System

Abstract

To provide a system that estimates the emotions of a customer and serves the customer accordingly.SOLUTION: A system according to an embodiment comprises an acquisition unit, an estimation unit, a determination unit, and an execution unit. The acquisition unit acquires emotion estimation information for estimating the emotions of a customer. The estimation unit estimates the emotions of the customer on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines a customer service according to the emotions of the customer as estimated by the estimation unit. The execution unit provides the customer service determined by the determination 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] With conventional technology, it is difficult to provide appropriate customer service that is in line with the customer's emotions, and there is room for improvement.

[0005] The system according to the embodiment aims to estimate the emotions of a customer and provide customer service in accordance with the emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and an execution 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 customer service according to the emotion of the customer estimated by the estimation unit. The execution unit executes the customer service determined by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can estimate the emotions of a customer and provide customer service 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 the processor.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The customer service support system according to the embodiment of the present invention is a system that uses an agent having an emotion engine to support customer service at a mobile phone shop. This customer service support system acquires information for estimating the customer's emotion, estimates the customer's emotion based on the acquired information, determines customer service according to the estimated emotion, and executes the determined customer service. For example, the customer service support system acquires information such as the customer's facial expression, movement, voice, heart rate, blood pressure, pupil reaction, brain wave, body temperature, breathing, and posted content. Next, based on the acquired information, the customer service support system estimates the customer's emotion. Depending on the estimated emotion, the customer service support system determines appropriate customer service. For example, if the customer is relaxed, the explanation is given at a slow pace, and if the customer is in a hurry, the customer is quickly responded to. Finally, the customer service support system executes the determined customer service. Furthermore, the customer service support system can accept information on the customer's request and budget. For example, the customer can input the type of terminal and the budget that he or she desires. Based on this, the customer service support system proposes a plan or terminal according to the customer's emotion and request. The customer service support system can also provide information on new products and campaigns. For example, the functions and benefits of a new smartphone can be explained. The customer service support system can also provide customer service in a manner according to the customer's emotion. For example, the timing of customer service, the manner of dialogue, the method of customer service, etc. can be adjusted. This allows the customer to have an optimal customer service experience. This allows the customer service support system to provide appropriate customer service based on the customer's emotions.

[0029] The customer service support system according to the embodiment includes an acquisition unit, an estimation unit, a decision unit, and an execution unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information includes, for example, the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content, but is not limited to such examples. The acquisition unit, for example, photographs the customer's facial expression using a camera and acquires facial expression data. The acquisition unit can also record the customer's voice using a microphone and acquire voice data. Furthermore, the acquisition unit can measure the customer's heart rate and blood pressure using a sensor and acquire these data. For example, the acquisition unit inputs facial expression data photographed by a camera to a generation AI, and the generation AI analyzes the facial expression data to estimate the emotion. The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit uses, for example, the generation AI to analyze the acquired information and estimate the customer's emotion. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. In the estimation unit, for example, the generation AI analyzes facial expression data and estimates the customer's emotions. In addition, the estimation unit can also analyze voice data and estimate the customer's emotions. In addition, the estimation unit can analyze heart rate data and blood pressure data and estimate the customer's emotions. The determination unit determines customer service according to the customer's emotions estimated by the estimation unit. For example, if the customer is relaxed, the determination unit determines to provide an explanation at a slow pace. In addition, if the customer is in a hurry, the determination unit can also determine to respond quickly. In addition, the determination unit can determine a plan and a terminal to be proposed according to the customer's emotions. For example, if the customer is relaxed, the determination unit determines to provide a detailed explanation. The execution unit executes the customer service determined by the determination unit. For example, the execution unit provides an explanation to the customer at a slow pace. In addition, the execution unit can respond quickly. In addition, the execution unit can explain the plan and the terminal to be proposed to the customer. For example, the execution unit explains the functions and benefits of a new smartphone to the customer. In this way, the customer service support system according to the embodiment can provide appropriate customer service based on the customer's emotions.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. The emotion estimation information includes, for example, the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content, but is not limited to such examples. For example, the acquisition unit uses a camera to capture the facial expression of the customer and acquires facial expression data. The camera has high resolution and can capture even subtle changes in facial expression. The acquisition unit can also record the customer's voice using a microphone and acquire voice data. The voice data includes the tone, speed, and volume of the voice, and important information for estimating the emotion can be obtained from these elements. Furthermore, the acquisition unit can measure the customer's heart rate and blood pressure using a sensor and acquire these data. Fluctuations in heart rate and blood pressure are indicators of stress and relaxation state. For example, the acquisition unit inputs facial expression data captured by a camera to the generation AI, which analyzes the facial expression data to estimate the emotion. The generation AI has learned a large amount of facial expression data in advance and can estimate the emotion from subtle changes in facial expression with high accuracy. This enables the acquisition unit to efficiently collect a variety of emotion estimation information for customers and provide basic data for emotion estimation.

[0031] The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses a generation AI to analyze the acquired information and estimate the emotion of the customer. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI analyzes facial expression data and estimates the emotion of the customer. The generation AI can capture subtle changes in facial expressions and classify emotions such as joy, anger, sadness, and surprise with high accuracy. The estimation unit can also analyze voice data and estimate the emotion of the customer. Voice recognition technology and emotion recognition technology are used to analyze the voice data, and emotions are estimated from changes in tone, speed, and volume of the voice. Furthermore, the estimation unit can analyze heart rate data and blood pressure data to estimate the emotion of the customer. Fluctuations in heart rate and blood pressure are indicators of stress and relaxation, and by analyzing these data, the emotional state of the customer can be grasped more accurately. The estimation unit analyzes these various data in an integrated manner and comprehensively evaluates the emotion of the customer. For example, more accurate emotion estimation is possible by combining and analyzing facial expression data and voice data. This allows the estimation unit to estimate the customer's emotion with high accuracy and provide important information for determining the next step, which is the content of customer service.

[0032] The determination unit determines the customer service according to the customer's emotion estimated by the estimation unit. For example, if the customer is relaxed, the determination unit determines to provide an explanation at a slow pace. For a relaxed customer, a detailed explanation and additional information can be provided to encourage deeper understanding. In addition, if the customer is in a hurry, it can also determine to respond quickly. For a customer in a hurry, efficient customer service can be provided by simply conveying the main points. Furthermore, the determination unit can also determine a plan and a terminal to be proposed according to the customer's emotion. For example, if the customer is relaxed, it determines to provide a detailed explanation. For a relaxed customer, it is possible to support the optimal selection by explaining the details of multiple plans and terminals. On the other hand, if the customer is stressed, it is determined to provide a simple and easy-to-understand proposal. As a result, the determination unit can determine the optimal customer service content according to the customer's emotion and improve customer satisfaction. Furthermore, the determination unit can continuously improve the accuracy of the customer service content by utilizing past customer service data and customer feedback. As a result, the determination unit can always provide optimal customer service based on the latest information and meet the needs of the customer.

[0033] The execution unit executes the customer service determined by the determination unit. For example, the execution unit provides explanations to the customer at a slow pace. For relaxed customers, detailed explanations and additional information can be provided to deepen understanding. The execution unit can also respond quickly. For customers who are in a hurry, the main points can be conveyed concisely to provide efficient customer service. Furthermore, the execution unit can also explain the plan and terminal to be proposed to the customer. For example, the execution unit explains the functions and benefits of a new smartphone to the customer. By explaining the functions and benefits of a new smartphone in detail, it is possible to attract the customer's interest and increase their desire to purchase. This allows the execution unit to execute the customer service content determined by the determination unit and improve customer satisfaction. Furthermore, the execution unit can monitor the customer's reaction in real time and adjust the customer service content as necessary. For example, if the customer shows interest, more detailed information can be provided, and conversely, if the customer does not show interest, another proposal can be made. This allows the execution unit to flexibly respond to the needs of the customer and provide optimal customer service.

