system

Abstract

The system according to this embodiment aims to improve the quality of service by selecting the most suitable operator based on the customer's attributes. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a matching unit, and an adjustment unit. The reception unit receives customer information. The analysis unit analyzes the information received by the reception unit and determines the customer's attributes. The matching unit selects the most suitable operator based on the attributes determined by the analysis unit. The adjustment unit adjusts the voice quality and image of the operator selected by the matching unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to select an operator optimal for the attributes of customers in a call center, and there is a problem that the quality of response is not constant.

[0005] The system according to the embodiment aims to select an optimal operator based on the attributes of customers and improve the quality of response.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a matching unit, and an adjustment unit. The reception unit receives customer information. The analysis unit analyzes the information received by the reception unit and determines the customer's attributes. The matching unit selects the most suitable operator based on the attributes determined by the analysis unit. The adjustment unit adjusts the voice quality and image of the operator selected by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can select the most suitable operator based on the customer's attributes and improve the quality of service. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The call center system according to an embodiment of the present invention is a system that improves the quality of service by matching the customer with the most suitable operator based on their attributes upon incoming calls. The call center system uses AI to analyze the content of the inquiry, the customer's identity verification attributes, and the tone of their voice when a call is made, and to determine the attributes of the most suitable operator for the customer. Next, it matches the customer with a crew member whose attributes are closest to those of an available operator. For example, if the customer's attributes are "female," "in her 40s," and "polite," the AI ​​selects the operator who most closely matches those attributes. If a suitable operator is unavailable, the AI ​​adjusts the operator's voice quality to match the customer's attributes. Furthermore, during the call, the AI ​​changes the operator's voice quality to be optimal for the customer. For example, it may lower the tone of voice by one level or eliminate noise such as breathing sounds. The AI ​​also assists in guiding the customer, providing advice such as "Please speak a little more slowly." This makes the interaction between the customer and the operator smoother and improves customer satisfaction. In the case of video calls, the AI ​​also changes the operator's image to match the customer's attributes. For example, if the customer requests a "woman in her 40s," the AI ​​changes the operator's image to that of a woman in her 40s. In this way, the quality of call center service can be improved. This allows the call center system to match the most suitable operator to the customer's attributes, thereby improving the quality of service.

[0029] The call center system according to this embodiment comprises a reception unit, an analysis unit, a matching unit, and an adjustment unit. The reception unit receives customer information. Customer information includes, but is not limited to, personal information, purchase history, and inquiry content. The reception unit can receive information, for example, through voice input or text input. The analysis unit analyzes the information received by the reception unit and determines the customer's attributes. The analysis unit performs analysis using, for example, data mining, statistical analysis, and machine learning. The analysis unit can analyze the tone of the customer's voice using, for example, voice analysis technology. The matching unit selects the most suitable operator based on the attributes determined by the analysis unit. The matching unit selects an operator based on attribute information such as age, gender, and purchase history. The matching unit can analyze the operator's attribute information using, for example, AI and select the most suitable operator. The adjustment unit adjusts the voice quality and image of the operator selected by the matching unit. The adjustment unit can adjust, for example, the tone, pitch, and speed of the voice. The adjustment unit can adjust, for example, the resolution, color tone, and brightness of the image. As a result, the call center system according to this embodiment can match the customer with the most suitable operator based on their attributes, thereby improving the quality of service.

[0030] The reception department receives customer information. This information includes, but is not limited to, personal information, purchase history, and inquiry details. The reception department can receive information through methods such as voice input and text input. Specifically, in the case of voice input, speech recognition technology is used to convert the customer's speech into text data, and in the case of text input, the customer directly inputs the information using a keyboard or touchscreen. The reception department centrally manages this information and stores it in a database. Furthermore, the reception department has a feedback function to verify the accuracy of the entered information and can prompt the customer to confirm the input. For example, in the case of voice input, the recognized text is played back to the customer as audio and asked for confirmation. The reception department also supports multiple languages ​​and can handle international customers. This allows the reception department to efficiently receive and accurately manage information from diverse customers. In addition, the reception department transmits the received information to the analysis department in real time, enabling a rapid response. In this way, the reception department plays a role in improving the efficiency and service quality of the entire call center system.

[0031] The analysis department analyzes information received by the reception department to determine customer attributes. The analysis department uses techniques such as data mining, statistical analysis, and machine learning for its analysis. Specifically, it uses data mining techniques to extract patterns and trends from customer purchase history and inquiries, and statistical analysis to numerically evaluate customer attributes. Furthermore, it can use machine learning algorithms to predict future behavior based on past customer behavior data. The analysis department can also use voice analysis techniques to analyze the emotional tone of a customer's voice. Voice analysis techniques analyze the tone, pitch, speed, and emotion of the voice to determine the customer's emotional state and urgency. This allows the analysis department to accurately understand customer needs and emotional states and provide information for optimal response. Furthermore, the analysis department analyzes data in real time, enabling rapid responses. For example, it can analyze customer inquiries and immediately provide relevant information. In this way, the analysis department plays a role in improving the overall service quality of the call center system.

[0032] The matching unit selects the most suitable operator based on attributes determined by the analysis unit. For example, the matching unit selects operators based on attribute information such as age, gender, and purchase history. Specifically, it considers the operator's skill set, experience, and past interaction history to select the operator best suited to the customer's needs. The matching unit can, for example, use AI to analyze operator attribute information and select the most suitable operator. The AI ​​uses machine learning algorithms to learn from past matching data and find the optimal matching pattern. This allows the matching unit to select the most suitable operator quickly and accurately. Furthermore, the matching unit can monitor operator schedules and work status in real time, enabling efficient resource management. For example, it can monitor operator work status and provide appropriate breaks to avoid overload. The matching unit can also collect operator feedback and continuously improve the accuracy of its matching algorithm. This allows the matching unit to play a role in improving the overall efficiency and service quality of the call center system.

