System

Abstract

To provide a system according to an embodiment that grasps a customer's needs and emotion and suggests an appropriate response.SOLUTION: A system according to an embodiment comprises a collection unit, a suggestion unit, and an alert unit. The collection unit collects information indicating a customer's needs and information indicating the customer's emotion. The suggestion unit suggests the details of a response to the customer to a front desk staff member on the basis of the information collected by the collection unit. The alert unit issues an alert when a specific emotion of the customer exceeds a set threshold.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, it was difficult to grasp customer needs and emotions in real time and respond appropriately.

[0005] The system according to the embodiment aims to understand the needs and feelings of customers and suggest appropriate responses. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a suggestion unit, and an alert unit. The collection unit collects information indicating customer needs and information indicating customer emotions. The suggestion unit suggests to the front staff how to respond to the customer based on the information collected by the collection unit. The alert unit issues an alert when a specific emotion of the customer exceeds a set threshold. Effect of the Invention

[0007] The system according to the embodiment can grasp the needs and feelings of customers and suggest appropriate responses. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The front desk operation support system according to the embodiment of the present invention is a system that grasps the needs and emotions of customers and suggests appropriate responses to front desk staff. This system collects information indicating the needs of customers and information indicating the emotions of customers, and suggests the content of responses to front desk staff based on the collected information. Furthermore, an alert is issued when the customer's negative emotions exceed a threshold. This mechanism allows hotel staff to understand the emotions of customers and respond more appropriately. It is also used for customer satisfaction evaluation. For example, when collecting information indicating the needs of customers and information indicating the emotions of customers, the information can be collected from an AI device installed in the guest room of the customer. The AI ​​can analyze the requests and comments made by the customer in the guest room and grasp the needs and emotions of the customer. It is also possible to select a collection method according to the customer's past behavior history. This allows more accurate information to be collected. Next, the content of responses to the front desk staff is suggested based on the collected information. For example, if a customer desires a specific service, it can be suggested that the service be provided. Furthermore, if a customer is dissatisfied, it can be suggested that the customer be provided with the service. Furthermore, if a customer is dissatisfied, it can be suggested that the customer be provided with the response to resolve the dissatisfaction. This allows the front desk staff to respond appropriately according to the needs of the customer. Furthermore, an alert is issued when the customer's negative emotions exceed a threshold. For example, if a customer is very dissatisfied, an alert can be sent to the front desk staff, urging them to take action quickly. This makes it possible to resolve customer dissatisfaction early and improve customer satisfaction. It is also possible to evaluate customer satisfaction based on the collected information. For example, it is possible to evaluate the extent to which a customer's request has been fulfilled and the extent to which the customer's feelings have improved. This makes it possible to improve the service quality of the hotel as a whole. This mechanism allows hotel staff to understand the feelings of customers and respond more appropriately. It is also used to evaluate customer satisfaction, making it possible to improve the service quality of the hotel as a whole.This enables the front desk operation support system to understand customers' needs and feelings and suggest appropriate responses to front desk staff, thereby improving customer satisfaction.

[0029] The front desk operation support system according to the embodiment includes a collection unit, a suggestion unit, and an alert unit. The collection unit collects information indicating the needs of the customer and information indicating the emotions of the customer. The information indicating the needs of the customer includes, for example, a purchase history and a survey result, but is not limited to such examples. The information indicating the emotions of the customer includes, for example, facial expression recognition data and voice tone analysis, but is not limited to such examples. The collection unit can collect information, for example, from an AI device installed in the guest room of the customer. The AI ​​device includes, for example, a smart speaker and a device with a camera, but is not limited to such examples. The collection unit can also select an appropriate method from predetermined collection methods based on the past behavior history of the customer. For example, the information collection method is selected based on the past purchase history, website browsing history, etc. The suggestion unit suggests the content of the response to the front desk staff based on the information collected by the collection unit. The suggestion is performed, for example, based on a recommendation algorithm or a form of suggestion, but is not limited to such examples. For example, if a customer desires a specific service, it can be suggested that the service be provided. Furthermore, if a customer is dissatisfied, a response to resolve the dissatisfaction can be suggested. The alert unit issues an alert when the customer's negative emotion exceeds a threshold. The specific emotion includes, but is not limited to, for example, anger, joy, sadness, etc. The threshold is set, but is not limited to, for example, based on the numerical range of the emotion score. For example, if a customer is very dissatisfied, an alert can be issued to the front staff to prompt them to respond quickly. As a result, the front desk operation support system according to the embodiment can improve customer satisfaction by grasping the needs and emotions of the customer and suggesting an appropriate response to the front desk staff.

[0030] The collection unit collects information indicating the needs of the customer and information indicating the emotions of the customer. The information indicating the needs of the customer includes, for example, a purchase history and a survey result, but is not limited to such examples. Specifically, the history of products and services that the customer has purchased in the past is obtained from a database, and the survey results are collected from an online form or paper media. These pieces of information are important for understanding the preferences and interests of the customer. The information indicating the emotions of the customer includes, for example, facial expression recognition data and voice tone analysis, but is not limited to such examples. The facial expression recognition data uses a device with a camera to analyze the facial expression of the customer in real time to identify emotions such as joy, anger, and sadness. The voice tone analysis analyzes the tone and pitch of the customer's voice through a smart speaker or microphone to detect changes in emotions. The collection unit can collect information, for example, from an AI device installed in the customer's guest room. The AI ​​device includes, for example, a smart speaker and a device with a camera, but is not limited to such examples. These devices are naturally used by the customer and can monitor the customer's actions and remarks in real time. The collection unit can also select an appropriate method from among predetermined collection methods based on the customer's past behavior history. For example, the information collection method is selected based on past purchase history, website browsing history, etc. This allows the collection unit to select the optimal information collection method according to the customer's characteristics and behavioral patterns, and efficiently collect data. Furthermore, the collection unit can centrally manage the collected data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the suggestion unit and alert unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The suggestion unit suggests the content of the response to the front staff based on the information collected by the collection unit. The suggestion is performed, for example, based on a recommendation algorithm or a form of suggestion, but is not limited to such examples. Specifically, the suggestion unit analyzes the collected customer needs and emotion data and suggests the optimal response method. For example, if a customer desires a specific service, it can suggest that the service be provided. Based on the services the customer has used in the past and the desired content in the questionnaire, an individually customized suggestion is made. In addition, if a customer is dissatisfied, it can suggest a response to resolve the dissatisfaction. For example, for a service about which the customer has expressed dissatisfaction in the past, it can suggest an improvement measure and encourage the customer to use it again. The suggestion unit uses AI to analyze these data and simulate multiple scenarios to identify the most effective response method. As a result, the suggestion unit can propose a quick and accurate response to the front staff and improve customer satisfaction. Furthermore, the suggestion unit can continuously revise the suggestion content based on data updated in real time to respond to the latest situation. For example, if the customer's emotions or needs change, the suggestion unit immediately takes in new data and updates the suggestion content. In addition, the suggestion department can make more accurate suggestions by taking into account the characteristics of each region and past customer history. This allows the suggestion department to always provide highly accurate suggestions based on the latest information, supporting quick and appropriate responses.