[0034] The execution unit can make a proposal, a procedure, or support according to the customer's emotions. The execution unit makes an appropriate proposal, for example, according to the customer's emotions. For example, when the customer is relaxed, a detailed proposal can be made. Also, when the customer is in a hurry, a proposal can be made quickly. Furthermore, the execution unit can perform a procedure according to the customer's emotions. For example, when the customer is relaxed, a procedure can be performed at a slow pace. Also, when the customer is in a hurry, a procedure can be performed quickly. Furthermore, the execution unit can provide support according to the customer's emotions. For example, when the customer is relaxed, detailed support can be provided. Also, when the customer is in a hurry, support can be provided quickly. This makes it possible to respond according to the customer's emotions. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit inputs customer emotion data into the generation AI, and the generation AI can execute a proposal, procedure, or support according to the customer's emotions.

[0035] The reception unit can receive information on the customer's requests and budget, and the determination unit can propose a plan or terminal according to the customer's emotions and the information received by the reception unit. The reception unit can, for example, input the type of terminal and budget desired by the customer. For example, the reception unit can input the model of the smartphone desired by the customer and the budget. The reception unit can also input the rate plan and service desired by the customer. The determination unit proposes a plan or terminal according to the customer's emotions and requests based on the information received by the reception unit. For example, the determination unit can propose a detailed plan or terminal when the customer is relaxed. Also, the determination unit can make a proposal quickly when the customer is in a hurry. Furthermore, the determination unit can adjust the proposed plan or terminal according to the customer's emotions. For example, the determination unit determines to provide a detailed explanation when the customer is relaxed. This makes it possible to make a proposal according to the customer's requests and budget. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input information on the customer's requests and budget to the generation AI, and the generation AI can execute a proposal of a plan or terminal according to the customer's emotions.

[0036] The determination unit determines the manner of customer service according to the customer's emotions, and the execution unit can perform the service according to the customer's emotions. For example, when the customer is relaxed, the determination unit determines to perform the service at a slow pace. Also, when the customer is in a hurry, the determination unit can determine to respond quickly. Furthermore, the determination unit can adjust the manner of customer service according to the customer's emotions. For example, when the customer is relaxed, the determination unit determines to provide a detailed explanation. The execution unit performs the customer service determined by the determination unit. For example, the execution unit provides an explanation to the customer at a slow pace. Also, the execution unit can respond quickly. Furthermore, the execution unit can explain the plan and terminal to be proposed to the customer. For example, the execution unit explains the functions and benefits of a new smartphone to the customer. This makes it possible to provide customer service in a manner according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit and the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit inputs customer emotion data to the generation AI, and the generation AI can determine the manner of customer service according to the customer's emotions. In addition, the execution unit can execute the manner of customer service determined by the generation AI.

[0037] The determination unit can determine the manner of customer service according to the customer's emotions. For example, when the customer is relaxed, the determination unit determines to provide customer service at a slow pace. Also, when the customer is in a hurry, the determination unit can determine to respond quickly. Furthermore, the determination unit can adjust the manner of customer service according to the customer's emotions. For example, when the customer is relaxed, the determination unit determines to provide a detailed explanation. This makes it possible to determine the manner of customer service according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data to a generation AI, and the generation AI can determine the manner of customer service according to the customer's emotions.

[0038] The acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content. For example, the acquisition unit captures the customer's facial expression using a camera to acquire facial expression data. The acquisition unit can also record the customer's voice using a microphone to acquire voice data. Furthermore, the acquisition unit can measure the customer's heart rate and blood pressure using a sensor to acquire these data. For example, the acquisition unit inputs facial expression data captured by a camera to a generation AI, which analyzes the facial expression data to estimate emotions. This improves the accuracy of emotion estimation by acquiring various information. Part or all of the above-mentioned processing in the acquisition unit may be performed using an AI, for example, or may be performed without using an AI. For example, the acquisition unit inputs facial expression data of the customer to a generation AI, which analyzes the facial expression data to estimate emotions.

[0039] 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 relaxed, the acquisition unit periodically acquires the emotion estimation information. Also, when the customer is nervous, the acquisition unit can frequently acquire the emotion estimation information. Furthermore, when the customer is in a hurry, the emotion estimation information can be acquired quickly. This makes it possible to acquire information at an appropriate timing according to the emotion of the customer. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the emotion data of the customer to the generation AI, and the generation AI can adjust the timing of acquiring the emotion estimation information.

[0040] The acquisition unit can analyze the past emotion data of the customer and select the optimal acquisition method. For example, if the customer was relaxed in the past, the acquisition unit can acquire emotion estimation information in a similar situation. Also, if the customer was nervous in the past, the acquisition unit can acquire emotion estimation information in a similar situation. Furthermore, if the customer was in a hurry in the past, the acquisition unit can acquire emotion estimation information in a similar situation. This makes it possible to acquire optimal information based on past emotion data. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the past emotion data of the customer to the generation AI, which can select the optimal acquisition method.

[0041] The acquisition unit can perform filtering based on the customer's current situation and field of interest when acquiring emotion estimation information. For example, if the customer is interested in a new product, the acquisition unit can preferentially acquire that information. In addition, if the customer is requesting troubleshooting, the acquisition unit can also preferentially acquire that information. Furthermore, if the customer is requesting campaign information, the acquisition unit can also preferentially acquire that information. This makes it possible to acquire information according to the customer's situation and interests. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the customer's field of interest data to the generation AI, and the generation AI can perform filtering.

[0042] 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 the customer is relaxed, the acquisition unit can prioritize acquiring detailed information. Also, when the customer is nervous, the acquisition unit can prioritize acquiring concise information. Furthermore, when the customer is in a hurry, the acquisition unit can prioritize acquiring information that can be acquired quickly. This makes it possible to determine the priority of information according to the customer's emotions. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input customer emotion data to a generation AI, which can then determine the priority of information.

[0043] 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 specific area, the acquisition unit preferentially acquires information related to the area. In addition, when the customer is traveling, the acquisition unit can preferentially acquire information related to the travel destination. Furthermore, when the customer is at home, the acquisition unit can preferentially acquire information around the home. This makes it possible to acquire highly relevant information based on the geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the geographical location data of the customer to the generation AI, which can then preferentially acquire highly relevant information.

[0044] The acquisition unit can analyze the social media activity of the customer and acquire related information when acquiring the emotion estimation information. For example, when the customer mentions a new product on social media, the acquisition unit can acquire the information with priority. In addition, when the customer mentions troubleshooting on social media, the acquisition unit can also acquire the information with priority. Furthermore, when the customer mentions a campaign on social media, the acquisition unit can also acquire the information with priority. In this way, related information based on social media activity can be acquired. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the social media data of the customer to the generation AI, and the generation AI can acquire the related information.

[0045] The estimation unit can estimate the customer's emotion and adjust the emotion estimation algorithm based on the estimated customer's emotion. For example, when the customer is relaxed, the estimation unit can gently adjust the emotion estimation algorithm. Also, when the customer is nervous, the estimation unit can sensitively adjust the emotion estimation algorithm. Furthermore, when the customer is in a hurry, the emotion estimation algorithm can be quickly adjusted. This enables the algorithm to be adjusted according to the customer's emotion. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the customer's emotion data to the generation AI, and the generation AI can adjust the emotion estimation algorithm.

[0046] 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. The estimation unit can perform detailed emotion estimation based on information of high importance, for example. It can also perform simple emotion estimation based on information of low importance. It can also perform emotion estimation with a moderate level of detail based on information of medium importance. This enables detailed estimation according to the importance of the information. A part or all of the above-mentioned processing in the estimation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the estimation unit can input emotion estimation information to a generation AI, and the generation AI can adjust the level of detail of the estimation based on the importance of the information.

[0047] The estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information at the time of estimation. The estimation unit can apply an algorithm for estimating an emotion based on facial expression information, for example. It can also apply an algorithm for estimating an emotion based on voice information. It can also apply an algorithm for estimating an emotion based on motion information. This enables appropriate estimation according to the category of information. A part or all of the above-mentioned processing in the estimation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the estimation unit can input emotion estimation information to a generation AI, and the generation AI can apply different estimation algorithms depending on the category of information.

