System

Abstract

To provide a system that estimates emotions of customers and provides appropriate responses according thereto.SOLUTION: A system comprises an acquisition unit, an estimation unit, and a determination unit. The acquisition unit acquires emotion estimation information for estimating emotions of customers. The estimation unit estimates the emotions of the customers on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines a response according to the emotions of the customers estimated by the estimation unit.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] Conventional techniques do not adequately estimate customer emotions accurately and respond appropriately, so there is room for improvement.

[0005] The system according to the embodiment aims to estimate the emotions of a customer and take appropriate action accordingly. [Means for solving the problem]

[0006] A system according to an embodiment includes an acquisition unit, an estimation unit, and a determination unit. The acquisition unit acquires emotion estimation information for estimating an emotion of a customer. The estimation unit estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The determination unit determines a response according to the emotion of the customer estimated by the estimation unit. Effect of the Invention

[0007] The system according to the embodiment can estimate the customer's emotions and take appropriate action accordingly. [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) A system according to an embodiment of the present invention is a system that estimates a customer's emotion and responds according to the emotion. The system includes an acquisition unit that acquires emotion estimation information for estimating the customer's emotion. Next, the system includes an estimation unit that estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The system further includes a decision unit that determines a response according to the customer's emotion estimated by the estimation unit. For example, when a customer is dissatisfied, the decision unit decides a response to resolve the dissatisfaction and notifies the staff through the notification unit. The notification unit can notify the staff of the content of the response using different notification methods according to the customer's emotion estimated by the estimation unit. The decision unit can also decide to provide different music or video depending on the customer's emotion. Furthermore, the decision unit determines a response according to the customer's emotion based on the customer's emotion and attributes estimated by the estimation unit. The acquisition unit acquires one or more pieces of information among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. This system can accurately grasp the customer's emotion and respond appropriately, thereby improving customer satisfaction. For example, when a customer is dissatisfied, an agent can sense the dissatisfaction and convey it to a staff member, which leads to early problem resolution. In addition, the system can improve the customer's mood by providing music and images that correspond to the customer's emotions. This allows the system to accurately grasp the customer's emotions and take appropriate measures.

[0029] The emotion estimation system according to the embodiment includes an acquisition unit, an estimation unit, and a determination unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The acquisition unit acquires, for example, one or more pieces of information among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. For example, the acquisition unit recognizes the customer's facial expression using a camera and acquires facial expression data. The acquisition unit can also collect the customer's voice using a microphone and acquire voice data. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire heart rate data. For example, the acquisition unit recognizes the customer's facial expression in real time using a camera and acquires facial expression data. The acquisition unit can also collect the customer's voice using a microphone and acquire voice data. The acquisition unit can also measure the customer's heart rate using a heart rate sensor and acquire heart rate data. The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the emotion using, for example, a machine learning algorithm. For example, the estimation unit receives facial expression data as an input and estimates the customer's emotion using a machine learning algorithm. The estimation unit can also receive voice data as an input and estimate the customer's emotion using a machine learning algorithm. The heart rate data can be received as an input, and the customer's emotions can be estimated using a machine learning algorithm. For example, the estimation unit receives facial expression data as an input, and estimates the customer's emotions using a machine learning algorithm. The voice data can be received as an input, and the customer's emotions can be estimated using a machine learning algorithm. The heart rate data can be received as an input, and the customer's emotions can be estimated using a machine learning algorithm. The determination unit determines a response according to the customer's emotions estimated by the estimation unit. For example, when a customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction. For example, when a customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction. When a customer is relaxed, a response to maintain the relaxation can also be determined. When a customer is excited, a response to relieve the excitement can also be determined. For example, when a customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction.If the customer is relaxed, it is also possible to determine a response to maintain the customer's relaxation. If the customer is excited, it is also possible to determine a response to calm the customer's excitement. In this way, the emotion estimation system according to the embodiment can accurately grasp the emotion of the customer and take an appropriate response.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. The acquisition unit acquires, for example, one or more pieces of information among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. Specifically, the acquisition unit recognizes the customer's facial expression in real time using a camera and acquires facial expression data. The camera has high resolution and can capture even subtle changes in facial expression. The acquisition unit can also collect the customer's voice using a microphone and acquire voice data. The voice data includes features such as tone, pitch, speed, and volume of the voice, which are important clues for estimating emotions from these features. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire heart rate data. The heart rate sensor is attached to the customer's wrist or chest and monitors the heart rate in real time. Fluctuations in heart rate reflect emotional states such as stress and excitement, and therefore play an important role in emotion estimation. In addition, the acquisition unit can measure the customer's blood pressure using a blood pressure sensor and acquire blood pressure data. Fluctuations in blood pressure are also related to emotional states, and are particularly an indicator of stress and tension. This allows the acquisition unit to comprehensively collect emotion estimation information of customers using various devices and grasp their emotional states in real time. Furthermore, the acquisition unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the estimation unit and the determination unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the acquisition unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the emotion using, for example, a machine learning algorithm. Specifically, the estimation unit receives facial expression data as an input and estimates the emotion of the customer using a machine learning algorithm. The facial expression data estimates emotions such as joy, sadness, anger, and surprise by analyzing the positions and movements of facial feature points and changes in facial expression. The estimation unit can also receive voice data as an input and estimate the emotion of the customer using a machine learning algorithm. The voice data analyzes features such as tone, pitch, speed, and volume of the voice to estimate the emotional state. For example, a high voice tone and a fast voice speed may indicate excitement or tension. The estimation unit can also receive heart rate data as an input and estimate the emotion of the customer using a machine learning algorithm. The heart rate data analyzes the fluctuations and patterns of the heart rate to estimate stress or relaxation state. For example, a sudden increase in the heart rate may indicate stress or excitement. The estimation unit can perform more accurate emotion estimation by integrating and analyzing these data and combining multiple emotion indicators. Furthermore, the estimation unit can also analyze long-term emotional fluctuations and trends by utilizing past data and statistical information. For example, based on past emotional data, it can predict emotional fluctuations in specific time periods and situations and plan future countermeasures. The estimation unit can also use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the estimation unit to handle not only real-time emotion estimation, but also long-term emotion management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The determination unit determines a response according to the emotion of the customer estimated by the estimation unit. For example, when a customer feels dissatisfied, the determination unit determines a response to resolve the dissatisfaction. Specifically, when a customer feels dissatisfied, the determination unit determines a response to resolve the dissatisfaction. For example, when a customer feels dissatisfied, the determination unit determines a response to resolve the dissatisfaction. When a customer is relaxed, the determination unit can also determine a response to maintain the relaxation. When a customer is excited, the determination unit can also determine a response to alleviate the excitement. For example, when a customer feels dissatisfied, the determination unit determines a response to resolve the dissatisfaction. When a customer is relaxed, the determination unit can also determine a response to maintain the relaxation. When a customer is excited, the determination unit can also determine a response to alleviate the excitement. In this way, the emotion estimation system according to the embodiment can accurately grasp the emotion of the customer and take an appropriate response.

[0033] The system includes a notification unit that notifies staff of the content of the response determined by the determination unit. The notification unit performs notification by a method such as email, SMS, or in-app notification. For example, the notification unit notifies staff of the content of the response determined by the determination unit by email. Notification can also be made using SMS. Notification can also be made using an in-app notification. For example, the notification unit notifies staff of the content of the response determined by the determination unit by email. Notification can also be made using SMS. Notification can also be made using an in-app notification. This enables prompt response by notifying staff of the determined response.

[0034] The notification unit can notify the staff of the content of the response using different notification methods according to the customer's emotions estimated by the estimation unit. For example, if the customer is dissatisfied, the notification unit notifies the staff using an emergency push notification. If the customer is relaxed, the notification unit can also notify the staff using a normal email notification. If the customer is excited, the notification unit can also notify the staff using SMS. For example, if the customer is dissatisfied, the notification unit notifies the staff using an emergency push notification. If the customer is relaxed, the notification unit can also notify the staff using a normal email notification. If the customer is excited, the notification unit can also notify the staff using SMS. This makes it possible to convey the content of the response to the staff using an appropriate notification method according to the customer's emotions.

