system

Abstract

The system according to this embodiment aims to efficiently collect, learn from, and provide feedback on guest information. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, and a feedback unit. The collection unit collects information on hotel guests. The learning unit learns the information collected by the collection unit. The feedback unit provides feedback on the information learned by the learning unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the process of efficiently collecting, learning, and feeding back the information of the guests is not sufficiently automated, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect, learn, and feed back the information of the guests.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a learning unit, and a feedback unit. The collection unit collects the information of the guests. The learning unit learns the information collected by the collection unit. The feedback unit feeds back the information learned by the learning unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently collect, learn from, and provide feedback on guest information. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 in 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The vacation rental reception system according to an embodiment of the present invention is a system that automates and efficiently executes vacation rental reception using a generating AI agent, a smartphone app, a fixed camera, digital signage, and a key box. This system begins with the guest using the smartphone app to complete the check-in procedure. The generating AI agent learns the guest's information and provides feedback to platforms such as Airbnb to encourage the guest's good manners. The fixed camera is used to authenticate the guest's identity and to securely hand over the key. The digital signage provides the guest with necessary information, and the key box automates the key handover. This ensures secure check-in and check-out for both the guest and the business owner. Furthermore, it is multilingual and can provide personalized services to guests. For example, when a guest uses the smartphone app to complete the check-in procedure, the guest enters their information and confirms their reservation information. The generating AI agent learns the guest's information and provides feedback to platforms such as Airbnb to encourage the guest's good manners. For example, based on the guest's past usage history and ratings, it sends messages to the guest encouraging appropriate behavior. Next, the fixed camera is used to authenticate the guest's identity. When a guest stands in front of the camera, the camera recognizes the guest's face and performs personal authentication. This verifies that the guest is a legitimate user. Once personal authentication is complete, the key box is unlocked, and the guest can receive their key. Digital signage is used to provide guests with necessary information, such as how to use the accommodation and information about nearby tourist attractions. Guests can obtain the necessary information through the digital signage. Finally, when guests check out, they also use a smartphone app to complete the process. The generated AI agent learns the guest's checkout information and provides feedback to platforms such as Airbnb. This updates the guest's usage history, which can be used to help with future stays. This system enables secure and efficient check-in and check-out for both guests and business owners.Furthermore, the system offers multilingual support, enabling personalized services for guests. For example, it can provide guidance in the guest's native language and tourist information tailored to their interests. This allows the private lodging reservation system to efficiently collect, learn from, and provide feedback on guest information.

[0029] The vacation rental reservation system according to the embodiment comprises a collection unit, a learning unit, and a feedback unit. The collection unit collects guest information. Guest information includes, but is not limited to, personal information, accommodation history, and preferences. The collection unit can collect guest information, for example, via a smartphone app. The learning unit learns the information collected by the collection unit. The learning unit learns guest information, for example, using a generating AI agent. The generating AI agent can learn guest behavior patterns based on the guest's past usage history and ratings. The feedback unit provides feedback based on the information learned by the learning unit. The feedback unit provides feedback to, for example, a platform such as Airbnb. The feedback unit can provide appropriate feedback based on the guest's behavior patterns. As a result, the vacation rental reservation system according to the embodiment can efficiently collect, learn, and provide feedback on guest information. Some or all of the above-described processes in the collection unit, learning unit, and feedback unit may be performed using, for example, AI, or without using AI. For example, the data collection unit can acquire guest information via a smartphone app and input that information into a generating AI agent. The learning unit can use the generating AI agent to learn from the guest information and understand guest behavior patterns. The feedback unit can provide feedback to platforms such as Airbnb based on the information learned by the learning unit. This allows for efficient collection, learning, and feedback of guest information.

[0030] The data collection unit collects guest information. This information includes, but is not limited to, personal information, stay history, and preferences. For example, the data collection unit can collect guest information via a smartphone app. Specifically, when guests check in through the smartphone app, they enter personal information such as their name, address, and contact information. Information on past stay history and ratings, preferred types of accommodation, and amenities is also collected. This information is collected with the guest's consent and stored in a secure database. Furthermore, the data collection unit also records activities and requests made by guests using the app during their stay. This includes information such as ordering room service, making reservations for sightseeing, and participating in events at the facility. This allows the data collection unit to create detailed guest profiles and build a foundation for providing services tailored to individual needs and preferences. The collected data is updated in real time, making it possible to always have the latest information on guests.

[0031] The learning unit learns from the information collected by the collection unit. For example, the learning unit learns from guest information using a generative AI agent. The generative AI agent can learn guest behavior patterns based on the guest's past usage history and ratings. Specifically, the generative AI agent analyzes the collected data and models the guest's preferences and behavior patterns. For example, it predicts the guest's preferences based on information such as what types of accommodations the guest has preferred in the past, what amenities they valued, and what services they used during their stay. The generative AI agent also analyzes guest ratings and feedback to evaluate the quality and satisfaction of the service. This allows the learning unit to understand what services guests were satisfied with and what aspects they were dissatisfied with. Furthermore, the generative AI agent can learn guest behavior patterns and predict future behavior. For example, it can predict how guests will behave and what services they will request in accordance with specific seasons or events, and take countermeasures in advance. As a result, the learning unit can build a foundation for providing personalized services that meet the needs of guests.