[0048] 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, when the customer is relaxed, the estimation unit can display a detailed estimation result. Also, when the customer is nervous, the estimation unit can display a concise estimation result. Furthermore, when the customer is in a hurry, the estimation unit can display an estimation result that can be displayed quickly. This makes it possible to adjust the display method according to the customer's emotions. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input customer emotion data to a generation AI, and the generation AI can adjust the display method of the estimation result.

[0049] The estimation unit can determine the priority order of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation unit preferentially estimates the latest emotion estimation information. Also, the estimation unit can make an estimation by referring to past emotion estimation information. Furthermore, the emotion estimation information acquired in a specific time period can be preferentially estimated. This makes it possible to determine the priority order based on the acquisition time. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the emotion estimation information to a generation AI, and the generation AI can determine the priority order of estimation based on the acquisition time.

[0050] 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 unit preferentially estimates information with high relevance. Also, the estimation unit can estimate information with medium relevance next. Furthermore, the estimation unit can estimate information with low relevance last. This makes it possible to adjust the estimation order based on the relevance of the information. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input emotion estimation information to a generation AI, and the generation AI can adjust the order of estimation based on the relevance of the information.

[0051] The determination unit can estimate the customer's emotions and adjust the customer service decision method based on the estimated customer's emotions. For example, when the customer is relaxed, the determination unit can determine to provide customer service at a slow pace. Also, when the customer is nervous, the determination unit can determine to provide customer service that gives a sense of security. Furthermore, when the customer is in a hurry, the determination unit can determine to provide customer service that responds quickly. This makes it possible to adjust the customer service decision method according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data to a generation AI, which can adjust the customer service decision method.

[0052] At the time of making a decision, the determination unit can select an appropriate service method by analyzing the customer's past customer service history. For example, the determination unit preferentially selects a service method that the customer has preferred in the past. The determination unit can also eliminate a service method that the customer has avoided in the past. Furthermore, the determination unit can select an optimal service method from the customer's past customer service history. This makes it possible to select an optimal service method based on the past customer service history. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the customer's past customer service history data to the generation AI, which can select the optimal service method.

[0053] The determination unit can customize the means of customer service based on the current situation of the customer at the time of the determination. For example, if the customer is relaxed, the determination unit can determine to provide a detailed explanation. Also, if the customer is nervous, the determination unit can determine to provide a brief explanation. Furthermore, if the customer is in a hurry, the determination unit can determine to respond quickly. This makes it possible to customize the means of customer service according to the current situation of the customer. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the current situation data of the customer to the generation AI, and the generation AI can customize the means of customer service.

[0054] The determination unit can estimate the customer's emotions and determine the priority of customer service based on the estimated customer's emotions. For example, when the customer is relaxed, the determination unit prioritizes detailed explanations. Also, when the customer is nervous, the determination unit can prioritize customer service that gives a sense of security. Furthermore, when the customer is in a hurry, the determination unit can prioritize quick responses. This makes it possible to determine the priority of customer service according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data to a generation AI, which can then determine the priority of customer service.

[0055] The determination unit can select an appropriate customer service method in consideration of the geographical location information of the customer at the time of determination. For example, when the customer is in a specific area, the determination unit provides information related to the area. In addition, when the customer is traveling, the determination unit can also provide information related to the travel destination. Furthermore, when the customer is at home, the determination unit can also provide information about the area around the customer's home. This makes it possible to select an optimal customer service method based on the geographical location information. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit inputs the geographical location data of the customer to the generation AI, which can select the optimal customer service method.

[0056] At the time of decision, the decision unit can analyze the social media activity of the customer and propose a means of customer service. For example, when the customer mentions a new product on social media, the decision unit can provide the information. In addition, when the customer mentions troubleshooting on social media, the decision unit can provide the information. Furthermore, when the customer mentions a campaign on social media, the decision unit can provide the information. This makes it possible to propose a means of customer service based on social media activity. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can input the social media data of the customer to the generation AI, which can then propose a means of customer service.

[0057] The execution unit can estimate the customer's emotions and adjust the method of performing customer service based on the estimated customer's emotions. For example, when the customer is relaxed, the execution unit can provide customer service at a slow pace. Also, when the customer is nervous, the execution unit can provide customer service that gives a sense of security. Furthermore, when the customer is in a hurry, the execution unit can provide customer service that responds quickly. This makes it possible to adjust the method of performing customer service according to the customer's emotions. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input customer emotion data to a generation AI, which can adjust the method of performing customer service.

[0058] At the time of execution, the execution unit can select an appropriate execution method by referring to the customer's past customer service history. For example, the execution unit preferentially selects a customer service method that the customer has preferred in the past. It can also eliminate a customer service method that the customer has avoided in the past. Furthermore, it can select an optimal execution method from the customer's past customer service history. In this way, it is possible to select an optimal execution method based on the past customer service history. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the customer's past customer service history data to the generation AI, which can select the optimal execution method.

[0059] The execution unit can customize the means of customer service based on the customer's current situation at the time of execution. For example, if the customer is relaxed, the execution unit can provide a detailed explanation. Also, if the customer is nervous, the execution unit can provide a brief explanation. Furthermore, if the customer is in a hurry, the execution unit can respond quickly. This makes it possible to customize the means of customer service according to the customer's current situation. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the customer's current situation data to the generation AI, which can customize the means of customer service.

[0060] The execution unit can estimate the customer's emotions and adjust the order of customer service execution based on the estimated customer's emotions. For example, if the customer is relaxed, the execution unit can prioritize detailed explanations. Also, if the customer is nervous, the execution unit can prioritize customer service that gives a sense of security. Furthermore, if the customer is in a hurry, the execution unit can prioritize quick responses. This makes it possible to adjust the order of customer service execution according to the customer's emotions. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input customer emotion data to a generation AI, which can then adjust the order of customer service execution.

[0061] At the time of execution, the execution unit can select an appropriate execution method in consideration of the geographical location information of the customer. For example, when the customer is in a specific area, the execution unit can provide information related to the area. Also, when the customer is traveling, the execution unit can provide information related to the travel destination. Furthermore, when the customer is at home, the execution unit can provide information around the home. This makes it possible to select an optimal execution method based on the geographical location information. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the geographical location data of the customer to the generation AI, which can select the optimal execution method.

[0062] During execution, the execution unit can analyze the social media activity of the customer and suggest ways to serve the customer. For example, when the customer mentions a new product on social media, the execution unit can provide the information. In addition, when the customer mentions troubleshooting on social media, the execution unit can provide the information. Furthermore, when the customer mentions a campaign on social media, the execution unit can provide the information. This makes it possible to suggest ways to serve the customer based on social media activity. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the social media data of the customer to the generation AI, which can then suggest ways to serve the customer.

[0063] The reception unit can estimate the customer's emotions and adjust the method of receiving the request and budget information based on the estimated customer's emotions. For example, the reception unit can provide detailed input options when the customer is relaxed. Also, the reception unit can provide concise input options when the customer is nervous. Furthermore, the reception unit can provide an option that allows quick input when the customer is in a hurry. This makes it possible to adjust the method of receiving the request and budget information according to the customer's emotions. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's emotion data to the generation AI, and the generation AI can adjust the method of receiving the request and budget information.

[0064] The reception unit can select an appropriate reception method by referring to the history of the customer's past requests and budgets at the time of reception. For example, the reception unit can automatically display requests and budgets input by the customer in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) used by the customer in the past. Furthermore, the reception unit can predict and suggest requests and budgets to be used in a specific time period from the history of the customer's past requests and budgets. This makes it possible to select an optimal reception method based on the history of the past requests and budgets. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the history data of the customer's past requests and budgets into a generation AI, which can select an optimal reception method.

