system

Abstract

The system according to the embodiment aims to automate the private lodging reception process and execute it efficiently and securely. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, a reception unit, an authentication unit, a display unit, and a management unit. The analysis unit analyzes reservation information. The generation unit generates a two-dimensional code based on the information analyzed by the analysis unit. The reception unit reads the two-dimensional code generated by the generation unit. The authentication unit performs facial authentication based on the two-dimensional code read by the reception unit. The display unit displays information authenticated by the authentication unit. The management unit manages keys when authenticated by the authentication unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the private lodging reception process is often manual, leaving room for improvement in efficiency and security.

[0005] The system according to the embodiment aims to automate the private lodging reception process and execute it efficiently and securely. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a reception unit, an authentication unit, a display unit, and a management unit. The analysis unit analyzes reservation information. The generation unit generates a two-dimensional code based on the information analyzed by the analysis unit. The reception unit reads the two-dimensional code generated by the generation unit. The authentication unit performs face authentication based on the two-dimensional code read by the reception unit. The display unit displays information authenticated by the authentication unit. The management unit manages keys when authenticated by the authentication unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the private lodging reception process and can execute it efficiently and securely. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A private lodging reception system according to an embodiment of the present invention automates and efficiently processes private lodging reservations. This system is a comprehensive package that combines a generation AI, a smartphone app, an installed camera, digital signage, and a key box, and is also multilingual. First, a guest makes a reservation using the smartphone app. The reservation information is analyzed by the generation AI, and the guest's information is learned. At check-in, the guest uses the smartphone app to generate a two-dimensional code (e.g., a QR code (registered trademark)) and reads it with the installed camera. The camera performs facial recognition of the guest and displays check-in information on digital signage. The key box is unlocked only if facial recognition is successful, allowing the guest to collect their key. At check-out, a two-dimensional code is generated using the smartphone app and read by the installed camera. The camera performs facial recognition again and displays check-out information on digital signage. The key box is locked after the key is confirmed to have been returned. Furthermore, the generation AI learns the guest's information and provides feedback to platforms such as AirBnB. This encourages guests to behave in a respectful manner. This system allows both guests and business owners to check in and out securely, enabling efficient management. This allows the private lodging reception system to learn information about guests and provide feedback to platforms such as AirBnB, encouraging guests to behave with good manners. It also allows check-in and check-out procedures to be carried out efficiently and securely.

[0029] A private lodging reception system according to an embodiment includes an analysis unit, a generation unit, a reception unit, an authentication unit, a display unit, and a management unit. The analysis unit analyzes reservation information. The reservation information includes, for example, the date of stay, the name of the guest, and the room type, but is not limited to these examples. The analysis unit analyzes the reservation information using, for example, a data analysis method. The generation unit generates a two-dimensional code based on the information analyzed by the analysis unit. The two-dimensional code encodes, for example, the guest's reservation information. The generation unit generates the two-dimensional code using, for example, a two-dimensional code generation algorithm. The reception unit reads the two-dimensional code generated by the generation unit. The reception unit reads the two-dimensional code using, for example, a two-dimensional code reader. The authentication unit performs facial authentication based on the two-dimensional code read by the reception unit. For example, a facial authentication algorithm is used for facial authentication. The authentication unit performs facial authentication using, for example, a facial authentication algorithm. The display unit displays information authenticated by the authentication unit. The display unit displays check-in and check-out information using, for example, digital signage. The management unit manages keys when authenticated by the authentication unit. The management unit manages, for example, electronic keys. This allows the private lodging reception system according to the embodiment to perform a comprehensive process from analyzing reservation information to generating two-dimensional codes, performing facial recognition, displaying information, and managing keys.

[0030] The analysis unit can learn information about guests and feed it back to the platform. The analysis unit learns information such as guests' personal information, accommodation history, and behavioral patterns. For example, the analysis unit learns information about guests using a machine learning algorithm. The analysis unit also feeds back the learned information to a platform such as AirBnB. For example, the analysis unit notifies the platform of guests' behavioral patterns. This can encourage guests to behave in a mannerly manner. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs information about guests into a generation AI, which then learns and feeds back the results to the platform.

[0031] The generation unit can generate a two-dimensional code based on the guest's reservation information. The generation unit, for example, generates the two-dimensional code by encoding the guest's reservation information. For example, the generation unit generates the two-dimensional code using a two-dimensional code generation algorithm. The generation unit also provides the generated two-dimensional code to the guest. For example, the generation unit displays the two-dimensional code to the guest via a smartphone app. By generating a two-dimensional code based on the guest's reservation information, it is possible to streamline check-in and check-out procedures. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's reservation information into the generation AI, which then generates the two-dimensional code.

[0032] The reception unit can read the two-dimensional code and perform facial authentication. The reception unit reads the two-dimensional code using, for example, a two-dimensional code reader. For example, the reception unit reads the two-dimensional code displayed through a smartphone app. The reception unit then performs facial authentication based on the read two-dimensional code. For example, the reception unit performs facial authentication using a facial recognition algorithm. This allows the identity of the guest to be reliably confirmed by reading the two-dimensional code and performing facial authentication. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the two-dimensional code into AI, which then performs facial authentication.

[0033] The authentication unit can manage keys only when facial authentication is successful. The authentication unit performs facial authentication using, for example, a facial recognition algorithm. For example, the authentication unit manages keys only when facial authentication is successful. The authentication unit also adjusts the key management method. For example, the authentication unit manages electronic keys. This enables secure key management by managing keys only when facial authentication is successful. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the results of facial authentication into AI, and the AI ​​manages keys.

[0034] The display unit can display check-in and check-out information. The display unit displays the check-in and check-out information using, for example, digital signage. For example, the display unit displays the check-in information of the guest. The display unit also displays the check-out information of the guest. In this way, by displaying the check-in and check-out information, necessary information can be provided to the guest. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the check-in and check-out information into AI, and the AI ​​displays the information.

[0035] The analysis unit can analyze the guest's past accommodation history and select an appropriate analysis method. The analysis unit, for example, analyzes the guest's past accommodation history. For example, the analysis unit selects the optimal analysis method based on data on accommodation facilities used by the guest in the past. The analysis unit also extracts specific patterns from the guest's past accommodation history and adjusts the analysis method. In this way, by analyzing the guest's past accommodation history, the optimal analysis method can be selected and the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the guest's accommodation history data into the generation AI, and the generation AI selects the optimal analysis method.

