system

Abstract

The system according to this embodiment aims to generate objective evaluations based on the behavioral data of accommodation facility users and to provide specific improvement suggestions. [Solution] The system according to the embodiment comprises a collection unit, an evaluation unit, a proposal unit, a reflection unit, and a support unit. The collection unit collects user behavior data. The evaluation unit analyzes the data collected by the collection unit and generates an objective evaluation. The proposal unit makes improvement suggestions based on the evaluation results generated by the evaluation unit. The reflection unit reflects the improvement suggestions made by the proposal unit in the reservation site and evaluation system. The support unit allows agents to act as concierges.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that subjective word-of-mouth is often relied on in improving the services of accommodation facilities, and there is a lack of objective evaluation and specific improvement proposals.

[0005] The system according to the embodiment aims to generate an objective evaluation based on the behavioral data of accommodation facility users and make specific improvement proposals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an evaluation unit, a proposal unit, a reflection unit, and a support unit. The data collection unit collects user behavior data. The evaluation unit analyzes the data collected by the data collection unit and generates an objective evaluation. The proposal unit makes improvement suggestions based on the evaluation results generated by the evaluation unit. The reflection unit reflects the improvements proposed by the proposal unit in the reservation site and evaluation system. The support unit allows agents to act as concierges. [Effects of the Invention]

[0007] The system according to this embodiment can generate objective evaluations based on the behavioral data of accommodation facility users and make specific improvement suggestions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI robot feedback system according to an embodiment of the present invention is a system that utilizes a generating AI agent to provide objective evaluations based on the behavioral data and reactions of accommodation facility users, thereby supporting the improvement of services at a ryokan (Japanese inn). The AI ​​robot feedback system collects information such as user behavior data, conversation content, interactions with staff, and frequency of facility use, and the AI ​​analyzes the collected data to generate a fair and objective evaluation. This accurately visualizes the current situation and reveals potential issues. Furthermore, it uses the collected data to periodically present specific improvement suggestions for facilities and services. Since the improvements are immediately reflected in the reservation site and evaluation system, it brings benefits to both users and ryokans. The agent also acts as a concierge, improving convenience during the ryokan stay by providing guidance during check-in, support on how to use facilities, and suggesting nearby tourist attractions. This contributes not only to improving user satisfaction but also to reducing the burden on staff. For example, the AI ​​robot feedback system collects detailed data such as which facilities the user used, what kind of conversations they had, and what kind of reactions they showed. This allows the system to understand the user's behavioral patterns and reactions. Next, the collected data is analyzed, and the AI ​​generates a fair and objective evaluation. For example, user behavior data and conversation content are analyzed to evaluate user satisfaction and dissatisfaction. This allows for accurate visualization of the current situation and identification of potential issues. Furthermore, the collected data is used to regularly provide specific improvement suggestions for facilities and services. For instance, specific improvements are proposed to enhance user satisfaction. Since the improvements are immediately reflected in the booking site and rating system, it benefits both users and the inn. In addition, the agent also acts as a concierge, improving the convenience of the inn's stay by providing guidance during check-in, supporting the use of facilities, and suggesting nearby tourist attractions. For example, guiding users on how to use the facilities and suggesting nearby tourist attractions during check-in improves user satisfaction. This contributes not only to increased user satisfaction but also to reducing the burden on staff.This allows the AI ​​robot feedback system to provide objective evaluations based on user behavior data, thereby supporting service improvement.

[0029] The AI ​​robot feedback system according to the embodiment comprises a collection unit, an evaluation unit, a proposal unit, a reflection unit, and a support unit. The collection unit collects user behavior data. The collection unit collects information such as user behavior data, conversation content, interactions with employees, and frequency of facility use. The collection unit collects detailed data such as which facilities the user used, what kind of conversations they had, and what kind of reactions they showed. The collection unit can collect behavior data to understand user behavior patterns and reactions, for example. The evaluation unit analyzes the data collected by the collection unit and generates an objective evaluation. The evaluation unit analyzes the collected data and evaluates the user's satisfaction and dissatisfaction, for example. The evaluation unit can analyze user behavior data and conversation content to evaluate the user's satisfaction and dissatisfaction, for example. The evaluation unit can analyze the collected data using AI and generate an objective evaluation. The proposal unit makes improvement suggestions based on the evaluation results generated by the evaluation unit. The proposal unit proposes specific improvements based on the evaluation results, for example. The proposal unit can, for example, propose specific improvements to enhance user satisfaction. The proposal unit can, for example, use AI to make improvement suggestions based on evaluation results. The implementation unit reflects the improvements proposed by the proposal unit into the reservation site and evaluation system. The implementation unit can, for example, reflect the proposed improvements into the reservation site and evaluation system immediately. The implementation unit can, for example, use AI to reflect the proposed improvements into the reservation site and evaluation system. The support unit allows the agent to act as a concierge. The support unit provides, for example, guidance during check-in, support on how to use the facilities, and suggestions for nearby tourist attractions. The support unit can, for example, guide users on how to use the facilities during check-in and suggest nearby tourist attractions. The support unit allows the agent to act as a concierge using AI. As a result, the AI ​​robot feedback system according to the embodiment can provide objective evaluations based on user behavior data and support service improvement.

[0030] The data collection unit collects user behavior data. For example, it collects information such as user behavior, conversation content, interactions with employees, and frequency of facility use. Specifically, it collects detailed data such as which facilities users used, what conversations they had, and how they reacted. For instance, it can record training content when a user uses the gym, meals at restaurants, and treatments at spas. It also collects information on conversations users had with employees, questions they asked, and feedback they provided. Furthermore, it can collect behavioral data to understand user behavior patterns and reactions. For example, it records how users moved within the facility, how long they spent in each area, and what activities they participated in. This allows the data collection unit to gain a detailed understanding of user behavior and reactions, providing foundational data to improve the quality of service for individual users. The collected data is stored on a secure cloud server, making it accessible to the analysis and proposal units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The evaluation unit analyzes data collected by the data collection unit to generate objective evaluations. For example, the evaluation unit analyzes collected data to assess user satisfaction and dissatisfaction. Specifically, it can analyze user behavior data and conversation content to assess user satisfaction and dissatisfaction. For example, it can use AI to analyze collected data and generate objective evaluations. The AI ​​uses natural language processing technology to analyze conversation content and understand user emotions and intentions. It also analyzes behavior data to identify what services users are satisfied with and what aspects they are dissatisfied with. Furthermore, the evaluation unit can utilize historical data and statistical information to analyze long-term trends and patterns. For example, based on past user data, it can predict fluctuations in satisfaction during specific seasons or events, providing insights for future service improvements. In addition, the evaluation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem detection. This allows the evaluation unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The proposal department makes improvement suggestions based on the evaluation results generated by the evaluation department. For example, the proposal department can propose specific improvements based on the evaluation results. Specifically, it can propose specific improvements to enhance user satisfaction. For example, it can use AI to make improvement suggestions based on the evaluation results. The AI ​​analyzes the evaluation results and generates specific improvement measures that meet user needs and expectations. For example, it may suggest diversifying the restaurant menu, customizing the gym's training program, or enhancing the spa's treatment offerings. Furthermore, the proposal department can make suggestions to continuously improve the quality of service based on user feedback. For example, it can collect user feedback and introduce new services or activities based on it. In addition, the proposal department can simulate multiple scenarios to identify the most effective improvement measures. This allows the proposal department to provide specific and actionable improvement suggestions to enhance user satisfaction and improve the quality of service.