[0065] The reception unit can estimate the customer's emotions and determine the priority of the information on requests and budgets based on the estimated customer's emotions. For example, when the customer is relaxed, the reception unit can preferentially receive detailed requests and budgets. Also, when the customer is nervous, the reception unit can preferentially receive brief requests and budgets. Furthermore, when the customer is in a hurry, the reception unit can preferentially receive requests and budgets that can be quickly received. This makes it possible to determine the priority of the information on requests and budgets according to the customer's emotions. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the customer's emotion data to the generation AI, which can then determine the priority of the information on requests and budgets.

[0066] The reception unit can select an appropriate reception method in consideration of the geographical location information of the customer at the time of reception. For example, when the customer is in a specific area, the reception unit can preferentially receive requests and budgets related to that area. In addition, when the customer is traveling, the reception unit can preferentially receive requests and budgets related to the travel destination. Furthermore, when the customer is at home, the reception unit can preferentially receive requests and budgets around the home. This makes it possible to select an optimal reception method based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location data of the customer to the generation AI, which can select the optimal reception method.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The acquisition unit acquires the customer's purchase history, and the estimation unit can estimate the customer's purchasing tendency based on the acquired purchase history. For example, the acquisition unit acquires data on products and services purchased in the past by the customer. The acquisition unit can also acquire data on products and services frequently purchased by the customer. Furthermore, the acquisition unit can acquire data on products and services purchased by the customer during specific seasons or events. The estimation unit can estimate what products and services the customer prefers based on the acquired purchase history. This makes it possible to make suggestions based on the customer's purchasing tendency.

[0069] The acquisition unit acquires the customer's purchase history, and the estimation unit can estimate the customer's purchasing tendency based on the acquired purchase history. For example, the acquisition unit acquires data on products and services purchased in the past by the customer. The acquisition unit can also acquire data on products and services frequently purchased by the customer. Furthermore, the acquisition unit can acquire data on products and services purchased by the customer during specific seasons or events. The estimation unit can estimate what products and services the customer prefers based on the acquired purchase history. This makes it possible to make suggestions based on the customer's purchasing tendency.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information includes the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. The acquisition unit photographs the customer's facial expression using a camera to acquire the facial expression data. It can also record the customer's voice using a microphone to acquire the voice data. Furthermore, it can measure the customer's heart rate and blood pressure using a sensor to acquire this data. Step 2: The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the acquired information using a generation AI and estimates the customer's emotion. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The estimation unit analyzes facial expression data, voice data, heart rate data, and blood pressure data to estimate the customer's emotion. Step 3: The determination unit determines how to serve the customer according to the customer's emotion estimated by the estimation unit. If the customer is relaxed, the determination unit determines to provide an explanation at a slow pace, and if the customer is in a hurry, the determination unit determines to respond quickly. Furthermore, the determination unit determines a plan and a terminal to propose according to the customer's emotion. Step 4: The execution unit executes the customer service determined by the determination unit. The execution unit can explain things to the customer at a leisurely pace or can respond quickly. Furthermore, the execution unit can explain the plans and devices to be proposed to the customer. For example, the execution unit can explain the functions and benefits of a new smartphone to the customer.

[0072] (Example 2) The customer service support system according to the embodiment of the present invention is a system that uses an agent having an emotion engine to support customer service at a mobile phone shop. This customer service support system acquires information for estimating the customer's emotion, estimates the customer's emotion based on the acquired information, determines customer service according to the estimated emotion, and executes the determined customer service. For example, the customer service support system acquires information such as the customer's facial expression, movement, voice, heart rate, blood pressure, pupil reaction, brain wave, body temperature, breathing, and posted content. Next, based on the acquired information, the customer service support system estimates the customer's emotion. Depending on the estimated emotion, the customer service support system determines appropriate customer service. For example, if the customer is relaxed, the explanation is given at a slow pace, and if the customer is in a hurry, the customer is quickly responded to. Finally, the customer service support system executes the determined customer service. Furthermore, the customer service support system can accept information on the customer's request and budget. For example, the customer can input the type of terminal and the budget that he or she desires. Based on this, the customer service support system proposes a plan or terminal according to the customer's emotion and request. The customer service support system can also provide information on new products and campaigns. For example, the functions and benefits of a new smartphone can be explained. The customer service support system can also provide customer service in a manner according to the customer's emotion. For example, the timing of customer service, the manner of dialogue, the method of customer service, etc. can be adjusted. This allows the customer to have an optimal customer service experience. This allows the customer service support system to provide appropriate customer service based on the customer's emotions.

[0073] The customer service support system according to the embodiment includes an acquisition unit, an estimation unit, a decision unit, and an execution unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information includes, for example, the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content, but is not limited to such examples. The acquisition unit, for example, photographs the customer's facial expression using a camera and acquires facial expression data. The acquisition unit can also record the customer's voice using a microphone and acquire voice data. Furthermore, the acquisition unit can measure the customer's heart rate and blood pressure using a sensor and acquire these data. For example, the acquisition unit inputs facial expression data photographed by a camera to a generation AI, and the generation AI analyzes the facial expression data to estimate the emotion. The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit uses, for example, the generation AI to analyze the acquired information and estimate the customer's emotion. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. In the estimation unit, for example, the generation AI analyzes facial expression data and estimates the customer's emotions. In addition, the estimation unit can also analyze voice data and estimate the customer's emotions. In addition, the estimation unit can analyze heart rate data and blood pressure data and estimate the customer's emotions. The determination unit determines customer service according to the customer's emotions estimated by the estimation unit. For example, if the customer is relaxed, the determination unit determines to provide an explanation at a slow pace. In addition, if the customer is in a hurry, the determination unit can also determine to respond quickly. In addition, the determination unit can determine a plan and a terminal to be proposed according to the customer's emotions. For example, if the customer is relaxed, the determination unit determines to provide a detailed explanation. The execution unit executes the customer service determined by the determination unit. For example, the execution unit provides an explanation to the customer at a slow pace. In addition, the execution unit can respond quickly. In addition, the execution unit can explain the plan and the terminal to be proposed to the customer. For example, the execution unit explains the functions and benefits of a new smartphone to the customer. In this way, the customer service support system according to the embodiment can provide appropriate customer service based on the customer's emotions.

[0074] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. The emotion estimation information includes, for example, the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content, but is not limited to such examples. For example, the acquisition unit uses a camera to capture the facial expression of the customer and acquires facial expression data. The camera has high resolution and can capture even subtle changes in facial expression. The acquisition unit can also record the customer's voice using a microphone and acquire voice data. The voice data includes the tone, speed, and volume of the voice, and important information for estimating the emotion can be obtained from these elements. Furthermore, the acquisition unit can measure the customer's heart rate and blood pressure using a sensor and acquire these data. Fluctuations in heart rate and blood pressure are indicators of stress and relaxation state. For example, the acquisition unit inputs facial expression data captured by a camera to the generation AI, which analyzes the facial expression data to estimate the emotion. The generation AI has learned a large amount of facial expression data in advance and can estimate the emotion from subtle changes in facial expression with high accuracy. This enables the acquisition unit to efficiently collect a variety of emotion estimation information for customers and provide basic data for emotion estimation.

[0075] The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses a generation AI to analyze the acquired information and estimate the emotion of the customer. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI analyzes facial expression data and estimates the emotion of the customer. The generation AI can capture subtle changes in facial expressions and classify emotions such as joy, anger, sadness, and surprise with high accuracy. The estimation unit can also analyze voice data and estimate the emotion of the customer. Voice recognition technology and emotion recognition technology are used to analyze the voice data, and emotions are estimated from changes in tone, speed, and volume of the voice. Furthermore, the estimation unit can analyze heart rate data and blood pressure data to estimate the emotion of the customer. Fluctuations in heart rate and blood pressure are indicators of stress and relaxation, and by analyzing these data, the emotional state of the customer can be grasped more accurately. The estimation unit analyzes these various data in an integrated manner and comprehensively evaluates the emotion of the customer. For example, more accurate emotion estimation is possible by combining and analyzing facial expression data and voice data. This allows the estimation unit to estimate the customer's emotion with high accuracy and provide important information for determining the next step, which is the content of customer service.

