1, (Appendix 30) The cleaning arrangement department includes: Analyze customers' social media activity to adjust cleaning arrangements 2. The system of claim 1, (Appendix 31) The reservation arrangement unit includes: Infer customer sentiment and adjust booking arrangements based on the inferred sentiment 2. The system of claim 1, (Appendix 32) The reservation arrangement unit includes: When making a reservation, the system selects the optimal arrangement method by referring to the customer's past behavior history. 2. The system of claim 1, (Appendix 33) The reservation arrangement unit includes: When making a reservation, adjust the arrangement based on the customer's current stay 2. The system of claim 1, (Appendix 34) The reservation arrangement unit includes: Estimating customer sentiment and prioritizing reservation arrangements based on the estimated customer sentiment 2. The system of claim 1, (Appendix 35) The reservation arrangement unit includes: When making a reservation, choose the best way to make the reservation by taking into account the customer's geographic location. 2. The system of claim 1, (Appendix 36) The reservation arrangement unit includes: Analyze customers' social media activity to tailor reservations 2. The system of claim 1, (Appendix 37) The sensor device includes: Inferring customer sentiment and adjusting a method for collecting sensor information based on the inferred customer sentiment 2. The system of claim 1, (Appendix 38) The sensor device includes: When collecting sensor information, the optimal collection method is selected by referring to the customer's past behavior history. 2. The system of claim 1, (Appendix 39) The sensor device includes: Estimate customer sentiment and prioritize sensor information based on the estimated customer sentiment 2. The system of claim 1, (Appendix 40) The sensor device includes: When collecting sensor information, the optimal collection method is selected taking into account the geographic location information of the customer. 2. The system of claim 1, [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving customer information indicating requests and schedules of hotel customers; an information management unit that manages the customer information received by the reception unit; A determination unit that determines a timing and route for cleaning the hotel based on the customer information managed by the information management unit; A cleaning arrangement unit that arranges the cleaning based on the determination result by the determination unit. A system characterized by:

2. The determination unit is The allocation of cleaning staff within the hotel is determined based on the customer information managed by the information management unit.

2. The system of claim 1 .

3. The reception unit is receiving sensor information output from a sensor device installed at each location in the hotel; The information management unit Manage the sensor information received by the reception unit; The determination unit is Based on the customer information and the sensor information, the timing and route of cleaning for the hotel are determined.

2. The system of claim 1 .

4. A reservation arrangement unit that makes reservations for facilities within the hotel based on the customer information and the sensor information.

4. The system of claim 3.

5. The sensor device includes: Output the congestion status and number of times the device is used at the installed location 4. The system of claim 3.

6. The reception unit is The customer information is received by an AI installed in the guest room of the customer.

2. The system of claim 1 .

7. The reception unit is The emotion of the customer is estimated, and a method of accepting requests is adjusted based on the estimated emotion of the customer.

2. The system of claim 1 .

8. The reception unit is Analyze the customer's past request history and select the optimal reception method 2. The system of claim 1 .

9. The reception unit is Filter requests based on the customer's current location and interests 2. The system of claim 1 .

10. The reception unit is Estimate customer sentiment and prioritize incoming requests based on the estimated sentiment 2. The system of claim 1 .

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