[0032] The alert unit issues an alert when the customer's negative emotion exceeds a threshold. The specific emotion includes, but is not limited to, anger, joy, sadness, and the like. Specifically, the customer's emotion score is calculated based on the facial expression recognition data and voice tone analysis data provided by the collection unit. The threshold is set based on, but is not limited to, the numerical range of the emotion score. For example, when a customer is very dissatisfied, an alert can be issued to the front staff to promptly respond. The alert is notified to the front staff's terminal, and an immediate response is required. The alert unit uses AI to analyze the emotion data and detect abnormal emotion changes. This allows the alert unit to monitor changes in the customer's emotions in real time and support rapid response. Furthermore, the alert unit can also perform long-term risk assessment and trend analysis using past data and statistical information. For example, based on past customer emotion data, the risk fluctuation in a specific time period or situation is predicted, and future countermeasures are planned. Furthermore, the alert unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning. This enables the alert unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The collection unit can collect information indicating the needs of the customer and information indicating the emotions of the customer from an AI device installed in the guest room of the customer. The collection unit collects information from an AI device such as a smart speaker or a camera-equipped device installed in the guest room of the customer. For example, the smart speaker can analyze the content of the customer's remarks and grasp the needs and emotions of the customer. In addition, the camera-equipped device can analyze the facial expression of the customer and estimate the emotions of the customer. In this way, more accurate customer information can be obtained by collecting information from the AI ​​device in the guest room. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input voice data acquired by the smart speaker to a generation AI and analyze the needs and emotions of the customer from the voice data.

[0034] The collection unit can select an appropriate method from among predetermined collection methods based on the customer's past behavior history. The collection unit, for example, analyzes the customer's behavior history, such as past purchase history and website browsing history, and selects the optimal information collection method. For example, based on services that the customer has frequently used in the past, related information is preferentially collected. In addition, the collection unit can analyze the customer's past behavior patterns and select the most efficient information collection method. Furthermore, the information collection method can be customized with reference to the customer's past feedback. As a result, more efficient information collection is possible by selecting an information collection method based on the customer's past behavior history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past behavior history data to the generation AI and have the generation AI select the optimal information collection method.

[0035] The collection unit can estimate the customer's emotion and adjust the timing of information collection based on the estimated customer's emotion. For example, when the customer is relaxed, the collection unit frequently collects information and collects detailed data. Also, when the customer is stressed, the frequency of information collection can be reduced to reduce the burden on the customer. Furthermore, when the customer is in a hurry, information can be collected quickly and the minimum amount of data required can be obtained. This reduces the burden on the customer and enables more appropriate information collection by adjusting the timing of information collection according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the customer's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0036] The collection unit can analyze the customer's past behavior history and select an appropriate method from among predetermined information collection methods. The collection unit, for example, preferentially collects related information based on services that the customer has frequently used in the past. The collection unit can also analyze the customer's past behavior patterns and select the most efficient information collection method. Furthermore, the information collection method can be customized by referring to the customer's past feedback. In this way, the optimal information collection method can be selected by analyzing the customer's past behavior history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past behavior history data to the generation AI and have the generation AI select the optimal information collection method.

[0037] The collection unit can perform filtering based on the customer's current stay status and areas of interest when collecting information. For example, if the customer is currently in a restaurant in a hotel, the collection unit preferentially collects information related to the restaurant. In addition, if the customer is interested in a tourist spot, the collection unit can collect information about the tourist spot. Furthermore, if the customer is staying for business purposes, business-related information can also be collected. This allows more relevant information to be collected by filtering information based on the customer's stay status and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's stay status data to the generation AI and have the generation AI perform filtering.

[0038] The collection unit can estimate the customer's emotions and determine the priority of information to be collected based on the estimated customer's emotions. For example, when the customer is dissatisfied, the collection unit preferentially collects information related to problem solving. Also, when the customer is satisfied, the collection unit can collect information related to additional service proposals. Furthermore, when the customer is excited, the collection unit can preferentially collect information related to entertainment. In this way, by determining the priority of information based on the customer's emotions, more important information can be preferentially collected. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input customer emotion data to the generation AI and have the generation AI determine the priority of information.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the customer. For example, when the customer is in the vicinity of a hotel, the collection unit can collect information about nearby tourist attractions and restaurants. Also, when the customer is at a specific event venue, the collection unit can collect information related to the event. Furthermore, when the customer is at an airport, the collection unit can also collect information about flight information and transportation. In this way, by taking into account the geographical location information of the customer, highly relevant information can be collected preferentially. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the location information data of the customer to the generation AI and have the generation AI collect highly relevant information.

[0040] The collection unit can analyze the social media activity of the customer and collect related information when collecting information. For example, if the customer mentions a specific restaurant on social media, the collection unit can collect information about the restaurant. Also, if the customer shows interest in a specific event on social media, the collection unit can collect information about the event. Furthermore, if the customer mentions a specific tourist destination on social media, information about the tourist destination can also be collected. In this way, related information can be collected by analyzing the customer's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the customer's social media data to the generation AI and have the generation AI collect related information.

[0041] The suggestion unit can estimate the customer's emotions and adjust the way of expressing the suggestions based on the estimated customer's emotions. For example, when the customer is relaxed, the suggestion unit can provide polite and detailed suggestions. Also, when the customer is in a hurry, the suggestion unit can provide concise and quick suggestions. Furthermore, when the customer is dissatisfied, the suggestion unit can provide suggestions that focus on problem solving. This allows for more appropriate suggestions by adjusting the way of expressing the suggestions based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input customer emotion data to the generation AI and cause the generation AI to adjust the way of expressing the suggestions.

[0042] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the customer's needs when making a suggestion. For example, if a customer strongly desires a specific service, the suggestion unit can provide detailed suggestions regarding the service. Also, if a customer is looking for general information, the suggestion unit can provide concise suggestions. Furthermore, if a customer has multiple needs, the level of detail of the suggestion can be adjusted according to the importance. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the customer's needs. A part or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input customer needs data to the generation AI and have the generation AI adjust the level of detail of the suggestion.

[0043] The suggestion unit can apply different suggestion algorithms depending on the customer category when making suggestions. For example, the suggestion unit can suggest efficient services and business-related information to business customers. Also, the suggestion unit can suggest information about tourist spots and leisure activities to tourists. Furthermore, it can suggest services and activities for families to families. This allows more appropriate suggestions to be made by applying different suggestion algorithms depending on the customer category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input customer category data to the generation AI and apply different suggestion algorithms to the generation AI.