[0033] The implementation unit reflects the improvements proposed by the suggestion unit into the reservation site and evaluation system. Specifically, it can immediately reflect the proposed improvements into the reservation site and evaluation system. For example, it can use AI to reflect the proposed improvements into the reservation site and evaluation system. The AI ​​analyzes the proposed improvements and reflects them into the reservation site and evaluation system in an appropriate format. For example, it can immediately reflect changes to restaurant menus, the addition of new training programs, and updates to spa treatments. The implementation unit can also monitor the status of the improvement implementation and make corrections and adjustments as needed. For example, it can check whether the improvements are properly reflected and whether user feedback is reflected, and make corrections as necessary. In this way, the implementation unit can quickly and accurately reflect the proposed improvements and improve the overall performance of the system.

[0034] The support department has agents who act as concierges. For example, the support department provides guidance during check-in, assistance with facility usage, and suggestions for nearby tourist attractions. Specifically, they can guide users on how to use the facilities during check-in and suggest nearby tourist destinations. For example, AI can be used to enable agents to act as concierges. The AI ​​can answer user questions quickly and accurately and provide necessary information. For example, it can provide information on how to use facilities and services within the property, information on nearby tourist attractions and restaurants, and transportation options. Furthermore, the support department can provide individualized support according to user requests. For example, they can respond quickly to special requests and needs to improve user satisfaction. In addition, the support department can collect user feedback and provide insights to continuously improve the quality of service. This allows the support department to provide users with quick and appropriate support, thereby improving user satisfaction.

[0035] The data collection unit can collect user behavior data, conversation content, interactions with employees, and facility usage frequency information. For example, the data collection unit collects user behavior data. For example, the data collection unit collects user conversation content. For example, the data collection unit collects interactions with employees. For example, the data collection unit collects facility usage frequency information. By collecting information such as user behavior data and conversation content, detailed data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into AI, and the AI ​​can analyze and collect the behavior data.

[0036] The evaluation unit can analyze the collected data and evaluate user satisfaction and dissatisfaction. For example, the evaluation unit analyzes the collected data. For example, the evaluation unit evaluates user satisfaction. For example, the evaluation unit evaluates user dissatisfaction. In this way, user satisfaction and dissatisfaction can be evaluated by analyzing the collected data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the collected data into AI, and the AI ​​can analyze and evaluate the data.

[0037] The proposal department can propose specific improvements based on the evaluation results. For example, the proposal department can propose improvements based on the evaluation results. The proposal department can propose specific improvements, for example. This makes it possible to improve the service by proposing specific improvements based on the evaluation results. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI. For example, the proposal department can input the evaluation results into AI, and the AI ​​can propose improvements.

[0038] The implementation unit can reflect the proposed improvements in the booking site and rating system. For example, the implementation unit can reflect the proposed improvements in the booking site. For example, the implementation unit can reflect the proposed improvements in the rating system. This immediately reflects the proposed improvements, bringing benefits to both users and the inn. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input the proposed improvements into the AI, which can then reflect them in the booking site and rating system.

[0039] The support department can provide guidance during check-in, support on how to use the facilities, and suggestions for nearby tourist attractions. For example, the support department can provide guidance during check-in. For example, the support department can provide support on how to use the facilities. For example, the support department can suggest nearby tourist attractions. By providing guidance during check-in and support on how to use the facilities, user satisfaction is improved. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input check-in instructions into the AI, and the AI ​​can provide the instructions.

[0040] The data collection unit can analyze the user's past behavioral data and select an appropriate collection method. For example, the data collection unit analyzes the user's past behavioral data. For example, the data collection unit selects the optimal collection method. For example, the data collection unit prioritizes collecting data on facilities that the user has frequently used in the past. For example, the data collection unit analyzes the user's past behavioral patterns and determines the optimal collection timing. For example, the data collection unit concentrates data collection during specific time periods based on the user's past behavioral data. This allows the optimal collection method to be selected by analyzing past behavioral data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then analyze the data and select the optimal collection method.

[0041] The data collection unit can filter behavioral data based on the user's current location and areas of interest. For example, the data collection unit considers the user's current location. For example, the data collection unit filters based on the user's areas of interest. For example, the data collection unit prioritizes collecting data on facilities the user is currently using. For example, the data collection unit filters and collects relevant behavioral data based on the user's areas of interest. For example, the data collection unit collects specific behavioral data according to the user's location. This allows for the collection of highly relevant data by filtering based on the current location and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current location and areas of interest into the AI, which can then filter and collect the data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting behavioral data. For example, the data collection unit considers the user's geographical location information. For example, the data collection unit prioritizes the collection of highly relevant data. For example, if the user is staying at a specific facility, the data collection unit prioritizes the collection of data related to that facility. For example, the data collection unit collects relevant behavioral data based on the user's current location. For example, the data collection unit collects relevant data based on the user's travel route. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the data and prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can analyze a user's social media activity. For example, the data collection unit can collect relevant data. For example, the data collection unit can analyze a user's social media posts and collect relevant behavioral data. For example, the data collection unit can collect relevant data based on a user's social media check-in information. For example, the data collection unit can analyze a user's interests on social media and collect relevant behavioral data. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's social media activity into AI, and the AI ​​can analyze the data and collect relevant data.

[0044] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the behavioral data during the evaluation process. The evaluation unit considers, for example, the importance of the behavioral data. The evaluation unit adjusts the level of detail of the evaluation. The evaluation unit provides a detailed evaluation based on important behavioral data. The evaluation unit provides a concise evaluation based on less important behavioral data. The evaluation unit adjusts the level of detail of the evaluation according to the importance of the behavioral data. This allows the evaluation to be based on important data by adjusting the level of detail of the evaluation based on the importance of the behavioral data. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the importance of the behavioral data into the AI, and the AI ​​can analyze the data and adjust the level of detail of the evaluation.

[0045] The evaluation unit can apply different evaluation algorithms depending on the category of behavioral data during evaluation. For example, the evaluation unit considers the category of behavioral data. For example, the evaluation unit applies a different evaluation algorithm. For example, the evaluation unit applies a specific evaluation algorithm based on the content of the user's conversation. For example, the evaluation unit applies a different evaluation algorithm based on the user's frequency of facility use. For example, the evaluation unit selects the optimal evaluation algorithm depending on the category of the user's behavioral data. This allows for a more accurate evaluation by applying the optimal evaluation algorithm according to the category of behavioral data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the category of behavioral data into the AI, and the AI ​​can analyze the data and apply the optimal evaluation algorithm.

[0046] The evaluation unit can determine the priority of evaluations based on the timing of behavioral data collection during the evaluation process. The evaluation unit considers, for example, the timing of behavioral data collection. The evaluation unit determines the priority of evaluations, for example. The evaluation unit determines the priority of evaluations based on, for example, the latest behavioral data. The evaluation unit determines the priority of evaluations based on, for example, past behavioral data. The evaluation unit adjusts the priority of evaluations according to the timing of behavioral data collection. This allows for evaluations based on the latest data by determining the priority of evaluations based on the timing of behavioral data collection. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the timing of behavioral data collection into AI, and the AI ​​can analyze the data to determine the priority of evaluations.

[0047] The evaluation unit can adjust the order of evaluations based on the relevance of behavioral data during the evaluation process. The evaluation unit considers, for example, the relevance of behavioral data. The evaluation unit adjusts the order of evaluations. The evaluation unit determines the order of evaluations based on highly relevant behavioral data. The evaluation unit determines the order of evaluations based on less relevant behavioral data. The evaluation unit adjusts the order of evaluations according to the relevance of behavioral data. By adjusting the order of evaluations based on the relevance of behavioral data, it is possible to provide evaluations based on highly relevant data. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the relevance of behavioral data into AI, and the AI ​​can analyze the data and adjust the order of evaluations.

