system

Abstract

The system according to this embodiment aims to analyze guest information and provide services tailored to individual needs. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects information about guests. The generation unit analyzes the information collected by the collection unit and generates appropriate services for guests. The provision unit provides the services generated by the generation unit to guests.

Description

Technical Field

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

Background Art

[0002] Patent Document No. 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide services according to the individual needs of guests, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the information of guests and provide services according to individual needs.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects information of guests. The generation unit analyzes the information collected by the collection unit and generates appropriate services for the guests. The provision unit provides the services generated by the generation unit to the guests.

Effects of the Invention

[0007] The system according to this embodiment can analyze guest information and provide services tailored to individual needs. [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 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 personalized hospitality system according to an embodiment of the present invention is a system that provides personalized hospitality to guests by utilizing generative AI. This personalized hospitality system collects guest information, analyzes the collected information, generates the optimal service for the guest, and provides the generated service to the guest. For example, it detects the guest's heart rate and facial expressions using cameras and sensors, and also collects information such as the guest's hobbies, interests, and food preferences. Next, it analyzes the collected information and generates music and lighting effects that are optimal for the guest. It also customizes wallpaper and artwork based on the guest's hobbies and interests. Furthermore, it displays videos on a display installed in the lobby that combine seasonal and local scenery generated by generative AI with the guest's preferences. An AI concierge responds to the guest's questions and requests and proposes personalized menus. As a result, guests can receive hospitality tailored to their needs and enjoy a comfortable stay. Thus, the personalized hospitality system can automatically collect and analyze guest information and provide the optimal service.

[0029] The personalized hospitality system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects information about guests. This information includes, but is not limited to, heart rate, facial expressions, hobbies, interests, and food preferences. For example, the collection unit can detect the guest's heart rate and facial expressions using cameras or sensors. The collection unit can also collect information such as the guest's hobbies, interests, and food preferences through surveys or social media analysis. The generation unit analyzes the information collected by the collection unit and generates the most suitable service for the guest. For example, the generation unit uses a generation AI to analyze data on the guest's heart rate and facial expressions and generates the most suitable music and lighting effects for that moment. The generation unit can also customize wallpaper and artwork based on the guest's hobbies and interests. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and uses these to generate the most suitable service for the guest. The provision unit provides the service generated by the generation unit to the guest. The service department, for example, displays videos on a screen installed in the lobby that combine seasonal and local scenery generated by a generative AI with the guest's preferences. Furthermore, the service department can act as an AI concierge, responding to guests' questions and requests and suggesting personalized menus. Thus, the personalized hospitality system according to this embodiment can collect and analyze guest information and provide optimal service, thereby realizing personalized hospitality.

[0030] The data collection unit can detect the guest's heart rate and facial expression using a camera and sensors. For example, the data collection unit can detect the guest's facial expression using a camera. For example, the camera captures the guest's face, and facial recognition technology is used to analyze the expression. The data collection unit can also measure the guest's heart rate using sensors. For example, a heart rate sensor detects the guest's pulse and measures the heart rate. This allows the data collection unit to detect the guest's heart rate and facial expression in real time and understand the guest's emotions. 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 image data acquired by the camera into a generating AI and have the generating AI perform facial expression analysis.

[0031] The data collection unit can collect information on guests' hobbies, interests, and food preferences. For example, the data collection unit can collect information on guests' hobbies, interests, and food preferences through surveys. For example, it can ask guests questions about their hobbies and interests and collect their responses. The data collection unit can also collect information on guests' hobbies, interests, and food preferences through social media analysis. For example, it can analyze guests' social media accounts and identify their hobbies and interests from their posts and the accounts they follow. Furthermore, the data collection unit can also collect information on guests' past dining history and allergies. For example, it can retrieve information on menus guests have ordered in the past and allergy information from a database. This allows the data collection unit to collect detailed information on guests' hobbies, interests, and food preferences and obtain basic information for providing personalized services. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input social media posting data into a generating AI and have the generating AI identify hobbies and interests.

[0032] The generation unit can analyze collected information and generate appropriate music and lighting effects for guests. For example, the generation unit can analyze collected heart rate and facial expression data and generate the optimal music and lighting effects for that moment. For instance, if the heart rate is high, the generation unit can generate relaxing music and soft lighting effects, and if the heart rate is low, it can generate lively music and bright lighting effects. The generation unit can also analyze the guest's facial expression data and generate cheerful music and lighting effects if the guest is smiling, and calming music and lighting effects if the guest has a serious expression. In this way, the generation unit can analyze guest information to generate optimal music and lighting effects and provide a comfortable environment. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input heart rate and facial expression data into a generation AI and have the generation AI generate the music and lighting effects.

[0033] The generation unit can customize wallpaper and artwork based on the guest's hobbies and interests. For example, if the guest likes nature, the generation unit will generate wallpaper and artwork depicting natural landscapes; if the guest likes art, it will generate wallpaper and artwork with abstract designs. The generation unit can also generate wallpaper and artwork based on a specific theme based on the guest's interests. For example, if the guest is interested in music, it will generate wallpaper and artwork with a music theme. In this way, the generation unit can customize wallpaper and artwork based on the guest's hobbies and interests, providing a unique space. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input data on the guest's hobbies and interests into a generation AI and have the generation AI perform the generation of wallpaper and artwork.

