System

Abstract

To provide a system that estimates the emotions of a customer and provides a response accordingly.SOLUTION: A system according to an embodiment comprises an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotions of a customer to be served. 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 response according to the emotions of the customer as estimated by the estimation unit. The response unit gives the response 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 was difficult to accurately read customer emotions and respond accordingly.

[0005] The system according to the embodiment aims to estimate the customer's emotions and respond accordingly. [Means for solving the problem]

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

[0007] The system according to the embodiment can estimate the customer's emotions and respond 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) A customer service support system according to an embodiment of the present invention is a system that provides a better customer service experience by reading the customer's emotions and taking appropriate measures. This customer service support system acquires emotion estimation information for estimating the customer's emotions, and estimates the customer's emotions based on the acquired emotion estimation information. An appropriate response is determined according to the estimated customer's emotions, and the response is executed. For example, the customer service support system can acquire information such as the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. These pieces of information may include AI processing. Next, the customer's emotions are estimated based on the acquired emotion estimation information. The AI ​​analyzes these pieces of information and estimates the customer's emotions, such as whether the customer is happy, sad, or angry. For example, it is possible to estimate whether the customer is happy from the customer's facial expression and tone of voice. An appropriate response is determined according to the estimated customer's emotions. For example, it is possible to determine responses such as comforting words when the customer is sad, rejoicing together with the customer when the customer is happy, and providing a topic to calm the customer when the customer is angry. These responses may include AI processing. Finally, the determined response is executed. For example, the agent can respond by speaking comforting words to the customer, sharing in the customer's joy, or offering a topic of conversation to calm the customer. The agent can also notify the person in charge of the decided response. This allows the agent to read the customer's emotions and respond appropriately, thereby providing a better customer service experience. For example, the agent can take the customer's order while checking the customer's mood, check the progress of the meal, and pay the bill. This allows the customer to have a more satisfying customer service experience. In this way, the customer service support system can provide a better customer service experience by reading the customer's emotions and responding appropriately.

[0029] The customer service support system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer to be served. The acquisition unit can acquire, for example, 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. These pieces of information may include AI processing. For example, the acquisition unit uses a camera to capture the customer's facial expression and acquires information for estimating the emotion using facial expression recognition technology. The acquisition unit can also use a microphone to record the customer's voice and acquire information for estimating the emotion using voice analysis technology. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire information for estimating the emotion. The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the acquired information and estimates the emotion of the customer, such as whether the customer is happy, sad, or angry. For example, the estimation unit uses facial expression recognition technology to estimate the emotion from the customer's facial expression. The estimation unit can also estimate the emotion from the tone of the customer's voice using a voice analysis technique. Furthermore, the estimation unit can also estimate the emotion from the fluctuation of the heart rate. The determination unit determines a response according to the emotion of the customer estimated by the estimation unit. The determination unit determines an appropriate response based on the emotion estimated by using, for example, AI. For example, the determination unit determines a response of speaking comforting words when the customer is sad. Furthermore, the determination unit can also determine a response of rejoicing together with the customer when the customer is happy. Furthermore, the determination unit can also determine a response of providing a topic to calm the customer when the customer is angry. The response unit executes the response determined by the determination unit. The response unit executes the response determined by using, for example, AI. For example, the response unit speaks comforting words to the customer. Furthermore, the response unit can rejoice together with the customer. Furthermore, the response unit can provide a topic to calm the customer. As a result, the customer service support system according to the embodiment can provide a better customer service experience by reading the customer's emotion and taking an appropriate response.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of a customer being served. The acquisition unit can acquire, for example, 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. These pieces of information may include AI processing. For example, the acquisition unit uses a camera to capture the facial expression of the customer and acquires information for estimating the emotion using facial expression recognition technology. Specifically, the camera has high resolution and can capture subtle changes in facial expression. The facial expression recognition technology uses a model using deep learning to classify emotions such as smile, anger, and sadness with high accuracy. The acquisition unit can also record the customer's voice using a microphone and acquire information for estimating the emotion using voice analysis technology. The voice analysis technology analyzes features such as tone, pitch, speed, and volume of the voice to detect changes in emotion. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire information for estimating the emotion. The heart rate sensor measures heart rate fluctuations in real time using photoplethysmography (PPG). This allows the acquisition unit to grasp the emotional state of the customer from multiple angles. Furthermore, the acquisition unit can centrally manage these data and transmit them to the analysis unit in real time. This allows the acquisition unit to provide basic data for estimating the customer's emotions with high accuracy, and improve the accuracy and reliability of the entire system.

[0031] The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the acquired information and estimates the customer's emotion, such as whether the customer is happy, sad, or angry. Specifically, the estimation unit estimates the emotion from the customer's facial expression using facial expression recognition technology. A model using deep learning can learn millions of facial expression images and classify subtle changes in facial expressions with high accuracy. The estimation unit can also estimate the emotion from the tone of the customer's voice using voice analysis technology. The voice analysis technology analyzes features such as the tone, pitch, speed, and volume of the voice to detect changes in emotion. Furthermore, the estimation unit can estimate the emotion from the fluctuation of the heart rate. Since the fluctuation of the heart rate reflects an emotional state such as stress or excitement, the emotion of the customer can be estimated by analyzing this. The estimation unit integrates these pieces of information and performs comprehensive emotion estimation. For example, by combining facial expression, voice, and heart rate data, more accurate emotion estimation is possible. In addition, the estimation unit can use past data and statistical information to analyze long-term emotional fluctuations and understand customer emotional patterns. This enables the estimation unit to handle not only real-time emotion estimation but also long-term emotion analysis, improving the accuracy and reliability of the entire system.

[0032] The determination unit determines a response according to the customer's emotions estimated by the estimation unit. The determination unit determines an appropriate response based on the estimated emotions, for example, using AI. Specifically, the determination unit determines a response of offering words of comfort when the customer is sad. For example, when the customer is sad, the AI ​​searches for appropriate words or phrases of comfort from a past database and selects the optimal one. The determination unit can also determine a response that makes the customer happy when the customer is happy. For example, when the customer is happy, the AI ​​suggests words or actions to share the customer's joy. Furthermore, the determination unit can also determine a response that provides a topic to calm the customer when the customer is angry. For example, when the customer is angry, the AI ​​searches for appropriate topics or phrases from a past database and selects the optimal one. In order to quickly and accurately determine these responses, the determination unit analyzes data in real time and selects the optimal response. Furthermore, the determination unit can learn from past response results and continuously improve the accuracy and effectiveness of the response. This allows the determination unit to quickly determine the optimal response according to the customer's emotions and improve the performance of the entire system.