[0036] The analysis unit can customize the analysis based on the guest's current travel purpose and areas of interest. The analysis unit, for example, collects the guest's current travel purpose and areas of interest. For example, if the guest is traveling for business purposes, the analysis unit performs a business-oriented analysis. Furthermore, if the guest is traveling for tourism purposes, the analysis unit performs an analysis that emphasizes tourism information. By customizing the analysis based on the guest's travel purpose and areas of interest, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs data on the guest's travel purpose and areas of interest into the generation AI, and the generation AI customizes the analysis.

[0037] The analysis unit performs analysis taking into account the geographical location information of the guest, thereby enabling it to meet regional needs. The analysis unit, for example, collects the geographical location information of the guest. For example, the analysis unit obtains the location information of the guest using GPS data or a location information service. The analysis unit also performs analysis that meets regional needs. For example, if the guest is staying in an urban area, the analysis is performed that meets regional needs. This makes it possible to perform analysis that meets regional needs by taking into account the geographical location information of the guest. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the geographical location information of the guest into a generation AI, which then performs an analysis that meets regional needs.

[0038] The analysis unit can analyze the social media activities of guests and reflect related information in the analysis. The analysis unit, for example, collects the social media activities of guests. For example, the analysis unit collects information such as the content of guests' posts, the number of likes, and the number of followers. The analysis unit also performs analysis based on the collected information. For example, based on information shared by guests on social media, related information is reflected in the analysis. This makes it possible to provide more relevant analysis results by analyzing the guests' social media activities. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the guests' social media data into the generation AI, and the generation AI reflects related information in the analysis.

[0039] The generation unit can adjust the method for generating the 2D code based on the level of detail in the guest's reservation information. The generation unit, for example, evaluates the level of detail in the guest's reservation information. For example, if the guest's reservation information is detailed, the generation unit generates a detailed 2D code. On the other hand, if the guest's reservation information is simple, the generation unit generates a simple 2D code. In this way, by adjusting the method for generating the 2D code based on the level of detail in the guest's reservation information, a more appropriate 2D code can be generated. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's reservation information into the generation AI, and the generation AI adjusts the method for generating the 2D code.

[0040] The generation unit can apply different 2D code generation algorithms depending on the reservation category of the guest. The generation unit, for example, classifies the reservation category of the guest. For example, the generation unit classifies the reservation category into a business category, a tourism category, a relaxation category, etc. The generation unit also applies a 2D code generation algorithm according to each category. For example, for a reservation in the business category, a 2D code generation algorithm for business is applied. This allows for the generation of a more appropriate 2D code by applying different 2D code generation algorithms depending on the reservation category of the guest. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the reservation category data of the guest into the generation AI, which then applies the 2D code generation algorithm.

[0041] The generation unit can set the expiration date of the two-dimensional code based on the time of the guest's reservation. The generation unit, for example, evaluates the time of the guest's reservation. For example, if the guest's reservation is made at short notice, the generation unit generates a two-dimensional code with a short expiration date. On the other hand, if the guest's reservation is made early, the generation unit generates a two-dimensional code with a long expiration date. In this way, by setting the expiration date of the two-dimensional code based on the time of the guest's reservation, a more appropriate two-dimensional code can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's reservation date data into the generation AI, which then sets the expiration date of the two-dimensional code.

[0042] The generation unit can adjust the range of use of the 2D code based on the guest's related information. The generation unit, for example, collects the guest's related information. For example, the generation unit collects information such as the guest's profile and past accommodation history. The generation unit also adjusts the range of use of the 2D code based on the collected information. For example, if the guest uses a specific facility, a 2D code limited to that facility is generated. This makes it possible to provide a more appropriate 2D code by adjusting the range of use of the 2D code based on the guest's related information. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's related information into the generation AI, which then adjusts the range of use of the 2D code.

[0043] The reception unit can analyze the guest's past check-in history and select an appropriate reading method. The reception unit, for example, analyzes the guest's past check-in history. For example, the reception unit selects the optimal reading method based on the check-in methods used by the guest in the past. The reception unit also extracts specific patterns from the guest's past check-in history and adjusts the reading method. In this way, by analyzing the guest's past check-in history, the optimal reading method can be selected and reading accuracy can be improved. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's check-in history data into AI, which selects the optimal reading method.

[0044] The reception unit can customize the 2D code reading process based on the guest's current situation. The reception unit, for example, collects information about the guest's current situation. For example, the reception unit collects information such as the guest's location, time of day, and congestion status. The reception unit also customizes the 2D code reading process based on the collected information. For example, if the guest is in a hurry, the reception unit will read the code quickly. This allows for flexible response by customizing the 2D code reading process based on the guest's current situation. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's current situation data into AI, which then customizes the reading process.

[0045] The reception unit can read the 2D code taking into account the geographical location information of the guest. The reception unit, for example, collects the geographical location information of the guest. For example, the reception unit obtains the location information of the guest using GPS data or a location information service. The reception unit also performs reading that meets the needs specific to the region. For example, if the guest is staying in an urban area, a reading method specific to the city is applied. This makes it possible to perform reading that meets the needs specific to the region by taking into account the geographical location information of the guest. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the geographical location information of the guest into AI, and the AI ​​performs reading that meets the needs specific to the region.

[0046] The reception unit can analyze the social media activity of the guest and reflect related information in the readout. The reception unit, for example, collects the guest's social media activity. For example, the reception unit collects information such as the guest's posted content, the number of likes, and the number of followers. The reception unit also performs readout based on the collected information. For example, based on the information the guest shared on social media, related information is reflected in the readout. This makes it possible to provide more relevant readout results by analyzing the guest's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's social media data into AI, and the AI ​​reflects the related information in the readout.

[0047] The authentication unit can analyze the guest's past authentication history and select an appropriate authentication method. The authentication unit, for example, analyzes the guest's past authentication history. For example, the authentication unit selects the optimal authentication method based on authentication methods used by the guest in the past. The authentication unit also extracts specific patterns from the guest's past authentication history and adjusts the authentication method. In this way, by analyzing the guest's past authentication history, the optimal authentication method can be selected and authentication accuracy can be improved. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's authentication history data into AI, which selects the optimal authentication method.

[0048] The authentication unit can customize the facial recognition process based on the guest's current situation. The authentication unit, for example, collects the guest's current situation. For example, the authentication unit collects information such as the guest's location, time of day, and congestion status. The authentication unit also customizes the facial recognition process based on the collected information. For example, if the guest is in a hurry, the authentication unit quickly performs facial recognition. This allows for flexible response by customizing the facial recognition process based on the guest's current situation. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's current situation data into AI, which then customizes the facial recognition process.