[0034] The service department can display videos on a display in the lobby that combine seasonal and local scenery generated by a generative AI with the guest's preferences. For example, the service department can display cherry blossom scenery in spring, ocean scenery in summer, autumn foliage scenery in fall, and snowscapes in winter, customizing these scenery according to the guest's preferences. The service department can also generate videos incorporating local landmarks and cultural buildings to convey the charm of the local area to guests. This allows the service department to display videos tailored to the guest's preferences and provide a more personalized hospitality. Some or all of the above processing in the service department may be performed using a generative AI, or not. For example, the service department can input seasonal and local scenery data into a generative AI and have the generative AI generate videos.

[0035] The service department can respond to guests' questions and requests as an AI concierge and propose personalized menus. For example, the service department can respond to guests' questions and requests as an AI concierge. For example, the service department can use natural language processing technology to understand guests' questions and provide appropriate answers. The service department can also propose personalized menus based on guests' requests. For example, the service department can consider guests' food preferences and allergy information and propose personalized meal menus. In this way, the service department can respond to guests' questions and requests as an AI concierge and provide personalized service without language barriers. Some or all of the above processing in the service department may be performed using, for example, generative AI, or not using generative AI. For example, the service department can input data on guests' questions and requests into generative AI and have the generative AI execute the responses and menu suggestions.

[0036] The data collection unit can analyze a guest's past stay history and select an appropriate information collection method. For example, the data collection unit can retrieve and analyze a guest's past stay history from a database. For example, the data collection unit can determine the priority of information collection based on services and activities that the guest has preferred in the past. The data collection unit can also analyze feedback from past stays and select an information collection method that reflects areas for improvement. Furthermore, the data collection unit can predict services that guests prefer at specific times based on their past stay history and collect information during those times. This allows the data collection unit to analyze a guest's past stay history and collect information more effectively. 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 past stay history data into a generating AI and have the generating AI select an information collection method.

[0037] The data collection unit can filter information based on the guest's current activities and areas of interest during data collection. For example, the data collection unit can monitor the guest's current activities and filter information based on their areas of interest. For instance, if the guest is currently reading, the data collection unit will prioritize collecting information related to reading. Similarly, if the guest is enjoying sports, the data collection unit can prioritize collecting information related to sports. Furthermore, if the guest is sightseeing, the data collection unit can prioritize collecting information related to tourist destinations. This allows the data collection unit to filter information based on the guest's current activities and areas of interest, thereby collecting more relevant information. 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 the guest's current activity data into a generating AI and have the generating AI perform the information filtering.

[0038] The data collection unit can prioritize collecting highly relevant information based on the guest's geographical location during information gathering. For example, the data collection unit can acquire the guest's geographical location and prioritize collecting highly relevant information. For instance, if the guest is in the hotel, the data collection unit will prioritize collecting information about services and events within the hotel. If the guest is out, the data collection unit can also prioritize collecting information about nearby tourist attractions and restaurants. Furthermore, if the guest is staying in a specific area, the data collection unit can prioritize collecting information related to that area. This allows the data collection unit to collect more relevant information by considering the guest's geographical location. 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 geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0039] The data collection unit can analyze guests' social media activity and collect relevant information during data collection. For example, the data collection unit can analyze guests' social media activity and collect relevant information. For example, the data collection unit can collect information based on the interests and concerns that guests share on social media. The data collection unit can also collect relevant information based on the accounts and groups that guests follow. Furthermore, the data collection unit can collect relevant information based on the location where guests checked in on social media. This allows the data collection unit to analyze guests' social media activity and collect more relevant 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 social media data into a generating AI and have the generating AI collect relevant information.

[0040] The generation unit can adjust the level of detail generated according to the importance of the guest when generating services. For example, the generation unit adjusts the level of detail based on the importance of the guest. For example, in the case of VIP guests, the generation unit generates detailed and high-quality services. The generation unit can also generate standard services for regular guests. Furthermore, in the case of repeat guests, the generation unit can generate customized services that take into account their past stay history. In this way, the generation unit can adjust the level of detail generated based on the importance of the guest and provide more appropriate services. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input guest importance data into a generation AI and have the generation AI perform the adjustment of the level of detail generated.

[0041] The generation unit can apply different generation algorithms based on the guest's category when generating services. For example, the generation unit can apply different generation algorithms depending on the guest's category. For instance, for business travelers, the generation unit can apply an algorithm that generates efficient services. For tourists, the generation unit can also apply an algorithm that generates services that prioritize tourist information. Furthermore, for families, the generation unit can apply an algorithm that generates services that the whole family can enjoy. This allows the generation unit to apply different generation algorithms depending on the guest's category and provide more appropriate services. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input guest category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0042] The generation unit can determine the priority of service generation based on the guest's length of stay. For example, the generation unit may prioritize generating efficient services if the guest is staying during peak season. It can also prioritize generating relaxed services if the guest is staying during off-peak season. Furthermore, if the guest is staying during a specific event period, the generation unit may prioritize generating services related to that event. This allows the generation unit to determine the priority of service generation based on the guest's length of stay and provide more appropriate services. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the length of stay data into the generation AI and have the generation AI determine the priority of service generation.