[0048] The proposal unit can adjust the level of detail of a proposal based on the importance of the evaluation results when making a proposal. For example, the proposal unit considers the importance of the evaluation results. The proposal unit adjusts the level of detail of the proposal. For example, the proposal unit provides a detailed proposal based on important evaluation results. For example, the proposal unit provides a concise proposal based on less important evaluation results. For example, the proposal unit adjusts the level of detail of the proposal according to the importance of the evaluation results. This allows the proposal to provide proposals based on important evaluation results by adjusting the level of detail of the proposal based on the importance of the evaluation results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the evaluation results into the AI, and the AI ​​can analyze the data and adjust the level of detail of the proposal.

[0049] The proposal unit can apply different proposal algorithms depending on the category of the evaluation result when making a proposal. For example, the proposal unit considers the category of the evaluation result. The proposal unit applies a different proposal algorithm. For example, the proposal unit applies a specific proposal algorithm based on the content of the user's conversation. For example, the proposal unit applies a different proposal algorithm based on the frequency of the user's use of the facility. For example, the proposal unit selects the optimal proposal algorithm according to the category of the user's behavioral data. This allows for more accurate proposals to be provided by applying the optimal proposal algorithm according to the category of the evaluation result. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the evaluation result into AI, and the AI ​​can analyze the data and apply the optimal proposal algorithm.

[0050] The proposal department can determine the priority of proposals based on the timing of evaluation result collection when submitting proposals. The proposal department considers, for example, the timing of evaluation result collection. The proposal department determines the priority of proposals, for example. The proposal department determines the priority of proposals based on, for example, the latest evaluation results. The proposal department determines the priority of proposals based on, for example, past evaluation results. The proposal department adjusts the priority of proposals according to the timing of evaluation result collection. This allows the proposal department to provide proposals based on the latest evaluation results by determining the priority of proposals based on the timing of evaluation result collection. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the timing of evaluation result collection into AI, and the AI ​​can analyze the data to determine the priority of proposals.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the evaluation results when making proposals. The proposal unit considers the relevance of the evaluation results, for example. The proposal unit adjusts the order of proposals, for example. The proposal unit determines the order of proposals based on highly relevant evaluation results, for example. The proposal unit determines the order of proposals based on less relevant evaluation results, for example. The proposal unit adjusts the order of proposals according to the relevance of the evaluation results, for example. By doing so, by adjusting the order of proposals based on the relevance of the evaluation results, it is possible to provide proposals based on highly relevant evaluation results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the evaluation results into AI, and the AI ​​can analyze the data and adjust the order of proposals.

[0052] The reflection unit can adjust the level of detail of the reflection based on the importance of the proposed content during the reflection process. The reflection unit considers, for example, the importance of the proposed content. The reflection unit adjusts the level of detail of the reflection. The reflection unit performs detailed reflection based on important proposed content. The reflection unit performs concise reflection based on less important proposed content. The reflection unit adjusts the level of detail of the reflection according to the importance of the proposed content. This allows important proposed content to be reflected in detail by adjusting the level of detail of the reflection based on the importance of the proposed content. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or without AI. For example, the reflection unit can input the importance of the proposed content into the AI, and the AI ​​can analyze the data and adjust the level of detail of the reflection.

[0053] The reflection unit can apply different reflection algorithms depending on the category of the proposed content during the reflection process. For example, the reflection unit considers the category of the proposed content. For example, the reflection unit applies a different reflection algorithm. For example, the reflection unit applies a specific reflection algorithm based on the user's conversation content. For example, the reflection unit applies a different reflection algorithm based on the user's frequency of facility use. For example, the reflection unit selects the optimal reflection algorithm according to the category of the user's behavioral data. This enables more accurate reflection by applying the optimal reflection algorithm according to the category of the proposed content. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input the category of the proposed content into AI, and the AI ​​can analyze the data and apply the optimal reflection algorithm.

[0054] The reflection unit can determine the priority of reflection based on the timing of proposal collection when the proposals are reflected. The reflection unit considers, for example, the timing of proposal collection. The reflection unit determines, for example, the priority of reflection. The reflection unit determines the priority of reflection based on, for example, the latest proposals. The reflection unit determines the priority of reflection based on, for example, past proposals. The reflection unit adjusts the priority of reflection according to, for example, the timing of proposal collection. This allows the latest proposals to be reflected preferentially by determining the priority of reflection based on the timing of proposal collection. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or not using AI. For example, the reflection unit can input the timing of proposal collection into AI, and the AI ​​can analyze the data to determine the priority of reflection.

[0055] The reflection unit can adjust the order of reflection based on the relevance of the proposed content during the reflection process. The reflection unit considers, for example, the relevance of the proposed content. The reflection unit adjusts the order of reflection, for example. The reflection unit determines the order of reflection based on the relevance of the proposed content. The reflection unit determines the order of reflection based on the relevance of the proposed content. The reflection unit adjusts the order of reflection according to the relevance of the proposed content. This allows for the reflection of highly relevant proposed content to be prioritized by adjusting the order of reflection based on the relevance of the proposed content. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or without AI. For example, the reflection unit can input the relevance of the proposed content into AI, and the AI ​​can analyze the data and adjust the order of reflection.

[0056] The support unit can select the optimal support method by referring to the user's past behavior data during support. For example, the support unit refers to the user's past behavior data. The support unit selects the optimal support method. For example, the support unit provides the optimal support based on the support methods the user has used in the past. For example, the support unit analyzes the user's past behavior data and selects the optimal support method. For example, the support unit provides the optimal support based on the user's past behavior patterns. In this way, the optimal support method can be selected by referring to past behavior data. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI. For example, the support unit can input the user's past behavior data into AI, and the AI ​​can analyze the data and select the optimal support method.

[0057] The support unit can customize the means of support based on the user's current location during support. The support unit considers, for example, the user's current location. The support unit customizes the means of support. The support unit provides optimal support based on the facilities the user is currently using. The support unit customizes the means of support according to the user's current location. The support unit provides specific support based on the user's location. This allows for more appropriate support to be provided by customizing the means of support based on the current location. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the user's current location into AI, and the AI ​​can analyze the data to customize the means of support.

[0058] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, the support unit considers the user's geographical location information. The support unit selects the optimal support method. For example, if the user is staying at a specific facility, the support unit provides support related to that facility. For example, the support unit selects the optimal support method based on the user's current location. For example, the support unit provides the optimal support based on the user's travel route. In this way, the optimal support method can be selected by considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to select the optimal support method.

[0059] The support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can analyze a user's social media activity. The support unit can propose support measures. For example, the support unit can analyze a user's social media posts and provide relevant support. For example, the support unit can provide optimal support based on a user's social media check-in information. For example, the support unit can analyze a user's interests on social media and provide relevant support. In this way, by analyzing social media activity, it is possible to propose relevant support measures. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input a user's social media activity into AI, and the AI ​​can analyze the data and propose support measures.

[0060] The support unit can customize the means of support based on the user's current location during support. The support unit considers, for example, the user's current location. The support unit customizes the means of support. The support unit provides optimal support based on the facilities the user is currently using. The support unit customizes the means of support according to the user's current location. The support unit provides specific support based on the user's location. This allows for more appropriate support to be provided by customizing the means of support based on the current location. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the user's current location into AI, and the AI ​​can analyze the data to customize the means of support.

[0061] The support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can analyze a user's social media activity. The support unit can propose support measures. For example, the support unit can analyze a user's social media posts and provide relevant support. For example, the support unit can provide optimal support based on a user's social media check-in information. For example, the support unit can analyze a user's interests on social media and provide relevant support. In this way, by analyzing social media activity, it is possible to propose relevant support measures. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input a user's social media activity into AI, and the AI ​​can analyze the data and propose support measures.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can analyze users' past behavioral data and select appropriate collection methods. For example, it can prioritize the collection of data from facilities that users have frequently used in the past. It can also analyze users' past behavioral patterns and determine the optimal collection timing. Furthermore, it can concentrate data collection during specific time periods based on users' past behavioral data. In this way, the optimal collection method can be selected by analyzing past behavioral data.