[0076] The determination unit determines the customer service according to the customer's emotion estimated by the estimation unit. For example, if the customer is relaxed, the determination unit determines to provide an explanation at a slow pace. For a relaxed customer, a detailed explanation and additional information can be provided to encourage deeper understanding. In addition, if the customer is in a hurry, it can also determine to respond quickly. For a customer in a hurry, efficient customer service can be provided by simply conveying the main points. Furthermore, the determination unit can also determine a plan and a terminal to be proposed according to the customer's emotion. For example, if the customer is relaxed, it determines to provide a detailed explanation. For a relaxed customer, it is possible to support the optimal selection by explaining the details of multiple plans and terminals. On the other hand, if the customer is stressed, it is determined to provide a simple and easy-to-understand proposal. As a result, the determination unit can determine the optimal customer service content according to the customer's emotion and improve customer satisfaction. Furthermore, the determination unit can continuously improve the accuracy of the customer service content by utilizing past customer service data and customer feedback. As a result, the determination unit can always provide optimal customer service based on the latest information and meet the needs of the customer.

[0077] The execution unit executes the customer service determined by the determination unit. For example, the execution unit provides explanations to the customer at a slow pace. For relaxed customers, detailed explanations and additional information can be provided to deepen understanding. The execution unit can also respond quickly. For customers who are in a hurry, the main points can be conveyed concisely to provide efficient customer service. Furthermore, the execution unit can also explain the plan and terminal to be proposed to the customer. For example, the execution unit explains the functions and benefits of a new smartphone to the customer. By explaining the functions and benefits of a new smartphone in detail, it is possible to attract the customer's interest and increase their desire to purchase. This allows the execution unit to execute the customer service content determined by the determination unit and improve customer satisfaction. Furthermore, the execution unit can monitor the customer's reaction in real time and adjust the customer service content as necessary. For example, if the customer shows interest, more detailed information can be provided, and conversely, if the customer does not show interest, another proposal can be made. This allows the execution unit to flexibly respond to the needs of the customer and provide optimal customer service.

[0078] The execution unit can make a proposal, a procedure, or support according to the customer's emotions. The execution unit makes an appropriate proposal, for example, according to the customer's emotions. For example, when the customer is relaxed, a detailed proposal can be made. Also, when the customer is in a hurry, a proposal can be made quickly. Furthermore, the execution unit can perform a procedure according to the customer's emotions. For example, when the customer is relaxed, a procedure can be performed at a slow pace. Also, when the customer is in a hurry, a procedure can be performed quickly. Furthermore, the execution unit can provide support according to the customer's emotions. For example, when the customer is relaxed, detailed support can be provided. Also, when the customer is in a hurry, support can be provided quickly. This makes it possible to respond according to the customer's emotions. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit inputs customer emotion data into the generation AI, and the generation AI can execute a proposal, procedure, or support according to the customer's emotions.

[0079] The reception unit can receive information on the customer's requests and budget, and the determination unit can propose a plan or terminal according to the customer's emotions and the information received by the reception unit. The reception unit can, for example, input the type of terminal and budget desired by the customer. For example, the reception unit can input the model of the smartphone desired by the customer and the budget. The reception unit can also input the rate plan and service desired by the customer. The determination unit proposes a plan or terminal according to the customer's emotions and requests based on the information received by the reception unit. For example, the determination unit can propose a detailed plan or terminal when the customer is relaxed. Also, the determination unit can make a proposal quickly when the customer is in a hurry. Furthermore, the determination unit can adjust the proposed plan or terminal according to the customer's emotions. For example, the determination unit determines to provide a detailed explanation when the customer is relaxed. This makes it possible to make a proposal according to the customer's requests and budget. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input information on the customer's requests and budget to the generation AI, and the generation AI can execute a proposal of a plan or terminal according to the customer's emotions.

[0080] The determination unit determines the manner of customer service according to the customer's emotions, and the execution unit can perform the service according to the customer's emotions. For example, when the customer is relaxed, the determination unit determines to perform the service at a slow pace. Also, when the customer is in a hurry, the determination unit can determine to respond quickly. Furthermore, the determination unit can adjust the manner of customer service according to the customer's emotions. For example, when the customer is relaxed, the determination unit determines to provide a detailed explanation. The execution unit performs the customer service determined by the determination unit. For example, the execution unit provides an explanation to the customer at a slow pace. Also, the execution unit can respond quickly. Furthermore, the execution unit can explain the plan and terminal to be proposed to the customer. For example, the execution unit explains the functions and benefits of a new smartphone to the customer. This makes it possible to provide customer service in a manner according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit and the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit inputs customer emotion data to the generation AI, and the generation AI can determine the manner of customer service according to the customer's emotions. In addition, the execution unit can execute the manner of customer service determined by the generation AI.

[0081] The determination unit can determine the manner of customer service according to the customer's emotions. For example, when the customer is relaxed, the determination unit determines to provide customer service at a slow pace. Also, when the customer is in a hurry, the determination unit can determine to respond quickly. Furthermore, the determination unit can adjust the manner of customer service according to the customer's emotions. For example, when the customer is relaxed, the determination unit determines to provide a detailed explanation. This makes it possible to determine the manner of customer service according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data to a generation AI, and the generation AI can determine the manner of customer service according to the customer's emotions.

[0082] The acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content. For example, the acquisition unit captures the customer's facial expression using a camera to acquire facial expression data. The acquisition unit can also record the customer's voice using a microphone to acquire voice data. Furthermore, the acquisition unit can measure the customer's heart rate and blood pressure using a sensor to acquire these data. For example, the acquisition unit inputs facial expression data captured by a camera to a generation AI, which analyzes the facial expression data to estimate emotions. This improves the accuracy of emotion estimation by acquiring various information. Part or all of the above-mentioned processing in the acquisition unit may be performed using an AI, for example, or may be performed without using an AI. For example, the acquisition unit inputs facial expression data of the customer to a generation AI, which analyzes the facial expression data to estimate emotions.

[0083] 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 relaxed, the acquisition unit periodically acquires the emotion estimation information. Also, when the customer is nervous, the acquisition unit can frequently acquire the emotion estimation information. Furthermore, when the customer is in a hurry, the emotion estimation information can be acquired quickly. This makes it possible to acquire information at an appropriate timing according to the emotion of the customer. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the emotion data of the customer to the generation AI, and the generation AI can adjust the timing of acquiring the emotion estimation information.

[0084] The acquisition unit can analyze the past emotion data of the customer and select the optimal acquisition method. For example, if the customer was relaxed in the past, the acquisition unit can acquire emotion estimation information in a similar situation. Also, if the customer was nervous in the past, the acquisition unit can acquire emotion estimation information in a similar situation. Furthermore, if the customer was in a hurry in the past, the acquisition unit can acquire emotion estimation information in a similar situation. This makes it possible to acquire optimal information based on past emotion data. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the past emotion data of the customer to the generation AI, which can select the optimal acquisition method.

[0085] The acquisition unit can perform filtering based on the customer's current situation and field of interest when acquiring emotion estimation information. For example, if the customer is interested in a new product, the acquisition unit can preferentially acquire that information. In addition, if the customer is requesting troubleshooting, the acquisition unit can also preferentially acquire that information. Furthermore, if the customer is requesting campaign information, the acquisition unit can also preferentially acquire that information. This makes it possible to acquire information according to the customer's situation and interests. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the customer's field of interest data to the generation AI, and the generation AI can perform filtering.

[0086] 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 the customer is relaxed, the acquisition unit can prioritize acquiring detailed information. Also, when the customer is nervous, the acquisition unit can prioritize acquiring concise information. Furthermore, when the customer is in a hurry, the acquisition unit can prioritize acquiring information that can be acquired quickly. This makes it possible to determine the priority of information according to the customer's emotions. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input customer emotion data to a generation AI, which can then determine the priority of information.