[0044] The suggestion unit can estimate the customer's emotion and adjust the length of the suggestion based on the estimated customer's emotion. For example, the suggestion unit can provide detailed suggestions when the customer is relaxed. Also, when the customer is in a hurry, the suggestion unit can provide concise suggestions. Furthermore, when the customer is dissatisfied, the suggestion unit can provide a quick solution suggestion. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input customer emotion data to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0045] The suggestion unit can determine the priority of suggestions based on the customer's stay period when making suggestions. For example, if the customer is staying for a long time, the suggestion unit can preferentially suggest long-term services and activities. Also, if the customer is staying for a short time, the suggestion unit can preferentially suggest services and activities that can be enjoyed in a short time. Furthermore, if the customer is staying during a specific event period, information related to the event can be preferentially suggested. This enables more appropriate suggestions by determining the priority of suggestions based on the customer's stay period. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input customer's stay period data to the generation AI and have the generation AI determine the priority of suggestions.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the customer when making suggestions. For example, if the customer shows a strong interest in a specific service, the suggestion unit can suggest that service first. Also, if the customer is interested in multiple services, the suggestion unit can suggest services in order of relevance. Furthermore, it is also possible to preferentially suggest services that the customer uses in a specific time period. This allows more appropriate suggestions to be made by adjusting the order of suggestions based on the relevance of the customer. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance data of the customer to the generation AI and cause the generation AI to adjust the order of suggestions.

[0047] The alert unit can estimate the customer's emotion and adjust the timing of issuing an alert based on the estimated customer's emotion. For example, if the customer is strongly dissatisfied, the alert unit immediately issues an alert. Also, if the customer is slightly dissatisfied, the alert unit can issue an alert after a certain time. Furthermore, if the customer is relaxed, the alert can be delayed. In this way, the timing of issuing an alert can be adjusted based on the customer's emotion, so that the alert can be issued at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the alert unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the alert unit can input customer emotion data to the generation AI and have the generation AI adjust the timing of issuing an alert.

[0048] When issuing an alert, the alert unit can customize the contents of the alert by referring to the customer's past complaint history. For example, if a customer has been dissatisfied with a particular service in the past, the alert unit issues an alert related to that service. Also, if a customer has been dissatisfied with a particular problem in the past, the alert unit can issue an alert related to that problem. Furthermore, the alert unit can analyze the customer's past complaint history and determine the most appropriate alert content. As a result, more appropriate alert content can be provided by referring to the customer's past complaint history. A part or all of the above-mentioned processing in the alert unit may be performed, for example, using AI or may be performed without using AI. For example, the alert unit can input customer complaint history data to the generation AI and have the generation AI customize the alert content.

[0049] The alert unit can select the means of alerting based on the customer's current stay status when issuing an alert. For example, when the customer is in a guest room, the alert unit issues an alert through an intercom in the guest room. Also, when the customer is in a restaurant in the hotel, the alert unit can issue an alert to restaurant staff. Furthermore, when the customer is outside the hotel, the alert unit can also issue an alert to a mobile phone. This allows for more effective alerting by selecting the optimal alert means based on the customer's stay status. Some or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input the customer's stay status data to the generation AI and have the generation AI select the means of alerting.

[0050] The alert unit can estimate the customer's emotion and determine the priority of the alert based on the estimated customer's emotion. For example, when the customer is strongly dissatisfied, the alert unit issues an alert with the highest priority. Also, when the customer is slightly dissatisfied, the alert unit can issue an alert with the next highest priority. Furthermore, when the customer is relaxed, the priority of the alert can be lowered. In this way, by determining the priority of the alert based on the customer's emotion, it is possible to issue a more important alert with priority. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the alert unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the alert unit can input the customer's emotion data to the generation AI and have the generation AI determine the priority of the alert.

[0051] The alert unit can select the optimal alert means in consideration of the geographical location information of the customer when issuing an alert. For example, when the customer is in the hotel, the alert unit issues an alert through the in-house broadcast. Also, when the customer is outside the hotel, the alert unit can issue an alert to a mobile phone. Furthermore, when the customer is at a specific event venue, the alert can also be issued to staff at the venue. In this way, the optimal alert means can be selected by considering the geographical location information of the customer. A part or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input the customer's location information data to the generation AI and have the generation AI select the optimal alert means.

[0052] The alert unit can adjust the content of the alert by analyzing the social media activity of the customer when issuing an alert. For example, if a customer mentions a specific problem on social media, the alert unit can issue an alert related to the problem. Also, if a customer is dissatisfied with a specific service on social media, the alert unit can issue an alert related to the service. Furthermore, the alert unit can analyze the social media activity of the customer and determine the most appropriate alert content. In this way, more appropriate alert content can be provided by analyzing the social media activity of the customer. Some or all of the above-mentioned processing in the alert unit can be performed, for example, using AI or can be performed without using AI. For example, the alert unit can input the social media data of the customer to the generation AI and have the generation AI adjust the content of the alert.

[0053] The evaluation unit can estimate the customer's emotions and adjust the criteria for satisfaction evaluation based on the estimated customer's emotions. For example, when the customer is relaxed, the evaluation unit can perform a detailed satisfaction evaluation. Also, when the customer is in a hurry, the evaluation unit can perform a concise satisfaction evaluation. Furthermore, when the customer is dissatisfied, the evaluation unit can perform a satisfaction evaluation focusing on problem solving. This allows for a more accurate satisfaction evaluation by adjusting the criteria for satisfaction evaluation based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input customer emotion data to the generation AI and have the generation AI adjust the criteria for satisfaction evaluation.

[0054] The evaluation unit can optimize the evaluation algorithm by referring to the customer's past evaluation history when evaluating the satisfaction level. The evaluation unit adjusts the evaluation criteria based on, for example, services that the customer has previously rated highly. The evaluation unit can also analyze the customer's past evaluation history and select the most appropriate evaluation algorithm. Furthermore, for services that the customer has previously rated poorly, an evaluation can be performed that reflects improvements. In this way, a more appropriate evaluation algorithm can be applied by referring to the customer's past evaluation history. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can input the customer's evaluation history data to the generation AI and cause the generation AI to optimize the evaluation algorithm.

[0055] The evaluation unit can estimate the customer's emotions and adjust the frequency of satisfaction evaluation based on the estimated customer's emotions. For example, when the customer is relaxed, the evaluation unit can frequently perform satisfaction evaluation. Also, when the customer is in a hurry, the evaluation unit can reduce the frequency of satisfaction evaluation. Furthermore, when the customer is dissatisfied, the evaluation unit can quickly perform satisfaction evaluation. This allows satisfaction evaluation at a more appropriate time by adjusting the frequency of satisfaction evaluation based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input customer emotion data to the generation AI and have the generation AI adjust the frequency of satisfaction evaluation.