[0033] The response unit executes the response determined by the determination unit. The response unit executes the response determined by, for example, using AI. Specifically, the response unit speaks words of comfort to the customer. For example, if the customer is sad, the AI ​​generates appropriate words of comfort and speaks them to the customer. The response unit can also rejoice with the customer. For example, if the customer is happy, the AI ​​generates words and actions to share the customer's joy and speaks them to the customer. Furthermore, the response unit can provide topics to calm the customer. For example, if the customer is angry, the AI ​​generates appropriate topics and phrases and speaks them to the customer. The response unit analyzes data in real time and selects the optimal response in order to quickly and accurately execute these responses. Furthermore, the response unit can learn from past response results and continuously improve the accuracy and effectiveness of the response. This allows the response unit to quickly execute the optimal response according to the customer's emotions and improve the performance of the entire system. Furthermore, the response unit can collect feedback from customers and continuously improve the accuracy and effectiveness of the response. For example, by recording how customers react to the services provided, the system can improve the accuracy and effectiveness of the services based on that data. This allows the service department to quickly implement the optimal service according to the customer's emotions, improving the performance of the entire system.

[0034] The determination unit determines a manner of customer service according to the customer's emotion as a response according to the customer's emotion, and the response unit can serve the customer in a manner according to the customer's emotion. For example, the determination unit determines a manner of speaking comforting words to the customer when the customer is sad. Furthermore, the determination unit can also determine a manner of rejoicing together with the customer when the customer is happy. Furthermore, the determination unit can also determine a manner of providing a topic to calm the customer when the customer is angry. The response unit serves the customer in the manner determined by the determination unit, for example. For example, the response unit speaks comforting words to the customer. Furthermore, the response unit can rejoice together with the customer. Furthermore, the response unit can also provide a topic to calm the customer. As a result, a better customer service experience can be provided by serving the customer in a manner according to the customer's emotion. A part or all of the above-mentioned processing in the determination unit and the response unit may be performed, for example, using AI, or may be performed without using AI.

[0035] The determination unit can determine a response according to the customer's emotions and attributes. The determination unit determines an appropriate response based on attributes such as the customer's age, sex, and purchase history. For example, the determination unit determines a response that uses casual language for younger customers. The determination unit can also determine a response that uses polite language for older customers. Furthermore, the determination unit can determine a response that provides topics related to products that the customer has previously purchased based on the customer's purchase history. This enables more personalized customer service by determining a response according to the customer's emotions and attributes. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI.

[0036] 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 as emotion estimation information. The acquisition unit can, for example, photograph the customer's facial expression using a camera and acquire information for estimating emotion using facial expression recognition technology. The acquisition unit can also record the customer's voice using a microphone and acquire information for estimating emotion using voice analysis technology. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire information for estimating emotion. In this way, by acquiring various information, the customer's emotion can be estimated more accurately. 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.

[0037] The response unit can notify the person in charge of the content of the response determined by the determination unit. The response unit, for example, notifies the person in charge of the content of the response determined by the determination unit. For example, the response unit notifies the person in charge that it has decided to respond by speaking comforting words when the customer is sad. The response unit can also notify the person in charge that it has decided to respond by sharing the joy with the customer when the customer is happy. Furthermore, the response unit can also notify the person in charge that it has decided to respond by providing a topic to calm the customer when the customer is angry. In this way, by notifying the person in charge of the content of the response, it is possible to confirm that an appropriate response is taken. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI.

[0038] 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 restaurant, the acquisition unit preferentially acquires information about the restaurant. Furthermore, when the customer is in a cafe, the acquisition unit can also preferentially acquire information about the cafe. Furthermore, when the customer is in a hotel, the acquisition unit can also preferentially acquire information about the hotel. In this way, more relevant information can be acquired by considering the geographical location information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI, or may be performed without using an AI.

[0039] The acquisition unit can analyze the social media activity of the customer and acquire related information when acquiring the emotion estimation information. The acquisition unit acquires the emotion estimation information based on, for example, the content posted by the customer on social media. The acquisition unit can also acquire the emotion estimation information based on the information of the account that the customer follows on social media. Furthermore, the acquisition unit can also acquire the emotion estimation information based on the post that the customer "liked" on social media. In this way, by analyzing the social media activity, information based on the customer's interests can be acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI.

[0040] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. For example, when the emotion information is important, the estimation unit performs a detailed facial expression analysis. Furthermore, when the audio information is important, the estimation unit can also perform a detailed audio analysis. Furthermore, when the action information is important, the estimation unit can also perform a detailed action analysis. This allows for efficient emotion estimation by adjusting the level of detail of the estimation based on the importance of the information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0041] The estimation unit can apply different estimation algorithms depending on the category of emotion estimation information at the time of estimation. For example, the estimation unit applies a facial expression recognition algorithm to facial expression information. The estimation unit can also apply a voice analysis algorithm to voice information. The estimation unit can also apply a motion analysis algorithm to motion information. This enables more accurate emotion estimation by applying an algorithm depending on the category of information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0042] The estimation unit can determine the priority of estimation based on the acquisition time of emotion estimation information at the time of estimation. For example, the estimation unit prioritizes the latest emotion estimation information. The estimation unit can also estimate by referring to past emotion estimation information. Furthermore, the estimation unit can also prioritize emotion estimation information at the time of a specific event. This enables efficient emotion estimation by determining the priority based on the acquisition time of information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0043] 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 prioritizes information related to the customer's current situation to estimate. The estimation unit can also prioritize information related to the customer's past emotion history to estimate. Furthermore, the estimation unit can also prioritize information related to the customer's field of interest to estimate. This enables efficient emotion estimation by adjusting the order of estimation based on the relevance of information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0044] At the time of decision, the decision unit can select the optimal response method by analyzing the past response history of the customer. For example, the decision unit selects a response method that the customer was pleased with in the past with a priority. The decision unit can also select a response method that avoids a response method that the customer was dissatisfied with in the past. Furthermore, the decision unit can select an optimal response method for a specific situation from the past response history of the customer. This makes it possible to respond more effectively by analyzing the past response history. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using an AI, or may be performed without using an AI.