[0049] The authentication unit can perform facial recognition taking into account the geographical location information of the guest. The authentication unit, for example, collects the geographical location information of the guest. For example, the authentication unit obtains the location information of the guest using GPS data or a location information service. The authentication unit also performs facial recognition that meets the needs specific to the region. For example, if the guest is staying in an urban area, a facial recognition method specific to the city is applied. This makes it possible to perform facial recognition that meets the needs specific to the region by taking into account the geographical location information of the guest. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the geographical location information of the guest into AI, which then performs facial recognition that meets the needs specific to the region.

[0050] The authentication unit can analyze the social media activity of the guest and reflect related information in the authentication. The authentication unit, for example, collects the guest's social media activity. For example, the authentication unit collects information such as the guest's posted content, the number of likes, and the number of followers. The authentication unit also performs authentication based on the collected information. For example, based on the information the guest shared on social media, related information is reflected in the authentication. This makes it possible to provide more relevant authentication results by analyzing the guest's social media activity. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's social media data into AI, and the AI ​​reflects the related information in the authentication.

[0051] The display unit can analyze the guest's past display history and select an appropriate display method. The display unit, for example, analyzes the guest's past display history. For example, the display unit selects the optimal display method based on the display methods used by the guest in the past. The display unit also extracts specific patterns from the guest's past display history and adjusts the display method. In this way, by analyzing the guest's past display history, the optimal display method can be selected and display accuracy can be improved. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's display history data into AI, which selects the optimal display method.

[0052] The display unit can customize the display process based on the guest's current situation. The display unit, for example, collects information about the guest's current situation. For example, the display unit collects information such as the guest's location, time of day, and congestion status. The display unit also customizes the display process based on the collected information. For example, if the guest is in a hurry, the display unit quickly displays the information. This allows for flexible response by customizing the display process based on the guest's current situation. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's current situation data into AI, and the AI ​​customizes the display process.

[0053] The display unit can determine the display content taking into account the geographical location information of the guest. The display unit, for example, collects the geographical location information of the guest. For example, the display unit obtains the location information of the guest using GPS data or a location information service. The display unit also provides display content that meets region-specific needs. For example, if the guest is staying in an urban area, display content that is specific to the city is provided. This makes it possible to provide display that meets region-specific needs by taking into account the geographical location information of the guest. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the geographical location information of the guest into AI, which then determines display content that meets region-specific needs.

[0054] The display unit can analyze the social media activity of the guest and reflect related information in the display. The display unit, for example, collects the guest's social media activity. For example, the display unit collects information such as the guest's posted content, the number of likes, and the number of followers. The display unit also displays information based on the collected information. For example, the display unit reflects related information in the display based on information shared by the guest on social media. This makes it possible to provide more relevant display results by analyzing the guest's social media activity. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's social media data into AI, and the AI ​​reflects the related information in the display.

[0055] The management unit can analyze the guest's past key management history and select an appropriate management method. The management unit, for example, analyzes the guest's past key management history. For example, the management unit selects the optimal management method based on the key management methods used by the guest in the past. The management unit also extracts specific patterns from the guest's past key management history and adjusts the management method. In this way, by analyzing the guest's past key management history, the optimal management method can be selected and management accuracy can be improved. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the guest's key management history data into AI, which selects the optimal management method.

[0056] The management unit can customize the key management process based on the guest's current situation. The management unit, for example, collects the guest's current situation. For example, the management unit collects information such as the guest's location, time of day, and congestion status. The management unit also customizes the key management process based on the collected information. For example, if the guest is in a hurry, the management unit performs key management quickly. This allows for flexible response by customizing the key management process based on the guest's current situation. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the guest's current situation data into AI, which then customizes the key management process.

[0057] The management unit can perform key management taking into account the geographical location information of guests. The management unit, for example, collects the geographical location information of guests. For example, the management unit obtains the location information of guests using GPS data or location information services. The management unit also performs key management that meets region-specific needs. For example, if a guest is staying in an urban area, a city-specific key management method is applied. This enables key management that meets region-specific needs by taking into account the geographical location information of guests. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the geographical location information of guests into AI, and the AI ​​performs key management that meets region-specific needs.

[0058] The management unit can analyze the social media activity of guests and reflect related information in key management. The management unit, for example, collects the social media activity of guests. For example, the management unit collects information such as the content of guests' posts, the number of likes, and the number of followers. The management unit also performs key management based on the collected information. For example, based on the information shared by guests on social media, related information is reflected in key management. This enables more relevant key management by analyzing guests' social media activity. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the guest's social media data into AI, and the AI ​​reflects the related information in key management.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can analyze the guest's past reviews and ratings and evaluate the guest's trustworthiness. For example, the analysis unit can collect the content and rating points of the guest's past reviews and calculate a trustworthiness score. The analysis unit can also provide special services or discounts to the guest based on the trustworthiness score. This makes it possible to provide better services by evaluating the guest's trustworthiness. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the guest's review and rating data into the generation AI, which then calculates the trustworthiness score.

[0061] The generation unit can generate a two-dimensional code tailored to the guest's preferences based on the guest's reservation information. For example, the generation unit collects information about services and facilities that the guest has used in the past and encodes the information about the specific service or facility into the two-dimensional code based on that information. The generation unit can also include benefits and coupons tailored to the guest's preferences in the two-dimensional code. This makes it possible to provide more personalized services by generating a two-dimensional code tailored to the guest's preferences. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit inputs the guest's preference data into the generation AI, which then generates the two-dimensional code.

[0062] The reception unit can analyze the guest's behavioral patterns at check-in and provide an efficient check-in process. For example, the reception unit can analyze the guest's past check-in procedures and present the optimal check-in procedure based on those patterns. The reception unit can also collect information the guest will need at check-in in advance to support a smooth check-in. This makes it possible to shorten check-in time by providing an efficient check-in process based on the guest's behavioral patterns. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's behavioral pattern data into AI, which then presents the optimal check-in procedure.

[0063] The authentication unit can perform voice authentication in addition to facial authentication of guests. For example, the authentication unit analyzes the characteristics of the guest's voice and verifies the guest's identity using a voice authentication algorithm. The authentication unit can also combine the results of facial authentication and voice authentication to achieve more accurate authentication. This combination of facial authentication and voice authentication strengthens security and enables more reliable identity verification. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's voice data into AI, which then performs voice authentication.