[0033] The adjustment unit adjusts the voice quality and image of the operator selected by the matching unit. For example, the adjustment unit can adjust the tone, pitch, and speed of the voice. Specifically, it uses voice processing technology to adjust the sound quality of the operator's voice to be easily heard by the customer. For example, it can adjust the tone of the voice to make it sound more approachable. It can also adjust the pitch and speed to improve intelligibility. Furthermore, the adjustment unit can adjust the resolution, color tone, and brightness of the image. Specifically, it uses image processing technology to adjust the image quality of the operator to be easily seen by the customer. For example, it can increase the resolution to provide a clearer image. It can also adjust the color tone and brightness to improve the quality of the image. In this way, the adjustment unit can optimize the voice quality and image of the operator and provide a comfortable customer service environment. Furthermore, the adjustment unit can monitor the quality of the voice and image in real time and make adjustments as needed. In this way, the adjustment unit plays a role in improving the overall service quality of the call center system.

[0034] The adjustment unit can adjust the operator's voice quality to match the customer's attributes. For example, the adjustment unit can adjust the tone of the voice. For example, the adjustment unit can lower the tone of the voice by one level. The adjustment unit can also adjust the pitch of the voice. For example, the adjustment unit can raise the pitch of the voice. The adjustment unit can also adjust the speed of the voice. For example, the adjustment unit can slow down the speed of the voice. By adjusting the operator's voice quality to match the customer's attributes, the quality of service can be improved. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's voice quality into the AI, and the AI ​​can output the optimal voice quality.

[0035] The adjustment unit can adjust the operator's image to match the customer's attributes. For example, the adjustment unit can adjust the image resolution. For example, the adjustment unit can increase the image resolution. The adjustment unit can also adjust the image's color tone. For example, the adjustment unit can brighten the image's color tone. The adjustment unit can also adjust the image's brightness. For example, the adjustment unit can darken the image's brightness. By adjusting the operator's image to match the customer's attributes, the quality of service can be improved. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's image into the AI, which can then output the optimal image.

[0036] The analysis unit can analyze the content of the call, the identity verification attributes, and the tone of the customer's voice during the call recording. For example, the analysis unit can analyze the content of the inquiry. For example, the analysis unit can analyze questions about products or inquiries about services. The analysis unit can also analyze the identity verification attributes. For example, the analysis unit can analyze identity verification attributes such as name, address, and telephone number. The analysis unit can also analyze the tone of the customer's voice. For example, the analysis unit can analyze the tone of the customer's voice using sentiment analysis technology. This allows for the selection of the most suitable operator by accurately analyzing the customer's attributes. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the content of the inquiry, identity verification attributes, and the tone of the customer's voice into the AI, and the AI ​​can output the analysis results.

[0037] The matching unit can select a crew member with similar attributes from among the available operators. The matching unit selects operators based on attribute information such as age, gender, and purchase history. For example, the matching unit can select an operator with a similar age. It can also select an operator with the same gender. It can also select an operator with a similar purchase history. This improves the quality of service by selecting an operator that is closest to the customer's attributes. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input operator attribute information into AI, and the AI ​​can output the most suitable operator.

[0038] The adjustment unit can change the operator's voice quality during a call. For example, the adjustment unit can change the tone of the voice. For example, the adjustment unit can lower the tone of the voice by one level. The adjustment unit can also change the pitch of the voice. For example, the adjustment unit can raise the pitch of the voice. The adjustment unit can also change the speed of the voice. For example, the adjustment unit can slow down the speed of the voice. By changing the operator's voice quality during a call, customer satisfaction can be improved. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's voice quality into the AI, and the AI ​​can output the optimal voice quality.

[0039] The reception department can analyze a customer's past inquiry history and select the most appropriate method of handling inquiries. For example, the reception department can automatically display as suggestions the type of inquiry the customer has frequently made in the past. It can also prioritize suggesting inquiry methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception department can predict and suggest inquiry methods to be used during specific time periods based on the customer's past inquiry history. This enables efficient handling by providing the most suitable method of handling inquiries based on past inquiry history. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the customer's past inquiry history into an AI, which can then output the most appropriate method of handling inquiries.

[0040] The reception desk can filter information received based on the customer's current situation and areas of interest. For example, when a customer enters their current situation, the reception desk will prioritize displaying relevant information. The reception desk can also automatically filter relevant inquiries based on the customer's areas of interest. Furthermore, if a customer is in a specific situation, the reception desk can suggest appropriate inquiry methods. This allows for more appropriate responses by providing information based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's current situation and areas of interest into an AI, which can then output the optimal filtering results.

[0041] The reception desk can prioritize receiving highly relevant information by considering the customer's geographical location when receiving information. For example, if the customer is in a specific region, the reception desk will prioritize receiving information related to that region. The reception desk can also suggest the most appropriate inquiry method based on the customer's geographical location. Furthermore, if the customer is on the move, the reception desk can prioritize receiving information based on their current location. This allows for more appropriate responses by providing information based on the customer's geographical location. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's geographical location information into an AI, which can then output the most appropriate information.

[0042] The reception desk can analyze the customer's social media activity when receiving information and accept relevant information. For example, the reception desk can extract topics of interest from the customer's social media activity and accept relevant information. The reception desk can also suggest the most appropriate way to make an inquiry based on the information the customer has shared on social media. Furthermore, the reception desk can analyze the customer's social media activity and prioritize receiving inquiries that are relevant to that activity. This allows for more appropriate responses by providing information based on the customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's social media activity into AI, which can then output the most appropriate information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during the analysis process. For example, the analysis unit can perform a detailed analysis on inquiries of high importance. It can also perform a simplified analysis on inquiries of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the inquiry. This allows for more appropriate responses by providing analyses tailored to the importance of the inquiry. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the inquiry into the AI, which can then output the optimal analysis method.