[0032] The Feedback Department provides feedback based on the information learned by the Learning Department. For example, the Feedback Department provides feedback to platforms such as Airbnb. The Feedback Department can provide appropriate feedback based on guest behavior patterns. Specifically, based on guest preferences and behavior patterns obtained by the Learning Department, the Feedback Department provides improvement suggestions and recommendations to accommodation owners and managers. For example, if a guest prefers to use a particular amenity, the Feedback Department suggests improving that amenity. Furthermore, for areas where guests have expressed dissatisfaction in the past, the Feedback Department provides solutions and concrete action plans to improve service quality. In addition, the Feedback Department also provides feedback to guests, offering information to improve their satisfaction during their stay. For example, based on services and activities guests have used in the past, it suggests recommended services and events they can use during their stay. It also provides special offers and discounts tailored to guests' preferences to encourage repeat visits. In this way, the Feedback Department can provide valuable information to both guests and accommodations, improving service quality. Furthermore, based on the collected data, the Feedback Department can also provide strategic feedback to improve the overall operation of the accommodation.

[0033] The private lodging reception system includes an authentication unit that authenticates the guest's identity. The authentication unit authenticates the guest's identity. The authentication unit can authenticate the guest's identity using methods such as facial recognition or fingerprint recognition. When a guest stands in front of a camera, the camera recognizes the guest's face and performs identity authentication. This confirms that the guest is a legitimate user. The authentication unit can, for example, use facial recognition technology to recognize the guest's face and perform identity authentication. The authentication unit can also use fingerprint recognition technology to recognize the guest's fingerprint and perform identity authentication. This enables secure check-in by authenticating the guest's identity. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the guest's facial image into a generating AI and have the generating AI perform facial recognition.

[0034] The private lodging reservation system includes a provision unit that provides information necessary for guests. The provision unit provides information necessary for guests. For example, the provision unit can provide information about the accommodation and information about nearby tourist attractions. The provision unit collects information that guests need and provides it to guests. For example, the provision unit can provide information on how to use the accommodation and information about nearby tourist attractions. The provision unit can also provide information tailored to the guest's interests. This improves the convenience for guests by providing them with the information they need. Some or all of the above processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can input relevant information into a generating AI based on the guest's interests and have the generating AI provide the information.

[0035] The short-term rental reception system includes a key handover unit that automates the key handover process. The key handover unit automates the key handover process. For example, the key handover unit can automate the key handover process using a smart lock. When a guest checks in, the key handover unit unlocks the smart lock and hands over the key. When a guest checks out, the key handover unit locks the smart lock and collects the key. This automates the key handover process, improving convenience for both guests and business owners. Some or all of the above processes in the key handover unit may be performed using AI, for example, or without AI. For example, the key handover unit can input the guest's check-in information into a generating AI and have the generating AI unlock the smart lock.

[0036] The data collection unit can collect guest information via a smartphone app. For example, the data collection unit collects guest information when a guest uses the smartphone app to check in. The data collection unit collects information entered by the guest into the smartphone app and obtains the guest's personal information and reservation information. For example, the data collection unit can collect the guest's name, address, contact information, reservation information, etc., when a guest uses the smartphone app to check in. This improves the efficiency of information collection by collecting guest information via a smartphone app. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the information entered by the guest into the smartphone app into a generating AI and have the generating AI perform the information collection.

[0037] The learning unit can learn guest information using a generative AI agent. For example, the learning unit can learn guest behavior patterns based on the guest's past usage history and ratings using a generative AI agent. The learning unit can efficiently learn guest information using a generative AI agent. For example, the learning unit can learn guest behavior patterns based on the guest's past usage history and ratings using a generative AI agent. This allows for efficient learning of guest information by using a generative AI agent. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input guest information into a generative AI and have the generative AI perform information learning.

[0038] The feedback unit can provide feedback to platforms such as Airbnb. For example, the feedback unit provides feedback to platforms such as Airbnb based on the guest's behavior patterns. The feedback unit can provide appropriate feedback based on the guest's behavior patterns. For example, the feedback unit provides feedback to platforms such as Airbnb based on the guest's past usage history and ratings. By providing feedback to platforms such as Airbnb, it is possible to encourage guests to behave in a mannerly manner. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the guest's behavior patterns into a generating AI and have the generating AI perform the feedback.

[0039] The authentication unit can perform personal authentication of guests using a stationary camera. For example, the authentication unit can recognize the guest's face using a stationary camera and perform personal authentication. When a guest stands in front of the camera, the camera recognizes the guest's face and performs personal authentication. This confirms that the guest is a legitimate user. The authentication unit can recognize the guest's face using a stationary camera and perform personal authentication. This improves the accuracy of guest personal authentication by using a stationary camera. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the guest's face image into a generating AI and have the generating AI perform face authentication.