[0043] The generation unit can adjust the order of service generation based on the guest's relevance. For example, the generation unit can adjust the order of generation based on the guest's relevance. For example, if a guest strongly desires a particular service, the generation unit will generate that service with the highest priority. The generation unit can also prioritize generating services that are highly relevant if a guest desires multiple services. Furthermore, the generation unit can prioritize generating services that are highly relevant based on services the guest has used in the past. In this way, the generation unit can adjust the order of generation based on the guest's relevance and provide more appropriate services. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input guest relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0044] The service provider can select an appropriate service delivery method by referring to the guest's past service usage history when providing services. For example, the service provider can retrieve and analyze the guest's past service usage history from a database. For example, the service provider can provide services that the guest has previously preferred. The service provider can also provide services that reflect improvements based on feedback from services previously used by the guest. Furthermore, the service provider can predict the guest's preferred services at specific times based on their past service usage history and provide them at those times. In this way, the service provider can refer to the guest's past service usage history and provide more appropriate services. 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 past service usage history data into a generating AI and have the generating AI select the delivery method.

[0045] The service provider can customize the means of service delivery based on the guest's current living situation. For example, the service provider can customize the means of service delivery considering the guest's current living situation. For example, if the guest is staying for business purposes, the service provider can provide efficient service. Also, if the guest is staying for sightseeing purposes, the service provider can provide service that emphasizes sightseeing information. Furthermore, if the guest is staying with their family, the service provider can provide service that the whole family can enjoy. In this way, the service provider can customize the means of service delivery based on the guest's current living situation and provide more appropriate service. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input guest living situation data into a generating AI and have the generating AI perform the customization of the means of service delivery.

[0046] The service provider can select an appropriate service delivery method based on the guest's geographical location information when providing services. For example, the service provider can acquire the guest's geographical location information and select an appropriate service delivery method. For example, if the guest is in the hotel, the service provider can prioritize providing services within the hotel. If the guest is out, the service provider can also provide services related to nearby tourist attractions and restaurants. Furthermore, if the guest is staying in a specific area, the service provider can also provide services related to that area. This allows the service provider to provide more appropriate services by taking into account the guest's geographical location information. 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 geographical location information into a generating AI and have the generating AI perform the selection of the service delivery method.

[0047] The service provider can analyze guests' social media activity and propose ways to provide services when providing services. For example, the service provider can analyze guests' social media activity and collect relevant information. For example, the service provider can provide services based on the interests and concerns shared by guests on social media. The service provider can also provide relevant services based on information about accounts and groups that guests follow. Furthermore, the service provider can provide relevant services based on the location where guests checked in on social media. This allows the service provider to analyze guests' social media activity and provide more appropriate services. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input social media data into a generating AI and have the generating AI propose ways to provide services.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The data collection unit can analyze guests' past travel history and suggest destinations for their next trip. For example, the unit retrieves and analyzes information from a database about places guests have visited and the length of their stays. Then, based on the characteristics of destinations guests have preferred in the past, the unit can suggest destinations for their next trip. For example, if a guest prefers nature, it will suggest tourist destinations rich in nature; if they prefer city sightseeing, it will suggest tourist destinations in urban areas. The unit can also consider guests' activity and dining preferences at past destinations and suggest activities and restaurants for their next trip. In this way, the data collection unit can leverage guests' past travel history to suggest destinations for their next trip and provide a more personalized service.

[0050] The data collection unit can monitor the health status of guests and provide health-conscious services. For example, the unit collects health data such as heart rate, blood pressure, and body temperature using sensors. Next, the unit analyzes the collected health data to understand the guest's health status. For example, if a guest's heart rate is high, it can provide services with a relaxing effect, and if their blood pressure is high, it can suggest a low-sodium meal. The unit can also suggest appropriate exercise programs and relaxation programs according to the guest's health status. In this way, the data collection unit can monitor the health status of guests and provide health-conscious services.

[0051] The service department can analyze guests' past stay history and offer special services during their next stay. For example, the service department can retrieve and analyze guests' past stay history from a database. Then, based on the services and activities that guests have preferred in the past, the service department can offer special services during their next stay. For example, if a guest has previously enjoyed using the spa, a discount coupon for the spa can be offered during their next stay. If a guest has preferred a particular restaurant, a special menu at that restaurant can be offered. The service department can also suggest specific events or activities based on guests' past stay history. In this way, the service department can leverage guests' past stay history to provide special services during their next stay and achieve more personalized hospitality.

[0052] The service department can monitor the current activities of guests and provide appropriate services. For example, the service department can monitor the activities guests are currently engaged in and provide services suited to those activities. For instance, if a guest is reading, they can provide a quiet environment; if a guest is exercising, they can provide sports drinks and towels. If a guest is sightseeing, they can provide information and maps about tourist attractions. This allows the service department to provide more appropriate services by considering the guest's current activities.

[0053] The service provider can utilize the geographical location information of guests to provide information on nearby tourist attractions and restaurants. For example, the service provider can obtain the guest's current geographical location and provide information on nearby tourist attractions and restaurants based on that location. For instance, if the guest is inside the hotel, it can provide information on restaurants and facilities within the hotel; if the guest is out, it can provide information on nearby tourist attractions and restaurants. Furthermore, if the guest is staying in a specific area, the service provider can also provide information on events and activities related to that area. In this way, the service provider can provide more appropriate information by utilizing the geographical location information of guests.

[0054] The service department can analyze guests' social media activity and suggest events and activities that are suitable for them. For example, the service department can analyze guests' social media accounts to understand their interests and preferences. Then, based on these interests, the service department can suggest events and activities that are suitable for the guests. For example, if a guest is interested in music, they can suggest music events or live performances; if a guest is interested in sports, they can suggest sports events or activities. If a guest is interested in art, they can also provide information on art galleries and museums. In this way, the service department can leverage guests' social media activity to suggest more personalized events and activities.