[0056] The evaluation unit can weight the evaluation data based on the customer's stay time when evaluating the satisfaction level. For example, if the customer is staying for a long time, the evaluation unit can emphasize the long-term satisfaction evaluation. Also, if the customer is staying for a short time, the evaluation unit can emphasize the short-term satisfaction evaluation. Furthermore, if the customer is staying during a specific event period, the evaluation unit can emphasize the satisfaction evaluation related to the event. This allows for more accurate satisfaction evaluation by weighting the evaluation data based on the customer's stay time. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the customer's stay time data to the generation AI and have the generation AI weight the evaluation data.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The front desk operation support system can further include a health management unit that monitors the health condition of customers. The health management unit collects vital data such as the customer's heart rate and body temperature, and can alert the front desk staff if an abnormality is detected. For example, if a customer has a high fever, it can suggest that medical services be provided promptly. Also, if a customer is feeling stressed, it can suggest that services that will help them relax be provided. This makes it possible to respond appropriately according to the customer's health condition.

[0059] The front desk operation support system can further include a personalization unit that provides personalized services based on the customer's hobbies and preferences. The personalization unit analyzes the customer's past usage history and survey results to suggest services and activities that the customer prefers. For example, if the customer has used a spa in the past, a special spa plan can be suggested. Also, if the customer likes a particular dish, a restaurant that serves that dish can be introduced. This makes it possible to improve customer satisfaction by providing services that meet the customer's preferences.

[0060] The front desk operation support system can further be equipped with a tracking unit that tracks the behavior of customers during their stay in real time. The tracking unit grasps what activities customers are doing within the hotel and provides services at the appropriate time. For example, if a customer is at the pool, it can suggest poolside drink service. Also, if a customer uses the gym, it can offer a refreshment service after training. This makes it possible to provide timely services according to customer behavior.

[0061] The front desk operation support system can further include a social media analysis unit that analyzes the social media activities of customers and proposes services based on the interests of the customers. The social media analysis unit collects the content that customers mention on social media and understands the interests of the customers. For example, if a customer mentions a specific tourist destination, information about that tourist destination can be provided. Also, if a customer shows interest in a specific event, tickets to that event can be arranged. This makes it possible to provide services based on the interests of the customers.

[0062] The front desk operation support system can further include an eco-management unit that monitors energy consumption during a customer's stay and suggests eco-friendly services. The eco-management unit collects energy consumption data of customers and suggests energy-efficient services. For example, if a customer consumes a lot of energy, it can suggest eco-friendly activities. Also, if a customer is highly eco-conscious, it can provide an eco-friendly room. This makes it possible to provide eco-friendly services according to the customer's energy consumption.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The collection unit collects information indicating customer needs and information indicating customer emotions. The information indicating customer needs includes, for example, purchase history and survey results. The information indicating customer emotions includes, for example, facial expression recognition data and voice tone analysis. The collection unit can collect information from an AI device installed in the customer's guest room. The AI ​​device includes, for example, a smart speaker and a device with a camera. The collection unit can also select an appropriate method from predetermined collection methods based on the customer's past behavioral history. Step 2: The suggestion unit suggests the content of the response to the front staff based on the information collected by the collection unit. The suggestion is made based on, for example, a recommendation algorithm or a suggestion format. For example, if the customer desires a specific service, it can suggest that the service be provided. Also, if the customer is dissatisfied, it can suggest a response to resolve the dissatisfaction. Step 3: The alert unit issues an alert when the customer's negative emotion exceeds a threshold. Specific emotions include, for example, anger, joy, sadness, etc. The threshold is set, for example, based on the numerical range of the emotion score. For example, if a customer is feeling strongly dissatisfied, an alert can be issued to the front staff to promptly respond.

[0065] (Example 2) The front desk operation support system according to the embodiment of the present invention is a system that grasps the needs and emotions of customers and suggests appropriate responses to front desk staff. This system collects information indicating the needs of customers and information indicating the emotions of customers, and suggests the content of responses to front desk staff based on the collected information. Furthermore, an alert is issued when the customer's negative emotions exceed a threshold. This mechanism allows hotel staff to understand the emotions of customers and respond more appropriately. It is also used for customer satisfaction evaluation. For example, when collecting information indicating the needs of customers and information indicating the emotions of customers, the information can be collected from an AI device installed in the guest room of the customer. The AI ​​can analyze the requests and comments made by the customer in the guest room and grasp the needs and emotions of the customer. It is also possible to select a collection method according to the customer's past behavior history. This allows more accurate information to be collected. Next, the content of responses to the front desk staff is suggested based on the collected information. For example, if a customer desires a specific service, it can be suggested that the service be provided. Furthermore, if a customer is dissatisfied, it can be suggested that the customer be provided with the service. Furthermore, if a customer is dissatisfied, it can be suggested that the customer be provided with the response to resolve the dissatisfaction. This allows the front desk staff to respond appropriately according to the needs of the customer. Furthermore, an alert is issued when the customer's negative emotions exceed a threshold. For example, if a customer is very dissatisfied, an alert can be sent to the front desk staff, urging them to take action quickly. This makes it possible to resolve customer dissatisfaction early and improve customer satisfaction. It is also possible to evaluate customer satisfaction based on the collected information. For example, it is possible to evaluate the extent to which a customer's request has been fulfilled and the extent to which the customer's feelings have improved. This makes it possible to improve the service quality of the hotel as a whole. This mechanism allows hotel staff to understand the feelings of customers and respond more appropriately. It is also used to evaluate customer satisfaction, making it possible to improve the service quality of the hotel as a whole.This enables the front desk operation support system to understand customers' needs and feelings and suggest appropriate responses to front desk staff, thereby improving customer satisfaction.

[0066] The front desk operation support system according to the embodiment includes a collection unit, a suggestion unit, and an alert unit. The collection unit collects information indicating the needs of the customer and information indicating the emotions of the customer. The information indicating the needs of the customer includes, for example, a purchase history and a survey result, but is not limited to such examples. The information indicating the emotions of the customer includes, for example, facial expression recognition data and voice tone analysis, but is not limited to such examples. The collection unit can collect information, for example, from an AI device installed in the guest room of the customer. The AI ​​device includes, for example, a smart speaker and a device with a camera, but is not limited to such examples. The collection unit can also select an appropriate method from predetermined collection methods based on the past behavior history of the customer. For example, the information collection method is selected based on the past purchase history, website browsing history, etc. The suggestion unit suggests the content of the response to the front desk staff based on the information collected by the collection unit. The suggestion is performed, for example, based on a recommendation algorithm or a form of suggestion, but is not limited to such examples. For example, if a customer desires a specific service, it can be suggested that the service be provided. Furthermore, if a customer is dissatisfied, a response to resolve the dissatisfaction can be suggested. The alert unit issues an alert when the customer's negative emotion exceeds a threshold. The specific emotion includes, but is not limited to, for example, anger, joy, sadness, etc. The threshold is set, but is not limited to, for example, based on the numerical range of the emotion score. For example, if a customer is very dissatisfied, an alert can be issued to the front staff to prompt them to respond quickly. As a result, the front desk operation support system according to the embodiment can improve customer satisfaction by grasping the needs and emotions of the customer and suggesting an appropriate response to the front desk staff.