[0064] The evaluation unit can adjust the level of detail in the evaluation based on the importance of the behavioral data during the evaluation process. For example, it can provide a detailed evaluation based on important behavioral data, or a concise evaluation based on less important behavioral data. Furthermore, it can adjust the level of detail in the evaluation according to the importance of the behavioral data. This allows for the provision of evaluations based on important data by adjusting the level of detail in the evaluation based on the importance of the behavioral data.

[0065] The proposal department can adjust the level of detail in the proposal based on the importance of the evaluation results. For example, it can provide a detailed proposal based on important evaluation results, or a concise proposal based on less important evaluation results. Furthermore, it can adjust the level of detail in the proposal according to the importance of the evaluation results. This allows for the provision of proposals based on important evaluation results by adjusting the level of detail in the proposal based on the importance of the evaluation results.

[0066] The reflection unit can adjust the level of detail in the reflection process based on the importance of the proposed content. For example, it can perform detailed reflection based on important proposals, and concise reflection based on less important proposals. Furthermore, it can adjust the level of detail in the reflection process according to the importance of the proposals. This allows important proposals to be reflected in detail by adjusting the level of detail based on the importance of the proposals.

[0067] The support department can select the optimal support method by referring to the user's past behavioral data during support. For example, it can provide the best support based on the support methods the user has used in the past. It can also analyze the user's past behavioral data to select the optimal support method. Furthermore, it can provide the best support based on the user's past behavioral patterns. In this way, the optimal support method can be selected by referring to past behavioral data.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects user behavior data. The data collection unit collects information such as user behavior data, conversation content, interactions with employees, and frequency of facility use. The data collection unit collects detailed data such as which facilities the user used, what kind of conversations they had, and what kind of reactions they showed. Step 2: The evaluation unit analyzes the data collected by the data collection unit and generates an objective evaluation. The evaluation unit analyzes the collected data and evaluates user satisfaction and dissatisfaction. The evaluation unit can use AI to analyze the collected data and generate an objective evaluation. Step 3: The proposal department makes improvement suggestions based on the evaluation results generated by the evaluation department. The proposal department proposes specific areas for improvement based on the evaluation results. The proposal department can use AI to make improvement suggestions based on the evaluation results. Step 4: The implementation unit reflects the improvements proposed by the proposal unit into the reservation site and evaluation system. The implementation unit can immediately reflect the proposed improvements into the reservation site and evaluation system. The implementation unit can use AI to reflect the proposed improvements into the reservation site and evaluation system. Step 5: The support department has agents act as concierges. The support department provides guidance during check-in, assistance with facility usage, and suggestions for nearby tourist attractions. The support department uses AI to enable agents to fulfill the role of concierge.

[0070] (Example of form 2) An AI robot feedback system according to an embodiment of the present invention is a system that utilizes a generating AI agent to provide objective evaluations based on the behavioral data and reactions of accommodation facility users, thereby supporting the improvement of services at a ryokan (Japanese inn). The AI ​​robot feedback system collects information such as user behavior data, conversation content, interactions with staff, and frequency of facility use, and the AI ​​analyzes the collected data to generate a fair and objective evaluation. This accurately visualizes the current situation and reveals potential issues. Furthermore, it uses the collected data to periodically present specific improvement suggestions for facilities and services. Since the improvements are immediately reflected in the reservation site and evaluation system, it brings benefits to both users and ryokans. The agent also acts as a concierge, improving convenience during the ryokan stay by providing guidance during check-in, support on how to use facilities, and suggesting nearby tourist attractions. This contributes not only to improving user satisfaction but also to reducing the burden on staff. For example, the AI ​​robot feedback system collects detailed data such as which facilities the user used, what kind of conversations they had, and what kind of reactions they showed. This allows the system to understand the user's behavioral patterns and reactions. Next, the collected data is analyzed, and the AI ​​generates a fair and objective evaluation. For example, user behavior data and conversation content are analyzed to evaluate user satisfaction and dissatisfaction. This allows for accurate visualization of the current situation and identification of potential issues. Furthermore, the collected data is used to regularly provide specific improvement suggestions for facilities and services. For instance, specific improvements are proposed to enhance user satisfaction. Since the improvements are immediately reflected in the booking site and rating system, it benefits both users and the inn. In addition, the agent also acts as a concierge, improving the convenience of the inn's stay by providing guidance during check-in, supporting the use of facilities, and suggesting nearby tourist attractions. For example, guiding users on how to use the facilities and suggesting nearby tourist attractions during check-in improves user satisfaction. This contributes not only to increased user satisfaction but also to reducing the burden on staff.This allows the AI ​​robot feedback system to provide objective evaluations based on user behavior data, thereby supporting service improvement.

[0071] The AI ​​robot feedback system according to the embodiment comprises a collection unit, an evaluation unit, a proposal unit, a reflection unit, and a support unit. The collection unit collects user behavior data. The collection unit collects information such as user behavior data, conversation content, interactions with employees, and frequency of facility use. The collection unit collects detailed data such as which facilities the user used, what kind of conversations they had, and what kind of reactions they showed. The collection unit can collect behavior data to understand user behavior patterns and reactions, for example. The evaluation unit analyzes the data collected by the collection unit and generates an objective evaluation. The evaluation unit analyzes the collected data and evaluates the user's satisfaction and dissatisfaction, for example. The evaluation unit can analyze user behavior data and conversation content to evaluate the user's satisfaction and dissatisfaction, for example. The evaluation unit can analyze the collected data using AI and generate an objective evaluation. The proposal unit makes improvement suggestions based on the evaluation results generated by the evaluation unit. The proposal unit proposes specific improvements based on the evaluation results, for example. The proposal unit can, for example, propose specific improvements to enhance user satisfaction. The proposal unit can, for example, use AI to make improvement suggestions based on evaluation results. The implementation unit reflects the improvements proposed by the proposal unit into the reservation site and evaluation system. The implementation unit can, for example, reflect the proposed improvements into the reservation site and evaluation system immediately. The implementation unit can, for example, use AI to reflect the proposed improvements into the reservation site and evaluation system. The support unit allows the agent to act as a concierge. The support unit provides, for example, guidance during check-in, support on how to use the facilities, and suggestions for nearby tourist attractions. The support unit can, for example, guide users on how to use the facilities during check-in and suggest nearby tourist attractions. The support unit allows the agent to act as a concierge using AI. As a result, the AI ​​robot feedback system according to the embodiment can provide objective evaluations based on user behavior data and support service improvement.