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

[0056] Step 1: The data collection unit collects information about the guests. This information includes heart rate, facial expressions, hobbies, interests, and food preferences. The data collection unit uses cameras and sensors to detect guests' heart rates and facial expressions. It can also collect information about guests' hobbies, interests, and food preferences through surveys and social media analysis. Step 2: The generation unit analyzes the information collected by the collection unit and generates the optimal service for the guest. The generation unit uses a generation AI to analyze the guest's heart rate and facial expression data and generates the optimal music and lighting effects for that moment. It can also customize wallpaper and artwork based on the guest's hobbies and interests. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The service department provides the services generated by the generation department to the guests. The service department displays videos on a screen in the lobby that combine seasonal and local scenery generated by the generation AI with the guest's preferences. It can also act as an AI concierge, responding to guests' questions and requests and suggesting personalized menus.

[0057] (Example of form 2) The personalized hospitality system according to an embodiment of the present invention is a system that provides personalized hospitality to guests by utilizing generative AI. This personalized hospitality system collects guest information, analyzes the collected information, generates the optimal service for the guest, and provides the generated service to the guest. For example, it detects the guest's heart rate and facial expressions using cameras and sensors, and also collects information such as the guest's hobbies, interests, and food preferences. Next, it analyzes the collected information and generates music and lighting effects that are optimal for the guest. It also customizes wallpaper and artwork based on the guest's hobbies and interests. Furthermore, it displays videos on a display installed in the lobby that combine seasonal and local scenery generated by generative AI with the guest's preferences. An AI concierge responds to the guest's questions and requests and proposes personalized menus. As a result, guests can receive hospitality tailored to their needs and enjoy a comfortable stay. Thus, the personalized hospitality system can automatically collect and analyze guest information and provide the optimal service.

[0058] The personalized hospitality system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects information about guests. This information includes, but is not limited to, heart rate, facial expressions, hobbies, interests, and food preferences. For example, the collection unit can detect the guest's heart rate and facial expressions using cameras or sensors. The collection unit can also collect information such as the guest's hobbies, interests, and food preferences through surveys or social media analysis. The generation unit analyzes the information collected by the collection unit and generates the most suitable service for the guest. For example, the generation unit uses a generation AI to analyze data on the guest's heart rate and facial expressions and generates the most suitable music and lighting effects for that moment. The generation unit can also customize wallpaper and artwork based on the guest's hobbies and interests. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and uses these to generate the most suitable service for the guest. The provision unit provides the service generated by the generation unit to the guest. The service department, for example, displays videos on a screen installed in the lobby that combine seasonal and local scenery generated by a generative AI with the guest's preferences. Furthermore, the service department can act as an AI concierge, responding to guests' questions and requests and suggesting personalized menus. Thus, the personalized hospitality system according to this embodiment can collect and analyze guest information and provide optimal service, thereby realizing personalized hospitality.

[0059] The data collection unit can detect the guest's heart rate and facial expression using a camera and sensors. For example, the data collection unit can detect the guest's facial expression using a camera. For example, the camera captures the guest's face, and facial recognition technology is used to analyze the expression. The data collection unit can also measure the guest's heart rate using sensors. For example, a heart rate sensor detects the guest's pulse and measures the heart rate. This allows the data collection unit to detect the guest's heart rate and facial expression in real time and understand the guest's emotions. 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 image data acquired by the camera into a generating AI and have the generating AI perform facial expression analysis.

[0060] The data collection unit can collect information on guests' hobbies, interests, and food preferences. For example, the data collection unit can collect information on guests' hobbies, interests, and food preferences through surveys. For example, it can ask guests questions about their hobbies and interests and collect their responses. The data collection unit can also collect information on guests' hobbies, interests, and food preferences through social media analysis. For example, it can analyze guests' social media accounts and identify their hobbies and interests from their posts and the accounts they follow. Furthermore, the data collection unit can also collect information on guests' past dining history and allergies. For example, it can retrieve information on menus guests have ordered in the past and allergy information from a database. This allows the data collection unit to collect detailed information on guests' hobbies, interests, and food preferences and obtain basic information for providing personalized services. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input social media posting data into a generating AI and have the generating AI identify hobbies and interests.

[0061] The generation unit can analyze collected information and generate appropriate music and lighting effects for guests. For example, the generation unit can analyze collected heart rate and facial expression data and generate the optimal music and lighting effects for that moment. For instance, if the heart rate is high, the generation unit can generate relaxing music and soft lighting effects, and if the heart rate is low, it can generate lively music and bright lighting effects. The generation unit can also analyze the guest's facial expression data and generate cheerful music and lighting effects if the guest is smiling, and calming music and lighting effects if the guest has a serious expression. In this way, the generation unit can analyze guest information to generate optimal music and lighting effects and provide a comfortable environment. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input heart rate and facial expression data into a generation AI and have the generation AI generate the music and lighting effects.

[0062] The generation unit can customize wallpaper and artwork based on the guest's hobbies and interests. For example, if the guest likes nature, the generation unit will generate wallpaper and artwork depicting natural landscapes; if the guest likes art, it will generate wallpaper and artwork with abstract designs. The generation unit can also generate wallpaper and artwork based on a specific theme based on the guest's interests. For example, if the guest is interested in music, it will generate wallpaper and artwork with a music theme. In this way, the generation unit can customize wallpaper and artwork based on the guest's hobbies and interests, providing a unique space. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input data on the guest's hobbies and interests into a generation AI and have the generation AI perform the generation of wallpaper and artwork.