[0045] The decision unit can customize the means of response based on the customer's current situation at the time of decision. For example, when the customer is eating, the decision unit customizes the response related to the meal. In addition, when the customer is talking, the decision unit can also customize the response related to the conversation. Furthermore, when the customer is waiting, the decision unit can also customize the response related to the waiting time. This enables a more appropriate response by customizing the response based on the customer's current situation. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the decision unit may be performed using, for example, an AI, or may be performed without using an AI.

[0046] The decision unit can select the optimal response method by considering the geographical location information of the customer when making a decision. For example, if the customer is in a restaurant, the decision unit selects the response method in the restaurant. Furthermore, if the customer is in a cafe, the decision unit can also select the response method in the cafe. Furthermore, if the customer is in a hotel, the decision unit can also select the response method in the hotel. This allows for a more appropriate response by considering the geographical location information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the decision unit may be performed using, for example, an AI, or may be performed without using an AI.

[0047] At the time of decision, the decision unit can analyze the social media activity of the customer and propose a means of response. The decision unit can propose a response method based on, for example, the content posted by the customer on social media. The decision unit can also propose a response method based on information on the accounts the customer follows on social media. Furthermore, the decision unit can also propose a response method based on the posts that the customer "liked" on social media. This makes it possible to respond based on the customer's interests by analyzing social media activity. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the decision unit may be performed using, for example, an AI, or may be performed without using an AI.

[0048] When performing a response, the response unit can select an optimal execution method by referring to the customer's past response history. For example, the response unit prioritizes and executes a response method that the customer was pleased with in the past. The response unit can also avoid and execute a response method that the customer was dissatisfied with in the past. Furthermore, the response unit can select an optimal execution method for a specific situation from the customer's past response history. This allows for more effective response by referring to the past response history. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed, for example, using an AI, or may be performed without using an AI.

[0049] The response unit can customize the means of response based on the customer's current situation when the response is executed. For example, when the customer is eating, the response unit customizes the response related to the meal. In addition, when the customer is talking, the response unit can also customize the response related to the conversation. Furthermore, when the customer is waiting, the response unit can also customize the response related to the waiting time. This enables a more appropriate response by customizing the response based on the customer's current situation. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI.

[0050] The response unit can select the optimal execution method in consideration of the geographical location information of the customer when performing the response. For example, when the customer is in a restaurant, the response unit selects the response method in the restaurant. Furthermore, when the customer is in a cafe, the response unit can also select the response method in the cafe. Furthermore, when the customer is in a hotel, the response unit can also select the response method in the hotel. This allows for more appropriate response by considering the geographical location information. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI.

[0051] The response unit can analyze the social media activity of the customer and propose a means of response when the response is executed. The response unit can propose a response method based on, for example, the content posted by the customer on social media. The response unit can also propose a response method based on information on the accounts the customer follows on social media. Furthermore, the response unit can also propose a response method based on the posts that the customer "liked" on social media. This makes it possible to respond based on the customer's interests by analyzing social media activities. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When acquiring the emotion estimation information of a customer, the acquisition unit can refer to the customer's past purchasing history and preferentially acquire information related to a specific product or service. For example, if the customer has frequently purchased products of a specific brand in the past, information related to that brand can be preferentially acquired. Also, if the customer has used a specific service in the past and was satisfied with it, information related to that service can be preferentially acquired. Furthermore, it is also possible to avoid acquiring information related to products or services that the customer was dissatisfied with in the past. This makes it possible to acquire more relevant information based on the customer's past purchasing history.

[0054] When determining a response according to the customer's emotions, the determination unit can determine the response by taking into consideration the cultural background of the customer. For example, if the customer is from a different cultural region, the determination unit can determine a response that uses language and gestures appropriate for that culture. In addition, if the customer has a specific religious background, the determination unit can also determine a response that takes the religion into consideration. Furthermore, if the customer is accustomed to the customs and practices of a specific region, the determination unit can also determine a response that is appropriate for that region. This enables more personalized customer service by providing a response according to the cultural background of the customer.

[0055] When acquiring information for estimating a customer's emotion, the acquisition unit can acquire the information taking into consideration the customer's current environmental sounds. For example, if the customer is in a quiet place, voice information can be acquired with priority. Also, if the customer is in a noisy place, facial expression and movement information can be acquired with priority. Furthermore, if the customer is in a natural environment, heart rate and breathing rhythm can be acquired with priority. This allows for more accurate emotion estimation by acquiring information according to the customer's environmental sounds.

[0056] When determining a response according to the customer's emotions, the determination unit can take into consideration the customer's current activity. For example, if the customer is eating, a response related to the meal is determined. Also, if the customer is talking, a response related to the conversation can be determined. Furthermore, if the customer is waiting, a response related to the waiting time can be determined. This makes it possible to provide more appropriate customer service by taking a response according to the customer's activity.

[0057] When acquiring information for emotion estimation of a customer, the acquisition unit can acquire the information taking into consideration the current weather conditions of the customer. For example, if the customer is in a sunny day, facial expression and movement information can be acquired with priority. Also, if the customer is in a rainy day, voice information can be acquired with priority. Furthermore, if the customer is in cold weather, body temperature and breathing rhythm can be acquired with priority. In this way, more accurate emotion estimation can be achieved by acquiring information according to the customer's weather conditions.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a customer being served. The acquisition unit can acquire, for example, one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. These pieces of information may include AI processing. For example, the acquisition unit may photograph the customer's facial expression using a camera and acquire information for estimating the emotion using facial expression recognition technology. The acquisition unit may also record the customer's voice using a microphone and acquire information for estimating the emotion using voice analysis technology. Furthermore, the acquisition unit may measure the customer's heart rate using a heart rate sensor and acquire information for estimating the emotion. 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, for example, AI, and estimates the customer's emotion, such as whether the customer is happy, sad, or angry. For example, the estimation unit estimates the emotion from the customer's facial expression using facial expression recognition technology. The estimation unit can also estimate the emotion from the tone of the customer's voice using voice analysis technology. Furthermore, the estimation unit can estimate the emotion from fluctuations in heart rate. Step 3: The determination unit determines a response according to the customer's emotion estimated by the estimation unit. The determination unit determines an appropriate response based on the estimated emotion, for example, using AI. For example, the determination unit determines a response to offer comforting words when the customer is sad. The determination unit can also determine a response to share the customer's joy when the customer is happy. Furthermore, the determination unit can also determine a response to provide a topic of conversation to calm the customer when the customer is angry. Step 4: The response unit executes the response determined by the determination unit. The response unit executes the response determined by using, for example, AI. For example, the response unit may speak comforting words to the customer. The response unit may also rejoice together with the customer. Furthermore, the response unit may provide the customer with a topic to calm them down.