[0040] The service provider can provide guests with necessary information using digital signage. For example, the service provider can use digital signage to provide information about the accommodation and surrounding tourist attractions. The service provider can collect information that guests need and provide it to them. For example, the service provider can use digital signage to provide information on how to use the accommodation and surrounding tourist attractions. In this way, information can be provided to guests visually using digital signage. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input relevant information into a generating AI based on the guest's interests and have the generating AI provide the information.

[0041] The data collection unit can analyze a guest's past accommodation history and select the most suitable information collection method. For example, the data collection unit can select a method for collecting information at similar accommodations based on information about accommodations the guest has used in the past. The data collection unit can adjust its information collection approach by referring to the guest's past ratings. The data collection unit can analyze the guest's past behavioral patterns and collect information at the optimal timing. This allows the data collection unit to select the most suitable information collection method by analyzing the guest's past accommodation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the guest's past accommodation history data into a generating AI and have the generating AI select the most suitable information collection method.

[0042] The data collection unit can filter and collect information based on the guest's current travel purpose and areas of interest. For example, if the guest is traveling for sightseeing, the data collection unit will prioritize collecting information about tourist destinations. If the guest is traveling for business, the data collection unit will prioritize collecting business-related information. The data collection unit can collect information on relevant events and activities based on the guest's areas of interest. This allows the collection unit to provide guests with useful information by collecting information tailored to their travel purpose and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the guest's travel purpose and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0043] The data collection unit can prioritize the collection of highly relevant information, taking into account the geographical location of the guest. For example, the data collection unit can prioritize the collection of information about tourist attractions near the guest's current location. The data collection unit can prioritize the collection of information about restaurants near the guest's current location. The data collection unit can prioritize the collection of information about events near the guest's current location. In this way, by collecting information based on the guest's geographical location, useful information can be provided to the guest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the guest's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0044] The data collection unit can analyze guests' social media activity and collect relevant information. For example, the data collection unit can collect information on events that guests have shown interest in on social media. The data collection unit can collect information on accounts that guests follow on social media. The data collection unit can collect information on places that guests have shared on social media. By analyzing guests' social media activity, the data collection unit can provide guests with useful information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input guests' social media activity data into a generating AI and have the generating AI collect relevant information.

[0045] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data and adjust the parameters of the learning algorithm. The learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0046] The learning unit can analyze the behavior patterns of guests and customize the learning content. For example, the learning unit can analyze the past behavior patterns of guests and customize the learning content. The learning unit can select the optimal learning content based on the guest's behavior patterns. The learning unit can analyze the guest's behavior patterns and determine the priority of the learning content. In this way, the learning content can be optimized by analyzing the guest's behavior patterns. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input guest behavior pattern data into a generating AI and have the generating AI perform the customization of the learning content.

[0047] The learning unit can weight the training data based on the guest's stay history. For example, the learning unit can weight the training data based on the guest's past stay history. The learning unit can weight the training data based on the guest's evaluation. The learning unit can weight the training data based on the guest's behavioral patterns. By weighting the training data based on the guest's stay history, the accuracy of the learning is improved. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the guest's stay history data into a generating AI and have the generating AI perform the training data weighting.

[0048] The learning unit can adjust the learning content based on the guest's interests and preferences. For example, the learning unit can adjust the learning content based on the guest's interests. The learning unit can adjust the learning content based on the guest's interests. The learning unit can adjust the learning content based on the guest's past behavioral patterns. This improves the effectiveness of learning by providing learning content that matches the guest's interests and preferences. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input guest interest and preference data into a generating AI and have the generating AI perform the adjustment of the learning content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] A short-term rental booking system can offer perks to guests based on their past stay history. For example, if a guest has given high ratings in the past, they can be offered a discount on their next stay. If a guest has made many stays in the past, they can be offered special services. If a guest has not caused any problems in the past, they can be offered priority check-in. In this way, by offering perks based on a guest's past stay history, the repeat rate of guests can be increased. The provision of perks may be done using AI, or not. For example, the guest's stay history data can be input into a generating AI, and the AI ​​can then be made to provide perks.

[0051] A private lodging reservation system can provide services to guests based on their current location. For example, when a guest approaches the accommodation, the system can prepare for check-in. If the guest is at a tourist spot, it can provide tourist information. If the guest is at a restaurant, it can provide the restaurant's menu and recommended dishes. By providing services based on the guest's current location, the system can improve guest convenience. The use of location information may be done using AI, or it may not. For example, the guest's location data can be input into a generating AI, and the AI ​​can then perform the service provision.

[0052] A short-term rental booking system can analyze guests' social media activity and provide services to them. For example, it can provide information about events that guests have shown interest in on social media, information about accounts that guests follow on social media, and information about places that guests have shared on social media. In this way, by analyzing guests' social media activity, it is possible to provide guests with useful information. The analysis of social media activity may be performed using AI, or it may be performed without AI. For example, guests' social media activity data can be input into a generating AI, and the generating AI can then perform the service provision.