[0063] The service department can display videos on a display in the lobby that combine seasonal and local scenery generated by a generative AI with the guest's preferences. For example, the service department can display cherry blossom scenery in spring, ocean scenery in summer, autumn foliage scenery in fall, and snowscapes in winter, customizing these scenery according to the guest's preferences. The service department can also generate videos incorporating local landmarks and cultural buildings to convey the charm of the local area to guests. This allows the service department to display videos tailored to the guest's preferences and provide a more personalized hospitality. Some or all of the above processing in the service department may be performed using a generative AI, or not. For example, the service department can input seasonal and local scenery data into a generative AI and have the generative AI generate videos.

[0064] The service department can respond to guests' questions and requests as an AI concierge and propose personalized menus. For example, the service department can respond to guests' questions and requests as an AI concierge. For example, the service department can use natural language processing technology to understand guests' questions and provide appropriate answers. The service department can also propose personalized menus based on guests' requests. For example, the service department can consider guests' food preferences and allergy information and propose personalized meal menus. In this way, the service department can respond to guests' questions and requests as an AI concierge and provide personalized service without language barriers. Some or all of the above processing in the service department may be performed using, for example, generative AI, or not using generative AI. For example, the service department can input data on guests' questions and requests into generative AI and have the generative AI execute the responses and menu suggestions.

[0065] The data collection unit can estimate the emotions of guests and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit can analyze the emotions of guests by analyzing their facial expressions and heart rate. For example, the data collection unit can analyze the emotions of guests using facial recognition technology. The data collection unit can also measure the emotions of guests using a heart rate sensor and estimate their emotions. This allows the data collection unit to grasp the emotions of guests in real time and adjust the timing of information collection. For example, if a guest is relaxed, the timing of information collection can be delayed so as not to disturb the guest's relaxation. If a guest is excited, information can be collected quickly to provide the best service before the excitement subsides. Furthermore, if a guest is stressed, information collection can be withheld and waited until the guest calms down. This allows the data collection unit to adjust the timing of information collection based on the emotions of guests and collect information while maintaining the guest's comfort. 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 facial expression and heart rate data into a generating AI, which can then perform emotion estimation and adjust the timing of information collection.

[0066] The data collection unit can analyze a guest's past stay history and select an appropriate information collection method. For example, the data collection unit can retrieve and analyze a guest's past stay history from a database. For example, the data collection unit can determine the priority of information collection based on services and activities that the guest has preferred in the past. The data collection unit can also analyze feedback from past stays and select an information collection method that reflects areas for improvement. Furthermore, the data collection unit can predict services that guests prefer at specific times based on their past stay history and collect information during those times. This allows the data collection unit to analyze a guest's past stay history and collect information more effectively. 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 past stay history data into a generating AI and have the generating AI select an information collection method.

[0067] The data collection unit can filter information based on the guest's current activities and areas of interest during data collection. For example, the data collection unit can monitor the guest's current activities and filter information based on their areas of interest. For instance, if the guest is currently reading, the data collection unit will prioritize collecting information related to reading. Similarly, if the guest is enjoying sports, the data collection unit can prioritize collecting information related to sports. Furthermore, if the guest is sightseeing, the data collection unit can prioritize collecting information related to tourist destinations. This allows the data collection unit to filter information based on the guest's current activities and areas of interest, thereby collecting more relevant information. 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 the guest's current activity data into a generating AI and have the generating AI perform the information filtering.

[0068] 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, the data collection unit can analyze the guest's facial expressions and heart rate to estimate emotions. For example, the data collection unit can analyze the guest's facial expressions using facial recognition technology to estimate emotions. The data collection unit can also measure the guest's heart rate using a heart rate sensor to estimate emotions. This allows the data collection unit to grasp the guest's emotions in real time and determine the priority of information to collect. For example, if a guest is relaxed, information related to relaxation can be collected preferentially. If a guest is excited, information related to entertainment can be collected preferentially. Furthermore, if a guest is stressed, information related to stress reduction can be collected preferentially. This allows the data collection unit to determine the priority of information based on the guest's emotions and collect more appropriate information. 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 facial expression and heart rate data into a generating AI and have the generating AI perform emotion estimation and information prioritization.

[0069] The data collection unit can prioritize collecting highly relevant information based on the guest's geographical location during information gathering. For example, the data collection unit can acquire the guest's geographical location and prioritize collecting highly relevant information. For instance, if the guest is in the hotel, the data collection unit will prioritize collecting information about services and events within the hotel. If the guest is out, the data collection unit can also prioritize collecting information about nearby tourist attractions and restaurants. Furthermore, if the guest is staying in a specific area, the data collection unit can prioritize collecting information related to that area. This allows the data collection unit to collect more relevant information by considering the guest's geographical location. 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 geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0070] The data collection unit can analyze guests' social media activity and collect relevant information during data collection. For example, the data collection unit can analyze guests' social media activity and collect relevant information. For example, the data collection unit can collect information based on the interests and concerns that guests share on social media. The data collection unit can also collect relevant information based on the accounts and groups that guests follow. Furthermore, the data collection unit can collect relevant information based on the location where guests checked in on social media. This allows the data collection unit to analyze guests' social media activity and collect more relevant 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 social media data into a generating AI and have the generating AI collect relevant information.