[0064] The display unit can display local tourist information and event information in addition to the guest's check-in and check-out information. For example, the display unit collects tourist spot and event information in the area where the guest is staying and displays it at check-in. The display unit can also provide customized information based on the guest's interests. This makes it possible to provide guests with useful information for their stay and improve their satisfaction with their stay. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without AI. For example, the display unit inputs tourist information and event information into AI, which then displays the customized information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The analysis unit analyzes the reservation information. The reservation information includes, but is not limited to, the date of stay, the name of the guest, and the type of room. The analysis unit analyzes the reservation information using, for example, a data analysis method. Step 2: The generation unit generates a two-dimensional code based on the information analyzed by the analysis unit. For example, the guest's reservation information is encoded in the two-dimensional code. The generation unit generates the two-dimensional code using, for example, a two-dimensional code generation algorithm. Step 3: The reception unit reads the two-dimensional code generated by the generation unit. The reception unit reads the two-dimensional code using, for example, a two-dimensional code reader. Step 4: The authentication unit performs face authentication based on the two-dimensional code read by the reception unit. For example, a face authentication algorithm is used for face authentication. For example, the authentication unit performs face authentication using the face authentication algorithm. Step 5: The display unit displays the information authenticated by the authentication unit. The display unit displays the check-in and check-out information using, for example, digital signage. Step 6: The management unit manages the key when authenticated by the authentication unit. The management unit manages, for example, an electronic key.

[0067] (Example 2) A private lodging reception system according to an embodiment of the present invention automates and efficiently processes private lodging reservations. This system is a comprehensive package that combines a generation AI, a smartphone app, an installed camera, digital signage, and a key box, and is also multilingual. First, a guest makes a reservation using the smartphone app. The reservation information is analyzed by the generation AI, and the guest's information is learned. At check-in, the guest uses the smartphone app to generate a two-dimensional code (e.g., a QR code) and reads it with the installed camera. The camera performs facial recognition of the guest and displays check-in information on digital signage. The key box is unlocked only if facial recognition is successful, and the guest can collect their key. At check-out, a two-dimensional code is generated using the smartphone app and read by the installed camera. The camera performs facial recognition again and displays check-out information on digital signage. The key box is locked after the key is confirmed to have been returned. Furthermore, the generation AI learns the guest's information and provides feedback to platforms such as AirBnB. This encourages guests to behave in a mannerly manner. This system allows both guests and business owners to check in and out securely, enabling efficient management. This allows the private lodging reception system to learn information about guests and provide feedback to platforms such as AirBnB, encouraging guests to behave with good manners. It also allows check-in and check-out procedures to be carried out efficiently and securely.

[0068] A private lodging reception system according to an embodiment includes an analysis unit, a generation unit, a reception unit, an authentication unit, a display unit, and a management unit. The analysis unit analyzes reservation information. The reservation information includes, for example, the date of stay, the name of the guest, and the room type, but is not limited to these examples. The analysis unit analyzes the reservation information using, for example, a data analysis method. The generation unit generates a two-dimensional code based on the information analyzed by the analysis unit. The two-dimensional code encodes, for example, the guest's reservation information. The generation unit generates the two-dimensional code using, for example, a two-dimensional code generation algorithm. The reception unit reads the two-dimensional code generated by the generation unit. The reception unit reads the two-dimensional code using, for example, a two-dimensional code reader. The authentication unit performs facial authentication based on the two-dimensional code read by the reception unit. For example, a facial authentication algorithm is used for facial authentication. The authentication unit performs facial authentication using, for example, a facial authentication algorithm. The display unit displays information authenticated by the authentication unit. The display unit displays check-in and check-out information using, for example, digital signage. The management unit manages keys when authenticated by the authentication unit. The management unit manages, for example, electronic keys. This allows the private lodging reception system according to the embodiment to perform a comprehensive process from analyzing reservation information to generating two-dimensional codes, performing facial recognition, displaying information, and managing keys.

[0069] The analysis unit can learn information about guests and feed it back to the platform. The analysis unit learns information such as guests' personal information, accommodation history, and behavioral patterns. For example, the analysis unit learns information about guests using a machine learning algorithm. The analysis unit also feeds back the learned information to a platform such as AirBnB. For example, the analysis unit notifies the platform of guests' behavioral patterns. This can encourage guests to behave in a mannerly manner. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs information about guests into a generation AI, which then learns and feeds back the results to the platform.

[0070] The generation unit can generate a two-dimensional code based on the guest's reservation information. The generation unit, for example, generates the two-dimensional code by encoding the guest's reservation information. For example, the generation unit generates the two-dimensional code using a two-dimensional code generation algorithm. The generation unit also provides the generated two-dimensional code to the guest. For example, the generation unit displays the two-dimensional code to the guest via a smartphone app. By generating a two-dimensional code based on the guest's reservation information, it is possible to streamline check-in and check-out procedures. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's reservation information into the generation AI, which then generates the two-dimensional code.

[0071] The reception unit can read the two-dimensional code and perform facial authentication. The reception unit reads the two-dimensional code using, for example, a two-dimensional code reader. For example, the reception unit reads the two-dimensional code displayed through a smartphone app. The reception unit then performs facial authentication based on the read two-dimensional code. For example, the reception unit performs facial authentication using a facial recognition algorithm. This allows the identity of the guest to be reliably confirmed by reading the two-dimensional code and performing facial authentication. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the two-dimensional code into AI, which then performs facial authentication.

[0072] The authentication unit can manage keys only when facial authentication is successful. The authentication unit performs facial authentication using, for example, a facial recognition algorithm. For example, the authentication unit manages keys only when facial authentication is successful. The authentication unit also adjusts the key management method. For example, the authentication unit manages electronic keys. This enables secure key management by managing keys only when facial authentication is successful. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the results of facial authentication into AI, and the AI ​​manages keys.

[0073] The display unit can display check-in and check-out information. The display unit displays the check-in and check-out information using, for example, digital signage. For example, the display unit displays the check-in information of the guest. The display unit also displays the check-out information of the guest. In this way, by displaying the check-in and check-out information, necessary information can be provided to the guest. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the check-in and check-out information into AI, and the AI ​​displays the information.