[0053] The vacation rental booking system can adjust services for guests based on their past ratings. For example, if a guest receives a high rating, special services can be provided. If a guest receives a low rating, services can be adjusted to reflect improvements. If a guest receives a moderate rating, standard services can be provided. This allows for improved guest satisfaction by adjusting services based on past ratings. The analysis of ratings may be performed using AI, or it may be performed without AI. For example, guest rating data can be input into a generating AI, and the AI ​​can then perform the service adjustments.

[0054] A short-term rental booking system can provide services to guests based on their interests and preferences. For example, if a guest is interested in sightseeing, it can provide sightseeing information. If a guest is interested in food, it can provide restaurant information. If a guest is interested in shopping, it can provide shopping information. By providing services based on guests' interests and preferences, it is possible to improve guest satisfaction. The analysis of interests and preferences may be performed using AI, or it may be performed without using AI. For example, guest interest data can be input into a generating AI, and the generating AI can then perform the service provision.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection unit collects guest information. This guest information includes, for example, personal information, stay history, and preferences. The collection unit can collect guest information, for example, through a smartphone app. Step 2: The learning unit learns from the information collected by the collection unit. For example, the learning unit can learn about guests' information using a generative AI agent and understand their behavioral patterns. Step 3: The feedback unit provides feedback based on the information learned by the learning unit. The feedback unit provides feedback to platforms such as Airbnb. The feedback unit can provide appropriate feedback based on the behavior patterns of guests.

[0057] (Example of form 2) The vacation rental reception system according to an embodiment of the present invention is a system that automates and efficiently executes vacation rental reception using a generating AI agent, a smartphone app, a fixed camera, digital signage, and a key box. This system begins with the guest using the smartphone app to complete the check-in procedure. The generating AI agent learns the guest's information and provides feedback to platforms such as Airbnb to encourage the guest's good manners. The fixed camera is used to authenticate the guest's identity and to securely hand over the key. The digital signage provides the guest with necessary information, and the key box automates the key handover. This ensures secure check-in and check-out for both the guest and the business owner. Furthermore, it is multilingual and can provide personalized services to guests. For example, when a guest uses the smartphone app to complete the check-in procedure, the guest enters their information and confirms their reservation information. The generating AI agent learns the guest's information and provides feedback to platforms such as Airbnb to encourage the guest's good manners. For example, based on the guest's past usage history and ratings, it sends messages to the guest encouraging appropriate behavior. Next, the fixed camera is used to authenticate the guest's identity. When a guest stands in front of the camera, the camera recognizes the guest's face and performs personal authentication. This verifies that the guest is a legitimate user. Once personal authentication is complete, the key box is unlocked, and the guest can receive their key. Digital signage is used to provide guests with necessary information, such as how to use the accommodation and information about nearby tourist attractions. Guests can obtain the necessary information through the digital signage. Finally, when guests check out, they also use a smartphone app to complete the process. The generated AI agent learns the guest's checkout information and provides feedback to platforms such as Airbnb. This updates the guest's usage history, which can be used to help with future stays. This system enables secure and efficient check-in and check-out for both guests and business owners.Furthermore, the system offers multilingual support, enabling personalized services for guests. For example, it can provide guidance in the guest's native language and tourist information tailored to their interests. This allows the private lodging reservation system to efficiently collect, learn from, and provide feedback on guest information.

[0058] The vacation rental reservation system according to the embodiment comprises a collection unit, a learning unit, and a feedback unit. The collection unit collects guest information. Guest information includes, but is not limited to, personal information, accommodation history, and preferences. The collection unit can collect guest information, for example, via a smartphone app. The learning unit learns the information collected by the collection unit. The learning unit learns guest information, for example, using a generating AI agent. The generating AI agent can learn guest behavior patterns based on the guest's past usage history and ratings. The feedback unit provides feedback based on the information learned by the learning unit. The feedback unit provides feedback to, for example, a platform such as Airbnb. The feedback unit can provide appropriate feedback based on the guest's behavior patterns. As a result, the vacation rental reservation system according to the embodiment can efficiently collect, learn, and provide feedback on guest information. Some or all of the above-described processes in the collection unit, learning unit, and feedback unit may be performed using, for example, AI, or without using AI. For example, the data collection unit can acquire guest information via a smartphone app and input that information into a generating AI agent. The learning unit can use the generating AI agent to learn from the guest information and understand guest behavior patterns. The feedback unit can provide feedback to platforms such as Airbnb based on the information learned by the learning unit. This allows for efficient collection, learning, and feedback of guest information.

[0059] The data collection unit collects guest information. This information includes, but is not limited to, personal information, stay history, and preferences. For example, the data collection unit can collect guest information via a smartphone app. Specifically, when guests check in through the smartphone app, they enter personal information such as their name, address, and contact information. Information on past stay history and ratings, preferred types of accommodation, and amenities is also collected. This information is collected with the guest's consent and stored in a secure database. Furthermore, the data collection unit also records activities and requests made by guests using the app during their stay. This includes information such as ordering room service, making reservations for sightseeing, and participating in events at the facility. This allows the data collection unit to create detailed guest profiles and build a foundation for providing services tailored to individual needs and preferences. The collected data is updated in real time, making it possible to always have the latest information on guests.