[0071] The generation unit can estimate the emotions of guests and adjust the way it expresses the services it generates based on the estimated emotions. For example, the generation unit can analyze the guest's facial expressions and heart rate to estimate emotions. For example, the generation unit can analyze the guest's facial expressions using facial recognition technology to estimate emotions. The generation unit can also measure the guest's heart rate using a heart rate sensor to estimate emotions. This allows the generation unit to grasp the guest's emotions in real time and adjust the way it expresses the services it generates. For example, if the guest is relaxed, it can generate calming music and soft lighting. If the guest is excited, it can generate lively music and bright lighting. Furthermore, if the guest is stressed, it can generate relaxing music and calming lighting. This allows the generation unit to adjust the way it expresses the services based on the guest's emotions and provide more appropriate services. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input facial expression and heart rate data into a generation AI and have the generation AI adjust the way it expresses the services.

[0072] The generation unit can adjust the level of detail generated according to the importance of the guest when generating services. For example, the generation unit adjusts the level of detail based on the importance of the guest. For example, in the case of VIP guests, the generation unit generates detailed and high-quality services. The generation unit can also generate standard services for regular guests. Furthermore, in the case of repeat guests, the generation unit can generate customized services that take into account their past stay history. In this way, the generation unit can adjust the level of detail generated based on the importance of the guest and provide more appropriate services. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input guest importance data into a generation AI and have the generation AI perform the adjustment of the level of detail generated.

[0073] The generation unit can apply different generation algorithms based on the guest's category when generating services. For example, the generation unit can apply different generation algorithms depending on the guest's category. For instance, for business travelers, the generation unit can apply an algorithm that generates efficient services. For tourists, the generation unit can also apply an algorithm that generates services that prioritize tourist information. Furthermore, for families, the generation unit can apply an algorithm that generates services that the whole family can enjoy. This allows the generation unit to apply different generation algorithms depending on the guest's category and provide more appropriate services. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input guest category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0074] The generation unit can estimate the emotions of the guest and adjust the length of the service it generates based on the estimated emotions. For example, the generation unit can analyze the guest's facial expressions and heart rate to estimate emotions. For example, the generation unit can analyze the guest's facial expressions using facial recognition technology to estimate emotions. The generation unit can also measure the guest's heart rate using a heart rate sensor to estimate emotions. This allows the generation unit to grasp the guest's emotions in real time and adjust the length of the service it generates. For example, if the guest is relaxed, it can generate a longer relaxation service. If the guest is in a hurry, it can generate a short but effective service. Furthermore, if the guest is excited, it can generate an entertainment service of an appropriate length. This allows the generation unit to adjust the length of the service based on the guest's emotions and provide a more appropriate service. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input facial expression and heart rate data into a generation AI and have the generation AI adjust the length of the service.

[0075] The generation unit can determine the priority of service generation based on the guest's length of stay. For example, the generation unit may prioritize generating efficient services if the guest is staying during peak season. It can also prioritize generating relaxed services if the guest is staying during off-peak season. Furthermore, if the guest is staying during a specific event period, the generation unit may prioritize generating services related to that event. This allows the generation unit to determine the priority of service generation based on the guest's length of stay and provide more appropriate services. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the length of stay data into the generation AI and have the generation AI determine the priority of service generation.

[0076] The generation unit can adjust the order of service generation based on the guest's relevance. For example, the generation unit can adjust the order of generation based on the guest's relevance. For example, if a guest strongly desires a particular service, the generation unit will generate that service with the highest priority. The generation unit can also prioritize generating services that are highly relevant if a guest desires multiple services. Furthermore, the generation unit can prioritize generating services that are highly relevant based on services the guest has used in the past. In this way, the generation unit can adjust the order of generation based on the guest's relevance and provide more appropriate services. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input guest relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0077] The service provider can estimate the emotions of guests and adjust the service delivery method based on the estimated emotions. For example, the service provider can analyze the guest's facial expressions and heart rate to estimate emotions. For example, the service provider can analyze the guest's facial expressions using facial recognition technology to estimate emotions. The service provider can also measure the guest's heart rate using a heart rate sensor to estimate emotions. This allows the service provider to grasp the guest's emotions in real time and adjust the service delivery method. For example, if the guest is relaxed, it can provide calming music and soft lighting. If the guest is excited, it can provide lively music and bright lighting. Furthermore, if the guest is stressed, it can provide relaxing music and calming lighting. This allows the service provider to adjust the service delivery method based on the guest's emotions and provide more appropriate service. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input facial expression and heart rate data into a generative AI and have the generative AI adjust the service delivery method.

[0078] The service provider can select an appropriate service delivery method by referring to the guest's past service usage history when providing services. For example, the service provider can retrieve and analyze the guest's past service usage history from a database. For example, the service provider can provide services that the guest has previously preferred. The service provider can also provide services that reflect improvements based on feedback from services previously used by the guest. Furthermore, the service provider can predict the guest's preferred services at specific times based on their past service usage history and provide them at those times. In this way, the service provider can refer to the guest's past service usage history and provide more appropriate services. 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 past service usage history data into a generating AI and have the generating AI select the delivery method.

[0079] The service provider can customize the means of service delivery based on the guest's current living situation. For example, the service provider can customize the means of service delivery considering the guest's current living situation. For example, if the guest is staying for business purposes, the service provider can provide efficient service. Also, if the guest is staying for sightseeing purposes, the service provider can provide service that emphasizes sightseeing information. Furthermore, if the guest is staying with their family, the service provider can provide service that the whole family can enjoy. In this way, the service provider can customize the means of service delivery based on the guest's current living situation and provide more appropriate service. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input guest living situation data into a generating AI and have the generating AI perform the customization of the means of service delivery.