[0035] The decision unit can determine to provide specific music or video as a response according to the customer's emotions, depending on the customer's emotions. For example, if the customer is dissatisfied, the decision unit determines to provide relaxing music. If the customer is relaxed, the decision unit can also determine to provide relaxing music. If the customer is excited, the decision unit can also determine to provide encouraging video. For example, if the customer is dissatisfied, the decision unit determines to provide relaxing music. If the customer is relaxed, the decision unit can also determine to provide relaxing music. If the customer is excited, the decision unit can also determine to provide encouraging video. In this way, the customer's mood can be improved by providing music or video according to the customer's emotions.

[0036] The determination unit can determine a response according to the customer's emotions based on the customer's emotions and attributes estimated by the estimation unit. For example, if the customer is dissatisfied, the determination unit determines an appropriate response based on the customer's age and gender. If the customer is relaxed, the determination unit can also determine an appropriate response based on the customer's purchase history. If the customer is excited, the determination unit can also determine an appropriate response based on the customer's attributes. For example, if the customer is dissatisfied, the determination unit determines an appropriate response based on the customer's age and gender. If the customer is relaxed, the determination unit can also determine an appropriate response based on the customer's purchase history. If the customer is excited, the determination unit can also determine an appropriate response based on the customer's attributes. This makes it possible to provide a more personalized service by determining a response based on the customer's emotions and attributes.

[0037] The acquisition unit can acquire at least one piece of information from among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. For example, the acquisition unit recognizes the customer's facial expression using a camera and acquires facial expression data. It is also possible to collect the customer's voice using a microphone and acquire voice data. It is also possible to measure the customer's heart rate using a heart rate sensor and acquire heart rate data. For example, the acquisition unit recognizes the customer's facial expression in real time using a camera and acquires facial expression data. It is also possible to collect the customer's voice using a microphone and acquire voice data. It is also possible to measure the customer's heart rate using a heart rate sensor and acquire heart rate data. In this way, by acquiring a variety of information as emotion estimation information, it is possible to more accurately estimate the customer's emotion.

[0038] The acquisition unit can analyze the customer's past emotion data and select an appropriate acquisition method. For example, the acquisition unit analyzes situations in which the customer felt dissatisfied in the past and focuses on acquiring emotion estimation information in similar situations. It is also possible to analyze situations in which the customer was relaxed in the past and optimize the method of acquiring emotion estimation information in that situation. It is also possible to select a method of acquiring emotion estimation information in a specific time period or location from the customer's past emotion data. For example, the acquisition unit analyzes situations in which the customer felt dissatisfied in the past and focuses on acquiring emotion estimation information in similar situations. It is also possible to analyze situations in which the customer was relaxed in the past and optimize the method of acquiring emotion estimation information in that situation. It is also possible to select a method of acquiring emotion estimation information in a specific time period or location from the customer's past emotion data. In this way, the optimal acquisition method can be selected by analyzing past emotion data.

[0039] When acquiring the emotion estimating information, the acquiring unit can perform filtering based on the customer's current activity or environment. For example, when the customer is eating, the acquiring unit preferentially acquires emotion estimating information related to eating. When the customer is in a meeting, the acquiring unit can also preferentially acquire emotion estimating information related to the meeting. When the customer is in a relaxing environment, the acquiring unit can also preferentially acquire emotion estimating information related to the environment. For example, when the customer is eating, the acquiring unit preferentially acquires emotion estimating information related to eating. When the customer is in a meeting, the acquiring unit can also preferentially acquire emotion estimating information related to the meeting. When the customer is in a relaxing environment, the acquiring unit can also preferentially acquire emotion estimating information related to the environment. In this way, by filtering information based on the customer's activity or environment, more relevant information can be acquired.

[0040] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the customer. For example, when the customer is in a hotel, the acquisition unit preferentially acquires information related to services in the hotel. When the customer is in a restaurant, the acquisition unit can also preferentially acquire information related to services in the restaurant. When the customer is in a tourist destination, the acquisition unit can also preferentially acquire information related to the tourist destination. For example, when the customer is in a hotel, the acquisition unit preferentially acquires information related to services in the hotel. When the customer is in a restaurant, the acquisition unit can also preferentially acquire information related to services in the restaurant. When the customer is in a tourist destination, the acquisition unit can also preferentially acquire information related to the tourist destination. In this way, highly relevant information can be preferentially acquired by considering the geographical location information.

[0041] When acquiring the emotion estimation information, the acquisition unit can analyze the social media activity of the customer and acquire related information. For example, when the customer expresses dissatisfaction on social media, the acquisition unit acquires information related to the dissatisfaction. When the customer posts a relaxed state on social media, the acquisition unit can also acquire information related to the relaxation. When the customer posts an excited state on social media, the acquisition unit can also acquire information related to the excitement. For example, when the customer expresses dissatisfaction on social media, the acquisition unit acquires information related to the dissatisfaction. When the customer posts a relaxed state on social media, the acquisition unit can also acquire information related to the relaxation. When the customer posts an excited state on social media, the acquisition unit can also acquire information related to the excitement. In this way, by analyzing social media activity, related information can be acquired.

[0042] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. For example, the estimation unit performs detailed emotion estimation based on emotion estimation information of high importance. The estimation unit can also perform simple emotion estimation based on emotion estimation information of low importance. The estimation unit can also perform emotion estimation with a moderate level of detail based on emotion estimation information of medium importance. For example, the estimation unit performs detailed emotion estimation based on emotion estimation information of high importance. The estimation unit can also perform simple emotion estimation based on emotion estimation information of low importance. The estimation unit can also perform emotion estimation with a moderate level of detail based on emotion estimation information of medium importance. Thereby, by adjusting the level of detail of the estimation based on the importance of the emotion estimation information, efficient emotion estimation is possible.

[0043] At the time of estimation, the estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information. For example, the estimation unit applies a facial expression recognition algorithm to emotion estimation information based on facial expressions. The estimation unit can also apply a voice recognition algorithm to emotion estimation information based on voice. The estimation unit can also apply a voice recognition algorithm to emotion estimation information based on movement. For example, the estimation unit applies a facial expression recognition algorithm to emotion estimation information based on facial expressions. The estimation unit can also apply a voice recognition algorithm to emotion estimation information based on voice. The estimation unit can also apply a voice recognition algorithm to emotion estimation information based on movement. In this way, by applying different estimation algorithms depending on the category of the emotion estimation information, more accurate emotion estimation is possible.

[0044] The estimation unit can determine a priority order of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation is performed by preferentially using the latest emotion estimation information. The estimation can also be performed by referring to past emotion estimation information and prioritizing the latest information. The estimation can also be performed by preferentially using emotion estimation information acquired in a specific time period. For example, the estimation unit can perform estimation by preferentially using the latest emotion estimation information. The estimation can also be performed by referring to past emotion estimation information and prioritizing the latest information. The estimation can also be performed by preferentially using emotion estimation information acquired in a specific time period. In this way, by determining the priority order of estimation based on the acquisition time of the emotion estimation information, estimation with emphasis on the latest information is possible.

[0045] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. For example, the estimation is performed by preferentially using emotion estimation information having high relevance. The estimation can also be performed by using emotion estimation information having medium relevance next. The estimation can also be performed by using emotion estimation information having low relevance last. For example, the estimation unit performs estimation by preferentially using emotion estimation information having high relevance. The estimation can also be performed by using emotion estimation information having medium relevance next. The estimation can also be performed by using emotion estimation information having low relevance last. In this way, by adjusting the order of estimation based on the relevance of the emotion estimation information, efficient emotion estimation is possible.