[0060] The learning unit learns from the information collected by the collection unit. For example, the learning unit learns from guest information using a generative AI agent. The generative AI agent can learn guest behavior patterns based on the guest's past usage history and ratings. Specifically, the generative AI agent analyzes the collected data and models the guest's preferences and behavior patterns. For example, it predicts the guest's preferences based on information such as what types of accommodations the guest has preferred in the past, what amenities they valued, and what services they used during their stay. The generative AI agent also analyzes guest ratings and feedback to evaluate the quality and satisfaction of the service. This allows the learning unit to understand what services guests were satisfied with and what aspects they were dissatisfied with. Furthermore, the generative AI agent can learn guest behavior patterns and predict future behavior. For example, it can predict how guests will behave and what services they will request in accordance with specific seasons or events, and take countermeasures in advance. As a result, the learning unit can build a foundation for providing personalized services that meet the needs of guests.

[0061] The Feedback Department provides feedback based on the information learned by the Learning Department. For example, the Feedback Department provides feedback to platforms such as Airbnb. The Feedback Department can provide appropriate feedback based on guest behavior patterns. Specifically, based on guest preferences and behavior patterns obtained by the Learning Department, the Feedback Department provides improvement suggestions and recommendations to accommodation owners and managers. For example, if a guest prefers to use a particular amenity, the Feedback Department suggests improving that amenity. Furthermore, for areas where guests have expressed dissatisfaction in the past, the Feedback Department provides solutions and concrete action plans to improve service quality. In addition, the Feedback Department also provides feedback to guests, offering information to improve their satisfaction during their stay. For example, based on services and activities guests have used in the past, it suggests recommended services and events they can use during their stay. It also provides special offers and discounts tailored to guests' preferences to encourage repeat visits. In this way, the Feedback Department can provide valuable information to both guests and accommodations, improving service quality. Furthermore, based on the collected data, the Feedback Department can also provide strategic feedback to improve the overall operation of the accommodation.

[0062] The private lodging reception system includes an authentication unit that authenticates the guest's identity. The authentication unit authenticates the guest's identity. The authentication unit can authenticate the guest's identity using methods such as facial recognition or fingerprint recognition. When a guest stands in front of a camera, the camera recognizes the guest's face and performs identity authentication. This confirms that the guest is a legitimate user. The authentication unit can, for example, use facial recognition technology to recognize the guest's face and perform identity authentication. The authentication unit can also use fingerprint recognition technology to recognize the guest's fingerprint and perform identity authentication. This enables secure check-in by authenticating the guest's identity. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the guest's facial image into a generating AI and have the generating AI perform facial recognition.

[0063] The private lodging reservation system includes a provision unit that provides information necessary for guests. The provision unit provides information necessary for guests. For example, the provision unit can provide information about the accommodation and information about nearby tourist attractions. The provision unit collects information that guests need and provides it to guests. For example, the provision unit can provide information on how to use the accommodation and information about nearby tourist attractions. The provision unit can also provide information tailored to the guest's interests. This improves the convenience for guests by providing them with the information they need. Some or all of the above processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can input relevant information into a generating AI based on the guest's interests and have the generating AI provide the information.

[0064] The short-term rental reception system includes a key handover unit that automates the key handover process. The key handover unit automates the key handover process. For example, the key handover unit can automate the key handover process using a smart lock. When a guest checks in, the key handover unit unlocks the smart lock and hands over the key. When a guest checks out, the key handover unit locks the smart lock and collects the key. This automates the key handover process, improving convenience for both guests and business owners. Some or all of the above processes in the key handover unit may be performed using AI, for example, or without AI. For example, the key handover unit can input the guest's check-in information into a generating AI and have the generating AI unlock the smart lock.

[0065] The data collection unit can collect guest information via a smartphone app. For example, the data collection unit collects guest information when a guest uses the smartphone app to check in. The data collection unit collects information entered by the guest into the smartphone app and obtains the guest's personal information and reservation information. For example, the data collection unit can collect the guest's name, address, contact information, reservation information, etc., when a guest uses the smartphone app to check in. This improves the efficiency of information collection by collecting guest information via a smartphone app. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the information entered by the guest into the smartphone app into a generating AI and have the generating AI perform the information collection.

[0066] The learning unit can learn guest information using a generative AI agent. For example, the learning unit can learn guest behavior patterns based on the guest's past usage history and ratings using a generative AI agent. The learning unit can efficiently learn guest information using a generative AI agent. For example, the learning unit can learn guest behavior patterns based on the guest's past usage history and ratings using a generative AI agent. This allows for efficient learning of guest information by using a generative AI agent. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input guest information into a generative AI and have the generative AI perform information learning.