[0072] The data collection unit collects user behavior data. For example, it collects information such as user behavior, conversation content, interactions with employees, and frequency of facility use. Specifically, it collects detailed data such as which facilities users used, what conversations they had, and how they reacted. For instance, it can record training content when a user uses the gym, meals at restaurants, and treatments at spas. It also collects information on conversations users had with employees, questions they asked, and feedback they provided. Furthermore, it can collect behavioral data to understand user behavior patterns and reactions. For example, it records how users moved within the facility, how long they spent in each area, and what activities they participated in. This allows the data collection unit to gain a detailed understanding of user behavior and reactions, providing foundational data to improve the quality of service for individual users. The collected data is stored on a secure cloud server, making it accessible to the analysis and proposal units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0073] The evaluation unit analyzes data collected by the data collection unit to generate objective evaluations. For example, the evaluation unit analyzes collected data to assess user satisfaction and dissatisfaction. Specifically, it can analyze user behavior data and conversation content to assess user satisfaction and dissatisfaction. For example, it can use AI to analyze collected data and generate objective evaluations. The AI ​​uses natural language processing technology to analyze conversation content and understand user emotions and intentions. It also analyzes behavior data to identify what services users are satisfied with and what aspects they are dissatisfied with. Furthermore, the evaluation unit can utilize historical data and statistical information to analyze long-term trends and patterns. For example, based on past user data, it can predict fluctuations in satisfaction during specific seasons or events, providing insights for future service improvements. In addition, the evaluation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem detection. This allows the evaluation unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0074] The proposal department makes improvement suggestions based on the evaluation results generated by the evaluation department. For example, the proposal department can propose specific improvements based on the evaluation results. Specifically, it can propose specific improvements to enhance user satisfaction. For example, it can use AI to make improvement suggestions based on the evaluation results. The AI ​​analyzes the evaluation results and generates specific improvement measures that meet user needs and expectations. For example, it may suggest diversifying the restaurant menu, customizing the gym's training program, or enhancing the spa's treatment offerings. Furthermore, the proposal department can make suggestions to continuously improve the quality of service based on user feedback. For example, it can collect user feedback and introduce new services or activities based on it. In addition, the proposal department can simulate multiple scenarios to identify the most effective improvement measures. This allows the proposal department to provide specific and actionable improvement suggestions to enhance user satisfaction and improve the quality of service.

[0075] The implementation unit reflects the improvements proposed by the suggestion unit into the reservation site and evaluation system. Specifically, it can immediately reflect the proposed improvements into the reservation site and evaluation system. For example, it can use AI to reflect the proposed improvements into the reservation site and evaluation system. The AI ​​analyzes the proposed improvements and reflects them into the reservation site and evaluation system in an appropriate format. For example, it can immediately reflect changes to restaurant menus, the addition of new training programs, and updates to spa treatments. The implementation unit can also monitor the status of the improvement implementation and make corrections and adjustments as needed. For example, it can check whether the improvements are properly reflected and whether user feedback is reflected, and make corrections as necessary. In this way, the implementation unit can quickly and accurately reflect the proposed improvements and improve the overall performance of the system.

[0076] The support department has agents who act as concierges. For example, the support department provides guidance during check-in, assistance with facility usage, and suggestions for nearby tourist attractions. Specifically, they can guide users on how to use the facilities during check-in and suggest nearby tourist destinations. For example, AI can be used to enable agents to act as concierges. The AI ​​can answer user questions quickly and accurately and provide necessary information. For example, it can provide information on how to use facilities and services within the property, information on nearby tourist attractions and restaurants, and transportation options. Furthermore, the support department can provide individualized support according to user requests. For example, they can respond quickly to special requests and needs to improve user satisfaction. In addition, the support department can collect user feedback and provide insights to continuously improve the quality of service. This allows the support department to provide users with quick and appropriate support, thereby improving user satisfaction.

[0077] The data collection unit can collect user behavior data, conversation content, interactions with employees, and facility usage frequency information. For example, the data collection unit collects user behavior data. For example, the data collection unit collects user conversation content. For example, the data collection unit collects interactions with employees. For example, the data collection unit collects facility usage frequency information. By collecting information such as user behavior data and conversation content, detailed data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into AI, and the AI ​​can analyze and collect the behavior data.

[0078] The evaluation unit can analyze the collected data and evaluate user satisfaction and dissatisfaction. For example, the evaluation unit analyzes the collected data. For example, the evaluation unit evaluates user satisfaction. For example, the evaluation unit evaluates user dissatisfaction. In this way, user satisfaction and dissatisfaction can be evaluated by analyzing the collected data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the collected data into AI, and the AI ​​can analyze and evaluate the data.

[0079] The proposal department can propose specific improvements based on the evaluation results. For example, the proposal department can propose improvements based on the evaluation results. The proposal department can propose specific improvements, for example. This makes it possible to improve the service by proposing specific improvements based on the evaluation results. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI. For example, the proposal department can input the evaluation results into AI, and the AI ​​can propose improvements.

[0080] The implementation unit can reflect the proposed improvements in the booking site and rating system. For example, the implementation unit can reflect the proposed improvements in the booking site. For example, the implementation unit can reflect the proposed improvements in the rating system. This immediately reflects the proposed improvements, bringing benefits to both users and the inn. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input the proposed improvements into the AI, which can then reflect them in the booking site and rating system.

[0081] The support department can provide guidance during check-in, support on how to use the facilities, and suggestions for nearby tourist attractions. For example, the support department can provide guidance during check-in. For example, the support department can provide support on how to use the facilities. For example, the support department can suggest nearby tourist attractions. By providing guidance during check-in and support on how to use the facilities, user satisfaction is improved. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input check-in instructions into the AI, and the AI ​​can provide the instructions.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. For example, the data collection unit estimates the user's emotions. For example, the data collection unit adjusts the timing of behavioral data collection based on the estimated user emotions. For example, if the user is relaxed, the data collection unit collects behavioral data frequently to obtain detailed data. For example, if the user is stressed, the data collection unit reduces the frequency of behavioral data collection to reduce the user's burden. For example, if the user is excited, the data collection unit prioritizes collecting specific behavioral data and records the user's reactions in detail. This allows for more appropriate data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into an AI, which can then estimate the emotion and adjust the timing of data collection.

[0083] The data collection unit can analyze the user's past behavioral data and select an appropriate collection method. For example, the data collection unit analyzes the user's past behavioral data. For example, the data collection unit selects the optimal collection method. For example, the data collection unit prioritizes collecting data on facilities that the user has frequently used in the past. For example, the data collection unit analyzes the user's past behavioral patterns and determines the optimal collection timing. For example, the data collection unit concentrates data collection during specific time periods based on the user's past behavioral data. This allows the optimal collection method to be selected by analyzing past behavioral data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then analyze the data and select the optimal collection method.

[0084] The data collection unit can filter behavioral data based on the user's current location and areas of interest. For example, the data collection unit considers the user's current location. For example, the data collection unit filters based on the user's areas of interest. For example, the data collection unit prioritizes collecting data on facilities the user is currently using. For example, the data collection unit filters and collects relevant behavioral data based on the user's areas of interest. For example, the data collection unit collects specific behavioral data according to the user's location. This allows for the collection of highly relevant data by filtering based on the current location and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current location and areas of interest into the AI, which can then filter and collect the data.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, the data collection unit estimates the user's emotions. For example, the data collection unit determines the priority of data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit prioritizes collecting detailed behavioral data. For example, if the user is stressed, the data collection unit prioritizes collecting only important data. For example, if the user is excited, the data collection unit prioritizes collecting specific behavioral data. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of the data.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting behavioral data. For example, the data collection unit considers the user's geographical location information. For example, the data collection unit prioritizes the collection of highly relevant data. For example, if the user is staying at a specific facility, the data collection unit prioritizes the collection of data related to that facility. For example, the data collection unit collects relevant behavioral data based on the user's current location. For example, the data collection unit collects relevant data based on the user's travel route. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the data and prioritize the collection of highly relevant data.

[0087] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can analyze a user's social media activity. For example, the data collection unit can collect relevant data. For example, the data collection unit can analyze a user's social media posts and collect relevant behavioral data. For example, the data collection unit can collect relevant data based on a user's social media check-in information. For example, the data collection unit can analyze a user's interests on social media and collect relevant behavioral data. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's social media activity into AI, and the AI ​​can analyze the data and collect relevant data.

[0088] The evaluation unit can estimate the user's emotions and adjust the way the evaluation is expressed based on the estimated user emotions. For example, the evaluation unit estimates the user's emotions. For example, the evaluation unit adjusts the way the evaluation is expressed based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit provides a detailed evaluation. For example, if the user is stressed, the evaluation unit provides a concise evaluation. For example, if the user is excited, the evaluation unit provides a visually stimulating evaluation. This allows for the provision of more appropriate evaluations by adjusting the way the evaluation is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into an AI, which can estimate the emotions and adjust the way the evaluation is expressed.