[0046] When making a decision, the decision unit can analyze the customer's past response history and select an appropriate response method. For example, the decision unit can refer to response methods that the customer was satisfied with in the past and select a similar response. It can also select a different response, avoiding response methods that the customer was dissatisfied with in the past. It can also select an optimal response method for a specific situation from the customer's past response history. For example, the decision unit can refer to response methods that the customer was satisfied with in the past and select a similar response. It can also select a different response, avoiding response methods that the customer was dissatisfied with in the past. It can also select an optimal response method for a specific situation from the customer's past response history. In this way, the optimal response method can be selected by analyzing past response history.

[0047] At the time of making the decision, the decision unit can customize the response measures based on the customer's current situation. For example, if the customer is eating, the decision unit customizes the response related to the meal. If the customer is in a meeting, the decision unit can also customize the response related to the meeting. If the customer is relaxed, the decision unit can also customize the response for maintaining the relaxation. For example, if the customer is eating, the decision unit customizes the response related to the meal. If the customer is in a meeting, the decision unit can also customize the response related to the meeting. If the customer is relaxed, the decision unit can also customize the response for maintaining the relaxation. This enables more appropriate responses by customizing the response measures based on the customer's current situation.

[0048] When making a decision, the decision unit can select an appropriate response method by taking into consideration the geographical location information of the customer. For example, if the customer is in a hotel, the decision unit selects a response related to services within the hotel. If the customer is in a restaurant, the decision unit can also select a response related to services within the restaurant. If the customer is in a tourist destination, the decision unit can also select a response related to the tourist destination. For example, if the customer is in a hotel, the decision unit selects a response related to services within the hotel. If the customer is in a restaurant, the decision unit can also select a response related to services within the restaurant. If the customer is in a tourist destination, the decision unit can also select a response related to the tourist destination. This makes it possible to select an optimal response method by taking into consideration the geographical location information.

[0049] At the time of making a decision, the decision unit can analyze the social media activity of the customer to suggest a means of response. For example, if the customer expresses dissatisfaction on social media, the decision unit can suggest a response to resolve the dissatisfaction. If the customer posts on social media how relaxed he or she is, the decision unit can also suggest a response to maintain the relaxation. If the customer posts on social media how excited he or she is, the decision unit can also suggest a response to ease the excitement. For example, if the customer expresses dissatisfaction on social media, the decision unit can suggest a response to resolve the dissatisfaction. If the customer posts on social media how relaxed he or she is, the decision unit can also suggest a response to maintain the relaxation. If the customer posts on social media how excited he or she is, the decision unit can also suggest a response to ease the excitement. In this way, by analyzing social media activity, appropriate means of response can be suggested.

[0050] When notifying, the notification unit can select the optimal notification method by referring to the customer's past notification history. For example, the notification unit can give priority to selecting a notification method that the customer has preferred in the past. It can also select a notification method that has been excluded from notification methods that the customer has avoided in the past. It can also select the optimal notification method for a specific situation from the customer's past notification history. For example, the notification unit can give priority to selecting a notification method that the customer has preferred in the past. It can also select a notification method that has been excluded from notification methods that the customer has avoided in the past. It can also select the optimal notification method for a specific situation from the customer's past notification history. In this way, the optimal notification method can be selected by referring to past notification history.

[0051] At the time of notification, the notification unit can customize the content of the notification based on the customer's current situation. For example, if the customer is eating, the notification unit customizes the notification content related to the meal. If the customer is in a meeting, the notification unit can also customize the notification content related to the meeting. If the customer is relaxed, the notification unit can also customize the notification content for maintaining that relaxation. For example, if the customer is eating, the notification unit customizes the notification content related to the meal. If the customer is in a meeting, the notification content related to the meeting can also be customized. If the customer is relaxed, the notification content for maintaining that relaxation can also be customized. This enables appropriate notifications to be provided by customizing the notification content based on the customer's current situation.

[0052] At the time of notification, the notification unit can select the optimal notification method by taking into consideration the device information of the customer. For example, if the customer is using a smartphone, the notification unit provides a notification method that matches the screen size. If the customer is using a tablet, the notification unit can also provide a notification method optimized for a large screen. If the customer is using a smartwatch, the notification unit can also provide a notification method that is concise and highly visible. For example, if the customer is using a smartphone, the notification unit provides a notification method that matches the screen size. If the customer is using a tablet, the notification unit can also provide a notification method that is optimized for a large screen. If the customer is using a smartwatch, the notification unit can also provide a notification method that is concise and highly visible. This makes it possible to select the optimal notification method by taking into consideration the device information.

[0053] The notification unit can provide notifications in multiple languages ​​depending on the language settings of the customer at the time of notification. For example, the notification unit automatically sets the language of the notification based on the language settings of the customer's device. If the customer uses multiple languages, the notification unit can also provide a language switching function. If the customer selects a specific language, the notification unit can also provide notifications in that language. For example, the notification unit automatically sets the language of the notification based on the language settings of the customer's device. If the customer uses multiple languages, the notification unit can also provide a language switching function. If the customer selects a specific language, the notification unit can also provide notifications in that language. This makes it possible to provide appropriate notifications to customers by providing notifications in multiple languages ​​depending on the language settings.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When acquiring emotion estimation information for estimating the emotion of a customer, the acquisition unit can analyze the customer's past purchase history and behavioral patterns to improve the accuracy of emotion estimation. For example, the acquisition unit collects data on products and services purchased by the customer in the past, and estimates the emotion of the customer based on this data. If the customer tends to take a specific action at a specific time period, the emotion estimation information can be acquired by taking into account the behavioral pattern. Furthermore, the acquisition unit can analyze data on places visited by the customer in the past and events attended by the customer, and acquire emotion estimation information based on this data. In this way, the accuracy of emotion estimation can be improved by utilizing the customer's past behavioral data.

[0056] When acquiring emotion estimation information for estimating the emotion of a customer, the acquisition unit can analyze the current environmental sound of the customer to improve the accuracy of emotion estimation. For example, the acquisition unit estimates the emotion of the customer by collecting environmental sounds in the place where the customer is located and analyzing the type and volume of the sounds. If the customer is in a quiet environment, it can be estimated that the customer is relaxed based on the environmental sounds. If the customer is in a noisy environment, it can also be estimated that the customer is stressed based on the environmental sounds. Furthermore, if the customer is listening to specific music, the acquisition unit can analyze the genre and tempo of the music and use it for emotion estimation. In this way, the accuracy of emotion estimation can be improved by utilizing the environmental sounds.

[0057] When estimating the customer's emotion, the estimation unit can analyze the customer's past health data and improve the accuracy of emotion estimation. For example, the estimation unit collects the customer's past heart rate and blood pressure data, and estimates the customer's emotion based on this data. If the customer felt stressed when he or she showed a high heart rate in the past, the estimation unit can analyze the current heart rate based on that data and estimate the emotion. Furthermore, the estimation unit can analyze the customer's past sleep data and estimate the emotion by taking into account the impact of lack of sleep on the emotion. In this way, the accuracy of emotion estimation can be improved by utilizing the past health data.

[0058] When acquiring emotion estimation information for estimating the emotion of a customer, the acquisition unit can analyze the current activity level of the customer and improve the accuracy of emotion estimation. For example, when the customer is exercising, the acquisition unit estimates the emotion based on the activity level. When the customer shows a high activity level, it can be estimated that the customer is excited based on the data. When the customer shows a low activity level, it can be estimated that the customer is relaxed based on the data. Furthermore, when the customer is performing a specific exercise, the acquisition unit can analyze the type and intensity of the exercise and use it for emotion estimation. In this way, the accuracy of emotion estimation can be improved by utilizing the activity level.