[0074] The analysis unit can estimate the guest's emotions and adjust the analysis priority based on the estimated guest's emotions. The analysis unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the analysis unit estimates emotions by analyzing the guest's facial expressions and voice data. The analysis unit also adjusts the analysis priority based on the estimated guest's emotions. For example, if the guest is feeling stressed, the analysis unit quickly analyzes and responds as a priority. This enables flexible responses according to the guest's situation. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the guest's emotion data into the generation AI, which estimates the guest's emotions and adjusts the analysis priority based on the results.

[0075] The analysis unit can analyze the guest's past accommodation history and select an appropriate analysis method. The analysis unit, for example, analyzes the guest's past accommodation history. For example, the analysis unit selects the optimal analysis method based on data on accommodation facilities used by the guest in the past. The analysis unit also extracts specific patterns from the guest's past accommodation history and adjusts the analysis method. In this way, by analyzing the guest's past accommodation history, the optimal analysis method can be selected and the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the guest's accommodation history data into the generation AI, and the generation AI selects the optimal analysis method.

[0076] The analysis unit can customize the analysis based on the guest's current travel purpose and areas of interest. The analysis unit, for example, collects the guest's current travel purpose and areas of interest. For example, if the guest is traveling for business purposes, the analysis unit performs a business-oriented analysis. Furthermore, if the guest is traveling for tourism purposes, the analysis unit performs an analysis that emphasizes tourism information. By customizing the analysis based on the guest's travel purpose and areas of interest, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs data on the guest's travel purpose and areas of interest into the generation AI, and the generation AI customizes the analysis.

[0077] The analysis unit can estimate the guest's emotions and adjust the display method of the analysis results based on the estimated guest's emotions. The analysis unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the analysis unit estimates the guest's emotions by analyzing the guest's facial expressions and voice data. The analysis unit also adjusts the display method of the analysis results based on the estimated guest's emotions. For example, if the guest is nervous, the analysis unit provides a simple, highly visible display method. This allows the display method of the analysis results to be adjusted based on the guest's emotions, making it easy for the guest to see. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the guest's emotion data into the generation AI, which then estimates the guest's emotions and adjusts the display method based on the results.

[0078] The analysis unit performs analysis taking into account the geographical location information of the guest, thereby enabling it to meet regional needs. The analysis unit, for example, collects the geographical location information of the guest. For example, the analysis unit obtains the location information of the guest using GPS data or a location information service. The analysis unit also performs analysis that meets regional needs. For example, if the guest is staying in an urban area, the analysis is performed that meets regional needs. This makes it possible to perform analysis that meets regional needs by taking into account the geographical location information of the guest. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the geographical location information of the guest into a generation AI, which then performs an analysis that meets regional needs.

[0079] The analysis unit can analyze the social media activities of guests and reflect related information in the analysis. The analysis unit, for example, collects the social media activities of guests. For example, the analysis unit collects information such as the content of guests' posts, the number of likes, and the number of followers. The analysis unit also performs analysis based on the collected information. For example, based on information shared by guests on social media, related information is reflected in the analysis. This makes it possible to provide more relevant analysis results by analyzing the guests' social media activities. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the guests' social media data into the generation AI, and the generation AI reflects related information in the analysis.

[0080] The generation unit can estimate the guest's emotions and adjust the timing of generating the 2D code based on the estimated guest's emotions. The generation unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the generation unit estimates the guest's emotions by analyzing the guest's facial expressions and voice data. The generation unit also adjusts the timing of generating the 2D code based on the estimated guest's emotions. For example, if the guest is in a hurry, the generation unit quickly generates a 2D code. This enables flexible response according to the guest's situation. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's emotion data into the generation AI, which estimates the guest's emotions, and adjusts the timing of generating the 2D code based on the result.

[0081] The generation unit can adjust the method for generating the 2D code based on the level of detail in the guest's reservation information. The generation unit, for example, evaluates the level of detail in the guest's reservation information. For example, if the guest's reservation information is detailed, the generation unit generates a detailed 2D code. On the other hand, if the guest's reservation information is simple, the generation unit generates a simple 2D code. In this way, by adjusting the method for generating the 2D code based on the level of detail in the guest's reservation information, a more appropriate 2D code can be generated. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's reservation information into the generation AI, and the generation AI adjusts the method for generating the 2D code.

[0082] The generation unit can apply different 2D code generation algorithms depending on the reservation category of the guest. The generation unit, for example, classifies the reservation category of the guest. For example, the generation unit classifies the reservation category into a business category, a tourism category, a relaxation category, etc. The generation unit also applies a 2D code generation algorithm according to each category. For example, for a reservation in the business category, a 2D code generation algorithm for business is applied. This allows for the generation of a more appropriate 2D code by applying different 2D code generation algorithms depending on the reservation category of the guest. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the reservation category data of the guest into the generation AI, which then applies the 2D code generation algorithm.

[0083] The generation unit can estimate the guest's emotions and adjust the design of the 2D code based on the estimated guest's emotions. The generation unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the generation unit estimates the guest's emotions by analyzing the guest's facial expressions and voice data. The generation unit also adjusts the design of the 2D code based on the estimated guest's emotions. For example, if the guest is relaxed, the generation unit generates a 2D code with a calm design. By adjusting the design of the 2D code based on the guest's emotions, it is possible to provide a 2D code that is easy for the guest to read. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's emotional data into the generation AI, which then estimates the guest's emotions and adjusts the design of the 2D code based on the results.

[0084] The generation unit can set the expiration date of the two-dimensional code based on the time of the guest's reservation. The generation unit, for example, evaluates the time of the guest's reservation. For example, if the guest's reservation is made at short notice, the generation unit generates a two-dimensional code with a short expiration date. On the other hand, if the guest's reservation is made early, the generation unit generates a two-dimensional code with a long expiration date. In this way, by setting the expiration date of the two-dimensional code based on the time of the guest's reservation, a more appropriate two-dimensional code can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's reservation date data into the generation AI, which then sets the expiration date of the two-dimensional code.

[0085] The generation unit can adjust the range of use of the 2D code based on the guest's related information. The generation unit, for example, collects the guest's related information. For example, the generation unit collects information such as the guest's profile and past accommodation history. The generation unit also adjusts the range of use of the 2D code based on the collected information. For example, if the guest uses a specific facility, a 2D code limited to that facility is generated. This makes it possible to provide a more appropriate 2D code by adjusting the range of use of the 2D code based on the guest's related information. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the guest's related information into the generation AI, which then adjusts the range of use of the 2D code.