[0067] The collection unit collects information indicating the needs of the customer and information indicating the emotions of the customer. The information indicating the needs of the customer includes, for example, a purchase history and a survey result, but is not limited to such examples. Specifically, the history of products and services that the customer has purchased in the past is obtained from a database, and the survey results are collected from an online form or paper media. These pieces of information are important for understanding the preferences and interests of the customer. The information indicating the emotions of the customer includes, for example, facial expression recognition data and voice tone analysis, but is not limited to such examples. The facial expression recognition data uses a device with a camera to analyze the facial expression of the customer in real time to identify emotions such as joy, anger, and sadness. The voice tone analysis analyzes the tone and pitch of the customer's voice through a smart speaker or microphone to detect changes in emotions. The collection unit can collect information, for example, from an AI device installed in the customer's guest room. The AI ​​device includes, for example, a smart speaker and a device with a camera, but is not limited to such examples. These devices are naturally used by the customer and can monitor the customer's actions and remarks in real time. The collection unit can also select an appropriate method from among predetermined collection methods based on the customer's past behavior history. For example, the information collection method is selected based on past purchase history, website browsing history, etc. This allows the collection unit to select the optimal information collection method according to the customer's characteristics and behavioral patterns, and efficiently collect data. Furthermore, the collection unit can centrally manage the collected data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the suggestion unit and alert unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0068] The suggestion unit suggests the content of the response to the front staff based on the information collected by the collection unit. The suggestion is performed, for example, based on a recommendation algorithm or a form of suggestion, but is not limited to such examples. Specifically, the suggestion unit analyzes the collected customer needs and emotion data and suggests the optimal response method. For example, if a customer desires a specific service, it can suggest that the service be provided. Based on the services the customer has used in the past and the desired content in the questionnaire, an individually customized suggestion is made. In addition, if a customer is dissatisfied, it can suggest a response to resolve the dissatisfaction. For example, for a service about which the customer has expressed dissatisfaction in the past, it can suggest an improvement measure and encourage the customer to use it again. The suggestion unit uses AI to analyze these data and simulate multiple scenarios to identify the most effective response method. As a result, the suggestion unit can propose a quick and accurate response to the front staff and improve customer satisfaction. Furthermore, the suggestion unit can continuously revise the suggestion content based on data updated in real time to respond to the latest situation. For example, if the customer's emotions or needs change, the suggestion unit immediately takes in new data and updates the suggestion content. In addition, the suggestion department can make more accurate suggestions by taking into account the characteristics of each region and past customer history. This allows the suggestion department to always provide highly accurate suggestions based on the latest information, supporting quick and appropriate responses.

[0069] The alert unit issues an alert when the customer's negative emotion exceeds a threshold. The specific emotion includes, but is not limited to, anger, joy, sadness, and the like. Specifically, the customer's emotion score is calculated based on the facial expression recognition data and voice tone analysis data provided by the collection unit. The threshold is set based on, but is not limited to, the numerical range of the emotion score. For example, when a customer is very dissatisfied, an alert can be issued to the front staff to promptly respond. The alert is notified to the front staff's terminal, and an immediate response is required. The alert unit uses AI to analyze the emotion data and detect abnormal emotion changes. This allows the alert unit to monitor changes in the customer's emotions in real time and support rapid response. Furthermore, the alert unit can also perform long-term risk assessment and trend analysis using past data and statistical information. For example, based on past customer emotion data, the risk fluctuation in a specific time period or situation is predicted, and future countermeasures are planned. Furthermore, the alert unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning. This enables the alert unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0070] The collection unit can collect information indicating the needs of the customer and information indicating the emotions of the customer from an AI device installed in the guest room of the customer. The collection unit collects information from an AI device such as a smart speaker or a camera-equipped device installed in the guest room of the customer. For example, the smart speaker can analyze the content of the customer's remarks and grasp the needs and emotions of the customer. In addition, the camera-equipped device can analyze the facial expression of the customer and estimate the emotions of the customer. In this way, more accurate customer information can be obtained by collecting information from the AI ​​device in the guest room. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input voice data acquired by the smart speaker to a generation AI and analyze the needs and emotions of the customer from the voice data.

[0071] The collection unit can select an appropriate method from among predetermined collection methods based on the customer's past behavior history. The collection unit, for example, analyzes the customer's behavior history, such as past purchase history and website browsing history, and selects the optimal information collection method. For example, based on services that the customer has frequently used in the past, related information is preferentially collected. In addition, the collection unit can analyze the customer's past behavior patterns and select the most efficient information collection method. Furthermore, the information collection method can be customized with reference to the customer's past feedback. As a result, more efficient information collection is possible by selecting an information collection method based on the customer's past behavior history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past behavior history data to the generation AI and have the generation AI select the optimal information collection method.

[0072] The collection unit can estimate the customer's emotion and adjust the timing of information collection based on the estimated customer's emotion. For example, when the customer is relaxed, the collection unit frequently collects information and collects detailed data. Also, when the customer is stressed, the frequency of information collection can be reduced to reduce the burden on the customer. Furthermore, when the customer is in a hurry, information can be collected quickly and the minimum amount of data required can be obtained. This reduces the burden on the customer and enables more appropriate information collection by adjusting the timing of information collection according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the customer's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0073] The collection unit can analyze the customer's past behavior history and select an appropriate method from among predetermined information collection methods. The collection unit, for example, preferentially collects related information based on services that the customer has frequently used in the past. The collection unit can also analyze the customer's past behavior patterns and select the most efficient information collection method. Furthermore, the information collection method can be customized by referring to the customer's past feedback. In this way, the optimal information collection method can be selected by analyzing the customer's past behavior history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past behavior history data to the generation AI and have the generation AI select the optimal information collection method.

[0074] The collection unit can perform filtering based on the customer's current stay status and areas of interest when collecting information. For example, if the customer is currently in a restaurant in a hotel, the collection unit preferentially collects information related to the restaurant. In addition, if the customer is interested in a tourist spot, the collection unit can collect information about the tourist spot. Furthermore, if the customer is staying for business purposes, business-related information can also be collected. This allows more relevant information to be collected by filtering information based on the customer's stay status and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's stay status data to the generation AI and have the generation AI perform filtering.

[0075] The collection unit can estimate the customer's emotions and determine the priority of information to be collected based on the estimated customer's emotions. For example, when the customer is dissatisfied, the collection unit preferentially collects information related to problem solving. Also, when the customer is satisfied, the collection unit can collect information related to additional service proposals. Furthermore, when the customer is excited, the collection unit can preferentially collect information related to entertainment. In this way, by determining the priority of information based on the customer's emotions, more important information can be preferentially collected. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input customer emotion data to the generation AI and have the generation AI determine the priority of information.