[0044] The analysis unit can apply different analysis algorithms depending on the customer's attributes during analysis. For example, the analysis unit can select an appropriate analysis algorithm based on the customer's age. It can also select an appropriate analysis algorithm based on the customer's gender. Furthermore, the analysis unit can apply the optimal analysis algorithm based on the customer's past inquiry history. This enables more appropriate analysis by providing analysis algorithms tailored to the customer's attributes. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer attribute information into AI, and the AI ​​can output the optimal analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on when the inquiry was submitted. For example, if the inquiry was recently submitted, the analysis unit will prioritize its analysis. Conversely, if the inquiry is old, the analysis unit may postpone its analysis. The analysis unit can also adjust the priority of analysis according to when the inquiry was submitted. This allows for more appropriate responses by providing analysis tailored to the submission date of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the inquiry into the AI, and the AI ​​can output the optimal analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the inquiry content during analysis. For example, if an inquiry is related to other content, the analysis unit will prioritize its analysis. Conversely, if an inquiry is independent, the analysis unit can postpone its analysis. The analysis unit can also adjust the order of analysis according to the relevance of the inquiry content. This allows for more appropriate responses by providing analysis tailored to the relevance of the inquiry content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the inquiry content into the AI, and the AI ​​can output the optimal analysis order.

[0047] The matching unit can improve the accuracy of matching by considering the relationships between operators during the matching process. For example, the matching unit can perform optimal matching based on the past collaborative relationships between operators. The matching unit can also perform optimal matching by considering the operators' areas of expertise and skills. Furthermore, the matching unit can also perform optimal matching by considering the operators' work status and workload. This allows for more appropriate responses by providing matching that takes into account the relationships between operators. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input operator relationship data into AI, and the AI ​​can output the optimal matching result.

[0048] The matching unit can perform matching while considering the operator's attribute information. For example, the matching unit can perform optimal matching by considering the operator's age and gender. The matching unit can also perform optimal matching based on the operator's past interaction history. Furthermore, the matching unit can perform optimal matching by considering the operator's area of ​​expertise and skills. By providing matching that takes operator attribute information into account, more appropriate responses become possible. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the operator's attribute information into the AI, and the AI ​​can output the optimal matching result.

[0049] The matching unit can perform matching while considering the geographical distribution of operators. For example, if an operator is nearby, the matching unit will perform matching to enable a quick response. Conversely, if an operator is far away, the matching unit can perform matching to enable a response over a longer period of time. Furthermore, the matching unit can perform optimal matching based on the geographical distribution of operators. This allows for more appropriate responses by providing matching that takes the geographical distribution of operators into account. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data of operators into AI, and the AI ​​can output the optimal matching result.

[0050] The matching unit can improve the accuracy of matching by referring to the operator's relevant literature during the matching process. For example, the matching unit performs optimal matching based on the operator's past research and publications. The matching unit can also perform optimal matching by referring to literature related to the operator's field of expertise. Furthermore, the matching unit can perform optimal matching based on the operator's past achievements. This allows for more appropriate responses by providing matching that references the operator's relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the operator's relevant literature data into AI, and the AI ​​can output the optimal matching result.

[0051] The adjustment unit can analyze the operator's past interaction history during adjustment to select the optimal adjustment method. For example, the adjustment unit can select the optimal voice quality and image from the operator's past interaction history. The adjustment unit can also customize the adjustment method based on the operator's past interaction history. Furthermore, the adjustment unit can analyze the operator's past interaction history and propose the optimal adjustment method. This enables more appropriate responses by providing the optimal adjustment method based on the operator's past interaction history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's past interaction history data into AI, and the AI ​​can output the optimal adjustment method.

[0052] The adjustment unit can customize the adjustment methods based on the operator's current condition during adjustment. For example, if the operator is tired, the adjustment unit can adjust the voice quality to be calmer. If the operator is energetic, the adjustment unit can also adjust the voice quality to be brighter. Furthermore, the adjustment unit can provide the optimal image according to the operator's current condition. This allows for a more appropriate response by providing adjustment methods tailored to the operator's current condition. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's current condition data into the AI, and the AI ​​can output the optimal adjustment method.

[0053] The adjustment unit can select the optimal adjustment method by considering the operator's geographical location information during adjustment. For example, if the operator is in a specific region, the adjustment unit can provide voice quality and images appropriate for that region. The adjustment unit can also propose the optimal adjustment method based on the operator's geographical location information. Furthermore, if the operator is on the move, the adjustment unit can provide voice quality and images based on their current location. This allows for a more appropriate response by providing an adjustment method based on the operator's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's geographical location information into the AI, and the AI ​​can output the optimal adjustment method.

[0054] The adjustment unit can analyze the operator's social media activity during adjustment and propose adjustment methods. For example, the adjustment unit can suggest the optimal voice quality and images based on the operator's social media activity. The adjustment unit can also customize the adjustment method based on information shared by the operator on social media. Furthermore, the adjustment unit can analyze the operator's social media activity and propose the optimal adjustment method. This enables more appropriate responses by providing adjustment methods based on the operator's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's social media activity data into AI, and the AI ​​can output the optimal adjustment method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception department can analyze a customer's past inquiry history and select the most appropriate method of handling inquiries. For example, it can automatically display as suggestions the customer has frequently inquired about in the past. It can also prioritize suggesting inquiry methods (voice, text, etc.) that the customer has used in the past. Furthermore, it can predict and suggest inquiry methods that the customer will use at specific times of day based on their past inquiry history. This enables efficient service by providing the most suitable method of handling inquiries based on past inquiry history. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the customer's past inquiry history into an AI, which can then output the most appropriate method of handling inquiries.

[0057] The reception desk can filter information based on the customer's current situation and areas of interest when receiving it. For example, when a customer enters their current situation, relevant information is displayed preferentially. It can also automatically filter relevant inquiries based on the customer's areas of interest. Furthermore, if a customer is in a specific situation, it can suggest appropriate inquiry methods. This allows for more appropriate responses by providing information based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's current situation and areas of interest into an AI, which can then output the optimal filtering results.

[0058] The reception desk can prioritize receiving highly relevant information by considering the customer's geographical location when receiving information. For example, if the customer is in a specific region, it will prioritize receiving information related to that region. It can also suggest the most appropriate inquiry method based on the customer's geographical location. Furthermore, if the customer is on the move, it can prioritize receiving information based on their current location. This allows for more appropriate responses by providing information based on the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's geographical location into an AI, which can then output the most appropriate information.