[0086] The reception unit can estimate the guest's emotions and adjust the accuracy of reading the 2D code based on the estimated guest's emotions. The reception unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the reception unit estimates the guest's emotions by analyzing the guest's facial expressions and voice data. The reception unit also adjusts the accuracy of reading the 2D code based on the estimated guest's emotions. For example, if the guest is nervous, the reception unit performs a highly accurate reading. This enables flexible response according to the guest's situation. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's emotional data into AI, which estimates the guest's emotions, and adjusts the accuracy of reading the 2D code based on the result.

[0087] The reception unit can analyze the guest's past check-in history and select an appropriate reading method. The reception unit, for example, analyzes the guest's past check-in history. For example, the reception unit selects the optimal reading method based on the check-in methods used by the guest in the past. The reception unit also extracts specific patterns from the guest's past check-in history and adjusts the reading method. In this way, by analyzing the guest's past check-in history, the optimal reading method can be selected and reading accuracy can be improved. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's check-in history data into AI, which selects the optimal reading method.

[0088] The reception unit can customize the 2D code reading process based on the guest's current situation. The reception unit, for example, collects information about the guest's current situation. For example, the reception unit collects information such as the guest's location, time of day, and congestion status. The reception unit also customizes the 2D code reading process based on the collected information. For example, if the guest is in a hurry, the reception unit will read the code quickly. This allows for flexible response by customizing the 2D code reading process based on the guest's current situation. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's current situation data into AI, which then customizes the reading process.

[0089] The reception unit can estimate the guest's emotions and adjust the reading order of the 2D codes based on the estimated guest's emotions. The reception unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the reception unit estimates the guest's emotions by analyzing the guest's facial expressions and voice data. The reception unit also adjusts the reading order of the 2D codes based on the estimated guest's emotions. For example, if the guest is nervous, the reception unit will read the codes in a simple order. This allows for flexible responses according to the guest's situation. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's emotional data into AI, which estimates the guest's emotions and adjusts the reading order based on the results.

[0090] The reception unit can read the 2D code taking into account the geographical location information of the guest. The reception unit, for example, collects the geographical location information of the guest. For example, the reception unit obtains the location information of the guest using GPS data or a location information service. The reception unit also performs reading that meets the needs specific to the region. For example, if the guest is staying in an urban area, a reading method specific to the city is applied. This makes it possible to perform reading that meets the needs specific to the region by taking into account the geographical location information of the guest. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the geographical location information of the guest into AI, and the AI ​​performs reading that meets the needs specific to the region.

[0091] The reception unit can analyze the social media activity of the guest and reflect related information in the readout. The reception unit, for example, collects the guest's social media activity. For example, the reception unit collects information such as the guest's posted content, the number of likes, and the number of followers. The reception unit also performs readout based on the collected information. For example, based on the information the guest shared on social media, related information is reflected in the readout. This makes it possible to provide more relevant readout results by analyzing the guest's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's social media data into AI, and the AI ​​reflects the related information in the readout.

[0092] The authentication unit can estimate the guest's emotions and adjust the accuracy of facial recognition based on the estimated guest's emotions. The authentication unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the authentication unit estimates emotions by analyzing the guest's facial expressions and voice data. The authentication unit also adjusts the accuracy of facial recognition based on the estimated guest's emotions. For example, if the guest is nervous, the authentication unit performs highly accurate facial recognition. This enables flexible responses according to the guest's situation. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's emotional data into AI, which estimates the guest's emotions, and adjusts the accuracy of facial recognition based on the results.

[0093] The authentication unit can analyze the guest's past authentication history and select an appropriate authentication method. The authentication unit, for example, analyzes the guest's past authentication history. For example, the authentication unit selects the optimal authentication method based on authentication methods used by the guest in the past. The authentication unit also extracts specific patterns from the guest's past authentication history and adjusts the authentication method. In this way, by analyzing the guest's past authentication history, the optimal authentication method can be selected and authentication accuracy can be improved. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's authentication history data into AI, which selects the optimal authentication method.

[0094] The authentication unit can customize the facial recognition process based on the guest's current situation. The authentication unit, for example, collects the guest's current situation. For example, the authentication unit collects information such as the guest's location, time of day, and congestion status. The authentication unit also customizes the facial recognition process based on the collected information. For example, if the guest is in a hurry, the authentication unit quickly performs facial recognition. This allows for flexible response by customizing the facial recognition process based on the guest's current situation. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's current situation data into AI, which then customizes the facial recognition process.

[0095] The authentication unit can estimate the guest's emotions and determine the priority of facial recognition based on the estimated guest's emotions. The authentication unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the authentication unit estimates emotions by analyzing the guest's facial expressions and voice data. The authentication unit also determines the priority of facial recognition based on the estimated guest's emotions. For example, if the guest is nervous, the authentication unit sets a high priority for facial recognition. This enables flexible response according to the guest's situation. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's emotional data into AI, which estimates the emotion, and determines the priority of facial recognition based on the result.

[0096] The authentication unit can perform facial recognition taking into account the geographical location information of the guest. The authentication unit, for example, collects the geographical location information of the guest. For example, the authentication unit obtains the location information of the guest using GPS data or a location information service. The authentication unit also performs facial recognition that meets the needs specific to the region. For example, if the guest is staying in an urban area, a facial recognition method specific to the city is applied. This makes it possible to perform facial recognition that meets the needs specific to the region by taking into account the geographical location information of the guest. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the geographical location information of the guest into AI, which then performs facial recognition that meets the needs specific to the region.

[0097] The authentication unit can analyze the social media activity of the guest and reflect related information in the authentication. The authentication unit, for example, collects the guest's social media activity. For example, the authentication unit collects information such as the guest's posted content, the number of likes, and the number of followers. The authentication unit also performs authentication based on the collected information. For example, based on the information the guest shared on social media, related information is reflected in the authentication. This makes it possible to provide more relevant authentication results by analyzing the guest's social media activity. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's social media data into AI, and the AI ​​reflects the related information in the authentication.

[0098] The display unit can estimate the guest's emotions and adjust the display content based on the estimated guest's emotions. The display unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the display unit estimates the guest's emotions by analyzing the guest's facial expressions and voice data. The display unit also adjusts the display content based on the estimated guest's emotions. For example, if the guest is nervous, the display unit provides simple, highly visible display content. This allows the display content to be adjusted based on the guest's emotions, making it easy for the guest to see. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's emotional data into AI, which estimates the guest's emotions, and adjusts the display content based on the result.