[0076] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the customer. For example, when the customer is in the vicinity of a hotel, the collection unit can collect information about nearby tourist attractions and restaurants. Also, when the customer is at a specific event venue, the collection unit can collect information related to the event. Furthermore, when the customer is at an airport, the collection unit can also collect information about flight information and transportation. In this way, by taking into account the geographical location information of the customer, highly relevant information can be collected preferentially. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the location information data of the customer to the generation AI and have the generation AI collect highly relevant information.

[0077] The collection unit can analyze the social media activity of the customer and collect related information when collecting information. For example, if the customer mentions a specific restaurant on social media, the collection unit can collect information about the restaurant. Also, if the customer shows interest in a specific event on social media, the collection unit can collect information about the event. Furthermore, if the customer mentions a specific tourist destination on social media, information about the tourist destination can also be collected. In this way, related information can be collected by analyzing the customer's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the customer's social media data to the generation AI and have the generation AI collect related information.

[0078] The suggestion unit can estimate the customer's emotions and adjust the way of expressing the suggestions based on the estimated customer's emotions. For example, when the customer is relaxed, the suggestion unit can provide polite and detailed suggestions. Also, when the customer is in a hurry, the suggestion unit can provide concise and quick suggestions. Furthermore, when the customer is dissatisfied, the suggestion unit can provide suggestions that focus on problem solving. This allows for more appropriate suggestions by adjusting the way of expressing the suggestions based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input customer emotion data to the generation AI and cause the generation AI to adjust the way of expressing the suggestions.

[0079] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the customer's needs when making a suggestion. For example, if a customer strongly desires a specific service, the suggestion unit can provide detailed suggestions regarding the service. Also, if a customer is looking for general information, the suggestion unit can provide concise suggestions. Furthermore, if a customer has multiple needs, the level of detail of the suggestion can be adjusted according to the importance. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the customer's needs. A part or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input customer needs data to the generation AI and have the generation AI adjust the level of detail of the suggestion.

[0080] The suggestion unit can apply different suggestion algorithms depending on the customer category when making suggestions. For example, the suggestion unit can suggest efficient services and business-related information to business customers. Also, the suggestion unit can suggest information about tourist spots and leisure activities to tourists. Furthermore, it can suggest services and activities for families to families. This allows more appropriate suggestions to be made by applying different suggestion algorithms depending on the customer category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input customer category data to the generation AI and apply different suggestion algorithms to the generation AI.

[0081] The suggestion unit can estimate the customer's emotion and adjust the length of the suggestion based on the estimated customer's emotion. For example, the suggestion unit can provide detailed suggestions when the customer is relaxed. Also, when the customer is in a hurry, the suggestion unit can provide concise suggestions. Furthermore, when the customer is dissatisfied, the suggestion unit can provide a quick solution suggestion. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input customer emotion data to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0082] The suggestion unit can determine the priority of suggestions based on the customer's stay period when making suggestions. For example, if the customer is staying for a long time, the suggestion unit can preferentially suggest long-term services and activities. Also, if the customer is staying for a short time, the suggestion unit can preferentially suggest services and activities that can be enjoyed in a short time. Furthermore, if the customer is staying during a specific event period, information related to the event can be preferentially suggested. This enables more appropriate suggestions by determining the priority of suggestions based on the customer's stay period. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input customer's stay period data to the generation AI and have the generation AI determine the priority of suggestions.

[0083] The suggestion unit can adjust the order of suggestions based on the relevance of the customer when making suggestions. For example, if the customer shows a strong interest in a specific service, the suggestion unit can suggest that service first. Also, if the customer is interested in multiple services, the suggestion unit can suggest services in order of relevance. Furthermore, it is also possible to preferentially suggest services that the customer uses in a specific time period. This allows more appropriate suggestions to be made by adjusting the order of suggestions based on the relevance of the customer. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance data of the customer to the generation AI and cause the generation AI to adjust the order of suggestions.

[0084] The alert unit can estimate the customer's emotion and adjust the timing of issuing an alert based on the estimated customer's emotion. For example, if the customer is strongly dissatisfied, the alert unit immediately issues an alert. Also, if the customer is slightly dissatisfied, the alert unit can issue an alert after a certain time. Furthermore, if the customer is relaxed, the alert can be delayed. In this way, the timing of issuing an alert can be adjusted based on the customer's emotion, so that the alert can be issued at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the alert unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the alert unit can input customer emotion data to the generation AI and have the generation AI adjust the timing of issuing an alert.

[0085] When issuing an alert, the alert unit can customize the contents of the alert by referring to the customer's past complaint history. For example, if a customer has been dissatisfied with a particular service in the past, the alert unit issues an alert related to that service. Also, if a customer has been dissatisfied with a particular problem in the past, the alert unit can issue an alert related to that problem. Furthermore, the alert unit can analyze the customer's past complaint history and determine the most appropriate alert content. As a result, more appropriate alert content can be provided by referring to the customer's past complaint history. A part or all of the above-mentioned processing in the alert unit may be performed, for example, using AI or may be performed without using AI. For example, the alert unit can input customer complaint history data to the generation AI and have the generation AI customize the alert content.

[0086] The alert unit can select the means of alerting based on the customer's current stay status when issuing an alert. For example, when the customer is in a guest room, the alert unit issues an alert through an intercom in the guest room. Also, when the customer is in a restaurant in the hotel, the alert unit can issue an alert to restaurant staff. Furthermore, when the customer is outside the hotel, the alert unit can also issue an alert to a mobile phone. This allows for more effective alerting by selecting the optimal alert means based on the customer's stay status. Some or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input the customer's stay status data to the generation AI and have the generation AI select the means of alerting.

[0087] The alert unit can estimate the customer's emotion and determine the priority of the alert based on the estimated customer's emotion. For example, when the customer is strongly dissatisfied, the alert unit issues an alert with the highest priority. Also, when the customer is slightly dissatisfied, the alert unit can issue an alert with the next highest priority. Furthermore, when the customer is relaxed, the priority of the alert can be lowered. In this way, by determining the priority of the alert based on the customer's emotion, it is possible to issue a more important alert with priority. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the alert unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the alert unit can input the customer's emotion data to the generation AI and have the generation AI determine the priority of the alert.

[0088] The alert unit can select the optimal alert means in consideration of the geographical location information of the customer when issuing an alert. For example, when the customer is in the hotel, the alert unit issues an alert through the in-house broadcast. Also, when the customer is outside the hotel, the alert unit can issue an alert to a mobile phone. Furthermore, when the customer is at a specific event venue, the alert can also be issued to staff at the venue. In this way, the optimal alert means can be selected by considering the geographical location information of the customer. A part or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input the customer's location information data to the generation AI and have the generation AI select the optimal alert means.