[0059] The analysis unit can adjust the level of detail in the analysis based on the importance of the inquiry. For example, it can perform a detailed analysis on high-importance inquiries and a simplified analysis on low-importance inquiries. Furthermore, it can determine the priority of the analysis according to the importance of the inquiry. This allows for more appropriate responses by providing analyses tailored to the importance of the inquiry. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the inquiry into the AI, which can then output the optimal analysis method.

[0060] The analysis unit can apply different analysis algorithms depending on the customer's attributes during analysis. For example, it can select an appropriate analysis algorithm based on the customer's age. It can also select an appropriate analysis algorithm based on the customer's gender. Furthermore, it can apply the optimal analysis algorithm based on the customer's past inquiry history. This allows for more accurate analysis by providing analysis algorithms tailored to the customer's attributes. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer attribute information into AI, and the AI ​​can output the optimal analysis algorithm.

[0061] The analysis unit can determine the priority of analysis based on when the inquiry was submitted. For example, if the inquiry was recently submitted, it will be analyzed first. If the inquiry is old, it can be analyzed later. Furthermore, the analysis priority can be adjusted according to when the inquiry was submitted. This allows for more appropriate responses by providing analysis tailored to when the inquiry was submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the inquiry into the AI, and the AI ​​can output the optimal analysis priority.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives customer information. This information may include, for example, personal information, purchase history, and inquiry details. The reception desk can receive information via voice input or text input. Step 2: The analysis unit analyzes the information received by the reception unit to determine the customer's attributes. The analysis unit uses techniques such as data mining, statistical analysis, and machine learning to perform the analysis. For example, it can use voice analysis technology to analyze the tone of the customer's voice. Step 3: The matching unit selects the most suitable operator based on the attributes determined by the analysis unit. The matching unit selects operators based on attribute information such as age, gender, and purchase history. For example, AI can be used to analyze the attribute information of operators and select the most suitable operator. Step 4: The adjustment unit adjusts the voice quality and image of the operator selected by the matching unit. The adjustment unit can adjust the tone, pitch, and speed of the voice. Furthermore, it can adjust the resolution, color tone, and brightness of the image.

[0064] (Example of form 2) The call center system according to an embodiment of the present invention is a system that improves the quality of service by matching the customer with the most suitable operator based on their attributes upon incoming calls. The call center system uses AI to analyze the content of the inquiry, the customer's identity verification attributes, and the tone of their voice when a call is made, and to determine the attributes of the most suitable operator for the customer. Next, it matches the customer with a crew member whose attributes are closest to those of an available operator. For example, if the customer's attributes are "female," "in her 40s," and "polite," the AI ​​selects the operator who most closely matches those attributes. If a suitable operator is unavailable, the AI ​​adjusts the operator's voice quality to match the customer's attributes. Furthermore, during the call, the AI ​​changes the operator's voice quality to be optimal for the customer. For example, it may lower the tone of voice by one level or eliminate noise such as breathing sounds. The AI ​​also assists in guiding the customer, providing advice such as "Please speak a little more slowly." This makes the interaction between the customer and the operator smoother and improves customer satisfaction. In the case of video calls, the AI ​​also changes the operator's image to match the customer's attributes. For example, if the customer requests a "woman in her 40s," the AI ​​changes the operator's image to that of a woman in her 40s. In this way, the quality of call center service can be improved. This allows the call center system to match the most suitable operator to the customer's attributes, thereby improving the quality of service.

[0065] The call center system according to this embodiment comprises a reception unit, an analysis unit, a matching unit, and an adjustment unit. The reception unit receives customer information. Customer information includes, but is not limited to, personal information, purchase history, and inquiry content. The reception unit can receive information, for example, through voice input or text input. The analysis unit analyzes the information received by the reception unit and determines the customer's attributes. The analysis unit performs analysis using, for example, data mining, statistical analysis, and machine learning. The analysis unit can analyze the tone of the customer's voice using, for example, voice analysis technology. The matching unit selects the most suitable operator based on the attributes determined by the analysis unit. The matching unit selects an operator based on attribute information such as age, gender, and purchase history. The matching unit can analyze the operator's attribute information using, for example, AI and select the most suitable operator. The adjustment unit adjusts the voice quality and image of the operator selected by the matching unit. The adjustment unit can adjust, for example, the tone, pitch, and speed of the voice. The adjustment unit can adjust, for example, the resolution, color tone, and brightness of the image. As a result, the call center system according to this embodiment can match the customer with the most suitable operator based on their attributes, thereby improving the quality of service.

[0066] The reception department receives customer information. This information includes, but is not limited to, personal information, purchase history, and inquiry details. The reception department can receive information through methods such as voice input and text input. Specifically, in the case of voice input, speech recognition technology is used to convert the customer's speech into text data, and in the case of text input, the customer directly inputs the information using a keyboard or touchscreen. The reception department centrally manages this information and stores it in a database. Furthermore, the reception department has a feedback function to verify the accuracy of the entered information and can prompt the customer to confirm the input. For example, in the case of voice input, the recognized text is played back to the customer as audio and asked for confirmation. The reception department also supports multiple languages ​​and can handle international customers. This allows the reception department to efficiently receive and accurately manage information from diverse customers. In addition, the reception department transmits the received information to the analysis department in real time, enabling a rapid response. In this way, the reception department plays a role in improving the efficiency and service quality of the entire call center system.

[0067] The analysis department analyzes information received by the reception department to determine customer attributes. The analysis department uses techniques such as data mining, statistical analysis, and machine learning for its analysis. Specifically, it uses data mining techniques to extract patterns and trends from customer purchase history and inquiries, and statistical analysis to numerically evaluate customer attributes. Furthermore, it can use machine learning algorithms to predict future behavior based on past customer behavior data. The analysis department can also use voice analysis techniques to analyze the emotional tone of a customer's voice. Voice analysis techniques analyze the tone, pitch, speed, and emotion of the voice to determine the customer's emotional state and urgency. This allows the analysis department to accurately understand customer needs and emotional states and provide information for optimal response. Furthermore, the analysis department analyzes data in real time, enabling rapid responses. For example, it can analyze customer inquiries and immediately provide relevant information. In this way, the analysis department plays a role in improving the overall service quality of the call center system.