[0060] (Example 2) A customer service support system according to an embodiment of the present invention is a system that provides a better customer service experience by reading the customer's emotions and taking appropriate measures. This customer service support system acquires emotion estimation information for estimating the customer's emotions, and estimates the customer's emotions based on the acquired emotion estimation information. An appropriate response is determined according to the estimated customer's emotions, and the response is executed. For example, the customer service support system can acquire information such as the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. These pieces of information may include AI processing. Next, the customer's emotions are estimated based on the acquired emotion estimation information. The AI ​​analyzes these pieces of information and estimates the customer's emotions, such as whether the customer is happy, sad, or angry. For example, it is possible to estimate whether the customer is happy from the customer's facial expression and tone of voice. An appropriate response is determined according to the estimated customer's emotions. For example, it is possible to determine responses such as comforting words when the customer is sad, rejoicing together with the customer when the customer is happy, and providing a topic to calm the customer when the customer is angry. These responses may include AI processing. Finally, the determined response is executed. For example, the agent can respond by speaking comforting words to the customer, sharing in the customer's joy, or offering a topic of conversation to calm the customer. The agent can also notify the person in charge of the decided response. This allows the agent to read the customer's emotions and respond appropriately, thereby providing a better customer service experience. For example, the agent can take the customer's order while checking the customer's mood, check the progress of the meal, and pay the bill. This allows the customer to have a more satisfying customer service experience. In this way, the customer service support system can provide a better customer service experience by reading the customer's emotions and responding appropriately.

[0061] The customer service support system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer to be served. The acquisition unit can acquire, for example, 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. These pieces of information may include AI processing. For example, the acquisition unit uses a camera to capture the customer's facial expression and acquires information for estimating the emotion using facial expression recognition technology. The acquisition unit can also use a microphone to record the customer's voice and acquire information for estimating the emotion using voice analysis technology. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire information for estimating the emotion. The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the acquired information and estimates the emotion of the customer, such as whether the customer is happy, sad, or angry. For example, the estimation unit uses facial expression recognition technology to estimate the emotion from the customer's facial expression. The estimation unit can also estimate the emotion from the tone of the customer's voice using a voice analysis technique. Furthermore, the estimation unit can also estimate the emotion from the fluctuation of the heart rate. The determination unit determines a response according to the emotion of the customer estimated by the estimation unit. The determination unit determines an appropriate response based on the emotion estimated by using, for example, AI. For example, the determination unit determines a response of speaking comforting words when the customer is sad. Furthermore, the determination unit can also determine a response of rejoicing together with the customer when the customer is happy. Furthermore, the determination unit can also determine a response of providing a topic to calm the customer when the customer is angry. The response unit executes the response determined by the determination unit. The response unit executes the response determined by using, for example, AI. For example, the response unit speaks comforting words to the customer. Furthermore, the response unit can rejoice together with the customer. Furthermore, the response unit can provide a topic to calm the customer. As a result, the customer service support system according to the embodiment can provide a better customer service experience by reading the customer's emotion and taking an appropriate response.

[0062] The acquisition unit acquires emotion estimation information for estimating the emotion of a customer being served. The acquisition unit can acquire, for example, 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. These pieces of information may include AI processing. For example, the acquisition unit uses a camera to capture the facial expression of the customer and acquires information for estimating the emotion using facial expression recognition technology. Specifically, the camera has high resolution and can capture subtle changes in facial expression. The facial expression recognition technology uses a model using deep learning to classify emotions such as smile, anger, and sadness with high accuracy. The acquisition unit can also record the customer's voice using a microphone and acquire information for estimating the emotion using voice analysis technology. The voice analysis technology analyzes features such as tone, pitch, speed, and volume of the voice to detect changes in emotion. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire information for estimating the emotion. The heart rate sensor measures heart rate fluctuations in real time using photoplethysmography (PPG). This allows the acquisition unit to grasp the emotional state of the customer from multiple angles. Furthermore, the acquisition unit can centrally manage these data and transmit them to the analysis unit in real time. This allows the acquisition unit to provide basic data for estimating the customer's emotions with high accuracy, and improve the accuracy and reliability of the entire system.

[0063] The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the acquired information and estimates the customer's emotion, such as whether the customer is happy, sad, or angry. Specifically, the estimation unit estimates the emotion from the customer's facial expression using facial expression recognition technology. A model using deep learning can learn millions of facial expression images and classify subtle changes in facial expressions with high accuracy. The estimation unit can also estimate the emotion from the tone of the customer's voice using voice analysis technology. The voice analysis technology analyzes features such as the tone, pitch, speed, and volume of the voice to detect changes in emotion. Furthermore, the estimation unit can estimate the emotion from the fluctuation of the heart rate. Since the fluctuation of the heart rate reflects an emotional state such as stress or excitement, the emotion of the customer can be estimated by analyzing this. The estimation unit integrates these pieces of information and performs comprehensive emotion estimation. For example, by combining facial expression, voice, and heart rate data, more accurate emotion estimation is possible. In addition, the estimation unit can use past data and statistical information to analyze long-term emotional fluctuations and understand customer emotional patterns. This enables the estimation unit to handle not only real-time emotion estimation but also long-term emotion analysis, improving the accuracy and reliability of the entire system.

[0064] The determination unit determines a response according to the customer's emotions estimated by the estimation unit. The determination unit determines an appropriate response based on the estimated emotions, for example, using AI. Specifically, the determination unit determines a response of offering words of comfort when the customer is sad. For example, when the customer is sad, the AI ​​searches for appropriate words or phrases of comfort from a past database and selects the optimal one. The determination unit can also determine a response that makes the customer happy when the customer is happy. For example, when the customer is happy, the AI ​​suggests words or actions to share the customer's joy. Furthermore, the determination unit can also determine a response that provides a topic to calm the customer when the customer is angry. For example, when the customer is angry, the AI ​​searches for appropriate topics or phrases from a past database and selects the optimal one. In order to quickly and accurately determine these responses, the determination unit analyzes data in real time and selects the optimal response. Furthermore, the determination unit can learn from past response results and continuously improve the accuracy and effectiveness of the response. This allows the determination unit to quickly determine the optimal response according to the customer's emotions and improve the performance of the entire system.