[0089] The alert unit can adjust the content of the alert by analyzing the social media activity of the customer when issuing an alert. For example, if a customer mentions a specific problem on social media, the alert unit can issue an alert related to the problem. Also, if a customer is dissatisfied with a specific service on social media, the alert unit can issue an alert related to the service. Furthermore, the alert unit can analyze the social media activity of the customer and determine the most appropriate alert content. In this way, more appropriate alert content can be provided by analyzing the social media activity of the customer. Some or all of the above-mentioned processing in the alert unit can be performed, for example, using AI or can be performed without using AI. For example, the alert unit can input the social media data of the customer to the generation AI and have the generation AI adjust the content of the alert.

[0090] The evaluation unit can estimate the customer's emotions and adjust the criteria for satisfaction evaluation based on the estimated customer's emotions. For example, when the customer is relaxed, the evaluation unit can perform a detailed satisfaction evaluation. Also, when the customer is in a hurry, the evaluation unit can perform a concise satisfaction evaluation. Furthermore, when the customer is dissatisfied, the evaluation unit can perform a satisfaction evaluation focusing on problem solving. This allows for a more accurate satisfaction evaluation by adjusting the criteria for satisfaction evaluation based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input customer emotion data to the generation AI and have the generation AI adjust the criteria for satisfaction evaluation.

[0091] The evaluation unit can optimize the evaluation algorithm by referring to the customer's past evaluation history when evaluating the satisfaction level. The evaluation unit adjusts the evaluation criteria based on, for example, services that the customer has previously rated highly. The evaluation unit can also analyze the customer's past evaluation history and select the most appropriate evaluation algorithm. Furthermore, for services that the customer has previously rated poorly, an evaluation can be performed that reflects improvements. In this way, a more appropriate evaluation algorithm can be applied by referring to the customer's past evaluation history. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can input the customer's evaluation history data to the generation AI and cause the generation AI to optimize the evaluation algorithm.

[0092] The evaluation unit can estimate the customer's emotions and adjust the frequency of satisfaction evaluation based on the estimated customer's emotions. For example, when the customer is relaxed, the evaluation unit can frequently perform satisfaction evaluation. Also, when the customer is in a hurry, the evaluation unit can reduce the frequency of satisfaction evaluation. Furthermore, when the customer is dissatisfied, the evaluation unit can quickly perform satisfaction evaluation. This allows satisfaction evaluation at a more appropriate time by adjusting the frequency of satisfaction evaluation based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input customer emotion data to the generation AI and have the generation AI adjust the frequency of satisfaction evaluation.

[0093] The evaluation unit can weight the evaluation data based on the customer's stay time when evaluating the satisfaction level. For example, if the customer is staying for a long time, the evaluation unit can emphasize the long-term satisfaction evaluation. Also, if the customer is staying for a short time, the evaluation unit can emphasize the short-term satisfaction evaluation. Furthermore, if the customer is staying during a specific event period, the evaluation unit can emphasize the satisfaction evaluation related to the event. This allows for more accurate satisfaction evaluation by weighting the evaluation data based on the customer's stay time. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the customer's stay time data to the generation AI and have the generation AI weight the evaluation data.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The front desk operation support system can further include a health management unit that monitors the health condition of customers. The health management unit collects vital data such as the customer's heart rate and body temperature, and can alert the front desk staff if an abnormality is detected. For example, if a customer has a high fever, it can suggest that medical services be provided promptly. Also, if a customer is feeling stressed, it can suggest that services that will help them relax be provided. This makes it possible to respond appropriately according to the customer's health condition.

[0096] The front desk operation support system can further include a personalization unit that provides personalized services based on the customer's hobbies and preferences. The personalization unit analyzes the customer's past usage history and survey results to suggest services and activities that the customer prefers. For example, if the customer has used a spa in the past, a special spa plan can be suggested. Also, if the customer likes a particular dish, a restaurant that serves that dish can be introduced. This makes it possible to improve customer satisfaction by providing services that meet the customer's preferences.

[0097] The front desk operation support system can further be equipped with a tracking unit that tracks the behavior of customers during their stay in real time. The tracking unit grasps what activities customers are doing within the hotel and provides services at the appropriate time. For example, if a customer is at the pool, it can suggest poolside drink service. Also, if a customer uses the gym, it can offer a refreshment service after training. This makes it possible to provide timely services according to customer behavior.

[0098] The front desk operation support system can further include a social media analysis unit that analyzes the social media activities of customers and proposes services based on the interests of the customers. The social media analysis unit collects the content that customers mention on social media and understands the interests of the customers. For example, if a customer mentions a specific tourist destination, information about that tourist destination can be provided. Also, if a customer shows interest in a specific event, tickets to that event can be arranged. This makes it possible to provide services based on the interests of the customers.

[0099] The front desk operation support system can further include an eco-management unit that monitors energy consumption during a customer's stay and suggests eco-friendly services. The eco-management unit collects energy consumption data of customers and suggests energy-efficient services. For example, if a customer consumes a lot of energy, it can suggest eco-friendly activities. Also, if a customer is highly eco-conscious, it can provide an eco-friendly room. This makes it possible to provide eco-friendly services according to the customer's energy consumption.

[0100] The front desk operation support system can further include a stress assessment unit that estimates the customer's emotions and evaluates the customer's stress level based on the estimated emotions. The stress assessment unit analyzes the customer's facial expressions and tone of voice to evaluate the stress level. For example, if the customer is feeling high stress, it can suggest a service that helps the customer relax. Also, if the customer is relaxed, it can suggest an active activity. This makes it possible to provide appropriate services according to the customer's stress level.

[0101] The front desk operation support system can further include a real-time satisfaction evaluation unit that estimates the customer's emotions and evaluates the customer's satisfaction level in real time based on the estimated emotions. The real-time satisfaction evaluation unit analyzes the customer's facial expressions and tone of voice to evaluate the customer's satisfaction level. For example, if the customer indicates high satisfaction, it can suggest additional services. Also, if the customer indicates low satisfaction, it can suggest measures to solve the problem. This makes it possible to provide services in real time according to the customer's satisfaction level.

[0102] The front desk operation support system may further include a preference prediction unit that estimates the emotions of a customer and predicts the customer's preferences based on the estimated emotions. The preference prediction unit analyzes the customer's facial expressions and tone of voice to predict the customer's preferences. For example, if the customer shows an emotion of joy, it may suggest a service related to that emotion. Also, if the customer is excited, it may suggest a service related to entertainment. This makes it possible to predict preferences and provide services based on the customer's emotions.

[0103] The front desk operation support system can further include a behavior prediction unit that estimates the customer's emotions and predicts the customer's behavior based on the estimated emotions. The behavior prediction unit analyzes the customer's facial expressions and tone of voice to predict the customer's next behavior. For example, if the customer is tired, it can suggest that the customer take a rest. Also, if the customer is excited, it can suggest an active activity. This makes it possible to predict behavior and provide services based on the customer's emotions.