[0059] When estimating the emotion of a customer, the estimation unit can analyze the customer's past purchase history and improve the accuracy of emotion estimation. For example, the estimation unit collects data on products and services purchased by the customer in the past, and estimates the customer's emotion based on this data. If the customer was satisfied when purchasing a specific product, the estimation unit can analyze the customer's current purchasing behavior based on that data and estimate the emotion. Furthermore, the estimation unit can analyze reviews and feedback of products purchased by the customer in the past, and estimate the emotion based on this data. In this way, the accuracy of emotion estimation can be improved by utilizing the past purchase history.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The acquisition unit acquires, for example, one or more pieces of information among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. Specifically, a camera is used to recognize the customer's facial expression and acquire facial expression data. A microphone can also be used to collect the customer's voice and acquire voice data. Furthermore, a heart rate sensor can be used to measure the customer's heart rate and acquire heart rate data. Step 2: The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the emotion using, for example, a machine learning algorithm. Specifically, the estimation unit receives facial expression data, voice data, and heart rate data as input, and estimates the emotion of the customer using the machine learning algorithm. Step 3: The determination unit determines a response according to the customer's emotion estimated by the estimation unit. For example, if the customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction. If the customer is relaxed, the determination unit can also determine a response to maintain the relaxation. If the customer is excited, the determination unit can also determine a response to alleviate the excitement.

[0062] (Example 2) A system according to an embodiment of the present invention is a system that estimates a customer's emotion and responds according to the emotion. The system includes an acquisition unit that acquires emotion estimation information for estimating the customer's emotion. Next, the system includes an estimation unit that estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The system further includes a decision unit that determines a response according to the customer's emotion estimated by the estimation unit. For example, when a customer is dissatisfied, the decision unit decides a response to resolve the dissatisfaction and notifies the staff through the notification unit. The notification unit can notify the staff of the content of the response using different notification methods according to the customer's emotion estimated by the estimation unit. The decision unit can also decide to provide different music or video depending on the customer's emotion. Furthermore, the decision unit determines a response according to the customer's emotion based on the customer's emotion and attributes estimated by the estimation unit. The acquisition unit acquires one or more pieces of information among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. This system can accurately grasp the customer's emotion and respond appropriately, thereby improving customer satisfaction. For example, when a customer is dissatisfied, an agent can sense the dissatisfaction and convey it to a staff member, which leads to early problem resolution. In addition, the system can improve the customer's mood by providing music and images that correspond to the customer's emotions. This allows the system to accurately grasp the customer's emotions and take appropriate measures.

[0063] The emotion estimation system according to the embodiment includes an acquisition unit, an estimation unit, and a determination unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The acquisition unit acquires, for example, one or more pieces of information among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. For example, the acquisition unit recognizes the customer's facial expression using a camera and acquires facial expression data. The acquisition unit can also collect the customer's voice using a microphone and acquire voice data. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire heart rate data. For example, the acquisition unit recognizes the customer's facial expression in real time using a camera and acquires facial expression data. The acquisition unit can also collect the customer's voice using a microphone and acquire voice data. The acquisition unit can also measure the customer's heart rate using a heart rate sensor and acquire heart rate data. The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the emotion using, for example, a machine learning algorithm. For example, the estimation unit receives facial expression data as an input and estimates the customer's emotion using a machine learning algorithm. The estimation unit can also receive voice data as an input and estimate the customer's emotion using a machine learning algorithm. The heart rate data can be received as an input, and the customer's emotions can be estimated using a machine learning algorithm. For example, the estimation unit receives facial expression data as an input, and estimates the customer's emotions using a machine learning algorithm. The voice data can be received as an input, and the customer's emotions can be estimated using a machine learning algorithm. The heart rate data can be received as an input, and the customer's emotions can be estimated using a machine learning algorithm. The determination unit determines a response according to the customer's emotions estimated by the estimation unit. For example, when a customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction. For example, when a customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction. When a customer is relaxed, a response to maintain the relaxation can also be determined. When a customer is excited, a response to relieve the excitement can also be determined. For example, when a customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction.If the customer is relaxed, it is also possible to determine a response to maintain the customer's relaxation. If the customer is excited, it is also possible to determine a response to calm the customer's excitement. In this way, the emotion estimation system according to the embodiment can accurately grasp the emotion of the customer and take an appropriate response.

[0064] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. The acquisition unit acquires, for example, one or more pieces of information among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. Specifically, the acquisition unit recognizes the customer's facial expression in real time using a camera and acquires facial expression data. The camera has high resolution and can capture even subtle changes in facial expression. The acquisition unit can also collect the customer's voice using a microphone and acquire voice data. The voice data includes features such as tone, pitch, speed, and volume of the voice, which are important clues for estimating emotions from these features. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire heart rate data. The heart rate sensor is attached to the customer's wrist or chest and monitors the heart rate in real time. Fluctuations in heart rate reflect emotional states such as stress and excitement, and therefore play an important role in emotion estimation. In addition, the acquisition unit can measure the customer's blood pressure using a blood pressure sensor and acquire blood pressure data. Fluctuations in blood pressure are also related to emotional states, and are particularly an indicator of stress and tension. This allows the acquisition unit to comprehensively collect emotion estimation information of customers using various devices and grasp their emotional states in real time. Furthermore, the acquisition unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the estimation unit and the determination unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the acquisition unit to efficiently and effectively collect data and improve the performance of the entire system.

[0065] The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the emotion using, for example, a machine learning algorithm. Specifically, the estimation unit receives facial expression data as an input and estimates the emotion of the customer using a machine learning algorithm. The facial expression data estimates emotions such as joy, sadness, anger, and surprise by analyzing the positions and movements of facial feature points and changes in facial expression. The estimation unit can also receive voice data as an input and estimate the emotion of the customer using a machine learning algorithm. The voice data analyzes features such as tone, pitch, speed, and volume of the voice to estimate the emotional state. For example, a high voice tone and a fast voice speed may indicate excitement or tension. The estimation unit can also receive heart rate data as an input and estimate the emotion of the customer using a machine learning algorithm. The heart rate data analyzes the fluctuations and patterns of the heart rate to estimate stress or relaxation state. For example, a sudden increase in the heart rate may indicate stress or excitement. The estimation unit can perform more accurate emotion estimation by integrating and analyzing these data and combining multiple emotion indicators. Furthermore, the estimation unit can also analyze long-term emotional fluctuations and trends by utilizing past data and statistical information. For example, based on past emotional data, it can predict emotional fluctuations in specific time periods and situations and plan future countermeasures. The estimation unit can also use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the estimation unit to handle not only real-time emotion estimation, but also long-term emotion management and anomaly detection, improving the reliability and safety of the entire system.

[0066] The determination unit determines a response according to the emotion of the customer estimated by the estimation unit. For example, when a customer feels dissatisfied, the determination unit determines a response to resolve the dissatisfaction. Specifically, when a customer feels dissatisfied, the determination unit determines a response to resolve the dissatisfaction. For example, when a customer feels dissatisfied, the determination unit determines a response to resolve the dissatisfaction. When a customer is relaxed, the determination unit can also determine a response to maintain the relaxation. When a customer is excited, the determination unit can also determine a response to alleviate the excitement. For example, when a customer feels dissatisfied, the determination unit determines a response to resolve the dissatisfaction. When a customer is relaxed, the determination unit can also determine a response to maintain the relaxation. When a customer is excited, the determination unit can also determine a response to alleviate the excitement. In this way, the emotion estimation system according to the embodiment can accurately grasp the emotion of the customer and take an appropriate response.

[0067] The system includes a notification unit that notifies staff of the content of the response determined by the determination unit. The notification unit performs notification by a method such as email, SMS, or in-app notification. For example, the notification unit notifies staff of the content of the response determined by the determination unit by email. Notification can also be made using SMS. Notification can also be made using an in-app notification. For example, the notification unit notifies staff of the content of the response determined by the determination unit by email. Notification can also be made using SMS. Notification can also be made using an in-app notification. This enables prompt response by notifying staff of the determined response.