[0087] 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 specific area, the acquisition unit preferentially acquires information related to the area. In addition, when the customer is traveling, the acquisition unit can preferentially acquire information related to the travel destination. Furthermore, when the customer is at home, the acquisition unit can preferentially acquire information around the home. This makes it possible to acquire highly relevant information based on the geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the geographical location data of the customer to the generation AI, which can then preferentially acquire highly relevant information.

[0088] The acquisition unit can analyze the social media activity of the customer and acquire related information when acquiring the emotion estimation information. For example, when the customer mentions a new product on social media, the acquisition unit can acquire the information with priority. In addition, when the customer mentions troubleshooting on social media, the acquisition unit can also acquire the information with priority. Furthermore, when the customer mentions a campaign on social media, the acquisition unit can also acquire the information with priority. In this way, related information based on social media activity can be acquired. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the social media data of the customer to the generation AI, and the generation AI can acquire the related information.

[0089] The estimation unit can estimate the customer's emotion and adjust the emotion estimation algorithm based on the estimated customer's emotion. For example, when the customer is relaxed, the estimation unit can gently adjust the emotion estimation algorithm. Also, when the customer is nervous, the estimation unit can sensitively adjust the emotion estimation algorithm. Furthermore, when the customer is in a hurry, the emotion estimation algorithm can be quickly adjusted. This enables the algorithm to be adjusted according to the customer's emotion. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the customer's emotion data to the generation AI, and the generation AI can adjust the emotion estimation algorithm.

[0090] 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. The estimation unit can perform detailed emotion estimation based on information of high importance, for example. It can also perform simple emotion estimation based on information of low importance. It can also perform emotion estimation with a moderate level of detail based on information of medium importance. This enables detailed estimation according to the importance of the information. A part or all of the above-mentioned processing in the estimation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the estimation unit can input emotion estimation information to a generation AI, and the generation AI can adjust the level of detail of the estimation based on the importance of the information.

[0091] The estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information at the time of estimation. The estimation unit can apply an algorithm for estimating an emotion based on facial expression information, for example. It can also apply an algorithm for estimating an emotion based on voice information. It can also apply an algorithm for estimating an emotion based on motion information. This enables appropriate estimation according to the category of information. A part or all of the above-mentioned processing in the estimation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the estimation unit can input emotion estimation information to a generation AI, and the generation AI can apply different estimation algorithms depending on the category of information.

[0092] 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, when the customer is relaxed, the estimation unit can display a detailed estimation result. Also, when the customer is nervous, the estimation unit can display a concise estimation result. Furthermore, when the customer is in a hurry, the estimation unit can display an estimation result that can be displayed quickly. This makes it possible to adjust the display method according to the customer's emotions. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input customer emotion data to a generation AI, and the generation AI can adjust the display method of the estimation result.

[0093] The estimation unit can determine the priority order of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation unit preferentially estimates the latest emotion estimation information. Also, the estimation unit can make an estimation by referring to past emotion estimation information. Furthermore, the emotion estimation information acquired in a specific time period can be preferentially estimated. This makes it possible to determine the priority order based on the acquisition time. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the emotion estimation information to a generation AI, and the generation AI can determine the priority order of estimation based on the acquisition time.

[0094] 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 unit preferentially estimates information with high relevance. Also, the estimation unit can estimate information with medium relevance next. Furthermore, the estimation unit can estimate information with low relevance last. This makes it possible to adjust the estimation order based on the relevance of the information. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input emotion estimation information to a generation AI, and the generation AI can adjust the order of estimation based on the relevance of the information.

[0095] The determination unit can estimate the customer's emotions and adjust the customer service decision method based on the estimated customer's emotions. For example, when the customer is relaxed, the determination unit can determine to provide customer service at a slow pace. Also, when the customer is nervous, the determination unit can determine to provide customer service that gives a sense of security. Furthermore, when the customer is in a hurry, the determination unit can determine to provide customer service that responds quickly. This makes it possible to adjust the customer service decision method according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data to a generation AI, which can adjust the customer service decision method.

[0096] At the time of making a decision, the determination unit can select an appropriate service method by analyzing the customer's past customer service history. For example, the determination unit preferentially selects a service method that the customer has preferred in the past. The determination unit can also eliminate a service method that the customer has avoided in the past. Furthermore, the determination unit can select an optimal service method from the customer's past customer service history. This makes it possible to select an optimal service method based on the past customer service history. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the customer's past customer service history data to the generation AI, which can select the optimal service method.

[0097] The determination unit can customize the means of customer service based on the current situation of the customer at the time of the determination. For example, if the customer is relaxed, the determination unit can determine to provide a detailed explanation. Also, if the customer is nervous, the determination unit can determine to provide a brief explanation. Furthermore, if the customer is in a hurry, the determination unit can determine to respond quickly. This makes it possible to customize the means of customer service according to the current situation of the customer. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the current situation data of the customer to the generation AI, and the generation AI can customize the means of customer service.

[0098] The determination unit can estimate the customer's emotions and determine the priority of customer service based on the estimated customer's emotions. For example, when the customer is relaxed, the determination unit prioritizes detailed explanations. Also, when the customer is nervous, the determination unit can prioritize customer service that gives a sense of security. Furthermore, when the customer is in a hurry, the determination unit can prioritize quick responses. This makes it possible to determine the priority of customer service according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input customer emotion data to a generation AI, which can then determine the priority of customer service.

[0099] The determination unit can select an appropriate customer service method in consideration of the geographical location information of the customer at the time of determination. For example, when the customer is in a specific area, the determination unit provides information related to the area. In addition, when the customer is traveling, the determination unit can also provide information related to the travel destination. Furthermore, when the customer is at home, the determination unit can also provide information about the area around the customer's home. This makes it possible to select an optimal customer service method based on the geographical location information. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit inputs the geographical location data of the customer to the generation AI, which can select the optimal customer service method.

[0100] At the time of decision, the decision unit can analyze the social media activity of the customer and propose a means of customer service. For example, when the customer mentions a new product on social media, the decision unit can provide the information. In addition, when the customer mentions troubleshooting on social media, the decision unit can provide the information. Furthermore, when the customer mentions a campaign on social media, the decision unit can provide the information. This makes it possible to propose a means of customer service based on social media activity. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can input the social media data of the customer to the generation AI, which can then propose a means of customer service.

[0101] The execution unit can estimate the customer's emotions and adjust the method of performing customer service based on the estimated customer's emotions. For example, when the customer is relaxed, the execution unit can provide customer service at a slow pace. Also, when the customer is nervous, the execution unit can provide customer service that gives a sense of security. Furthermore, when the customer is in a hurry, the execution unit can provide customer service that responds quickly. This makes it possible to adjust the method of performing customer service according to the customer's emotions. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input customer emotion data to a generation AI, which can adjust the method of performing customer service.

[0102] At the time of execution, the execution unit can select an appropriate execution method by referring to the customer's past customer service history. For example, the execution unit preferentially selects a customer service method that the customer has preferred in the past. It can also eliminate a customer service method that the customer has avoided in the past. Furthermore, it can select an optimal execution method from the customer's past customer service history. In this way, it is possible to select an optimal execution method based on the past customer service history. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the customer's past customer service history data to the generation AI, which can select the optimal execution method.

[0103] The execution unit can customize the means of customer service based on the customer's current situation at the time of execution. For example, if the customer is relaxed, the execution unit can provide a detailed explanation. Also, if the customer is nervous, the execution unit can provide a brief explanation. Furthermore, if the customer is in a hurry, the execution unit can respond quickly. This makes it possible to customize the means of customer service according to the customer's current situation. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the customer's current situation data to the generation AI, which can customize the means of customer service.