[0089] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the behavioral data during the evaluation process. The evaluation unit considers, for example, the importance of the behavioral data. The evaluation unit adjusts the level of detail of the evaluation. The evaluation unit provides a detailed evaluation based on important behavioral data. The evaluation unit provides a concise evaluation based on less important behavioral data. The evaluation unit adjusts the level of detail of the evaluation according to the importance of the behavioral data. This allows the evaluation to be based on important data by adjusting the level of detail of the evaluation based on the importance of the behavioral data. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the importance of the behavioral data into the AI, and the AI ​​can analyze the data and adjust the level of detail of the evaluation.

[0090] The evaluation unit can apply different evaluation algorithms depending on the category of behavioral data during evaluation. For example, the evaluation unit considers the category of behavioral data. For example, the evaluation unit applies a different evaluation algorithm. For example, the evaluation unit applies a specific evaluation algorithm based on the content of the user's conversation. For example, the evaluation unit applies a different evaluation algorithm based on the user's frequency of facility use. For example, the evaluation unit selects the optimal evaluation algorithm depending on the category of the user's behavioral data. This allows for a more accurate evaluation by applying the optimal evaluation algorithm according to the category of behavioral data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the category of behavioral data into the AI, and the AI ​​can analyze the data and apply the optimal evaluation algorithm.

[0091] The evaluation unit can estimate the user's emotions and adjust the length of the evaluation based on the estimated user emotions. For example, the evaluation unit estimates the user's emotions. For example, the evaluation unit adjusts the length of the evaluation based on the estimated user emotions. For example, the evaluation unit provides a detailed evaluation when the user is relaxed. For example, the evaluation unit provides a concise evaluation when the user is stressed. For example, the evaluation unit provides a visually stimulating evaluation when the user is excited. By adjusting the length of the evaluation based on the user's emotions, a more appropriate evaluation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into an AI, which can estimate the emotions and adjust the length of the evaluation.

[0092] The evaluation unit can determine the priority of evaluations based on the timing of behavioral data collection during the evaluation process. The evaluation unit considers, for example, the timing of behavioral data collection. The evaluation unit determines the priority of evaluations, for example. The evaluation unit determines the priority of evaluations based on, for example, the latest behavioral data. The evaluation unit determines the priority of evaluations based on, for example, past behavioral data. The evaluation unit adjusts the priority of evaluations according to the timing of behavioral data collection. This allows for evaluations based on the latest data by determining the priority of evaluations based on the timing of behavioral data collection. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the timing of behavioral data collection into AI, and the AI ​​can analyze the data to determine the priority of evaluations.

[0093] The evaluation unit can adjust the order of evaluations based on the relevance of behavioral data during the evaluation process. The evaluation unit considers, for example, the relevance of behavioral data. The evaluation unit adjusts the order of evaluations. The evaluation unit determines the order of evaluations based on highly relevant behavioral data. The evaluation unit determines the order of evaluations based on less relevant behavioral data. The evaluation unit adjusts the order of evaluations according to the relevance of behavioral data. By adjusting the order of evaluations based on the relevance of behavioral data, it is possible to provide evaluations based on highly relevant data. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the relevance of behavioral data into AI, and the AI ​​can analyze the data and adjust the order of evaluations.

[0094] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, the suggestion unit estimates the user's emotions. For example, the suggestion unit adjusts the way it presents suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. For example, if the user is stressed, the suggestion unit provides concise suggestions. For example, if the user is excited, the suggestion unit provides visually stimulating suggestions. By adjusting the way it presents suggestions based on the user's emotions, it is possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotions and adjust the way it presents suggestions.

[0095] The proposal unit can adjust the level of detail of a proposal based on the importance of the evaluation results when making a proposal. For example, the proposal unit considers the importance of the evaluation results. The proposal unit adjusts the level of detail of the proposal. For example, the proposal unit provides a detailed proposal based on important evaluation results. For example, the proposal unit provides a concise proposal based on less important evaluation results. For example, the proposal unit adjusts the level of detail of the proposal according to the importance of the evaluation results. This allows the proposal to provide proposals based on important evaluation results by adjusting the level of detail of the proposal based on the importance of the evaluation results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the evaluation results into the AI, and the AI ​​can analyze the data and adjust the level of detail of the proposal.

[0096] The proposal unit can apply different proposal algorithms depending on the category of the evaluation result when making a proposal. For example, the proposal unit considers the category of the evaluation result. The proposal unit applies a different proposal algorithm. For example, the proposal unit applies a specific proposal algorithm based on the content of the user's conversation. For example, the proposal unit applies a different proposal algorithm based on the frequency of the user's use of the facility. For example, the proposal unit selects the optimal proposal algorithm according to the category of the user's behavioral data. This allows for more accurate proposals to be provided by applying the optimal proposal algorithm according to the category of the evaluation result. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the evaluation result into AI, and the AI ​​can analyze the data and apply the optimal proposal algorithm.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, the suggestion unit estimates the user's emotions. For example, the suggestion unit adjusts the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. For example, if the user is stressed, the suggestion unit provides concise suggestions. For example, if the user is excited, the suggestion unit provides visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotions and adjust the length of the suggestions.

[0098] The proposal department can determine the priority of proposals based on the timing of evaluation result collection when submitting proposals. The proposal department considers, for example, the timing of evaluation result collection. The proposal department determines the priority of proposals, for example. The proposal department determines the priority of proposals based on, for example, the latest evaluation results. The proposal department determines the priority of proposals based on, for example, past evaluation results. The proposal department adjusts the priority of proposals according to the timing of evaluation result collection. This allows the proposal department to provide proposals based on the latest evaluation results by determining the priority of proposals based on the timing of evaluation result collection. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the timing of evaluation result collection into AI, and the AI ​​can analyze the data to determine the priority of proposals.

[0099] The proposal unit can adjust the order of proposals based on the relevance of the evaluation results when making proposals. The proposal unit considers the relevance of the evaluation results, for example. The proposal unit adjusts the order of proposals, for example. The proposal unit determines the order of proposals based on highly relevant evaluation results, for example. The proposal unit determines the order of proposals based on less relevant evaluation results, for example. The proposal unit adjusts the order of proposals according to the relevance of the evaluation results, for example. By doing so, by adjusting the order of proposals based on the relevance of the evaluation results, it is possible to provide proposals based on highly relevant evaluation results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the evaluation results into AI, and the AI ​​can analyze the data and adjust the order of proposals.

[0100] The reflection unit can estimate the user's emotions and determine the priority of the content to reflect based on the estimated user emotions. For example, the reflection unit estimates the user's emotions. For example, the reflection unit determines the priority of the content to reflect based on the estimated user emotions. For example, if the user is relaxed, the reflection unit prioritizes reflecting detailed improvements. For example, if the user is stressed, the reflection unit prioritizes reflecting only important improvements. For example, if the user is excited, the reflection unit prioritizes reflecting specific improvements. In this way, by determining the priority of the content to reflect based on the user's emotions, important content can be reflected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without using AI. For example, the reflection unit can input user emotion data into the AI, which can then estimate the emotion and determine the priority of the content to reflect.

[0101] The reflection unit can adjust the level of detail of the reflection based on the importance of the proposed content during the reflection process. The reflection unit considers, for example, the importance of the proposed content. The reflection unit adjusts the level of detail of the reflection. The reflection unit performs detailed reflection based on important proposed content. The reflection unit performs concise reflection based on less important proposed content. The reflection unit adjusts the level of detail of the reflection according to the importance of the proposed content. This allows important proposed content to be reflected in detail by adjusting the level of detail of the reflection based on the importance of the proposed content. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or without AI. For example, the reflection unit can input the importance of the proposed content into the AI, and the AI ​​can analyze the data and adjust the level of detail of the reflection.