[0068] The notification unit can notify the staff of the content of the response using different notification methods according to the customer's emotions estimated by the estimation unit. For example, if the customer is dissatisfied, the notification unit notifies the staff using an emergency push notification. If the customer is relaxed, the notification unit can also notify the staff using a normal email notification. If the customer is excited, the notification unit can also notify the staff using SMS. For example, if the customer is dissatisfied, the notification unit notifies the staff using an emergency push notification. If the customer is relaxed, the notification unit can also notify the staff using a normal email notification. If the customer is excited, the notification unit can also notify the staff using SMS. This makes it possible to convey the content of the response to the staff using an appropriate notification method according to the customer's emotions.

[0069] The decision unit can determine to provide specific music or video as a response according to the customer's emotions, depending on the customer's emotions. For example, if the customer is dissatisfied, the decision unit determines to provide relaxing music. If the customer is relaxed, the decision unit can also determine to provide relaxing music. If the customer is excited, the decision unit can also determine to provide encouraging video. For example, if the customer is dissatisfied, the decision unit determines to provide relaxing music. If the customer is relaxed, the decision unit can also determine to provide relaxing music. If the customer is excited, the decision unit can also determine to provide encouraging video. In this way, the customer's mood can be improved by providing music or video according to the customer's emotions.

[0070] The determination unit can determine a response according to the customer's emotions based on the customer's emotions and attributes estimated by the estimation unit. For example, if the customer is dissatisfied, the determination unit determines an appropriate response based on the customer's age and gender. If the customer is relaxed, the determination unit can also determine an appropriate response based on the customer's purchase history. If the customer is excited, the determination unit can also determine an appropriate response based on the customer's attributes. For example, if the customer is dissatisfied, the determination unit determines an appropriate response based on the customer's age and gender. If the customer is relaxed, the determination unit can also determine an appropriate response based on the customer's purchase history. If the customer is excited, the determination unit can also determine an appropriate response based on the customer's attributes. This makes it possible to provide a more personalized service by determining a response based on the customer's emotions and attributes.

[0071] The acquisition unit can acquire at least one piece of information from among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. For example, the acquisition unit recognizes the customer's facial expression using a camera and acquires facial expression data. It is also possible to collect the customer's voice using a microphone and acquire voice data. It is also possible to measure the customer's heart rate using a heart rate sensor and acquire heart rate data. For example, the acquisition unit recognizes the customer's facial expression in real time using a camera and acquires facial expression data. It is also possible to collect the customer's voice using a microphone and acquire voice data. It is also possible to measure the customer's heart rate using a heart rate sensor and acquire heart rate data. In this way, by acquiring a variety of information as emotion estimation information, it is possible to more accurately estimate the customer's emotion.

[0072] The acquisition unit can estimate the emotion of the customer and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the customer. For example, when the customer is stressed, the acquisition unit increases the frequency of acquiring the emotion estimation information and tracks changes in emotion in real time. When the customer is relaxed, the acquisition unit can also reduce the frequency of acquiring the emotion estimation information and acquire the minimum necessary information. When the customer is in a hurry, the acquisition unit can speed up the timing of acquiring the emotion estimation information to enable immediate response. For example, when the customer is stressed, the acquisition unit increases the frequency of acquiring the emotion estimation information and tracks changes in emotion in real time. When the customer is relaxed, the acquisition unit can also reduce the frequency of acquiring the emotion estimation information and acquire the minimum necessary information. When the customer is in a hurry, the acquisition unit can speed up the timing of acquiring the emotion estimation information to enable immediate response. In this way, more appropriate information can be acquired by adjusting the acquisition timing according to the emotion of the customer.

[0073] The acquisition unit can analyze the customer's past emotion data and select an appropriate acquisition method. For example, the acquisition unit analyzes situations in which the customer felt dissatisfied in the past and focuses on acquiring emotion estimation information in similar situations. It is also possible to analyze situations in which the customer was relaxed in the past and optimize the method of acquiring emotion estimation information in that situation. It is also possible to select a method of acquiring emotion estimation information in a specific time period or location from the customer's past emotion data. For example, the acquisition unit analyzes situations in which the customer felt dissatisfied in the past and focuses on acquiring emotion estimation information in similar situations. It is also possible to analyze situations in which the customer was relaxed in the past and optimize the method of acquiring emotion estimation information in that situation. It is also possible to select a method of acquiring emotion estimation information in a specific time period or location from the customer's past emotion data. In this way, the optimal acquisition method can be selected by analyzing past emotion data.

[0074] When acquiring the emotion estimating information, the acquiring unit can perform filtering based on the customer's current activity or environment. For example, when the customer is eating, the acquiring unit preferentially acquires emotion estimating information related to eating. When the customer is in a meeting, the acquiring unit can also preferentially acquire emotion estimating information related to the meeting. When the customer is in a relaxing environment, the acquiring unit can also preferentially acquire emotion estimating information related to the environment. For example, when the customer is eating, the acquiring unit preferentially acquires emotion estimating information related to eating. When the customer is in a meeting, the acquiring unit can also preferentially acquire emotion estimating information related to the meeting. When the customer is in a relaxing environment, the acquiring unit can also preferentially acquire emotion estimating information related to the environment. In this way, by filtering information based on the customer's activity or environment, more relevant information can be acquired.

[0075] The acquisition unit can estimate the customer's emotions and determine the priority of information to be acquired based on the estimated customer's emotions. For example, when a customer is dissatisfied, the acquisition unit prioritizes acquiring information related to the dissatisfaction. When a customer is relaxed, the acquisition unit can also prioritize acquiring information related to relaxation. When a customer is excited, the acquisition unit can also prioritize acquiring information related to the excitement. For example, when a customer is dissatisfied, the acquisition unit prioritizes acquiring information related to the dissatisfaction. When a customer is relaxed, the acquisition unit can also prioritize acquiring information related to relaxation. When a customer is excited, the acquisition unit can also prioritize acquiring information related to the excitement. In this way, by determining the priority of information based on the customer's emotions, important information can be acquired preferentially.

[0076] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the customer. For example, when the customer is in a hotel, the acquisition unit preferentially acquires information related to services in the hotel. When the customer is in a restaurant, the acquisition unit can also preferentially acquire information related to services in the restaurant. When the customer is in a tourist destination, the acquisition unit can also preferentially acquire information related to the tourist destination. For example, when the customer is in a hotel, the acquisition unit preferentially acquires information related to services in the hotel. When the customer is in a restaurant, the acquisition unit can also preferentially acquire information related to services in the restaurant. When the customer is in a tourist destination, the acquisition unit can also preferentially acquire information related to the tourist destination. In this way, highly relevant information can be preferentially acquired by considering the geographical location information.

[0077] When acquiring the emotion estimation information, the acquisition unit can analyze the social media activity of the customer and acquire related information. For example, when the customer expresses dissatisfaction on social media, the acquisition unit acquires information related to the dissatisfaction. When the customer posts a relaxed state on social media, the acquisition unit can also acquire information related to the relaxation. When the customer posts an excited state on social media, the acquisition unit can also acquire information related to the excitement. For example, when the customer expresses dissatisfaction on social media, the acquisition unit acquires information related to the dissatisfaction. When the customer posts a relaxed state on social media, the acquisition unit can also acquire information related to the relaxation. When the customer posts an excited state on social media, the acquisition unit can also acquire information related to the excitement. In this way, by analyzing social media activity, related information can be acquired.

[0078] The estimation unit can estimate the customer's emotions and adjust the estimation algorithm based on the estimated customer's emotions. For example, when a customer is feeling stressed, the estimation unit applies an estimation algorithm specialized for stress. When a customer is relaxed, the estimation unit can also apply an estimation algorithm specialized for relaxation. When a customer is excited, the estimation unit can also apply an estimation algorithm specialized for excitement. For example, when a customer is feeling stressed, the estimation unit applies an estimation algorithm specialized for stress. When a customer is relaxed, the estimation unit can also apply an estimation algorithm specialized for relaxation. When a customer is excited, the estimation unit can also apply an estimation algorithm specialized for excitement. In this way, by adjusting the estimation algorithm based on the customer's emotions, more accurate emotion estimation is possible.