[0104] The execution unit can estimate the customer's emotions and adjust the order of customer service execution based on the estimated customer's emotions. For example, if the customer is relaxed, the execution unit can prioritize detailed explanations. Also, if the customer is nervous, the execution unit can prioritize customer service that gives a sense of security. Furthermore, if the customer is in a hurry, the execution unit can prioritize quick responses. This makes it possible to adjust the order of customer service execution according to the customer's emotions. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input customer emotion data to a generation AI, which can then adjust the order of customer service execution.

[0105] At the time of execution, the execution unit can select an appropriate execution method in consideration of the geographical location information of the customer. For example, when the customer is in a specific area, the execution unit can provide information related to the area. Also, when the customer is traveling, the execution unit can provide information related to the travel destination. Furthermore, when the customer is at home, the execution unit can provide information around the home. This makes it possible to select an optimal execution method based on the geographical location information. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the geographical location data of the customer to the generation AI, which can select the optimal execution method.

[0106] During execution, the execution unit can analyze the social media activity of the customer and suggest ways to serve the customer. For example, when the customer mentions a new product on social media, the execution unit can provide the information. In addition, when the customer mentions troubleshooting on social media, the execution unit can provide the information. Furthermore, when the customer mentions a campaign on social media, the execution unit can provide the information. This makes it possible to suggest ways to serve the customer based on social media activity. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the social media data of the customer to the generation AI, which can then suggest ways to serve the customer.

[0107] The reception unit can estimate the customer's emotions and adjust the method of receiving the request and budget information based on the estimated customer's emotions. For example, the reception unit can provide detailed input options when the customer is relaxed. Also, the reception unit can provide concise input options when the customer is nervous. Furthermore, the reception unit can provide an option that allows quick input when the customer is in a hurry. This makes it possible to adjust the method of receiving the request and budget information according to the customer's emotions. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's emotion data to the generation AI, and the generation AI can adjust the method of receiving the request and budget information.

[0108] The reception unit can select an appropriate reception method by referring to the history of the customer's past requests and budgets at the time of reception. For example, the reception unit can automatically display requests and budgets input by the customer in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) used by the customer in the past. Furthermore, the reception unit can predict and suggest requests and budgets to be used in a specific time period from the history of the customer's past requests and budgets. This makes it possible to select an optimal reception method based on the history of the past requests and budgets. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the history data of the customer's past requests and budgets into a generation AI, which can select an optimal reception method.

[0109] The reception unit can estimate the customer's emotions and determine the priority of the information on requests and budgets based on the estimated customer's emotions. For example, when the customer is relaxed, the reception unit can preferentially receive detailed requests and budgets. Also, when the customer is nervous, the reception unit can preferentially receive brief requests and budgets. Furthermore, when the customer is in a hurry, the reception unit can preferentially receive requests and budgets that can be quickly received. This makes it possible to determine the priority of the information on requests and budgets according to the customer's emotions. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the customer's emotion data to the generation AI, which can then determine the priority of the information on requests and budgets.

[0110] The reception unit can select an appropriate reception method in consideration of the geographical location information of the customer at the time of reception. For example, when the customer is in a specific area, the reception unit can preferentially receive requests and budgets related to that area. In addition, when the customer is traveling, the reception unit can preferentially receive requests and budgets related to the travel destination. Furthermore, when the customer is at home, the reception unit can preferentially receive requests and budgets around the home. This makes it possible to select an optimal reception method based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location data of the customer to the generation AI, which can select the optimal reception method.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The acquisition unit acquires the customer's purchase history, and the estimation unit can estimate the customer's purchasing tendency based on the acquired purchase history. For example, the acquisition unit acquires data on products and services purchased in the past by the customer. The acquisition unit can also acquire data on products and services frequently purchased by the customer. Furthermore, the acquisition unit can acquire data on products and services purchased by the customer during specific seasons or events. The estimation unit can estimate what products and services the customer prefers based on the acquired purchase history. This makes it possible to make suggestions based on the customer's purchasing tendency.

[0113] The estimation unit can estimate the customer's emotions and predict new products and services that the customer is likely to be interested in based on the estimated customer's emotions. For example, the estimation unit can suggest new relaxation products when the customer is relaxed. Also, the estimation unit can suggest new entertainment services when the customer is excited. Furthermore, the estimation unit can suggest new learning materials when the customer is calm. This makes it possible to suggest new products and services according to the customer's emotions.

[0114] The determination unit can learn the customer service style that the customer prefers based on the customer's emotions and reflect that in the next service. For example, if the customer is relaxed, the determination unit can select a service style that will allow the customer to be relaxed the next time. If the customer is in a hurry, the determination unit can also select a service style that will allow the customer to respond quickly the next time. Furthermore, if the customer is nervous, the determination unit can also select a service style that gives the customer a sense of security the next time. This makes it possible to learn and reflect service styles based on the customer's emotions.

[0115] The executive department can select a communication channel preferred by the customer based on the customer's emotions. For example, if the customer is relaxed, communication can be carried out via email or chat. If the customer is in a hurry, communication can be carried out quickly via telephone or video call. Furthermore, if the customer is nervous, face-to-face communication can be selected. In this way, the optimal communication channel can be selected according to the customer's emotions.

[0116] The reception unit can provide an interface that the customer prefers based on the customer's emotions. For example, if the customer is relaxed, detailed input options can be provided. If the customer is in a hurry, simple input options can be provided. Furthermore, if the customer is nervous, an intuitive and easy-to-use interface can be provided. In this way, an optimal interface can be provided according to the customer's emotions.

[0117] The acquisition unit acquires the customer's purchase history, and the estimation unit can estimate the customer's purchasing tendency based on the acquired purchase history. For example, the acquisition unit acquires data on products and services purchased in the past by the customer. The acquisition unit can also acquire data on products and services frequently purchased by the customer. Furthermore, the acquisition unit can acquire data on products and services purchased by the customer during specific seasons or events. The estimation unit can estimate what products and services the customer prefers based on the acquired purchase history. This makes it possible to make suggestions based on the customer's purchasing tendency.

[0118] The estimation unit can estimate the customer's emotions and predict new products and services that the customer is likely to be interested in based on the estimated customer's emotions. For example, the estimation unit can suggest new relaxation products when the customer is relaxed. Also, the estimation unit can suggest new entertainment services when the customer is excited. Furthermore, the estimation unit can suggest new learning materials when the customer is calm. This makes it possible to suggest new products and services according to the customer's emotions.

[0119] The determination unit can learn the customer service style that the customer prefers based on the customer's emotions and reflect that in the next service. For example, if the customer is relaxed, the determination unit can select a service style that will allow the customer to be relaxed the next time. If the customer is in a hurry, the determination unit can also select a service style that will allow the customer to respond quickly the next time. Furthermore, if the customer is nervous, the determination unit can also select a service style that gives the customer a sense of security the next time. This makes it possible to learn and reflect service styles based on the customer's emotions.

[0120] The executive department can select a communication channel preferred by the customer based on the customer's emotions. For example, if the customer is relaxed, communication can be carried out via email or chat. If the customer is in a hurry, communication can be carried out quickly via telephone or video call. Furthermore, if the customer is nervous, face-to-face communication can be selected. In this way, the optimal communication channel can be selected according to the customer's emotions.

[0121] The reception unit can provide an interface that the customer prefers based on the customer's emotions. For example, if the customer is relaxed, detailed input options can be provided. If the customer is in a hurry, simple input options can be provided. Furthermore, if the customer is nervous, an intuitive and easy-to-use interface can be provided. In this way, an optimal interface can be provided according to the customer's emotions.

[0122] The process flow of the second embodiment will be briefly described below.