[0065] The response unit executes the response determined by the determination unit. The response unit executes the response determined by, for example, using AI. Specifically, the response unit speaks words of comfort to the customer. For example, if the customer is sad, the AI ​​generates appropriate words of comfort and speaks them to the customer. The response unit can also rejoice with the customer. For example, if the customer is happy, the AI ​​generates words and actions to share the customer's joy and speaks them to the customer. Furthermore, the response unit can provide topics to calm the customer. For example, if the customer is angry, the AI ​​generates appropriate topics and phrases and speaks them to the customer. The response unit analyzes data in real time and selects the optimal response in order to quickly and accurately execute these responses. Furthermore, the response unit can learn from past response results and continuously improve the accuracy and effectiveness of the response. This allows the response unit to quickly execute the optimal response according to the customer's emotions and improve the performance of the entire system. Furthermore, the response unit can collect feedback from customers and continuously improve the accuracy and effectiveness of the response. For example, by recording how customers react to the services provided, the system can improve the accuracy and effectiveness of the services based on that data. This allows the service department to quickly implement the optimal service according to the customer's emotions, improving the performance of the entire system.

[0066] The determination unit determines a manner of customer service according to the customer's emotion as a response according to the customer's emotion, and the response unit can serve the customer in a manner according to the customer's emotion. For example, the determination unit determines a manner of speaking comforting words to the customer when the customer is sad. Furthermore, the determination unit can also determine a manner of rejoicing together with the customer when the customer is happy. Furthermore, the determination unit can also determine a manner of providing a topic to calm the customer when the customer is angry. The response unit serves the customer in the manner determined by the determination unit, for example. For example, the response unit speaks comforting words to the customer. Furthermore, the response unit can rejoice together with the customer. Furthermore, the response unit can also provide a topic to calm the customer. As a result, a better customer service experience can be provided by serving the customer in a manner according to the customer's emotion. A part or all of the above-mentioned processing in the determination unit and the response unit may be performed, for example, using AI, or may be performed without using AI.

[0067] The determination unit can determine a response according to the customer's emotions and attributes. The determination unit determines an appropriate response based on attributes such as the customer's age, sex, and purchase history. For example, the determination unit determines a response that uses casual language for younger customers. The determination unit can also determine a response that uses polite language for older customers. Furthermore, the determination unit can determine a response that provides topics related to products that the customer has previously purchased based on the customer's purchase history. This enables more personalized customer service by determining a response according to the customer's emotions and attributes. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI.

[0068] 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 as emotion estimation information. The acquisition unit can, for example, photograph the customer's facial expression using a camera and acquire information for estimating emotion using facial expression recognition technology. The acquisition unit can also record the customer's voice using a microphone and acquire information for estimating emotion using voice analysis technology. Furthermore, the acquisition unit can measure the customer's heart rate using a heart rate sensor and acquire information for estimating emotion. In this way, by acquiring various information, the customer's emotion can be estimated more accurately. 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.

[0069] The response unit can notify the person in charge of the content of the response determined by the determination unit. The response unit, for example, notifies the person in charge of the content of the response determined by the determination unit. For example, the response unit notifies the person in charge that it has decided to respond by speaking comforting words when the customer is sad. The response unit can also notify the person in charge that it has decided to respond by sharing the joy with the customer when the customer is happy. Furthermore, the response unit can also notify the person in charge that it has decided to respond by providing a topic to calm the customer when the customer is angry. In this way, by notifying the person in charge of the content of the response, it is possible to confirm that an appropriate response is taken. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI.

[0070] 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 acquisition unit can reduce the frequency of acquiring the emotion estimation information. In this way, by adjusting the timing of acquiring information according to the emotion of the customer, more appropriate information can be acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI.

[0071] The acquisition unit can analyze the customer's past emotion history and select an optimal acquisition method. For example, the acquisition unit acquires emotion estimation information during a time period when the customer was happy in the past. The acquisition unit can also acquire emotion estimation information by avoiding situations in which the customer was stressed in the past. Furthermore, the acquisition unit can acquire emotion estimation information at a specific event from the customer's past emotion history. This enables more effective information acquisition by analyzing the past emotion history. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI.

[0072] The acquisition unit can perform filtering based on the customer's current situation and areas of interest when acquiring emotion estimation information. For example, when the customer is eating, the acquisition unit prioritizes acquiring information related to the meal. In addition, when the customer is talking, the acquisition unit can also prioritize acquiring information related to the content of the conversation. Furthermore, when the customer is waiting, the acquisition unit can also prioritize acquiring information related to the waiting time. In this way, by filtering information based on the customer's situation and areas of interest, more relevant information can be acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI.

[0073] The acquisition unit can estimate the emotion of the customer and determine the priority of emotion estimation information to be acquired based on the estimated emotion of the customer. For example, when the customer is happy, the acquisition unit prioritizes acquiring facial expression information. Furthermore, when the customer is sad, the acquisition unit can also prioritize acquiring voice information. Furthermore, when the customer is angry, the acquisition unit can also prioritize acquiring motion information. In this way, important information can be acquired preferentially by determining the priority of information based on the emotion of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI.

[0074] 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 restaurant, the acquisition unit preferentially acquires information about the restaurant. Furthermore, when the customer is in a cafe, the acquisition unit can also preferentially acquire information about the cafe. Furthermore, when the customer is in a hotel, the acquisition unit can also preferentially acquire information about the hotel. In this way, more relevant information can be acquired by considering the geographical location information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI, or may be performed without using an AI.

[0075] The acquisition unit can analyze the social media activity of the customer and acquire related information when acquiring the emotion estimation information. The acquisition unit acquires the emotion estimation information based on, for example, the content posted by the customer on social media. The acquisition unit can also acquire the emotion estimation information based on the information of the account that the customer follows on social media. Furthermore, the acquisition unit can also acquire the emotion estimation information based on the post that the customer "liked" on social media. In this way, by analyzing the social media activity, information based on the customer's interests can be acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI.