[0079] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. For example, the estimation unit performs detailed emotion estimation based on emotion estimation information of high importance. The estimation unit can also perform simple emotion estimation based on emotion estimation information of low importance. The estimation unit can also perform emotion estimation with a moderate level of detail based on emotion estimation information of medium importance. For example, the estimation unit performs detailed emotion estimation based on emotion estimation information of high importance. The estimation unit can also perform simple emotion estimation based on emotion estimation information of low importance. The estimation unit can also perform emotion estimation with a moderate level of detail based on emotion estimation information of medium importance. Thereby, by adjusting the level of detail of the estimation based on the importance of the emotion estimation information, efficient emotion estimation is possible.

[0080] At the time of estimation, the estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information. For example, the estimation unit applies a facial expression recognition algorithm to emotion estimation information based on facial expressions. The estimation unit can also apply a voice recognition algorithm to emotion estimation information based on voice. The estimation unit can also apply a voice recognition algorithm to emotion estimation information based on movement. For example, the estimation unit applies a facial expression recognition algorithm to emotion estimation information based on facial expressions. The estimation unit can also apply a voice recognition algorithm to emotion estimation information based on voice. The estimation unit can also apply a voice recognition algorithm to emotion estimation information based on movement. In this way, by applying different estimation algorithms depending on the category of the emotion estimation information, more accurate emotion estimation is possible.

[0081] The estimation unit can estimate the customer's emotions and adjust the display method of the estimation result based on the estimated customer's emotions. For example, if the customer is nervous, the estimation unit provides a simple and highly visible display method. If the customer is relaxed, the estimation unit can also provide a display method including detailed information. If the customer is in a hurry, the estimation unit can also provide a display method that focuses on the main points. For example, if the customer is nervous, the estimation unit provides a simple and highly visible display method. If the customer is relaxed, the estimation unit can also provide a display method including detailed information. If the customer is in a hurry, the estimation unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the estimation result based on the customer's emotions, a highly visible display is possible.

[0082] The estimation unit can determine a priority order of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation is performed by preferentially using the latest emotion estimation information. The estimation can also be performed by referring to past emotion estimation information and prioritizing the latest information. The estimation can also be performed by preferentially using emotion estimation information acquired in a specific time period. For example, the estimation unit can perform estimation by preferentially using the latest emotion estimation information. The estimation can also be performed by referring to past emotion estimation information and prioritizing the latest information. The estimation can also be performed by preferentially using emotion estimation information acquired in a specific time period. In this way, by determining the priority order of estimation based on the acquisition time of the emotion estimation information, estimation with emphasis on the latest information is possible.

[0083] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. For example, the estimation is performed by preferentially using emotion estimation information having high relevance. The estimation can also be performed by using emotion estimation information having medium relevance next. The estimation can also be performed by using emotion estimation information having low relevance last. For example, the estimation unit performs estimation by preferentially using emotion estimation information having high relevance. The estimation can also be performed by using emotion estimation information having medium relevance next. The estimation can also be performed by using emotion estimation information having low relevance last. In this way, by adjusting the order of estimation based on the relevance of the emotion estimation information, efficient emotion estimation is possible.

[0084] The determination unit can estimate the customer's emotions and adjust the response determination method based on the estimated customer's emotions. For example, when a customer is dissatisfied, the determination unit preferentially determines a response to resolve the dissatisfaction. When a customer is relaxed, the determination unit can also determine a response to maintain the relaxation. When a customer is excited, the determination unit can also determine a response to alleviate the excitement. For example, when a customer is dissatisfied, the determination unit preferentially determines a response to resolve the dissatisfaction. When a customer is relaxed, the determination unit can also determine a response to maintain the relaxation. When a customer is excited, the determination unit can also determine a response to alleviate the excitement. In this way, by adjusting the response determination method based on the customer's emotions, a more appropriate response can be provided.

[0085] When making a decision, the decision unit can analyze the customer's past response history and select an appropriate response method. For example, the decision unit can refer to response methods that the customer was satisfied with in the past and select a similar response. It can also select a different response, avoiding response methods that the customer was dissatisfied with in the past. It can also select an optimal response method for a specific situation from the customer's past response history. For example, the decision unit can refer to response methods that the customer was satisfied with in the past and select a similar response. It can also select a different response, avoiding response methods that the customer was dissatisfied with in the past. It can also select an optimal response method for a specific situation from the customer's past response history. In this way, the optimal response method can be selected by analyzing past response history.

[0086] At the time of making the decision, the decision unit can customize the response measures based on the customer's current situation. For example, if the customer is eating, the decision unit customizes the response related to the meal. If the customer is in a meeting, the decision unit can also customize the response related to the meeting. If the customer is relaxed, the decision unit can also customize the response for maintaining the relaxation. For example, if the customer is eating, the decision unit customizes the response related to the meal. If the customer is in a meeting, the decision unit can also customize the response related to the meeting. If the customer is relaxed, the decision unit can also customize the response for maintaining the relaxation. This enables more appropriate responses by customizing the response measures based on the customer's current situation.

[0087] The determination unit can estimate the customer's emotions and determine the priority of responses based on the estimated customer's emotions. For example, if the customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction as a first priority. If the customer is relaxed, the determination unit can also determine a response to maintain relaxation next. If the customer is excited, the determination unit can also determine a response to ease the excitement last. For example, if the customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction as a first priority. If the customer is relaxed, the determination unit can also determine a response to maintain relaxation next. If the customer is excited, the determination unit can also determine a response to ease the excitement last. In this way, by determining the priority of responses based on the customer's emotions, important responses can be given priority.

[0088] When making a decision, the decision unit can select an appropriate response method by taking into consideration the geographical location information of the customer. For example, if the customer is in a hotel, the decision unit selects a response related to services within the hotel. If the customer is in a restaurant, the decision unit can also select a response related to services within the restaurant. If the customer is in a tourist destination, the decision unit can also select a response related to the tourist destination. For example, if the customer is in a hotel, the decision unit selects a response related to services within the hotel. If the customer is in a restaurant, the decision unit can also select a response related to services within the restaurant. If the customer is in a tourist destination, the decision unit can also select a response related to the tourist destination. This makes it possible to select an optimal response method by taking into consideration the geographical location information.

[0089] At the time of making a decision, the decision unit can analyze the social media activity of the customer to suggest a means of response. For example, if the customer expresses dissatisfaction on social media, the decision unit can suggest a response to resolve the dissatisfaction. If the customer posts on social media how relaxed he or she is, the decision unit can also suggest a response to maintain the relaxation. If the customer posts on social media how excited he or she is, the decision unit can also suggest a response to ease the excitement. For example, if the customer expresses dissatisfaction on social media, the decision unit can suggest a response to resolve the dissatisfaction. If the customer posts on social media how relaxed he or she is, the decision unit can also suggest a response to maintain the relaxation. If the customer posts on social media how excited he or she is, the decision unit can also suggest a response to ease the excitement. In this way, by analyzing social media activity, appropriate means of response can be suggested.

[0090] The notification unit can estimate the customer's emotions and adjust the notification method based on the estimated customer's emotions. For example, if the customer is dissatisfied, the notification unit selects a quick and clear notification method. If the customer is relaxed, the notification unit can also select a gentle notification method. If the customer is excited, the notification unit can also select a notification method that encourages the customer to stay calm. For example, if the customer is dissatisfied, the notification unit selects a quick and clear notification method. If the customer is relaxed, the notification unit can also select a gentle notification method. If the customer is excited, the notification unit can also select a notification method that encourages the customer to stay calm. In this way, by adjusting the notification method based on the customer's emotions, appropriate notification is possible.

[0091] When notifying, the notification unit can select the optimal notification method by referring to the customer's past notification history. For example, the notification unit can give priority to selecting a notification method that the customer has preferred in the past. It can also select a notification method that has been excluded from notification methods that the customer has avoided in the past. It can also select the optimal notification method for a specific situation from the customer's past notification history. For example, the notification unit can give priority to selecting a notification method that the customer has preferred in the past. It can also select a notification method that has been excluded from notification methods that the customer has avoided in the past. It can also select the optimal notification method for a specific situation from the customer's past notification history. In this way, the optimal notification method can be selected by referring to past notification history.