[0123] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information includes the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. The acquisition unit photographs the customer's facial expression using a camera to acquire the facial expression data. It can also record the customer's voice using a microphone to acquire the voice data. Furthermore, it can measure the customer's heart rate and blood pressure using a sensor to acquire this data. Step 2: The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the acquired information using a generation AI and estimates the customer's emotion. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The estimation unit analyzes facial expression data, voice data, heart rate data, and blood pressure data to estimate the customer's emotion. Step 3: The determination unit determines how to serve the customer according to the customer's emotion estimated by the estimation unit. If the customer is relaxed, the determination unit determines to provide an explanation at a slow pace, and if the customer is in a hurry, the determination unit determines to respond quickly. Furthermore, the determination unit determines a plan and a terminal to propose according to the customer's emotion. Step 4: The execution unit executes the customer service determined by the determination unit. The execution unit can explain things to the customer at a leisurely pace or can respond quickly. Furthermore, the execution unit can explain the plans and devices to be proposed to the customer. For example, the execution unit can explain the functions and benefits of a new smartphone to the customer.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] For example, the acquisition unit can acquire the facial expression and voice of the customer using the camera 42 and microphone 38B of the smart device 14. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the acquired information to estimate the customer's emotions. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines appropriate customer service based on the estimated emotions. The execution unit is realized by the control unit 46A of the smart device 14, and executes the determined customer service. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] For example, the acquisition unit can acquire the facial expression and voice of the customer using the camera 42 and microphone 238 of the smart glasses 214. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the acquired information to estimate the customer's emotions. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines appropriate customer service based on the estimated emotions. The execution unit is realized by the control unit 46A of the smart glasses 214, and executes the determined customer service. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

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

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

[0154] In the 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.

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

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0158] The data processing system 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.

[0159] For example, the acquisition unit can acquire the facial expression and voice of the customer using the camera 42 and microphone 238 of the headset type terminal 314. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the acquired information to estimate the customer's emotions. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines appropriate customer service based on the estimated emotions. The execution unit is realized by the control unit 46A of the headset type terminal 314, and executes the determined customer service. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] For example, the acquisition unit can acquire the facial expression and voice of the customer using the camera 42 and microphone 238 of the robot 414. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the acquired information to estimate the customer's emotions. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines appropriate customer service based on the estimated emotions. The execution unit is realized by the control unit 46A of the robot 414, and executes the determined customer service. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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 experiences positive feelings such as "I want more" or "I want to know more."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (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 customer service according to the emotion of the customer estimated by the estimation unit; an execution unit that executes the customer service determined by the determination unit; A system characterized by: (Appendix 2) The execution unit is Offer suggestions, procedures, or support based on customer emotions 2. The system of claim 1, (Appendix 3) It has a reception desk that accepts customer requests and budget information, The determination unit is Proposing a plan or a terminal according to the emotion of the customer and the information received by the reception unit 2. The system of claim 1, (Appendix 4) The determination unit is Decide on a customer service style according to the customer's emotions, The execution unit is Serve the customer in a manner that reflects the customer's emotions 2. The system of claim 1, (Appendix 5) The determination unit is Decide on the style of customer service according to the customer's emotions 2. The system of claim 1, (Appendix 6) The acquisition unit is Acquire one or more pieces of information from the customer's facial expressions, movements, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, respiration, and posted content 2. The system of claim 1, (Appendix 7) The acquisition unit is Estimate customer sentiment, Adjusting the 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 customer's past sentiment data, Choose the appropriate acquisition method 2. The system of claim 1, (Appendix 9) The acquisition unit is When acquiring emotion estimation information, Filter based on your customers' current circumstances and interests 2. The system of claim 1, (Appendix 10) The acquisition unit is Estimate customer sentiment, Prioritize information to be retrieved based on estimated customer sentiment 2. The system of claim 1, (Appendix 11) The acquisition unit is When acquiring emotion estimation information, Prioritize relevant information based on customer geographic location 2. The system of claim 1, (Appendix 12) The acquisition unit is When acquiring emotion estimation information, Analyze your customers' social media activity and get relevant information 2. The system of claim 1, (Appendix 13) The estimation unit is Estimate customer sentiment, Adjust the sentiment estimation algorithm based on estimated customer sentiment 2. The system of claim 1, (Appendix 14) The estimation unit is When estimating, Adjusting the level of detail of emotion estimation based on the importance of information for emotion estimation 2. The system of claim 1, (Appendix 15) The estimation unit is When estimating, Apply different estimation algorithms depending on the category of emotion estimation information 2. The system of claim 1, (Appendix 16) The estimation unit is Estimate customer sentiment, Adjust how predictions are displayed based on predicted customer sentiment 2. The system of claim 1, (Appendix 17) The estimation unit is When estimating, Determine the priority of estimation based on the time when emotion estimation information was acquired 2. The system of claim 1, (Appendix 18) The estimation unit is When estimating, Adjusting the order of estimation based on the relevance of emotion estimation information 2. The system of claim 1, (Appendix 19) The determination unit is Estimate customer sentiment, Adjust customer service decisions based on estimated customer sentiment 2. The system of claim 1, (Appendix 20) The determination unit is At the time of decision, Analyze the customer's past customer service history to select the appropriate customer service method 2. The system of claim 1, (Appendix 21) The determination unit is At the time of decision, Customize interactions based on the customer's current situation 2. The system of claim 1, (Appendix 22) The determination unit is Estimate customer sentiment, Deciding priorities for customer service based on estimated customer sentiment 2. The system of claim 1, (Appendix 23) The determination unit is At the time of decision, Choose the right way to serve your customers by taking their geographic location into account 2. The system of claim 1, (Appendix 24) The determination unit is At the time of decision, Analyze customer social media activity to suggest ways to serve them 2. The system of claim 1, (Appendix 25) The execution unit is Estimate customer sentiment, Adjust customer service execution based on estimated customer sentiment 2. The system of claim 1, (Appendix 26) The execution unit is At runtime, Select the appropriate execution method by referring to the customer's past customer service history 2. The system of claim 1, (Appendix 27) The execution unit is At runtime, Customize interactions based on the customer's current situation 2. The system of claim 1, (Appendix 28) The execution unit is Estimate customer sentiment, Adjusting the order of customer service based on estimated customer sentiment 2. The system of claim 1, (Appendix 29) The execution unit is At runtime, Choose the right execution method considering the geographic location of the customer 2. The system of claim 1, (Appendix 30) The execution unit is At runtime, Analyze customer social media activity to suggest ways to serve them 2. The system of claim 1, (Appendix 31) The reception unit is Estimate customer sentiment, Adjust how you accept requests and budget information based on estimated customer sentiment 4. The system of claim 3. (Appendix 32) The reception unit is At the time of reception, Select the appropriate reception method by referring to the customer's past requests and budget history 4. The system of claim 3. (Appendix 33) The reception unit is Estimate customer sentiment, Prioritize requests and budget information based on estimated customer sentiment 4. The system of claim 3. (Appendix 34) The reception unit is At the time of reception, Choose the appropriate reception method based on the customer's geographic location 4. The system of claim 3. [Explanation of symbols]

[0196] 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 customer service according to the emotion of the customer estimated by the estimation unit; an execution unit that executes the customer service determined by the determination unit; A system characterized by:

2. The execution unit is Offer suggestions, procedures, or support according to the customer's feelings 2. The system of claim 1 .

3. a reception unit for receiving information on the customer's request and budget, The determination unit is Proposing a plan or a terminal according to the emotion of the customer and the information received by the reception unit 2. The system of claim 1 .

4. The determination unit is determining a manner of serving the customer in accordance with the customer's emotions; The execution unit is Serve the customer in a manner that reflects the customer's emotions 2. The system of claim 1 .

5. The determination unit is Deciding the manner of customer service according to the emotion of the customer 2. The system of claim 1 .

6. The acquisition unit is Acquire one or more pieces of information on the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, respiration, and posted content.

2. The system of claim 1 .

7. The acquisition unit is Estimating a sentiment of the customer; The timing of acquiring the emotion estimation information is adjusted based on the estimated emotion of the customer.

2. The system of claim 1 .

8. The acquisition unit is Analyzing past emotion data of said customers; Choose the appropriate acquisition method 2. The system of claim 1 .

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