[0068] The matching unit selects the most suitable operator based on attributes determined by the analysis unit. For example, the matching unit selects operators based on attribute information such as age, gender, and purchase history. Specifically, it considers the operator's skill set, experience, and past interaction history to select the operator best suited to the customer's needs. The matching unit can, for example, use AI to analyze operator attribute information and select the most suitable operator. The AI ​​uses machine learning algorithms to learn from past matching data and find the optimal matching pattern. This allows the matching unit to select the most suitable operator quickly and accurately. Furthermore, the matching unit can monitor operator schedules and work status in real time, enabling efficient resource management. For example, it can monitor operator work status and provide appropriate breaks to avoid overload. The matching unit can also collect operator feedback and continuously improve the accuracy of its matching algorithm. This allows the matching unit to play a role in improving the overall efficiency and service quality of the call center system.

[0069] The adjustment unit adjusts the voice quality and image of the operator selected by the matching unit. For example, the adjustment unit can adjust the tone, pitch, and speed of the voice. Specifically, it uses voice processing technology to adjust the sound quality of the operator's voice to be easily heard by the customer. For example, it can adjust the tone of the voice to make it sound more approachable. It can also adjust the pitch and speed to improve intelligibility. Furthermore, the adjustment unit can adjust the resolution, color tone, and brightness of the image. Specifically, it uses image processing technology to adjust the image quality of the operator to be easily seen by the customer. For example, it can increase the resolution to provide a clearer image. It can also adjust the color tone and brightness to improve the quality of the image. In this way, the adjustment unit can optimize the voice quality and image of the operator and provide a comfortable customer service environment. Furthermore, the adjustment unit can monitor the quality of the voice and image in real time and make adjustments as needed. In this way, the adjustment unit plays a role in improving the overall service quality of the call center system.

[0070] The adjustment unit can adjust the operator's voice quality to match the customer's attributes. For example, the adjustment unit can adjust the tone of the voice. For example, the adjustment unit can lower the tone of the voice by one level. The adjustment unit can also adjust the pitch of the voice. For example, the adjustment unit can raise the pitch of the voice. The adjustment unit can also adjust the speed of the voice. For example, the adjustment unit can slow down the speed of the voice. By adjusting the operator's voice quality to match the customer's attributes, the quality of service can be improved. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's voice quality into the AI, and the AI ​​can output the optimal voice quality.

[0071] The adjustment unit can adjust the operator's image to match the customer's attributes. For example, the adjustment unit can adjust the image resolution. For example, the adjustment unit can increase the image resolution. The adjustment unit can also adjust the image's color tone. For example, the adjustment unit can brighten the image's color tone. The adjustment unit can also adjust the image's brightness. For example, the adjustment unit can darken the image's brightness. By adjusting the operator's image to match the customer's attributes, the quality of service can be improved. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's image into the AI, which can then output the optimal image.

[0072] The analysis unit can analyze the content of the call, the identity verification attributes, and the tone of the customer's voice during the call recording. For example, the analysis unit can analyze the content of the inquiry. For example, the analysis unit can analyze questions about products or inquiries about services. The analysis unit can also analyze the identity verification attributes. For example, the analysis unit can analyze identity verification attributes such as name, address, and telephone number. The analysis unit can also analyze the tone of the customer's voice. For example, the analysis unit can analyze the tone of the customer's voice using sentiment analysis technology. This allows for the selection of the most suitable operator by accurately analyzing the customer's attributes. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the content of the inquiry, identity verification attributes, and the tone of the customer's voice into the AI, and the AI ​​can output the analysis results.

[0073] The matching unit can select a crew member with similar attributes from among the available operators. The matching unit selects operators based on attribute information such as age, gender, and purchase history. For example, the matching unit can select an operator with a similar age. It can also select an operator with the same gender. It can also select an operator with a similar purchase history. This improves the quality of service by selecting an operator that is closest to the customer's attributes. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input operator attribute information into AI, and the AI ​​can output the most suitable operator.

[0074] The adjustment unit can change the operator's voice quality during a call. For example, the adjustment unit can change the tone of the voice. For example, the adjustment unit can lower the tone of the voice by one level. The adjustment unit can also change the pitch of the voice. For example, the adjustment unit can raise the pitch of the voice. The adjustment unit can also change the speed of the voice. For example, the adjustment unit can slow down the speed of the voice. By changing the operator's voice quality during a call, customer satisfaction can be improved. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's voice quality into the AI, and the AI ​​can output the optimal voice quality.

[0075] The reception desk can estimate the customer's emotions and adjust how information is received based on those estimates. For example, if the customer is stressed, the reception desk can provide a simple interface and minimize the input steps. If the customer is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the customer is in a hurry, the reception desk can prioritize voice input and receive information quickly. This allows for more appropriate responses by providing information receiving methods that are tailored to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI, which can then output the optimal reception method.

[0076] The reception department can analyze a customer's past inquiry history and select the most appropriate method of handling inquiries. For example, the reception department can automatically display as suggestions the type of inquiry the customer has frequently made in the past. It can also prioritize suggesting inquiry methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception department can predict and suggest inquiry methods to be used during specific time periods based on the customer's past inquiry history. This enables efficient handling by providing the most suitable method of handling inquiries based on past inquiry history. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the customer's past inquiry history into an AI, which can then output the most appropriate method of handling inquiries.

[0077] The reception desk can filter information received based on the customer's current situation and areas of interest. For example, when a customer enters their current situation, the reception desk will prioritize displaying relevant information. The reception desk can also automatically filter relevant inquiries based on the customer's areas of interest. Furthermore, if a customer is in a specific situation, the reception desk can suggest appropriate inquiry methods. This allows for more appropriate responses by providing information based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's current situation and areas of interest into an AI, which can then output the optimal filtering results.

[0078] The reception desk can estimate the customer's emotions and determine the priority of information to receive based on those emotions. For example, if the customer is nervous, the reception desk will prioritize receiving important information. If the customer is relaxed, the reception desk may also prioritize receiving detailed information. If the customer is in a hurry, the reception desk may also prioritize receiving information that requires a quick response. This allows for more appropriate service by prioritizing information according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI, which can then output the optimal information priority.