[0092] At the time of notification, the notification unit can customize the content of the notification based on the customer's current situation. For example, if the customer is eating, the notification unit customizes the notification content related to the meal. If the customer is in a meeting, the notification unit can also customize the notification content related to the meeting. If the customer is relaxed, the notification unit can also customize the notification content for maintaining that relaxation. For example, if the customer is eating, the notification unit customizes the notification content related to the meal. If the customer is in a meeting, the notification content related to the meeting can also be customized. If the customer is relaxed, the notification content for maintaining that relaxation can also be customized. This enables appropriate notifications to be provided by customizing the notification content based on the customer's current situation.

[0093] The notification unit can estimate the customer's emotions and determine the priority of notifications based on the estimated customer's emotions. For example, if the customer is dissatisfied, the notification unit determines that a notification for resolving the dissatisfaction is given top priority. If the customer is relaxed, the notification unit can also determine that a notification for maintaining relaxation is given next. If the customer is excited, the notification unit can also determine that a notification for easing excitement is given last. For example, if the customer is dissatisfied, the notification unit determines that a notification for resolving the dissatisfaction is given top priority. If the customer is relaxed, the notification unit can also determine that a notification for maintaining relaxation is given next. If the customer is excited, the notification unit can also determine that a notification for easing excitement is given last. In this way, by determining the priority of notifications based on the customer's emotions, important notifications can be given priority.

[0094] At the time of notification, the notification unit can select the optimal notification method by taking into consideration the device information of the customer. For example, if the customer is using a smartphone, the notification unit provides a notification method that matches the screen size. If the customer is using a tablet, the notification unit can also provide a notification method optimized for a large screen. If the customer is using a smartwatch, the notification unit can also provide a notification method that is concise and highly visible. For example, if the customer is using a smartphone, the notification unit provides a notification method that matches the screen size. If the customer is using a tablet, the notification unit can also provide a notification method that is optimized for a large screen. If the customer is using a smartwatch, the notification unit can also provide a notification method that is concise and highly visible. This makes it possible to select the optimal notification method by taking into consideration the device information.

[0095] The notification unit can provide notifications in multiple languages ​​depending on the language settings of the customer at the time of notification. For example, the notification unit automatically sets the language of the notification based on the language settings of the customer's device. If the customer uses multiple languages, the notification unit can also provide a language switching function. If the customer selects a specific language, the notification unit can also provide notifications in that language. For example, the notification unit automatically sets the language of the notification based on the language settings of the customer's device. If the customer uses multiple languages, the notification unit can also provide a language switching function. If the customer selects a specific language, the notification unit can also provide notifications in that language. This makes it possible to provide appropriate notifications to customers by providing notifications in multiple languages ​​depending on the language settings.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] When acquiring emotion estimation information for estimating the emotion of a customer, the acquisition unit can analyze the customer's past purchase history and behavioral patterns to improve the accuracy of emotion estimation. For example, the acquisition unit collects data on products and services purchased by the customer in the past, and estimates the emotion of the customer based on this data. If the customer tends to take a specific action at a specific time period, the emotion estimation information can be acquired by taking into account the behavioral pattern. Furthermore, the acquisition unit can analyze data on places visited by the customer in the past and events attended by the customer, and acquire emotion estimation information based on this data. In this way, the accuracy of emotion estimation can be improved by utilizing the customer's past behavioral data.

[0098] When estimating the emotions of a customer, the estimation unit can analyze the social media activities of the customer to improve the accuracy of emotion estimation. For example, the estimation unit collects the contents and comments posted by the customer on social media, and estimates the emotions of the customer based on this data. If the customer uses a specific hashtag, posts related to the hashtag can be analyzed and used for emotion estimation. Furthermore, the estimation unit can analyze data on accounts the customer follows on social media and groups the customer participates in, and estimate emotions based on this data. In this way, the accuracy of emotion estimation can be improved by utilizing social media activities.

[0099] The decision unit can provide discounts and special offers for specific products in accordance with the customer's emotions. For example, if the customer is dissatisfied, the decision unit decides to provide a discount coupon for a specific product in order to resolve the dissatisfaction. If the customer is relaxed, the decision unit can also decide to provide a special offer for a product that has a relaxing effect in order to maintain the relaxation. If the customer is excited, the decision unit can also decide to provide a free trial of a specific service in order to relieve the excitement. In this way, customer satisfaction can be improved by providing special offers and discounts in accordance with the customer's emotions.

[0100] The notification unit can provide different notification contents depending on the customer's emotions. For example, if the customer is dissatisfied, the notification unit can notify the customer of specific measures to resolve the dissatisfaction. If the customer is relaxed, the notification unit can also notify the customer of advice for maintaining relaxation or content that has a relaxing effect. If the customer is excited, the notification unit can also notify the customer of relaxation methods to relieve the excitement. In this way, by providing appropriate notification contents according to the customer's emotions, the customer's emotions can be appropriately managed.

[0101] When acquiring emotion estimation information for estimating the emotion of a customer, the acquisition unit can analyze the current environmental sound of the customer to improve the accuracy of emotion estimation. For example, the acquisition unit estimates the emotion of the customer by collecting environmental sounds in the place where the customer is located and analyzing the type and volume of the sounds. If the customer is in a quiet environment, it can be estimated that the customer is relaxed based on the environmental sounds. If the customer is in a noisy environment, it can also be estimated that the customer is stressed based on the environmental sounds. Furthermore, if the customer is listening to specific music, the acquisition unit can analyze the genre and tempo of the music and use it for emotion estimation. In this way, the accuracy of emotion estimation can be improved by utilizing the environmental sounds.

[0102] When estimating the customer's emotion, the estimation unit can analyze the customer's past health data and improve the accuracy of emotion estimation. For example, the estimation unit collects the customer's past heart rate and blood pressure data, and estimates the customer's emotion based on this data. If the customer felt stressed when he or she showed a high heart rate in the past, the estimation unit can analyze the current heart rate based on that data and estimate the emotion. Furthermore, the estimation unit can analyze the customer's past sleep data and estimate the emotion by taking into account the impact of lack of sleep on the emotion. In this way, the accuracy of emotion estimation can be improved by utilizing the past health data.

[0103] The decision unit can provide specific feedback depending on the customer's emotions. For example, if the customer is dissatisfied, the decision unit can provide specific feedback to resolve the dissatisfaction. If the customer is relaxed, the decision unit can also provide positive feedback to maintain the relaxation. If the customer is excited, the decision unit can also provide calm feedback to reduce the excitement. In this way, the customer's emotions can be appropriately managed by providing appropriate feedback according to the customer's emotions.

[0104] The notification unit can set different notification timings depending on the customer's emotions. For example, if the customer is dissatisfied, the notification unit can quickly notify and respond early. If the customer is relaxed, the notification unit can also delay the timing of the notification so as not to disturb the customer's relaxation. If the customer is excited, the notification unit can also notify at an appropriate time to encourage the customer to calm down. In this way, by setting appropriate notification timings depending on the customer's emotions, the customer's emotions can be appropriately managed.

[0105] When acquiring emotion estimation information for estimating the emotion of a customer, the acquisition unit can analyze the current activity level of the customer and improve the accuracy of emotion estimation. For example, when the customer is exercising, the acquisition unit estimates the emotion based on the activity level. When the customer shows a high activity level, it can be estimated that the customer is excited based on the data. When the customer shows a low activity level, it can be estimated that the customer is relaxed based on the data. Furthermore, when the customer is performing a specific exercise, the acquisition unit can analyze the type and intensity of the exercise and use it for emotion estimation. In this way, the accuracy of emotion estimation can be improved by utilizing the activity level.