[0076] 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 sets the sensitivity of the emotion estimation algorithm to low. Also, when the customer is nervous, the estimation unit can set the sensitivity of the emotion estimation algorithm to high. Furthermore, when the customer is in a hurry, the estimation unit can increase the processing speed of the emotion estimation algorithm. This allows for more accurate emotion estimation by adjusting the algorithm based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0077] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. For example, when the emotion information is important, the estimation unit performs a detailed facial expression analysis. Furthermore, when the audio information is important, the estimation unit can also perform a detailed audio analysis. Furthermore, when the action information is important, the estimation unit can also perform a detailed action analysis. This allows for efficient emotion estimation by adjusting the level of detail of the estimation based on the importance of the information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0078] The estimation unit can apply different estimation algorithms depending on the category of emotion estimation information at the time of estimation. For example, the estimation unit applies a facial expression recognition algorithm to facial expression information. The estimation unit can also apply a voice analysis algorithm to voice information. The estimation unit can also apply a motion analysis algorithm to motion information. This enables more accurate emotion estimation by applying an algorithm depending on the category of information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0079] The estimation unit can estimate the customer's emotion and adjust the display method of the estimation result based on the estimated emotion of the customer. For example, when the customer is relaxed, the estimation unit displays a detailed estimation result. Furthermore, when the customer is nervous, the estimation unit can also display a concise estimation result. Furthermore, when the customer is in a hurry, the estimation unit can also display an estimation result that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0080] The estimation unit can determine the priority of estimation based on the acquisition time of emotion estimation information at the time of estimation. For example, the estimation unit prioritizes the latest emotion estimation information. The estimation unit can also estimate by referring to past emotion estimation information. Furthermore, the estimation unit can also prioritize emotion estimation information at the time of a specific event. This enables efficient emotion estimation by determining the priority based on the acquisition time of information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0081] 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 prioritizes information related to the customer's current situation to estimate. The estimation unit can also prioritize information related to the customer's past emotion history to estimate. Furthermore, the estimation unit can also prioritize information related to the customer's field of interest to estimate. This enables efficient emotion estimation by adjusting the order of estimation based on the relevance of information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0082] The determination unit can estimate the customer's emotion and adjust the response decision method based on the estimated customer's emotion. For example, when the customer is relaxed, the determination unit determines a gentle response. Also, when the customer is nervous, the determination unit can determine a reassuring response. Furthermore, when the customer is in a hurry, the determination unit can determine a quick response. This allows for a more appropriate response by adjusting the response decision method based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI.

[0083] At the time of decision, the decision unit can select the optimal response method by analyzing the past response history of the customer. For example, the decision unit selects a response method that the customer was pleased with in the past with a priority. The decision unit can also select a response method that avoids a response method that the customer was dissatisfied with in the past. Furthermore, the decision unit can select an optimal response method for a specific situation from the past response history of the customer. This makes it possible to respond more effectively by analyzing the past response history. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using an AI, or may be performed without using an AI.

[0084] The decision unit can customize the means of response based on the customer's current situation at the time of decision. For example, when the customer is eating, the decision unit customizes the response related to the meal. In addition, when the customer is talking, the decision unit can also customize the response related to the conversation. Furthermore, when the customer is waiting, the decision unit can also customize the response related to the waiting time. This enables a more appropriate response by customizing the response based on the customer's current situation. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the decision unit may be performed using, for example, an AI, or may be performed without using an AI.

[0085] The determination unit can estimate the emotion of the customer and determine the priority of the response based on the estimated emotion of the customer. For example, when the customer is relaxed, the determination unit sets the priority of the response to low. Also, when the customer is nervous, the determination unit can set the priority of the response to high. Furthermore, when the customer is in a hurry, the determination unit can set the priority of the response to the highest priority. This enables more appropriate response by determining the priority of the response based on the emotion of the customer. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI.

[0086] The decision unit can select the optimal response method by considering the geographical location information of the customer when making a decision. For example, if the customer is in a restaurant, the decision unit selects the response method in the restaurant. Furthermore, if the customer is in a cafe, the decision unit can also select the response method in the cafe. Furthermore, if the customer is in a hotel, the decision unit can also select the response method in the hotel. This allows for a more appropriate response by considering the geographical location information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the decision unit may be performed using, for example, an AI, or may be performed without using an AI.

[0087] At the time of decision, the decision unit can analyze the social media activity of the customer and propose a means of response. The decision unit can propose a response method based on, for example, the content posted by the customer on social media. The decision unit can also propose a response method based on information on the accounts the customer follows on social media. Furthermore, the decision unit can also propose a response method based on the posts that the customer "liked" on social media. This makes it possible to respond based on the customer's interests by analyzing social media activity. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the decision unit may be performed using, for example, an AI, or may be performed without using an AI.

[0088] The response unit can estimate the customer's emotion and adjust the execution method of the response based on the estimated emotion of the customer. For example, when the customer is relaxed, the response unit executes a gentle response. Also, when the customer is nervous, the response unit can execute a reassuring response. Furthermore, when the customer is in a hurry, the response unit can execute a quick response. This allows for a more appropriate response by adjusting the execution method based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI.

[0089] When performing a response, the response unit can select an optimal execution method by referring to the customer's past response history. For example, the response unit prioritizes and executes a response method that the customer was pleased with in the past. The response unit can also avoid and execute a response method that the customer was dissatisfied with in the past. Furthermore, the response unit can select an optimal execution method for a specific situation from the customer's past response history. This allows for more effective response by referring to the past response history. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed, for example, using an AI, or may be performed without using an AI.

[0090] The response unit can customize the means of response based on the customer's current situation when the response is executed. For example, when the customer is eating, the response unit customizes the response related to the meal. In addition, when the customer is talking, the response unit can also customize the response related to the conversation. Furthermore, when the customer is waiting, the response unit can also customize the response related to the waiting time. This enables a more appropriate response by customizing the response based on the customer's current situation. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI.

[0091] The response unit can estimate the emotion of the customer and determine the priority of the response based on the estimated emotion of the customer. For example, when the customer is relaxed, the response unit sets the priority of the response to low. Also, when the customer is nervous, the response unit can set the priority of the response to high. Furthermore, when the customer is in a hurry, the response unit can set the priority of the response to the highest priority. This enables more appropriate response by determining the priority of the response based on the emotion of the customer. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI.