[0067] The feedback unit can provide feedback to platforms such as Airbnb. For example, the feedback unit provides feedback to platforms such as Airbnb based on the guest's behavior patterns. The feedback unit can provide appropriate feedback based on the guest's behavior patterns. For example, the feedback unit provides feedback to platforms such as Airbnb based on the guest's past usage history and ratings. By providing feedback to platforms such as Airbnb, it is possible to encourage guests to behave in a mannerly manner. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the guest's behavior patterns into a generating AI and have the generating AI perform the feedback.

[0068] The authentication unit can perform personal authentication of guests using a stationary camera. For example, the authentication unit can recognize the guest's face using a stationary camera and perform personal authentication. When a guest stands in front of the camera, the camera recognizes the guest's face and performs personal authentication. This confirms that the guest is a legitimate user. The authentication unit can recognize the guest's face using a stationary camera and perform personal authentication. This improves the accuracy of guest personal authentication by using a stationary camera. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the guest's face image into a generating AI and have the generating AI perform face authentication.

[0069] The service provider can provide guests with necessary information using digital signage. For example, the service provider can use digital signage to provide information about the accommodation and surrounding tourist attractions. The service provider can collect information that guests need and provide it to them. For example, the service provider can use digital signage to provide information on how to use the accommodation and surrounding tourist attractions. In this way, information can be provided to guests visually using digital signage. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input relevant information into a generating AI based on the guest's interests and have the generating AI provide the information.

[0070] The data collection unit can estimate the guest's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the guest is stressed, the data collection unit will collect information when the guest is relaxed. If the guest is relaxed, the data collection unit can proactively approach them to collect detailed information. If the guest is in a hurry, the data collection unit can quickly collect only the minimum necessary information. This allows for more appropriate information collection by adjusting the timing of information collection according to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input guest emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can analyze a guest's past accommodation history and select the most suitable information collection method. For example, the data collection unit can select a method for collecting information at similar accommodations based on information about accommodations the guest has used in the past. The data collection unit can adjust its information collection approach by referring to the guest's past ratings. The data collection unit can analyze the guest's past behavioral patterns and collect information at the optimal timing. This allows the data collection unit to select the most suitable information collection method by analyzing the guest's past accommodation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the guest's past accommodation history data into a generating AI and have the generating AI select the most suitable information collection method.

[0072] The data collection unit can filter and collect information based on the guest's current travel purpose and areas of interest. For example, if the guest is traveling for sightseeing, the data collection unit will prioritize collecting information about tourist destinations. If the guest is traveling for business, the data collection unit will prioritize collecting business-related information. The data collection unit can collect information on relevant events and activities based on the guest's areas of interest. This allows the collection unit to provide guests with useful information by collecting information tailored to their travel purpose and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the guest's travel purpose and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0073] The data collection unit can estimate the emotions of guests and determine the priority of information to collect based on the estimated emotions. For example, if a guest is feeling stressed, the data collection unit will prioritize collecting information that promotes relaxation. If a guest is relaxed, the data collection unit can prioritize collecting detailed information. If a guest is in a hurry, the data collection unit can prioritize collecting only the essential information. This allows for more appropriate information to be provided by prioritizing information according to the emotions of the guests. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input guest emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The data collection unit can prioritize the collection of highly relevant information, taking into account the geographical location of the guest. For example, the data collection unit can prioritize the collection of information about tourist attractions near the guest's current location. The data collection unit can prioritize the collection of information about restaurants near the guest's current location. The data collection unit can prioritize the collection of information about events near the guest's current location. In this way, by collecting information based on the guest's geographical location, useful information can be provided to the guest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the guest's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0075] The data collection unit can analyze guests' social media activity and collect relevant information. For example, the data collection unit can collect information on events that guests have shown interest in on social media. The data collection unit can collect information on accounts that guests follow on social media. The data collection unit can collect information on places that guests have shared on social media. By analyzing guests' social media activity, the data collection unit can provide guests with useful information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input guests' social media activity data into a generating AI and have the generating AI collect relevant information.

[0076] The learning unit can estimate the emotions of guests and select training data based on the estimated emotions. For example, if a guest is relaxed, the learning unit can select detailed training data. If a guest is stressed, the learning unit can select concise training data. If a guest is in a hurry, the learning unit can select data that can be learned quickly. This allows for more appropriate learning by selecting training data according to the emotions of guests. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input guest emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data and adjust the parameters of the learning algorithm. The learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0078] The learning unit can analyze the behavior patterns of guests and customize the learning content. For example, the learning unit can analyze the past behavior patterns of guests and customize the learning content. The learning unit can select the optimal learning content based on the guest's behavior patterns. The learning unit can analyze the guest's behavior patterns and determine the priority of the learning content. In this way, the learning content can be optimized by analyzing the guest's behavior patterns. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input guest behavior pattern data into a generating AI and have the generating AI perform the customization of the learning content.