[0080] The service provider can estimate the emotions of guests and determine the priority of service provision based on the estimated emotions. For example, the service provider can estimate emotions by analyzing the guest's facial expressions and heart rate. For example, the service provider can analyze the guest's facial expressions using facial recognition technology and estimate emotions. The service provider can also measure the guest's heart rate using a heart rate sensor and estimate emotions. This allows the service provider to grasp the emotions of guests in real time and determine the priority of service provision. For example, if a guest is relaxed, relaxation services can be provided preferentially. If a guest is excited, entertainment services can be provided preferentially. Furthermore, if a guest is stressed, stress reduction services can be provided preferentially. This allows the service provider to determine the priority of service provision based on the emotions of guests and provide more appropriate services. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input facial expression and heart rate data into a generative AI and have the generative AI perform the determination of service provision priorities.

[0081] The service provider can select an appropriate service delivery method based on the guest's geographical location information when providing services. For example, the service provider can acquire the guest's geographical location information and select an appropriate service delivery method. For example, if the guest is in the hotel, the service provider can prioritize providing services within the hotel. If the guest is out, the service provider can also provide services related to nearby tourist attractions and restaurants. Furthermore, if the guest is staying in a specific area, the service provider can also provide services related to that area. This allows the service provider to provide more appropriate services by taking into account the guest's geographical location information. 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 geographical location information into a generating AI and have the generating AI perform the selection of the service delivery method.

[0082] The service provider can analyze guests' social media activity and propose ways to provide services when providing services. For example, the service provider can analyze guests' social media activity and collect relevant information. For example, the service provider can provide services based on the interests and concerns shared by guests on social media. The service provider can also provide relevant services based on information about accounts and groups that guests follow. Furthermore, the service provider can provide relevant services based on the location where guests checked in on social media. This allows the service provider to analyze guests' social media activity and provide more appropriate services. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input social media data into a generating AI and have the generating AI propose ways to provide services. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the smart device 14 to detect the guest's heart rate and facial expressions, and the control unit 46A collects information such as the guest's hobbies, interests, and food preferences. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and uses generation AI to generate music and lighting effects that are optimal for the guest. The provision unit provides the generated service to the guest, for example, by using the output device 40 of the smart device 14. The collection unit, generation unit, and provision unit may also be implemented, for example, by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the smart glasses 214 to detect the guest's heart rate and facial expressions, and the control unit 46A collects information such as the guest's hobbies, interests, and food preferences. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and uses generation AI to generate music and lighting effects optimized for the guest. The provision unit provides the generated service to the guest, for example, using the speaker 240 of the smart glasses 214. The collection unit, generation unit, and provision unit may also be implemented, for example, in the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the headset terminal 314 to detect the guest's heart rate and facial expressions, and the control unit 46A collects information such as the guest's hobbies, interests, and food preferences. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and uses generation AI to generate music and lighting effects optimized for the guest. The provision unit provides the generated service to the guest, for example, using the display 343 of the headset terminal 314. The collection unit, generation unit, and provision unit may also be implemented, for example, in the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the robot 414 to detect the guest's heart rate and facial expressions, and the control unit 46A collects information such as the guest's hobbies, interests, and food preferences. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and uses a generation AI to generate music and lighting effects that are optimal for the guest. The provision unit provides the generated service to the guest, for example, by using the speaker 240 of the robot 414. The collection unit, generation unit, and provision unit may also be implemented, for example, by the specific processing unit 290 of the data processing unit 12.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The data collection unit can analyze guests' past travel history and suggest destinations for their next trip. For example, the unit retrieves and analyzes information from a database about places guests have visited and the length of their stays. Then, based on the characteristics of destinations guests have preferred in the past, the unit can suggest destinations for their next trip. For example, if a guest prefers nature, it will suggest tourist destinations rich in nature; if they prefer city sightseeing, it will suggest tourist destinations in urban areas. The unit can also consider guests' activity and dining preferences at past destinations and suggest activities and restaurants for their next trip. In this way, the data collection unit can leverage guests' past travel history to suggest destinations for their next trip and provide a more personalized service.

[0085] The data collection unit can monitor the health status of guests and provide health-conscious services. For example, the unit collects health data such as heart rate, blood pressure, and body temperature using sensors. Next, the unit analyzes the collected health data to understand the guest's health status. For example, if a guest's heart rate is high, it can provide services with a relaxing effect, and if their blood pressure is high, it can suggest a low-sodium meal. The unit can also suggest appropriate exercise programs and relaxation programs according to the guest's health status. In this way, the data collection unit can monitor the health status of guests and provide health-conscious services.

[0086] The data collection unit can estimate the emotions of guests and suggest activities suitable for them based on those estimated emotions. For example, the data collection unit analyzes the guest's facial expressions and heart rate to estimate their emotions. Then, based on the estimated emotions, the data collection unit suggests activities suitable for the guest. For example, if a guest is relaxed, it can suggest a spa or massage with relaxation effects; if they are excited, it can suggest active sports or adventure activities. Also, if a guest is stressed, it can suggest yoga or meditation with stress-reducing effects. In this way, the data collection unit can suggest activities suitable for guests based on their emotions and provide a more personalized service.

[0087] The generation unit can estimate the emotions of guests and provide entertainment tailored to them based on those estimated emotions. For example, the generation unit analyzes the guest's facial expressions and heart rate to estimate their emotions. Then, based on the estimated emotions, the generation unit provides entertainment tailored to the guest. For example, if a guest is relaxed, it can provide relaxing music or movies; if they are excited, it can provide action movies or live performances. If a guest is stressed, it can also provide stress-reducing nature sounds or meditation music. In this way, the generation unit can provide entertainment tailored to the guest's emotions and offer a more personalized service.