[0092] The response unit can select the optimal execution method in consideration of the geographical location information of the customer when performing the response. For example, when the customer is in a restaurant, the response unit selects the response method in the restaurant. Furthermore, when the customer is in a cafe, the response unit can also select the response method in the cafe. Furthermore, when the customer is in a hotel, the response unit can also select the response method in the hotel. This allows for more appropriate response by considering the geographical location information. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI.

[0093] The response unit can analyze the social media activity of the customer and propose a means of response when the response is executed. The response unit can propose a response method based on, for example, the content posted by the customer on social media. The response unit can also propose a response method based on information on the accounts the customer follows on social media. Furthermore, the response unit can also propose a response method based on the posts that the customer "liked" on social media. This makes it possible to respond based on the customer's interests by analyzing social media activities. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI.

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

[0095] When acquiring the emotion estimation information of a customer, the acquisition unit can refer to the customer's past purchasing history and preferentially acquire information related to a specific product or service. For example, if the customer has frequently purchased products of a specific brand in the past, information related to that brand can be preferentially acquired. Also, if the customer has used a specific service in the past and was satisfied with it, information related to that service can be preferentially acquired. Furthermore, it is also possible to avoid acquiring information related to products or services that the customer was dissatisfied with in the past. This makes it possible to acquire more relevant information based on the customer's past purchasing history.

[0096] When estimating the customer's emotions, the estimation unit can make the estimation taking into account the customer's current health condition. For example, if the customer is tired, the estimation unit estimates the sense of fatigue from the customer's facial expression and tone of voice. In addition, if the customer is feeling stressed, the estimation unit can estimate the stress level from fluctuations in heart rate and blood pressure. Furthermore, if the customer is relaxed, the degree of relaxation can be estimated from changes in breathing rhythm and body temperature. This enables more accurate emotion estimation based on the customer's health condition.

[0097] When determining a response according to the customer's emotions, the determination unit can determine the response by taking into consideration the cultural background of the customer. For example, if the customer is from a different cultural region, the determination unit can determine a response that uses language and gestures appropriate for that culture. In addition, if the customer has a specific religious background, the determination unit can also determine a response that takes the religion into consideration. Furthermore, if the customer is accustomed to the customs and practices of a specific region, the determination unit can also determine a response that is appropriate for that region. This enables more personalized customer service by providing a response according to the cultural background of the customer.

[0098] When acquiring information for emotion estimation of a customer, the acquisition unit can acquire the information taking into consideration the current activity level of the customer. For example, if the customer is exercising, priority can be given to acquiring the heart rate and breathing rhythm. Also, if the customer is taking a break, priority can be given to acquiring facial expressions and tone of voice. Furthermore, if the customer is working, priority can be given to acquiring the concentration level and stress level. In this way, more appropriate emotion estimation can be achieved by acquiring information according to the customer's activity level.

[0099] When performing the response determined by the determination unit, the response unit can adjust the tone of the response based on the customer's current mood. For example, if the customer is happy, the response can be made in a bright and cheerful tone. If the customer is sad, the response can be made in a calm and gentle tone. Furthermore, if the customer is angry, the response can be made in a calm and collected tone. This allows for more effective customer service by adjusting the tone of the response depending on the customer's mood.

[0100] When acquiring information for estimating a customer's emotion, the acquisition unit can acquire the information taking into consideration the customer's current environmental sounds. For example, if the customer is in a quiet place, voice information can be acquired with priority. Also, if the customer is in a noisy place, facial expression and movement information can be acquired with priority. Furthermore, if the customer is in a natural environment, heart rate and breathing rhythm can be acquired with priority. This allows for more accurate emotion estimation by acquiring information according to the customer's environmental sounds.

[0101] When estimating the emotion of a customer, the estimation unit can perform estimation by analyzing the customer's past emotion patterns. For example, it analyzes what emotion the customer showed in a specific situation in the past, and estimates the emotion by comparing it with the current situation. It can also analyze what emotion the customer showed in a specific event in the past, and estimate the emotion by comparing it with the current event. It can also analyze what emotion the customer showed in a specific time period in the past, and estimate the emotion by comparing it with the current time period. This enables more accurate emotion estimation based on the customer's past emotion patterns.

[0102] When determining a response according to the customer's emotions, the determination unit can take into consideration the customer's current activity. For example, if the customer is eating, a response related to the meal is determined. Also, if the customer is talking, a response related to the conversation can be determined. Furthermore, if the customer is waiting, a response related to the waiting time can be determined. This makes it possible to provide more appropriate customer service by taking a response according to the customer's activity.

[0103] When carrying out the action determined by the determination unit, the response unit can take into consideration the customer's current physical condition. For example, if the customer is tired, a relaxing action can be carried out. Also, if the customer is in good health, an active action can be carried out. Furthermore, if the customer is not feeling well, a gentle and considerate action can be carried out. This allows for more effective customer service by carrying out an action according to the customer's physical condition.

[0104] When acquiring information for emotion estimation of a customer, the acquisition unit can acquire the information taking into consideration the current weather conditions of the customer. For example, if the customer is in a sunny day, facial expression and movement information can be acquired with priority. Also, if the customer is in a rainy day, voice information can be acquired with priority. Furthermore, if the customer is in cold weather, body temperature and breathing rhythm can be acquired with priority. In this way, more accurate emotion estimation can be achieved by acquiring information according to the customer's weather conditions.

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

[0106] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a customer being served. The acquisition unit can acquire, for example, one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. These pieces of information may include AI processing. For example, the acquisition unit may photograph the customer's facial expression using a camera and acquire information for estimating the emotion using facial expression recognition technology. The acquisition unit may also record the customer's voice using a microphone and acquire information for estimating the emotion using voice analysis technology. Furthermore, the acquisition unit may measure the customer's heart rate using a heart rate sensor and acquire information for estimating the emotion. 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, for example, AI, and estimates the customer's emotion, such as whether the customer is happy, sad, or angry. For example, the estimation unit estimates the emotion from the customer's facial expression using facial expression recognition technology. The estimation unit can also estimate the emotion from the tone of the customer's voice using voice analysis technology. Furthermore, the estimation unit can estimate the emotion from fluctuations in heart rate. Step 3: The determination unit determines a response according to the customer's emotion estimated by the estimation unit. The determination unit determines an appropriate response based on the estimated emotion, for example, using AI. For example, the determination unit determines a response to offer comforting words when the customer is sad. The determination unit can also determine a response to share the customer's joy when the customer is happy. Furthermore, the determination unit can also determine a response to provide a topic of conversation to calm the customer when the customer is angry. Step 4: The response unit executes the response determined by the determination unit. The response unit executes the response determined by using, for example, AI. For example, the response unit may speak comforting words to the customer. The response unit may also rejoice together with the customer. Furthermore, the response unit may provide the customer with a topic to calm them down.