[0079] The learning unit can estimate the guest's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency if the guest is relaxed. It can decrease the learning frequency if the guest is stressed. It can adjust the learning frequency if the guest is in a hurry. This allows for more appropriate learning by adjusting the learning frequency according to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input guest emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The learning unit can weight the training data based on the guest's stay history. For example, the learning unit can weight the training data based on the guest's past stay history. The learning unit can weight the training data based on the guest's evaluation. The learning unit can weight the training data based on the guest's behavioral patterns. By weighting the training data based on the guest's stay history, the accuracy of the learning is improved. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the guest's stay history data into a generating AI and have the generating AI perform the training data weighting.

[0081] The learning unit can adjust the learning content based on the guest's interests and preferences. For example, the learning unit can adjust the learning content based on the guest's interests. The learning unit can adjust the learning content based on the guest's interests. The learning unit can adjust the learning content based on the guest's past behavioral patterns. This improves the effectiveness of learning by providing learning content that matches the guest's interests and preferences. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input guest interest and preference data into a generating AI and have the generating AI perform the adjustment of the learning content.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The vacation rental booking system can estimate the guest's emotions and customize messages based on those emotions. For example, if a guest is stressed, a relaxing message can be sent. If the guest is relaxed, sightseeing information and recommended activities can be suggested. If the guest is in a hurry, a message prompting quick response can be sent. This improves guest satisfaction by providing messages tailored to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Message customization may be done using AI or not. For example, guest emotion data can be input into a generative AI, and the generative AI can perform emotion estimation.

[0084] A short-term rental booking system can estimate the emotions of guests and predict their behavior based on those emotions. For example, if a guest is feeling stressed, the system can predict what actions they will take to provide a relaxing environment. If a guest is relaxed, the system can predict they are more likely to participate in sightseeing or activities. If a guest is in a hurry, the system can predict they are more likely to check out quickly. This allows for more appropriate service by predicting behavior based on the guest's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Behavioral prediction may be performed using AI or not. For example, guest emotion data can be input into a generative AI, and the generative AI can perform emotion estimation.

[0085] The short-term rental booking system can estimate the emotions of guests and adjust the check-in and check-out procedures based on those estimated emotions. For example, if a guest is feeling stressed, the check-in process can be simplified and expedited. If a guest is relaxed, detailed instructions can be provided, and the process can proceed at a leisurely pace. If a guest is in a hurry, only the essential procedures can be performed quickly. This improves guest convenience by providing check-in and check-out procedures tailored to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Procedure adjustments may be performed using AI or not. For example, guest emotion data can be input into a generative AI, and the generative AI can perform emotion estimation.

[0086] A short-term rental booking system can estimate the emotions of guests and customize services based on those emotions. For example, if a guest is stressed, it can provide relaxing services. If a guest is relaxed, it can suggest sightseeing or activities. If a guest is in a hurry, it can provide quick service. By providing services tailored to the guest's emotions, guest satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Service customization may be done using AI or not. For example, guest emotion data can be input into a generative AI, and the generative AI can perform emotion estimation.

[0087] A short-term rental booking system can estimate the emotions of guests and collect feedback based on those emotions. For example, if a guest is relaxed, detailed feedback can be requested. If a guest is stressed, concise feedback can be requested. If a guest is in a hurry, feedback can be collected quickly. This allows for more appropriate feedback to be obtained by collecting feedback that is tailored to the guest's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Feedback collection may be performed using AI or not. For example, guest emotion data can be input into a generative AI, and the generative AI can be made to perform emotion estimation.

[0088] A short-term rental booking system can offer perks to guests based on their past stay history. For example, if a guest has given high ratings in the past, they can be offered a discount on their next stay. If a guest has made many stays in the past, they can be offered special services. If a guest has not caused any problems in the past, they can be offered priority check-in. In this way, by offering perks based on a guest's past stay history, the repeat rate of guests can be increased. The provision of perks may be done using AI, or not. For example, the guest's stay history data can be input into a generating AI, and the AI ​​can then be made to provide perks.

[0089] A private lodging reservation system can provide services to guests based on their current location. For example, when a guest approaches the accommodation, the system can prepare for check-in. If the guest is at a tourist spot, it can provide tourist information. If the guest is at a restaurant, it can provide the restaurant's menu and recommended dishes. By providing services based on the guest's current location, the system can improve guest convenience. The use of location information may be done using AI, or it may not. For example, the guest's location data can be input into a generating AI, and the AI ​​can then perform the service provision.

[0090] A short-term rental booking system can analyze guests' social media activity and provide services to them. For example, it can provide information about events that guests have shown interest in on social media, information about accounts that guests follow on social media, and information about places that guests have shared on social media. In this way, by analyzing guests' social media activity, it is possible to provide guests with useful information. The analysis of social media activity may be performed using AI, or it may be performed without AI. For example, guests' social media activity data can be input into a generating AI, and the generating AI can then perform the service provision.

[0091] The vacation rental booking system can adjust services for guests based on their past ratings. For example, if a guest receives a high rating, special services can be provided. If a guest receives a low rating, services can be adjusted to reflect improvements. If a guest receives a moderate rating, standard services can be provided. This allows for improved guest satisfaction by adjusting services based on past ratings. The analysis of ratings may be performed using AI, or it may be performed without AI. For example, guest rating data can be input into a generating AI, and the AI ​​can then perform the service adjustments.