[0079] The reception desk can prioritize receiving highly relevant information by considering the customer's geographical location when receiving information. For example, if the customer is in a specific region, the reception desk will prioritize receiving information related to that region. The reception desk can also suggest the most appropriate inquiry method based on the customer's geographical location. Furthermore, if the customer is on the move, the reception desk can prioritize receiving information based on their current location. This allows for more appropriate responses by providing information based on the customer's geographical location. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's geographical location information into an AI, which can then output the most appropriate information.

[0080] The reception desk can analyze the customer's social media activity when receiving information and accept relevant information. For example, the reception desk can extract topics of interest from the customer's social media activity and accept relevant information. The reception desk can also suggest the most appropriate way to make an inquiry based on the information the customer has shared on social media. Furthermore, the reception desk can analyze the customer's social media activity and prioritize receiving inquiries that are relevant to that activity. This allows for more appropriate responses by providing information based on the customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's social media activity into AI, which can then output the most appropriate information.

[0081] The analysis unit can estimate the customer's emotions and adjust the analysis method based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can perform a detailed analysis. If the customer is in a hurry, the analysis unit can also perform a rapid analysis. Furthermore, if the customer is excited, the analysis unit can provide analysis results with visually stimulating effects. This allows for more appropriate analysis by providing an analysis method tailored to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input customer emotion data into a generative AI, which can then output the optimal analysis method.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during the analysis process. For example, the analysis unit can perform a detailed analysis on inquiries of high importance. It can also perform a simplified analysis on inquiries of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the inquiry. This allows for more appropriate responses by providing analyses tailored to the importance of the inquiry. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the inquiry into the AI, which can then output the optimal analysis method.

[0083] The analysis unit can apply different analysis algorithms depending on the customer's attributes during analysis. For example, the analysis unit can select an appropriate analysis algorithm based on the customer's age. It can also select an appropriate analysis algorithm based on the customer's gender. Furthermore, the analysis unit can apply the optimal analysis algorithm based on the customer's past inquiry history. This enables more appropriate analysis by providing analysis algorithms tailored to the customer's attributes. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer attribute information into AI, and the AI ​​can output the optimal analysis algorithm.

[0084] The analysis unit can estimate the customer's emotions and determine the priority of analysis based on the estimated emotions. For example, if the customer is nervous, the analysis unit will prioritize analyzing important information. If the customer is relaxed, the analysis unit can also analyze detailed information. If the customer is in a hurry, the analysis unit can also prioritize analyzing information that requires a quick response. This allows for a more appropriate response by providing analysis priorities according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input customer emotion data into a generative AI, and the generative AI can output the optimal analysis priority.

[0085] The analysis unit can determine the priority of analysis based on when the inquiry was submitted. For example, if the inquiry was recently submitted, the analysis unit will prioritize its analysis. Conversely, if the inquiry is old, the analysis unit may postpone its analysis. The analysis unit can also adjust the priority of analysis according to when the inquiry was submitted. This allows for more appropriate responses by providing analysis tailored to the submission date of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the inquiry into the AI, and the AI ​​can output the optimal analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the inquiry content during analysis. For example, if an inquiry is related to other content, the analysis unit will prioritize its analysis. Conversely, if an inquiry is independent, the analysis unit can postpone its analysis. The analysis unit can also adjust the order of analysis according to the relevance of the inquiry content. This allows for more appropriate responses by providing analysis tailored to the relevance of the inquiry content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the inquiry content into the AI, and the AI ​​can output the optimal analysis order.

[0087] The matching unit can estimate the customer's emotions and adjust the matching criteria based on the estimated emotions. For example, if the customer is relaxed, the matching unit can apply detailed matching criteria. If the customer is in a hurry, the matching unit can also apply criteria for quick matching. If the customer is excited, the matching unit can also apply matching criteria with visually stimulating effects. This allows for a more appropriate response by providing matching criteria that are tailored to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input customer emotion data into a generative AI, which can then output the optimal matching criteria.

[0088] The matching unit can improve the accuracy of matching by considering the relationships between operators during the matching process. For example, the matching unit can perform optimal matching based on the past collaborative relationships between operators. The matching unit can also perform optimal matching by considering the operators' areas of expertise and skills. Furthermore, the matching unit can also perform optimal matching by considering the operators' work status and workload. This allows for more appropriate responses by providing matching that takes into account the relationships between operators. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input operator relationship data into AI, and the AI ​​can output the optimal matching result.

[0089] The matching unit can perform matching while considering the operator's attribute information. For example, the matching unit can perform optimal matching by considering the operator's age and gender. The matching unit can also perform optimal matching based on the operator's past interaction history. Furthermore, the matching unit can perform optimal matching by considering the operator's area of ​​expertise and skills. By providing matching that takes operator attribute information into account, more appropriate responses become possible. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the operator's attribute information into the AI, and the AI ​​can output the optimal matching result.

[0090] The matching unit can estimate the customer's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the customer is nervous, the matching unit will prioritize displaying important information. If the customer is relaxed, the matching unit can also display detailed information. If the customer is in a hurry, the matching unit can also prioritize displaying information that requires a quick response. This allows for a more appropriate response by providing a display order of matching results that is tailored to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input customer emotion data into a generative AI, which can then output the optimal display order.

[0091] The matching unit can perform matching while considering the geographical distribution of operators. For example, if an operator is nearby, the matching unit will perform matching to enable a quick response. Conversely, if an operator is far away, the matching unit can perform matching to enable a response over a longer period of time. Furthermore, the matching unit can perform optimal matching based on the geographical distribution of operators. This allows for more appropriate responses by providing matching that takes the geographical distribution of operators into account. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data of operators into AI, and the AI ​​can output the optimal matching result.