[0102] The reflection unit can apply different reflection algorithms depending on the category of the proposed content during the reflection process. For example, the reflection unit considers the category of the proposed content. For example, the reflection unit applies a different reflection algorithm. For example, the reflection unit applies a specific reflection algorithm based on the user's conversation content. For example, the reflection unit applies a different reflection algorithm based on the user's frequency of facility use. For example, the reflection unit selects the optimal reflection algorithm according to the category of the user's behavioral data. This enables more accurate reflection by applying the optimal reflection algorithm according to the category of the proposed content. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input the category of the proposed content into AI, and the AI ​​can analyze the data and apply the optimal reflection algorithm.

[0103] The reflection unit can estimate the user's emotions and adjust the display method of the reflected content based on the estimated user emotions. For example, the reflection unit estimates the user's emotions. For example, the reflection unit adjusts the display method of the reflected content based on the estimated user emotions. For example, if the user is relaxed, the reflection unit provides a detailed display method. For example, if the user is stressed, the reflection unit provides a concise display method. For example, if the user is excited, the reflection unit provides a visually stimulating display method. By adjusting the display method based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the reflected content can be adjusted.

[0104] The reflection unit can determine the priority of reflection based on the timing of proposal collection when the proposals are reflected. The reflection unit considers, for example, the timing of proposal collection. The reflection unit determines, for example, the priority of reflection. The reflection unit determines the priority of reflection based on, for example, the latest proposals. The reflection unit determines the priority of reflection based on, for example, past proposals. The reflection unit adjusts the priority of reflection according to, for example, the timing of proposal collection. This allows the latest proposals to be reflected preferentially by determining the priority of reflection based on the timing of proposal collection. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or not using AI. For example, the reflection unit can input the timing of proposal collection into AI, and the AI ​​can analyze the data to determine the priority of reflection.

[0105] The reflection unit can adjust the order of reflection based on the relevance of the proposed content during the reflection process. The reflection unit considers, for example, the relevance of the proposed content. The reflection unit adjusts the order of reflection, for example. The reflection unit determines the order of reflection based on the relevance of the proposed content. The reflection unit determines the order of reflection based on the relevance of the proposed content. The reflection unit adjusts the order of reflection according to the relevance of the proposed content. This allows for the reflection of highly relevant proposed content to be prioritized by adjusting the order of reflection based on the relevance of the proposed content. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or without AI. For example, the reflection unit can input the relevance of the proposed content into AI, and the AI ​​can analyze the data and adjust the order of reflection.

[0106] The support unit can estimate the user's emotions and adjust the way it expresses support based on the estimated emotions. For example, the support unit estimates the user's emotions. For example, the support unit adjusts the way it expresses support based on the estimated emotions. For example, if the user is relaxed, the support unit provides detailed support. For example, if the user is stressed, the support unit provides concise support. For example, if the user is excited, the support unit provides visually stimulating support. This allows for more appropriate support to be provided by adjusting the way it expresses support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into an AI, which can estimate the emotions and adjust the way it expresses support.

[0107] The support unit can select the optimal support method by referring to the user's past behavior data during support. For example, the support unit refers to the user's past behavior data. The support unit selects the optimal support method. For example, the support unit provides the optimal support based on the support methods the user has used in the past. For example, the support unit analyzes the user's past behavior data and selects the optimal support method. For example, the support unit provides the optimal support based on the user's past behavior patterns. In this way, the optimal support method can be selected by referring to past behavior data. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI. For example, the support unit can input the user's past behavior data into AI, and the AI ​​can analyze the data and select the optimal support method.

[0108] The support unit can customize the means of support based on the user's current location during support. The support unit considers, for example, the user's current location. The support unit customizes the means of support. The support unit provides optimal support based on the facilities the user is currently using. The support unit customizes the means of support according to the user's current location. The support unit provides specific support based on the user's location. This allows for more appropriate support to be provided by customizing the means of support based on the current location. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the user's current location into AI, and the AI ​​can analyze the data to customize the means of support.

[0109] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, the support unit estimates the user's emotions. For example, the support unit determines the priority of support based on the estimated emotions. For example, if the user is relaxed, the support unit will prioritize detailed support. For example, if the user is stressed, the support unit will prioritize only important support. For example, if the user is agitated, the support unit will prioritize specific support. This allows for the priority of important support by determining the priority of support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of support.

[0110] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, the support unit considers the user's geographical location information. The support unit selects the optimal support method. For example, if the user is staying at a specific facility, the support unit provides support related to that facility. For example, the support unit selects the optimal support method based on the user's current location. For example, the support unit provides the optimal support based on the user's travel route. In this way, the optimal support method can be selected by considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to select the optimal support method.

[0111] The support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can analyze a user's social media activity. The support unit can propose support measures. For example, the support unit can analyze a user's social media posts and provide relevant support. For example, the support unit can provide optimal support based on a user's social media check-in information. For example, the support unit can analyze a user's interests on social media and provide relevant support. In this way, by analyzing social media activity, it is possible to propose relevant support measures. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input a user's social media activity into AI, and the AI ​​can analyze the data and propose support measures.

[0112] The support unit can customize the means of support based on the user's current location during support. The support unit considers, for example, the user's current location. The support unit customizes the means of support. The support unit provides optimal support based on the facilities the user is currently using. The support unit customizes the means of support according to the user's current location. The support unit provides specific support based on the user's location. This allows for more appropriate support to be provided by customizing the means of support based on the current location. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the user's current location into AI, and the AI ​​can analyze the data to customize the means of support.

[0113] The support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can analyze a user's social media activity. The support unit can propose support measures. For example, the support unit can analyze a user's social media posts and provide relevant support. For example, the support unit can provide optimal support based on a user's social media check-in information. For example, the support unit can analyze a user's interests on social media and provide relevant support. In this way, by analyzing social media activity, it is possible to propose relevant support measures. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input a user's social media activity into AI, and the AI ​​can analyze the data and propose support measures.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The AI ​​robot feedback system comprises a collection unit that collects user behavior data, an evaluation unit that analyzes the collected data, a proposal unit that makes improvement suggestions based on the evaluation results, a reflection unit that implements the proposed improvements, and a support unit in which the agent acts as a concierge. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of behavior data collection based on the estimated emotions. For example, if the user is relaxed, behavior data can be collected frequently to obtain detailed data. If the user is stressed, the frequency of behavior data collection can be reduced to lessen the user's burden. Moreover, if the user is excited, specific behavior data can be prioritized for collection, and the user's reactions can be recorded in detail. In this way, by adjusting the collection timing based on the user's emotions, more appropriate data collection becomes possible.

[0116] The evaluation unit can analyze the collected data and assess user satisfaction and dissatisfaction. Furthermore, it can estimate the user's emotions and adjust the evaluation's presentation based on those emotions. For example, if the user is relaxed, a detailed evaluation can be provided. If the user is stressed, a concise evaluation can be provided. If the user is excited, a visually stimulating evaluation can be provided. This allows for more appropriate evaluations by adjusting the evaluation's presentation based on the user's emotions.

[0117] The proposal department can suggest specific improvements based on the evaluation results. Furthermore, the proposal department can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is stressed, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. In this way, by adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided.

[0118] The implementation unit can reflect the proposed improvements in the booking site and rating system. Furthermore, the implementation unit can estimate the user's emotions and determine the priority of the improvements to implement based on those emotions. For example, if the user is relaxed, detailed improvements can be prioritized. If the user is stressed, only important improvements can be prioritized. Moreover, if the user is excited, specific improvements can be prioritized. In this way, by determining the priority of the improvements to implement based on the user's emotions, important improvements can be prioritized.