[0092] A short-term rental booking system can provide services to guests based on their interests and preferences. For example, if a guest is interested in sightseeing, it can provide sightseeing information. If a guest is interested in food, it can provide restaurant information. If a guest is interested in shopping, it can provide shopping information. By providing services based on guests' interests and preferences, it is possible to improve guest satisfaction. The analysis of interests and preferences may be performed using AI, or it may be performed without using AI. For example, guest interest data can be input into a generating AI, and the generating AI can then perform the service provision.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The collection unit collects guest information. This guest information includes, for example, personal information, stay history, and preferences. The collection unit can collect guest information, for example, through a smartphone app. Step 2: The learning unit learns from the information collected by the collection unit. For example, the learning unit can learn about guests' information using a generative AI agent and understand their behavioral patterns. Step 3: The feedback unit provides feedback based on the information learned by the learning unit. The feedback unit provides feedback to platforms such as Airbnb. The feedback unit can provide appropriate feedback based on the behavior patterns of guests.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] Each of the multiple elements described above, including the collection unit, learning unit, feedback unit, authentication unit, provision unit, and key handover unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects guest information via a smartphone app on the smart device 14. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns guest information using a generating AI agent. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and feeds the learned information back to a platform such as Airbnb. The authentication unit recognizes the guest's face using the camera 42 of the smart device 14 and performs personal authentication. The provision unit provides guests with necessary information using the digital signage on the smart device 14. The key handover unit automates key handover using the key box on the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, learning unit, feedback unit, authentication unit, provision unit, and key handover unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects guest information via a smartphone app on the smart glasses 214. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns guest information using a generating AI agent. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and feeds back the learned information to a platform such as Airbnb. The authentication unit recognizes the guest's face using the camera 42 of the smart glasses 214 and performs personal authentication. The provision unit provides guests with necessary information using the digital signage on the smart glasses 214. The key handover unit automates key handover using the key box on the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, learning unit, feedback unit, authentication unit, provision unit, and key handover unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects guest information via a smartphone app on the headset terminal 314. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns guest information using a generating AI agent. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and feeds the learned information back to a platform such as Airbnb. The authentication unit recognizes the guest's face using the camera 42 of the headset terminal 314 and performs personal authentication. The provision unit provides guests with necessary information using the digital signage on the headset terminal 314. The key handover unit automates key handover using the key box on the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, learning unit, feedback unit, authentication unit, provision unit, and key handover unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects guest information via the robot 414's smartphone app. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns guest information using a generating AI agent. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and feeds the learned information back to a platform such as Airbnb. The authentication unit recognizes the guest's face using the robot 414's camera 42 and performs personal authentication. The provision unit provides guests with necessary information using the robot 414's digital signage. The key handover unit automates key handover using the robot 414's key box. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0148] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0158] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] (Note 1) A collection department that collects information on hotel guests, A learning unit that learns the information collected by the aforementioned collection unit, A feedback unit that provides feedback on the information learned by the learning unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with an authentication unit for verifying the personal identity of guests. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a service area that provides necessary information to guests. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a key handover unit that automates the key exchange process. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collecting guest information via a smartphone app The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, Learn guest information using a generative AI agent. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is Provide feedback to platforms such as Airbnb. The system described in Appendix 1, characterized by the features described herein. (Note 8) The authentication unit, Personal identification of guests is performed using stationary cameras. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned supply unit is, Providing guests with necessary information using digital signage. The system described in Appendix 3, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of the guests and adjusts the timing of information collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We analyze guests' past stay history and select the most suitable method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Information is filtered and collected based on the guest's current travel purpose and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The system estimates the emotions of guests and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Prioritize the collection of highly relevant information, taking into account the geographical location of guests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is Analyze guests' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, The system estimates the emotions of hotel guests and selects training data based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, Analyze the behavior patterns of guests and customize the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, The system estimates the emotions of the guests and adjusts the frequency of learning based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, The training data is weighted based on the guests' accommodation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, The learning content is tailored to the interests and concerns of the guests. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information on hotel guests, A learning unit that learns the information collected by the aforementioned collection unit, A feedback unit that provides feedback on the information learned by the learning unit, Equipped with A system characterized by the following features.

2. It is equipped with an authentication unit for verifying the personal identity of guests. The system according to feature 1.

3. It is equipped with a service area that provides necessary information to guests. The system according to feature 1.

4. It is equipped with a key handover unit that automates the key exchange process. The system according to feature 1.

5. The aforementioned collection unit is Collecting guest information via a smartphone app The system according to feature 1.

6. The aforementioned learning unit, Learn guest information using a generative AI agent. The system according to feature 1.

7. The aforementioned feedback unit is Provide feedback to platforms such as Airbnb. The system according to feature 1.

8. The authentication unit, Personal identification of guests is performed using stationary cameras. The system according to feature 2.

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