[0092] The matching unit can improve the accuracy of matching by referring to the operator's relevant literature during the matching process. For example, the matching unit performs optimal matching based on the operator's past research and publications. The matching unit can also perform optimal matching by referring to literature related to the operator's field of expertise. Furthermore, the matching unit can perform optimal matching based on the operator's past achievements. This allows for more appropriate responses by providing matching that references the operator's relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the operator's relevant literature data into AI, and the AI ​​can output the optimal matching result.

[0093] The adjustment unit can estimate the customer's emotions and determine how to adjust the voice and image based on the estimated emotions. For example, if the customer is nervous, the adjustment unit will provide a calm voice and image. If the customer is relaxed, the adjustment unit can also provide a cheerful voice and image. If the customer is in a hurry, the adjustment unit can also provide a quick and concise voice and image. This allows for a more appropriate response by providing voice and image adjustments that match the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input customer emotion data into the generative AI, and the generative AI can output the optimal adjustment method.

[0094] The adjustment unit can analyze the operator's past interaction history during adjustment to select the optimal adjustment method. For example, the adjustment unit can select the optimal voice quality and image from the operator's past interaction history. The adjustment unit can also customize the adjustment method based on the operator's past interaction history. Furthermore, the adjustment unit can analyze the operator's past interaction history and propose the optimal adjustment method. This enables more appropriate responses by providing the optimal adjustment method based on the operator's past interaction history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's past interaction history data into AI, and the AI ​​can output the optimal adjustment method.

[0095] The adjustment unit can customize the adjustment methods based on the operator's current condition during adjustment. For example, if the operator is tired, the adjustment unit can adjust the voice quality to be calmer. If the operator is energetic, the adjustment unit can also adjust the voice quality to be brighter. Furthermore, the adjustment unit can provide the optimal image according to the operator's current condition. This allows for a more appropriate response by providing adjustment methods tailored to the operator's current condition. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's current condition data into the AI, and the AI ​​can output the optimal adjustment method.

[0096] The adjustment unit can estimate the customer's emotions and determine the priority of voice quality and images based on the estimated emotions. For example, if the customer is nervous, the adjustment unit can prioritize providing calm voice quality and images. It can also prioritize providing cheerful voice quality and images if the customer is relaxed. Furthermore, if the customer is in a hurry, the adjustment unit can prioritize providing quick and concise voice quality and images. This allows for a more appropriate response by providing voice quality and image priorities according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input customer emotion data into a generative AI, which can then output the optimal priority order.

[0097] The adjustment unit can select the optimal adjustment method by considering the operator's geographical location information during adjustment. For example, if the operator is in a specific region, the adjustment unit can provide voice quality and images appropriate for that region. The adjustment unit can also propose the optimal adjustment method based on the operator's geographical location information. Furthermore, if the operator is on the move, the adjustment unit can provide voice quality and images based on their current location. This allows for a more appropriate response by providing an adjustment method based on the operator's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's geographical location information into the AI, and the AI ​​can output the optimal adjustment method.

[0098] The adjustment unit can analyze the operator's social media activity during adjustment and propose adjustment methods. For example, the adjustment unit can suggest the optimal voice quality and images based on the operator's social media activity. The adjustment unit can also customize the adjustment method based on information shared by the operator on social media. Furthermore, the adjustment unit can analyze the operator's social media activity and propose the optimal adjustment method. This enables more appropriate responses by providing adjustment methods based on the operator's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the operator's social media activity data into AI, and the AI ​​can output the optimal adjustment method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can estimate the customer's emotions and adjust how information is received based on those estimates. For example, if the customer is stressed, a simple interface can be provided, minimizing the input steps. If the customer is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the customer is in a hurry, voice input can be prioritized to receive information quickly. This allows for more appropriate responses by providing information receiving methods tailored to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI, which can then output the optimal reception method.

[0101] The reception department can analyze a customer's past inquiry history and select the most appropriate method of handling inquiries. For example, it can automatically display as suggestions the customer has frequently inquired about in the past. It can also prioritize suggesting inquiry methods (voice, text, etc.) that the customer has used in the past. Furthermore, it can predict and suggest inquiry methods that the customer will use at specific times of day based on their past inquiry history. This enables efficient service by providing the most suitable method of handling inquiries based on past inquiry history. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the customer's past inquiry history into an AI, which can then output the most appropriate method of handling inquiries.

[0102] The reception desk can filter information based on the customer's current situation and areas of interest when receiving it. For example, when a customer enters their current situation, relevant information is displayed preferentially. It can also automatically filter relevant inquiries based on the customer's areas of interest. Furthermore, if a customer is in a specific situation, it can suggest appropriate inquiry methods. This allows for more appropriate responses by providing information based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's current situation and areas of interest into an AI, which can then output the optimal filtering results.

[0103] The reception desk can estimate the customer's emotions and determine the priority of information to receive based on those emotions. For example, if the customer is nervous, important information will be given priority. If the customer is relaxed, detailed information may be given priority. Furthermore, if the customer is in a hurry, information requiring a quick response may be given priority. This allows for more appropriate responses by prioritizing information according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI, which can then output the optimal information priority.

[0104] The reception desk can prioritize receiving highly relevant information by considering the customer's geographical location when receiving information. For example, if the customer is in a specific region, it will prioritize receiving information related to that region. It can also suggest the most appropriate inquiry method based on the customer's geographical location. Furthermore, if the customer is on the move, it can prioritize receiving information based on their current location. This allows for more appropriate responses by providing information based on the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's geographical location into an AI, which can then output the most appropriate information.

[0105] The analysis unit can estimate the customer's emotions and adjust the analysis method based on the estimated emotions. For example, if the customer is relaxed, a detailed analysis can be performed. If the customer is in a hurry, a rapid analysis can be performed. Furthermore, if the customer is excited, the analysis results can be provided with visually stimulating effects. This allows for more appropriate analysis by providing an analysis method that matches the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input customer emotion data into a generative AI, and the generative AI can output the optimal analysis method.

[0106] The analysis unit can adjust the level of detail in the analysis based on the importance of the inquiry. For example, it can perform a detailed analysis on high-importance inquiries and a simplified analysis on low-importance inquiries. Furthermore, it can determine the priority of the analysis according to the importance of the inquiry. This allows for more appropriate responses by providing analyses tailored to the importance of the inquiry. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the inquiry into the AI, which can then output the optimal analysis method.