[0099] The display unit can analyze the guest's past display history and select an appropriate display method. The display unit, for example, analyzes the guest's past display history. For example, the display unit selects the optimal display method based on the display methods used by the guest in the past. The display unit also extracts specific patterns from the guest's past display history and adjusts the display method. In this way, by analyzing the guest's past display history, the optimal display method can be selected and display accuracy can be improved. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's display history data into AI, which selects the optimal display method.

[0100] The display unit can customize the display process based on the guest's current situation. The display unit, for example, collects information about the guest's current situation. For example, the display unit collects information such as the guest's location, time of day, and congestion status. The display unit also customizes the display process based on the collected information. For example, if the guest is in a hurry, the display unit quickly displays the information. This allows for flexible response by customizing the display process based on the guest's current situation. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's current situation data into AI, and the AI ​​customizes the display process.

[0101] The display unit can estimate the guest's emotions and adjust the display order based on the estimated guest's emotions. The display unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the display unit estimates the guest's emotions by analyzing the guest's facial expressions and voice data. The display unit also adjusts the display order based on the estimated guest's emotions. For example, if the guest is nervous, the display unit displays the information in a simple order. This enables flexible response according to the guest's situation. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's emotional data into AI, which estimates the guest's emotions, and adjusts the display order based on the result.

[0102] The display unit can determine the display content taking into account the geographical location information of the guest. The display unit, for example, collects the geographical location information of the guest. For example, the display unit obtains the location information of the guest using GPS data or a location information service. The display unit also provides display content that meets region-specific needs. For example, if the guest is staying in an urban area, display content that is specific to the city is provided. This makes it possible to provide display that meets region-specific needs by taking into account the geographical location information of the guest. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the geographical location information of the guest into AI, which then determines display content that meets region-specific needs.

[0103] The display unit can analyze the social media activity of the guest and reflect related information in the display. The display unit, for example, collects the guest's social media activity. For example, the display unit collects information such as the guest's posted content, the number of likes, and the number of followers. The display unit also displays information based on the collected information. For example, the display unit reflects related information in the display based on information shared by the guest on social media. This makes it possible to provide more relevant display results by analyzing the guest's social media activity. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's social media data into AI, and the AI ​​reflects the related information in the display.

[0104] The management unit can estimate the guest's emotions and adjust the key management method based on the estimated guest's emotions. The management unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the management unit estimates emotions by analyzing the guest's facial expressions and voice data. The management unit also adjusts the key management method based on the estimated guest's emotions. For example, if the guest is nervous, the management unit performs highly accurate key management. This enables flexible response according to the guest's situation. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the guest's emotional data into AI, which estimates the guest's emotions, and adjusts the key management method based on the results.

[0105] The management unit can analyze the guest's past key management history and select an appropriate management method. The management unit, for example, analyzes the guest's past key management history. For example, the management unit selects the optimal management method based on the key management methods used by the guest in the past. The management unit also extracts specific patterns from the guest's past key management history and adjusts the management method. In this way, by analyzing the guest's past key management history, the optimal management method can be selected and management accuracy can be improved. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the guest's key management history data into AI, which selects the optimal management method.

[0106] The management unit can customize the key management process based on the guest's current situation. The management unit, for example, collects the guest's current situation. For example, the management unit collects information such as the guest's location, time of day, and congestion status. The management unit also customizes the key management process based on the collected information. For example, if the guest is in a hurry, the management unit performs key management quickly. This allows for flexible response by customizing the key management process based on the guest's current situation. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the guest's current situation data into AI, which then customizes the key management process.

[0107] The management unit can estimate the guest's emotions and determine the priority of key management based on the estimated guest's emotions. The management unit estimates the guest's emotions using, for example, an emotion analysis algorithm. For example, the management unit estimates the guest's emotions by analyzing the guest's facial expressions and voice data. The management unit also determines the priority of key management based on the estimated guest's emotions. For example, if the guest is nervous, the management unit sets a high priority for key management. This enables flexible response according to the guest's situation. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the guest's emotional data into AI, which estimates the guest's emotions, and determines the priority of key management based on the result.

[0108] The management unit can perform key management taking into account the geographical location information of guests. The management unit, for example, collects the geographical location information of guests. For example, the management unit obtains the location information of guests using GPS data or location information services. The management unit also performs key management that meets region-specific needs. For example, if a guest is staying in an urban area, a city-specific key management method is applied. This enables key management that meets region-specific needs by taking into account the geographical location information of guests. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the geographical location information of guests into AI, and the AI ​​performs key management that meets region-specific needs.

[0109] The management unit can analyze the social media activity of guests and reflect related information in key management. The management unit, for example, collects the social media activity of guests. For example, the management unit collects information such as the content of guests' posts, the number of likes, and the number of followers. The management unit also performs key management based on the collected information. For example, based on the information shared by guests on social media, related information is reflected in key management. This enables more relevant key management by analyzing guests' social media activity. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit inputs the guest's social media data into AI, and the AI ​​reflects the related information in key management. === Hard Collateral 1-1 === Each of the multiple elements including the above-described analysis unit, generation unit, reception unit, authentication unit, display unit, and management unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes reservation information. The generation unit is realized, for example, by the control unit 46A of the smart device 14 and generates a two-dimensional code. The reception unit reads the two-dimensional code using, for example, the camera 42 of the smart device 14. The authentication unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs face authentication. The display unit displays check-in and check-out information using, for example, the output device 40 of the smart device 14. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages keys. === Hard Collateral 1-2 === Each of the multiple elements including the above-described analysis unit, generation unit, reception unit, authentication unit, display unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes reservation information. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates a two-dimensional code. The reception unit reads the two-dimensional code using, for example, the camera 42 of the smart glasses 214. The authentication unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs face authentication. The display unit displays check-in and check-out information using, for example, the speaker 240 of the smart glasses 214. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages keys. === Hard Collateral 1-3 === Each of the multiple elements including the above-described analysis unit, generation unit, reception unit, authentication unit, display unit, and management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes reservation information. The generation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and generates a two-dimensional code. The reception unit reads the two-dimensional code using the camera 42 of the headset type terminal 314. The authentication unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs face authentication. The display unit displays check-in and check-out information on the display 343 of the headset type terminal 314, for example. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages keys. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, reception unit, authentication unit, display unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes reservation information. The generation unit is realized, for example, by the control unit 46A of the robot 414 and generates a two-dimensional code. The reception unit reads the two-dimensional code using, for example, the camera 42 of the robot 414. The authentication unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs face authentication. The display unit displays check-in and check-out information using, for example, the speaker 240 of the robot 414. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages keys.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The analysis unit can analyze the guest's past reviews and ratings and evaluate the guest's trustworthiness. For example, the analysis unit can collect the content and rating points of the guest's past reviews and calculate a trustworthiness score. The analysis unit can also provide special services or discounts to the guest based on the trustworthiness score. This makes it possible to provide better services by evaluating the guest's trustworthiness. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the guest's review and rating data into the generation AI, which then calculates the trustworthiness score.