[0106] When estimating the emotion of a customer, the estimation unit can analyze the customer's past purchase history and improve the accuracy of emotion estimation. For example, the estimation unit collects data on products and services purchased by the customer in the past, and estimates the customer's emotion based on this data. If the customer was satisfied when purchasing a specific product, the estimation unit can analyze the customer's current purchasing behavior based on that data and estimate the emotion. Furthermore, the estimation unit can analyze reviews and feedback of products purchased by the customer in the past, and estimate the emotion based on this data. In this way, the accuracy of emotion estimation can be improved by utilizing the past purchase history.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The acquisition unit acquires, for example, one or more pieces of information among the customer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. Specifically, a camera is used to recognize the customer's facial expression and acquire facial expression data. A microphone can also be used to collect the customer's voice and acquire voice data. Furthermore, a heart rate sensor can be used to measure the customer's heart rate and acquire heart rate data. Step 2: The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the emotion using, for example, a machine learning algorithm. Specifically, the estimation unit receives facial expression data, voice data, and heart rate data as input, and estimates the emotion of the customer using the machine learning algorithm. Step 3: The determination unit determines a response according to the customer's emotion estimated by the estimation unit. For example, if the customer is dissatisfied, the determination unit determines a response to resolve the dissatisfaction. If the customer is relaxed, the determination unit can also determine a response to maintain the relaxation. If the customer is excited, the determination unit can also determine a response to alleviate the excitement.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the acquisition unit acquires the facial expression and voice of the customer using the camera 42 and microphone 38B of the smart device 14, and the acquired facial expression and voice are processed as emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the customer based on the information from the acquisition unit. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the staff of the determined response. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the acquisition unit acquires the facial expression and voice of the customer using the camera 42 and microphone 238 of the smart glasses 214, and the acquired facial expression and voice are processed as emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the customer based on the information from the acquisition unit. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the staff of the determined response. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the acquisition unit acquires the facial expression and voice of the customer using the camera 42 and microphone 238 of the headset type terminal 314, and the acquired facial expression and voice are processed as emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the customer based on the information from the acquisition unit. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the staff of the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the acquisition unit acquires the facial expression and voice of the customer using the camera 42 and microphone 238 of the robot 414, and the acquired facial expression and voice are processed as emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the customer based on the information from the acquisition unit. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the staff of the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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 in which a processor is used 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of a customer; an estimation unit that estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response according to the emotion of the customer estimated by the estimation unit. A system characterized by: (Appendix 2) A notification unit is provided for notifying staff of the content of the response determined by the determination unit. 2. The system of claim 1, (Appendix 3) The notification unit is The content of the response determined by the determination unit is notified to the staff using different notification methods according to the emotion of the customer estimated by the estimation unit. 3. The system of claim 2, (Appendix 4) The determination unit is Providing specific music or video in response to the customer's emotion is determined as a response to the customer's emotion. 2. The system of claim 1, (Appendix 5) The determination unit is A response according to the emotion of the customer is determined based on the emotion of the customer estimated by the estimation unit and an attribute of the customer. 2. The system of claim 1, (Appendix 6) The acquisition unit is At least one of the facial expression, movement, voice, heart rate, and blood pressure of the customer is acquired as the emotion estimation information. 2. The system of claim 1, (Appendix 7) The acquisition unit is To estimate a customer's emotion and adjust timing of acquiring emotion estimation information based on the estimated emotion of the customer. 2. The system of claim 1, (Appendix 8) The acquisition unit is Analyze past customer sentiment data and select the appropriate acquisition method 2. The system of claim 1, (Appendix 9) The acquisition unit is Filtering sentiment estimation information based on the customer's current activity or environment 2. The system of claim 1, (Appendix 10) The acquisition unit is Estimate customer sentiment and prioritize information to be retrieved based on the estimated customer sentiment 2. The system of claim 1, (Appendix 11) The acquisition unit is When acquiring information for emotion estimation, the system prioritizes acquiring information that is highly relevant by taking into account the geographic location information of the customer. 2. The system of claim 1, (Appendix 12) The acquisition unit is When obtaining sentiment estimation information, analyze the customer's social media activity to obtain related information. 2. The system of claim 1, (Appendix 13) The estimation unit is Infer customer sentiment and adjust the inference algorithm based on the inferred customer sentiment 2. The system of claim 1, (Appendix 14) The estimation unit is During estimation, the level of detail of the estimation is adjusted based on the importance of the emotion estimation information. 2. The system of claim 1, (Appendix 15) The estimation unit is During estimation, different estimation algorithms are applied depending on the category of emotion estimation information. 2. The system of claim 1, (Appendix 16) The estimation unit is Estimate customer sentiment and adjust how the results are displayed based on the estimated customer sentiment 2. The system of claim 1, (Appendix 17) The estimation unit is During estimation, the priority of estimation is determined based on the time when emotion estimation information was acquired. 2. The system of claim 1, (Appendix 18) The estimation unit is During estimation, the order of estimation is adjusted based on the relevance of emotion estimation information. 2. The system of claim 1, (Appendix 19) The determination unit is Infer customer sentiment and adjust response decisions based on inferred customer sentiment 2. The system of claim 1, (Appendix 20) The determination unit is When making a decision, analyze the customer's past response history to select the appropriate response method. 2. The system of claim 1, (Appendix 21) The determination unit is When making a decision, tailor your response based on the customer's current situation 2. The system of claim 1, (Appendix 22) The determination unit is Estimate customer sentiment and prioritize interactions based on the estimated customer sentiment 2. The system of claim 1, (Appendix 23) The determination unit is When making decisions, consider the customer's geographic location to select the appropriate response 2. The system of claim 1, (Appendix 24) The determination unit is Analyze your customers' social media activity and suggest action steps when making decisions 2. The system of claim 1, (Appendix 25) The notification unit is Estimate customer sentiment and tailor notifications based on estimated customer sentiment 3. The system of claim 2, (Appendix 26) The notification unit is When notifying, select the optimal notification method by referring to the customer's past notification history 3. The system of claim 2, (Appendix 27) The notification unit is At the time of notification, customize the content of the notification based on the customer's current situation 3. The system of claim 2, (Appendix 28) The notification unit is Estimate customer sentiment and prioritize notifications based on estimated customer sentiment 3. The system of claim 2, (Appendix 29) The notification unit is When notifying, the optimal notification method is selected taking into account the customer's device information. 3. The system of claim 2, (Appendix 30) The notification unit is Upon notification, provide multilingual notifications based on customer's language settings 3. The system of claim 2, [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires emotion estimation information for estimating an emotion of a customer; an estimation unit that estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response according to the emotion of the customer estimated by the estimation unit. A system characterized by:

2. A notification unit is provided for notifying staff of the content of the response determined by the determination unit.

2. The system of claim 1 .

3. The notification unit is The content of the response determined by the determination unit is notified to the staff using different notification methods according to the emotion of the customer estimated by the estimation unit.

3. The system of claim 2.

4. The determination unit is Providing specific music or video in response to the customer's emotion is determined as a response to the customer's emotion.

2. The system of claim 1 .

5. The determination unit is A response according to the emotion of the customer is determined based on the emotion of the customer estimated by the estimation unit and an attribute of the customer.

2. The system of claim 1 .

6. The acquisition unit is At least one of the facial expression, movement, voice, heart rate, and blood pressure of the customer is acquired as the emotion estimation information.

2. The system of claim 1 .

7. The acquisition unit is To estimate a customer's emotion and adjust timing of acquiring emotion estimation information based on the estimated emotion of the customer.

2. The system of claim 1 .

8. The acquisition unit is Analyze past customer sentiment data and select the appropriate acquisition method 2. The system of claim 1 .

9. The acquisition unit is Filtering sentiment estimation information based on the customer's current activity or environment 2. The system of claim 1 .

10. The acquisition unit is Estimate customer sentiment and prioritize information to be retrieved based on the estimated customer sentiment 2. The system of claim 1 .

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