[0107] The analysis unit can apply different analysis algorithms depending on the customer's attributes during analysis. For example, it can select an appropriate analysis algorithm based on the customer's age. It can also select an appropriate analysis algorithm based on the customer's gender. Furthermore, it can apply the optimal analysis algorithm based on the customer's past inquiry history. This allows for more accurate analysis by providing analysis algorithms tailored to the customer's attributes. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer attribute information into AI, and the AI ​​can output the optimal analysis algorithm.

[0108] The analysis unit can estimate the customer's emotions and determine the priority of analysis based on the estimated emotions. For example, if the customer is nervous, it can prioritize the analysis of important information. If the customer is relaxed, it can also prioritize the analysis of detailed information. Furthermore, if the customer is in a hurry, it can prioritize the analysis of information that requires a quick response. This allows for a more appropriate response by providing analysis priorities according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input customer emotion data into a generative AI, and the generative AI can output the optimal analysis priority.

[0109] The analysis unit can determine the priority of analysis based on when the inquiry was submitted. For example, if the inquiry was recently submitted, it will be analyzed first. If the inquiry is old, it can be analyzed later. Furthermore, the analysis priority can be adjusted according to when the inquiry was submitted. This allows for more appropriate responses by providing analysis tailored to when the inquiry was submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the inquiry into the AI, and the AI ​​can output the optimal analysis priority.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives customer information. This information may include, for example, personal information, purchase history, and inquiry details. The reception desk can receive information via voice input or text input. Step 2: The analysis unit analyzes the information received by the reception unit to determine the customer's attributes. The analysis unit uses techniques such as data mining, statistical analysis, and machine learning to perform the analysis. For example, it can use voice analysis technology to analyze the tone of the customer's voice. Step 3: The matching unit selects the most suitable operator based on the attributes determined by the analysis unit. The matching unit selects operators based on attribute information such as age, gender, and purchase history. For example, AI can be used to analyze the attribute information of operators and select the most suitable operator. Step 4: The adjustment unit adjusts the voice quality and image of the operator selected by the matching unit. The adjustment unit can adjust the tone, pitch, and speed of the voice. Furthermore, it can adjust the resolution, color tone, and brightness of the image.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives customer information through voice input or text input. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines customer attributes using data mining and machine learning techniques. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the optimal operator using AI. The adjustment unit is implemented by the control unit 46A of the smart device 14 and adjusts the operator's voice quality and image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives customer information through voice input. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the customer's attributes using statistical analysis and voice analysis technology. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the optimal operator using AI. The adjustment unit is implemented, for example, by the control unit 46A of the smart glasses 214 and adjusts the operator's voice quality and image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives customer information through voice input. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines customer attributes using machine learning and voice analysis technology. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the optimal operator using AI. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the operator's voice quality and image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, and adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives customer information through voice input. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines customer attributes using data mining and voice analysis technology. The matching unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and selects the optimal operator using AI. The adjustment unit is implemented by, for example, the control unit 46A of the robot 414 and adjusts the operator's voice quality and image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The reception area receives customer information, An analysis unit analyzes the information received by the reception unit and determines the customer's attributes, A matching unit selects the optimal operator based on the attributes determined by the analysis unit, The system includes an adjustment unit that adjusts the voice quality and image of the operator selected by the matching unit. A system characterized by the following features. (Note 2) The adjustment unit is, The operator's voice quality is adjusted to match the customer's characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, Adjust the operator's image to match the customer's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system analyzes the content of inquiries recorded during calls, the identity verification attributes, and the tone of the customer's voice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The matching unit is Select a crew member with similar attributes from among the available operators. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Change the operator's voice tone during the call. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the customer's emotions and adjust how information is received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the customer's past inquiry history and select the most suitable method of handling their inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving information, filtering is performed based on the customer's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We estimate the customer's emotions and prioritize the information they receive based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving information, we prioritize receiving highly relevant information by taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving information, we analyze the customer's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the customer's emotions and adjust the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the customer's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, We estimate the customer's emotions and determine the priority of analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis process, we will prioritize the analysis based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis will be adjusted based on the relevance of the inquiry content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is We estimate the customer's emotions and adjust the matching criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is The matching process takes into account the relationships between operators to improve matching accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is The matching process takes into account the operator's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is We estimate the customer's emotions and adjust the order in which matching results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is The matchmaking process takes into account the geographical distribution of operators. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is Referencing relevant literature from operators during the matching process improves matching accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, The system estimates the customer's emotions and determines how to adjust the voice quality and images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During the adjustment process, the operator's past interaction history is analyzed to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During adjustment, customize the adjustment method based on the operator's current status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, The system estimates the customer's emotions and prioritizes voice quality and images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, During adjustment, the operator's geographical location information is taken into consideration to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, Analyze the operator's social media activity during adjustments and propose adjustment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area that receives customer information, An analysis unit analyzes the information received by the reception unit and determines the customer's attributes, A matching unit selects the optimal operator based on the attributes determined by the analysis unit, The system includes an adjustment unit that adjusts the voice quality and image of the operator selected by the matching unit. A system characterized by the following features.

2. The adjustment unit is, The operator's voice quality is adjusted to match the customer's characteristics. The system according to feature 1.

3. The adjustment unit is, Adjust the operator's image to match the customer's attributes. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes the content of inquiries recorded during calls, the identity verification attributes, and the tone of the customer's voice. The system according to feature 1.

5. The matching unit is Select a crew member with similar attributes from among the available operators. The system according to feature 1.

6. The adjustment unit is, Change the operator's voice tone during the call. The system according to feature 1.

7. The aforementioned reception unit is We estimate the customer's emotions and adjust how information is received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the customer's past inquiry history and select the most suitable method of handling their inquiry. The system according to feature 1.

9. The aforementioned reception unit is When receiving information, filtering is performed based on the customer's current situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is We estimate the customer's emotions and prioritize the information they receive based on those estimated emotions. The system according to feature 1.

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