[0104] The front desk operation support system can further include a needs prediction unit that estimates the emotions of the customer and predicts the customer's needs based on the estimated emotions. The needs prediction unit analyzes the customer's facial expressions and tone of voice to predict the customer's needs. For example, if the customer shows dissatisfaction, it can propose a service to resolve the dissatisfaction. Also, if the customer is satisfied, it can propose an additional service. This makes it possible to predict needs and provide services based on the customer's emotions.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The collection unit collects information indicating customer needs and information indicating customer emotions. The information indicating customer needs includes, for example, purchase history and survey results. The information indicating customer emotions includes, for example, facial expression recognition data and voice tone analysis. The collection unit can collect information from an AI device installed in the customer's guest room. The AI ​​device includes, for example, a smart speaker and a device with a camera. The collection unit can also select an appropriate method from predetermined collection methods based on the customer's past behavioral history. Step 2: The suggestion unit suggests the content of the response to the front staff based on the information collected by the collection unit. The suggestion is made based on, for example, a recommendation algorithm or a suggestion format. For example, if the customer desires a specific service, it can suggest that the service be provided. Also, if the customer is dissatisfied, it can suggest a response to resolve the dissatisfaction. Step 3: The alert unit issues an alert when the customer's negative emotion exceeds a threshold. Specific emotions include, for example, anger, joy, sadness, etc. The threshold is set, for example, based on the numerical range of the emotion score. For example, if a customer is feeling strongly dissatisfied, an alert can be issued to the front staff to promptly respond.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] For example, the collection unit can detect the facial expression and speech of the customer using the camera 42 and microphone 38B of the smart device 14, and collect the needs and emotions of the customer using the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the content of the response to the front staff based on the collected information. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert when the customer's negative emotions exceed a threshold. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0118] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] For example, the collection unit can detect the facial expression and speech of the customer using the camera 42 and microphone 238 of the smart glasses 214, and collect the needs and emotions of the customer using the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the content of the response to the front staff based on the collected information. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert when the customer's negative emotions exceed a threshold. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] For example, the collection unit can detect the facial expression and speech of the customer using the camera 42 and microphone 238 of the headset terminal 314, and collect the needs and emotions of the customer using the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the content of the response to the front staff based on the collected information. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert when the customer's negative emotions exceed a threshold. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] For example, the collection unit can detect the facial expression and speech of the customer using the camera 42 and microphone 238 of the robot 414, and collect the needs and emotions of the customer using the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the content of the response to the front staff based on the collected information. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert when the customer's negative emotions exceed a threshold. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) A collection unit for collecting information indicating customer needs and information indicating the emotions of the customer; a suggestion unit that suggests to a front staff how to respond to the customer based on the information collected by the collection unit; An alert unit that issues an alert when the specific emotion of the customer exceeds a set threshold. A system characterized by: (Appendix 2) The collecting unit includes: Collect information indicating the needs of the customer and information indicating the emotions of the customer from an AI device installed in the guest room of the customer. 2. The system of claim 1, (Appendix 3) The collecting unit includes: Select the appropriate method from among predefined collection methods based on the customer's past behavioral history 2. The system of claim 1, (Appendix 4) The collecting unit includes: Estimate customer sentiment and adjust the timing of information collection based on the estimated customer sentiment 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze the customer's past behavioral history and select the appropriate method from among predefined information collection methods 2. The system of claim 1, (Appendix 6) The collecting unit includes: When collecting information, filter it based on the customer’s current location and interests 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate customer sentiment and prioritize information to be collected based on the estimated customer sentiment 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting information, prioritize collecting information that is highly relevant by taking into account the customer's geographic location information 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting information, analyze the social media activity of customers and collect relevant information 2. The system of claim 1, (Appendix 10) The suggestion unit, Infer customer sentiment and tailor suggestions based on the inferred sentiment 2. The system of claim 1, (Appendix 11) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the customer's needs 2. The system of claim 1, (Appendix 12) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the customer category 2. The system of claim 1, (Appendix 13) The suggestion unit, Infer customer sentiment and adjust the length of suggestions based on the inferred customer sentiment 2. The system of claim 1, (Appendix 14) The suggestion unit is When making suggestions, prioritize them based on when the customer was in the room 2. The system of claim 1, (Appendix 15) The suggestion unit, When making suggestions, adjust the order of suggestions based on customer relevance 2. The system of claim 1, (Appendix 16) The alert unit is Infer customer sentiment and adjust the timing of alerts based on the inferred customer sentiment 2. The system of claim 1, (Appendix 17) The alert unit is When sending an alert, customize the content of the alert by referencing the customer's past complaint history 2. The system of claim 1, (Appendix 18) The alert unit is When an alert is sent, the method of sending the alert is selected based on the customer's current location. 2. The system of claim 1, (Appendix 19) The alert unit is Infer customer sentiment and prioritize alerts based on inferred customer sentiment 2. The system of claim 1, (Appendix 20) The alert unit is When sending an alert, the most appropriate method of alerting is selected based on the customer's geographic location. 2. The system of claim 1, (Appendix 21) The alert unit is When an alert is sent, it analyzes the social media activity of the customer to tailor the content of the alert. 2. The system of claim 1, (Appendix 22) The evaluation unit is Infer customer sentiment and adjust satisfaction rating scales based on inferred customer sentiment 2. The system of claim 1, (Appendix 23) The evaluation unit is When rating satisfaction, optimize the rating algorithm by looking at the customer's past rating history 2. The system of claim 1, (Appendix 24) The evaluation unit is Estimate customer sentiment and adjust the frequency of satisfaction ratings based on the estimated customer sentiment 2. The system of claim 1, (Appendix 25) The evaluation unit is Weighting satisfaction ratings based on when customers were on the site 2. The system of claim 1, [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit for collecting information indicating customer needs and information indicating the emotions of the customer; a suggestion unit that suggests to a front staff how to respond to the customer based on the information collected by the collection unit; An alert unit that issues an alert when the specific emotion of the customer exceeds a set threshold. A system characterized by:

2. The collecting unit includes: Collecting information indicating the needs of the customer and information indicating the emotions of the customer from an AI device installed in the guest room of the customer 2. The system of claim 1 .

3. The collecting unit includes: Selecting an appropriate method from among predetermined collection methods based on the past behavioral history of the customer.

2. The system of claim 1 .

4. The collecting unit includes: The customer's feelings are estimated, and the timing of information collection is adjusted based on the estimated customer's feelings.

2. The system of claim 1 .

5. The collecting unit includes: Analyzing the past behavioral history of the customer and selecting an appropriate method from among predetermined information collection methods.

2. The system of claim 1 .

6. The collecting unit includes: When collecting information, filter it based on the customer's current location and interests.

2. The system of claim 1 .

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