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

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

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

[0110] For example, the 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 decision unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotions. The response unit is realized by the control unit 46A of the smart device 14, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0126] For example, the 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 emotion. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The response unit is realized by the control unit 46A of the smart glasses 214, and executes the determined response. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0142] For example, the 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 decision unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The response unit is realized by the control unit 46A of the headset type terminal 314, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0159] For example, the 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 decision unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotions. The response unit is realized by the control unit 46A of the robot 414, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0178] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of a customer to be served; an estimation unit that estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response according to the emotion of the customer estimated by the estimation unit; a response unit that executes the response determined by the determination unit. A system characterized by: (Appendix 2) The determination unit is determining a manner of customer service according to the emotion of the customer as a response according to the emotion of the customer; The corresponding portion is Serve the customer in a manner that corresponds to the customer's emotions 2. The system of claim 1, (Appendix 3) The determination unit is Decide a response according to the customer's emotions and attributes 2. The system of claim 1, (Appendix 4) The acquisition unit is One or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, respiration, and posted content are acquired as the emotion estimation information. 2. The system of claim 1, (Appendix 5) The corresponding portion is The content of the response determined by the determination unit is notified to the person in charge. 2. The system of claim 1, (Appendix 6) The acquisition unit is To estimate a customer's emotion and adjust timing of acquiring emotion estimation information based on the estimated emotion of the customer. 2. The system of claim 1, (Appendix 7) The acquisition unit is Analyze the customer's past emotion history and select the optimal acquisition method 2. The system of claim 1, (Appendix 8) The acquisition unit is When acquiring information for sentiment estimation, filtering is performed based on the customer's current situation and areas of interest. 2. The system of claim 1, (Appendix 9) The acquisition unit is To estimate a customer's emotion and determine a priority order of emotion estimation information to be acquired based on the estimated customer's emotion. 2. The system of claim 1, (Appendix 10) The acquisition unit is When acquiring information for emotion estimation, the system prioritizes acquiring information that is highly relevant by taking into account the geographic location information of the customer. 2. The system of claim 1, (Appendix 11) The acquisition unit is When obtaining sentiment estimation information, analyze the customer's social media activity to obtain related information. 2. The system of claim 1, (Appendix 12) The estimation unit is Estimate customer sentiment and adjust sentiment estimation algorithms based on estimated customer sentiment 2. The system of claim 1, (Appendix 13) The estimation unit is During estimation, the level of detail of the estimation is adjusted based on the importance of the emotion estimation information. 2. The system of claim 1, (Appendix 14) The estimation unit is During estimation, different estimation algorithms are applied depending on the category of emotion estimation information. 2. The system of claim 1, (Appendix 15) The estimation unit is Estimate customer sentiment and adjust how the results are displayed based on the estimated customer sentiment 2. The system of claim 1, (Appendix 16) The estimation unit is During estimation, the priority of estimation is determined based on the time when emotion estimation information was acquired. 2. The system of claim 1, (Appendix 17) The estimation unit is During estimation, the order of estimation is adjusted based on the relevance of emotion estimation information. 2. The system of claim 1, (Appendix 18) The determination unit is Infer customer sentiment and adjust response decisions based on inferred customer sentiment 2. The system of claim 1, (Appendix 19) The determination unit is When making a decision, analyze the customer's past response history to select the most appropriate response method. 2. The system of claim 1, (Appendix 20) The determination unit is When making a decision, tailor your response based on the customer's current situation 2. The system of claim 1, (Appendix 21) The determination unit is Estimate customer sentiment and prioritize interactions based on the estimated customer sentiment 2. The system of claim 1, (Appendix 22) The determination unit is When making decisions, consider the customer's geographic location to determine the best course of action 2. The system of claim 1, (Appendix 23) The determination unit is Analyze your customers' social media activity and suggest action steps when making decisions 2. The system of claim 1, (Appendix 24) The corresponding portion is Infer customer sentiment and adjust response execution based on inferred customer sentiment 2. The system of claim 1, (Appendix 25) The corresponding portion is When responding, the optimal response method is selected by referring to the customer's past response history. 2. The system of claim 1, (Appendix 26) The corresponding portion is Customize the interaction based on the customer's current situation when the interaction is being carried out 2. The system of claim 1, (Appendix 27) The corresponding portion is Estimate customer sentiment and prioritize interactions based on the estimated customer sentiment 2. The system of claim 1, (Appendix 28) The corresponding portion is When responding, the optimal response method is selected based on the customer's geographic location. 2. The system of claim 1, (Appendix 29) The corresponding portion is Analyze customer social media activity to suggest action steps when taking action 2. The system of claim 1, [Explanation of symbols]

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

Claims

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

2. The determination unit is determining a manner of customer service according to the emotion of the customer as a response according to the emotion of the customer; The corresponding portion is Serve the customer in a manner that corresponds to the customer's emotions 2. The system of claim 1 .

3. The determination unit is Decide a response according to the customer's emotions and attributes 2. The system of claim 1 .

4. The acquisition unit is One or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, respiration, and posted content are acquired as the emotion estimation information.

2. The system of claim 1 .

5. The corresponding portion is The content of the response determined by the determination unit is notified to the person in charge.

2. The system of claim 1 .

6. The acquisition unit is A feeling of the customer is estimated, and a timing for acquiring the feeling estimation information is adjusted based on the estimated feeling of the customer.

2. The system of claim 1 .

7. The acquisition unit is Analyze the customer's past emotion history and select the optimal acquisition method.

2. The system of claim 1 .

8. The acquisition unit is When acquiring the emotion estimation information, filtering is performed based on the current situation and areas of interest of the customer.

2. The system of claim 1 .

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