[0088] The service department can estimate the emotions of guests and suggest a meal menu suitable for them based on those estimated emotions. For example, the service department analyzes the guest's facial expressions and heart rate to estimate their emotions. Then, based on the estimated emotions, the service department suggests a meal menu suitable for the guest. For example, if the guest is relaxed, it can suggest relaxing herbal tea or a light snack; if they are excited, it can suggest a meal suitable for replenishing energy. Furthermore, if the guest is stressed, it can suggest a menu using ingredients that have stress-reducing effects. In this way, the service department can suggest a meal menu suitable for the guest based on their emotions and provide a more personalized service.

[0089] The service department can analyze guests' past stay history and offer special services during their next stay. For example, the service department can retrieve and analyze guests' past stay history from a database. Then, based on the services and activities that guests have preferred in the past, the service department can offer special services during their next stay. For example, if a guest has previously enjoyed using the spa, a discount coupon for the spa can be offered during their next stay. If a guest has preferred a particular restaurant, a special menu at that restaurant can be offered. The service department can also suggest specific events or activities based on guests' past stay history. In this way, the service department can leverage guests' past stay history to provide special services during their next stay and achieve more personalized hospitality.

[0090] The service department can monitor the current activities of guests and provide appropriate services. For example, the service department can monitor the activities guests are currently engaged in and provide services suited to those activities. For instance, if a guest is reading, they can provide a quiet environment; if a guest is exercising, they can provide sports drinks and towels. If a guest is sightseeing, they can provide information and maps about tourist attractions. This allows the service department to provide more appropriate services by considering the guest's current activities.

[0091] The service provider can estimate the emotions of guests and provide relaxation programs tailored to those emotions. For example, the service provider analyzes the guest's facial expressions and heart rate to estimate their emotions. Then, based on the estimated emotions, the service provider provides relaxation programs tailored to the guest. For example, if a guest is relaxed, they can be offered a relaxing massage or aromatherapy; if they are excited, they can be offered relaxing yoga or meditation. If a guest is stressed, they can be offered a hot spring or sauna with stress-reducing effects. In this way, the service provider can provide relaxation programs tailored to the emotions of guests, enabling them to offer more personalized services.

[0092] The service provider can utilize the geographical location information of guests to provide information on nearby tourist attractions and restaurants. For example, the service provider can obtain the guest's current geographical location and provide information on nearby tourist attractions and restaurants based on that location. For instance, if the guest is inside the hotel, it can provide information on restaurants and facilities within the hotel; if the guest is out, it can provide information on nearby tourist attractions and restaurants. Furthermore, if the guest is staying in a specific area, the service provider can also provide information on events and activities related to that area. In this way, the service provider can provide more appropriate information by utilizing the geographical location information of guests.

[0093] The service department can analyze guests' social media activity and suggest events and activities that are suitable for them. For example, the service department can analyze guests' social media accounts to understand their interests and preferences. Then, based on these interests, the service department can suggest events and activities that are suitable for the guests. For example, if a guest is interested in music, they can suggest music events or live performances; if a guest is interested in sports, they can suggest sports events or activities. If a guest is interested in art, they can also provide information on art galleries and museums. In this way, the service department can leverage guests' social media activity to suggest more personalized events and activities.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The data collection unit collects information about the guests. This information includes heart rate, facial expressions, hobbies, interests, and food preferences. The data collection unit uses cameras and sensors to detect guests' heart rates and facial expressions. It can also collect information about guests' hobbies, interests, and food preferences through surveys and social media analysis. Step 2: The generation unit analyzes the information collected by the collection unit and generates the optimal service for the guest. The generation unit uses a generation AI to analyze the guest's heart rate and facial expression data and generates the optimal music and lighting effects for that moment. It can also customize wallpaper and artwork based on the guest's hobbies and interests. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The service department provides the services generated by the generation department to the guests. The service department displays videos on a screen in the lobby that combine seasonal and local scenery generated by the generation AI with the guest's preferences. It can also act as an AI concierge, responding to guests' questions and requests and suggesting personalized menus.

[0096] 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.

[0097] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0098] 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.

[0099] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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. 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.

[0114] 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.

[0115] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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. 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.

[0130] 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.

[0131] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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. 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.

[0147] 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.

[0148] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Explanation of symbols]

[0168] 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 generation unit analyzes the information collected by the collection unit and generates appropriate services for guests, The system includes a provisioning unit that provides the services generated by the generation unit to the guests. A system characterized by the following features.

2. The aforementioned collection unit is The camera and sensors are used to detect the heart rate and facial expressions of the guests. The system according to feature 1.

3. The aforementioned collection unit is Collect information on guests' hobbies, interests, and food preferences. The system according to feature 1.

4. The generating unit is The collected information is analyzed to generate appropriate music and lighting effects for each guest. The system according to feature 1.

5. The generating unit is Customize wallpaper and artwork based on the guest's tastes and interests. The system according to feature 1.

6. The aforementioned supply unit is, A display in the lobby shows videos created by AI that combine seasonal and local scenery with the preferences of the guests. The system according to feature 1.

7. The aforementioned supply unit is, As an AI concierge, it responds to guests' questions and requests and proposes personalized menus. The system according to feature 1.

8. The aforementioned collection unit is We estimate the emotions of the guests and adjust the timing of information collection based on those estimated emotions. The system according to feature 1.

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