[0112] The generation unit can generate a two-dimensional code tailored to the guest's preferences based on the guest's reservation information. For example, the generation unit collects information about services and facilities that the guest has used in the past and encodes the information about the specific service or facility into the two-dimensional code based on that information. The generation unit can also include benefits and coupons tailored to the guest's preferences in the two-dimensional code. This makes it possible to provide more personalized services by generating a two-dimensional code tailored to the guest's preferences. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit inputs the guest's preference data into the generation AI, which then generates the two-dimensional code.

[0113] The reception unit can analyze the guest's behavioral patterns at check-in and provide an efficient check-in process. For example, the reception unit can analyze the guest's past check-in procedures and present the optimal check-in procedure based on those patterns. The reception unit can also collect information the guest will need at check-in in advance to support a smooth check-in. This makes it possible to shorten check-in time by providing an efficient check-in process based on the guest's behavioral patterns. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the guest's behavioral pattern data into AI, which then presents the optimal check-in procedure.

[0114] The authentication unit can perform voice authentication in addition to facial authentication of guests. For example, the authentication unit analyzes the characteristics of the guest's voice and verifies the guest's identity using a voice authentication algorithm. The authentication unit can also combine the results of facial authentication and voice authentication to achieve more accurate authentication. This combination of facial authentication and voice authentication strengthens security and enables more reliable identity verification. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's voice data into AI, which then performs voice authentication.

[0115] The display unit can display local tourist information and event information in addition to the guest's check-in and check-out information. For example, the display unit collects tourist spot and event information in the area where the guest is staying and displays it at check-in. The display unit can also provide customized information based on the guest's interests. This makes it possible to provide guests with useful information for their stay and improve their satisfaction with their stay. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without AI. For example, the display unit inputs tourist information and event information into AI, which then displays the customized information.

[0116] The analysis unit can estimate the guest's emotions and adjust how the guest is treated based on the estimated emotions. For example, if the guest is feeling stressed, the analysis unit can respond quickly and courteously. Also, if the guest is relaxed, the analysis unit can respond flexibly. This makes it possible to improve guest satisfaction by responding according to the guest's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the guest's emotional data into the generation AI, which then estimates the emotion and adjusts how the guest is treated based on the results.

[0117] The generation unit can estimate the guest's emotions and adjust the expiration date of the 2D code based on the estimated guest's emotions. For example, if the guest is in a hurry, the generation unit can generate a 2D code with a short expiration date. Conversely, if the guest is relaxed, the generation unit can generate a 2D code with a long expiration date. This allows for flexible responses according to the guest's emotions. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit inputs the guest's emotional data into the generation AI, which then estimates the emotion and adjusts the expiration date of the 2D code based on the result.

[0118] The reception unit can estimate the guest's emotions and adjust the check-in process based on the estimated guest's emotions. For example, if the guest is nervous, the reception unit can provide simple and easy-to-understand check-in procedures. If the guest is relaxed, the reception unit can provide detailed explanations. By providing a check-in process that suits the guest's emotions, it is possible to reduce the guest's stress and ensure a smooth check-in. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the guest's emotional data into AI, which then estimates the guest's emotions and adjusts the check-in process based on the results.

[0119] The authentication unit can estimate the guest's emotions and adjust the timing of facial recognition based on the estimated guest's emotions. For example, if the guest is nervous, the authentication unit can delay the timing of facial recognition. Alternatively, if the guest is relaxed, the authentication unit can quickly perform facial recognition. This allows for flexible responses according to the guest's emotions. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit inputs the guest's emotional data into AI, which estimates the emotion and adjusts the timing of facial recognition based on the result.

[0120] The display unit can estimate the guest's emotions and adjust the color and font of the displayed content based on the estimated guest's emotions. For example, if the guest is nervous, the display unit can use subdued colors and large fonts. On the other hand, if the guest is relaxed, the display unit can use bright colors and small fonts. This allows the display content to be provided according to the guest's emotions, making it easy for the guest to see. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the guest's emotional data into AI, which estimates the emotion and adjusts the color and font of the displayed content based on the result.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The analysis unit analyzes the reservation information. The reservation information includes, but is not limited to, the date of stay, the name of the guest, and the type of room. The analysis unit analyzes the reservation information using, for example, a data analysis method. Step 2: The generation unit generates a two-dimensional code based on the information analyzed by the analysis unit. For example, the guest's reservation information is encoded in the two-dimensional code. The generation unit generates the two-dimensional code using, for example, a two-dimensional code generation algorithm. Step 3: The reception unit reads the two-dimensional code generated by the generation unit. The reception unit reads the two-dimensional code using, for example, a two-dimensional code reader. Step 4: The authentication unit performs face authentication based on the two-dimensional code read by the reception unit. For example, a face authentication algorithm is used for face authentication. For example, the authentication unit performs face authentication using the face authentication algorithm. Step 5: The display unit displays the information authenticated by the authentication unit. The display unit displays the check-in and check-out information using, for example, digital signage. Step 6: The management unit manages the key when authenticated by the authentication unit. The management unit manages, for example, an electronic key.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes reservation information; a generation unit that generates a two-dimensional code based on the information analyzed by the analysis unit; a reception unit that reads the two-dimensional code generated by the generation unit; an authentication unit that performs face authentication based on the two-dimensional code read by the reception unit; a display unit that displays information authenticated by the authentication unit; a management unit that manages a key when authenticated by the authentication unit; A system characterized by:

2. The analysis unit Learn guest information and provide feedback to the platform The system of claim 1 .

3. The generation unit Generate a 2D code based on the guest's reservation information The system of claim 1 .

4. The reception unit Scan the 2D code and perform facial recognition The system of claim 1 .

5. The authentication unit Manage keys only if face authentication is successful The system of claim 1 .

6. The display unit View check-in and check-out information The system of claim 1 .

7. The analysis unit Estimate guest sentiment and adjust analysis priorities based on the estimated sentiment The system of claim 1 .

8. The analysis unit Analyze the guest's past stay history and select the appropriate analysis method The system of claim 1 .

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