[0119] The support department can provide guidance during check-in, support on how to use the facilities, and suggestions for nearby tourist attractions. Furthermore, the support department can estimate the user's emotions and adjust the way support is expressed based on those estimates. For example, if the user is relaxed, detailed support can be provided. If the user is stressed, concise support can be provided. Moreover, if the user is excited, visually stimulating support can be provided. In this way, by adjusting the way support is expressed based on the user's emotions, more appropriate support can be provided.

[0120] The data collection unit can analyze users' past behavioral data and select appropriate collection methods. For example, it can prioritize the collection of data from facilities that users have frequently used in the past. It can also analyze users' past behavioral patterns and determine the optimal collection timing. Furthermore, it can concentrate data collection during specific time periods based on users' past behavioral data. In this way, the optimal collection method can be selected by analyzing past behavioral data.

[0121] The evaluation unit can adjust the level of detail in the evaluation based on the importance of the behavioral data during the evaluation process. For example, it can provide a detailed evaluation based on important behavioral data, or a concise evaluation based on less important behavioral data. Furthermore, it can adjust the level of detail in the evaluation according to the importance of the behavioral data. This allows for the provision of evaluations based on important data by adjusting the level of detail in the evaluation based on the importance of the behavioral data.

[0122] The proposal department can adjust the level of detail in the proposal based on the importance of the evaluation results. For example, it can provide a detailed proposal based on important evaluation results, or a concise proposal based on less important evaluation results. Furthermore, it can adjust the level of detail in the proposal according to the importance of the evaluation results. This allows for the provision of proposals based on important evaluation results by adjusting the level of detail in the proposal based on the importance of the evaluation results.

[0123] The reflection unit can adjust the level of detail in the reflection process based on the importance of the proposed content. For example, it can perform detailed reflection based on important proposals, and concise reflection based on less important proposals. Furthermore, it can adjust the level of detail in the reflection process according to the importance of the proposals. This allows important proposals to be reflected in detail by adjusting the level of detail based on the importance of the proposals.

[0124] The support department can select the optimal support method by referring to the user's past behavioral data during support. For example, it can provide the best support based on the support methods the user has used in the past. It can also analyze the user's past behavioral data to select the optimal support method. Furthermore, it can provide the best support based on the user's past behavioral patterns. In this way, the optimal support method can be selected by referring to past behavioral data.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The data collection unit collects user behavior data. The data collection unit collects information such as user behavior data, conversation content, interactions with employees, and frequency of facility use. The data collection unit collects detailed data such as which facilities the user used, what kind of conversations they had, and what kind of reactions they showed. Step 2: The evaluation unit analyzes the data collected by the data collection unit and generates an objective evaluation. The evaluation unit analyzes the collected data and evaluates user satisfaction and dissatisfaction. The evaluation unit can use AI to analyze the collected data and generate an objective evaluation. Step 3: The proposal department makes improvement suggestions based on the evaluation results generated by the evaluation department. The proposal department proposes specific areas for improvement based on the evaluation results. The proposal department can use AI to make improvement suggestions based on the evaluation results. Step 4: The implementation unit reflects the improvements proposed by the proposal unit into the reservation site and evaluation system. The implementation unit can immediately reflect the proposed improvements into the reservation site and evaluation system. The implementation unit can use AI to reflect the proposed improvements into the reservation site and evaluation system. Step 5: The support department has agents act as concierges. The support department provides guidance during check-in, assistance with facility usage, and suggestions for nearby tourist attractions. The support department uses AI to enable agents to fulfill the role of concierge.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, evaluation unit, proposal unit, reflection unit, and support unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The evaluation unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and generates an objective evaluation. The proposal unit makes improvement suggestions based on the evaluation results with the specific processing unit 290 of the data processing unit 12. The reflection unit reflects the proposed improvements with the specific processing unit 290 of the data processing unit 12 in the reservation site and evaluation system. The support unit enables the agent to act as a concierge with the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, evaluation unit, proposal unit, reflection unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The evaluation unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and generates an objective evaluation. The proposal unit makes improvement suggestions based on the evaluation results with the specific processing unit 290 of the data processing unit 12. The reflection unit reflects the proposed improvements with the specific processing unit 290 of the data processing unit 12 in the reservation site and evaluation system. The support unit enables the agent to act as a concierge with the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, evaluation unit, proposal unit, reflection unit, and support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The evaluation unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and generates an objective evaluation. The proposal unit makes improvement suggestions based on the evaluation results with the specific processing unit 290 of the data processing unit 12. The reflection unit reflects the proposed improvements with the specific processing unit 290 of the data processing unit 12 in the reservation site and evaluation system. The support unit enables the agent to act as a concierge with the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the collection unit, evaluation unit, proposal unit, reflection unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The evaluation unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and generates an objective evaluation. The proposal unit makes improvement suggestions based on the evaluation results with the specific processing unit 290 of the data processing unit 12. The reflection unit reflects the proposed improvements with the specific processing unit 290 of the data processing unit 12 in the reservation site and evaluation system. The support unit enables the agent to act as a concierge with the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0180] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) A data collection unit that collects user behavior data, An evaluation unit analyzes the data collected by the aforementioned collection unit and generates an objective evaluation, A proposal unit that makes improvement suggestions based on the evaluation results generated by the evaluation unit, The reflection unit reflects the improvements proposed by the proposal unit into the reservation site and evaluation system, It includes a support department where agents act as concierges. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect user behavior data, conversation content, interactions with employees, and information on facility usage frequency. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, We analyze the collected data to evaluate user satisfaction and areas of dissatisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the evaluation results, we propose specific areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reflection unit is, The suggested improvements will be reflected in the booking site and rating system. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We provide guidance during check-in, support with facility usage, and suggestions for nearby tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past behavioral data and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current location and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, It estimates the user's emotions and adjusts the way evaluations are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, During evaluation, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, It estimates the user's emotions and adjusts the length of the rating based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, During the evaluation process, the priority of the evaluation is determined based on when the behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, During evaluation, the order of evaluations will be adjusted based on the relevance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the evaluation results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reflection unit is, It estimates the user's emotions and determines the priority of content to reflect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reflection unit is, When implementing the changes, the level of detail will be adjusted based on the importance of the proposed content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reflection unit is, When implementing changes, different implementation algorithms are applied depending on the category of the proposed content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reflection unit is, We estimate the user's emotions and adjust how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reflection unit is, When implementing the changes, the priority of implementation will be determined based on when the proposals were collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reflection unit is, When implementing the changes, the order in which they are implemented will be adjusted based on the relevance of the proposed content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is It estimates the user's emotions and adjusts the way support is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is During support, the appropriate support method is selected based on the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is During support, customize the support method based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit is During support, customize the support method based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user behavior data, An evaluation unit analyzes the data collected by the aforementioned collection unit and generates an objective evaluation, A proposal unit that makes improvement suggestions based on the evaluation results generated by the evaluation unit, The reflection unit reflects the improvements proposed by the proposal unit into the reservation site and evaluation system, It includes a support department where agents act as concierges. A system characterized by the following features.

2. The aforementioned collection unit is We collect user behavior data, conversation content, interactions with employees, and information on facility usage frequency. The system according to feature 1.

3. The evaluation unit, We analyze the collected data to evaluate user satisfaction and areas of dissatisfaction. The system according to feature 1.

4. The aforementioned proposal section is, Based on the evaluation results, we propose specific areas for improvement. The system according to feature 1.

5. The aforementioned reflection unit is, The suggested improvements will be reflected in the booking site and rating system. The system according to feature 1.

6. The aforementioned support unit is We provide guidance during check-in, support with facility usage, and suggestions for nearby tourist attractions. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze users' past behavioral data and select the appropriate data collection method. The system according to feature 1.

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