system

Abstract

The system according to this embodiment aims to analyze customer emotions and propose, learn, and automatically respond to appropriate countermeasures. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a learning unit, and a response unit. The analysis unit analyzes customer emotions in real time. The proposal unit proposes appropriate countermeasures based on the emotions analyzed by the analysis unit. The learning unit learns the countermeasures proposed by the proposal unit. The response unit generates automated responses to simple inquiries and complaints based on the countermeasures learned by the learning unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that employees in the customer service department have strong mental stress in claim handling and it is difficult to respond efficiently.

[0005] The system according to the embodiment aims to analyze the emotions of customers and propose, learn, and automatically respond with appropriate countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a learning unit, and a response unit. The analysis unit analyzes customer emotions in real time. The proposal unit proposes appropriate countermeasures based on the emotions analyzed by the analysis unit. The learning unit learns the countermeasures proposed by the proposal unit. The response unit generates automated responses to simple inquiries and complaints based on the countermeasures learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze customer emotions and propose, learn, and automatically respond to appropriate countermeasures. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 complaint handling support system according to an embodiment of the present invention is a system that reduces the mental stress on employees in a company's customer service department and enables efficient complaint handling. This system utilizes generative AI to analyze customer emotions in real time and propose appropriate countermeasures. This reduces the burden on staff and enables faster complaint handling and improved customer satisfaction. For example, when the complaint handling support system receives a complaint from a customer, the generative AI analyzes the customer's emotions in real time. For example, it analyzes the content of the customer's statements and tone of voice to determine whether the customer is angry or distressed. Next, the generative AI proposes appropriate countermeasures based on the analysis results. For example, if the customer is angry, it suggests encouraging a calm response, and if the customer is distressed, it proposes specific solutions. Furthermore, the generative AI analyzes past complaint data and learns the optimal response method. For example, if a similar complaint has occurred in the past, it learns the response method so that it can respond quickly the next time a similar complaint occurs. This improves the efficiency of complaint handling and reduces the burden on staff. The generative AI can also generate automated responses to simple inquiries and complaints. For example, it reduces the burden on staff by automatically providing answers to frequently asked questions and responses to simple complaints. This allows staff to focus on handling more complex complaints. The system reduces mental stress for customer service employees, leading to faster complaint resolution and improved customer satisfaction. It also significantly improves operational efficiency and contributes to cost reduction. For example, it is particularly effective for companies with large customer bases or those employing more than 100 customer service representatives. The complaint handling support system analyzes customer emotions in real time and proposes appropriate solutions, thereby enabling faster complaint resolution and improved customer satisfaction.

[0029] The complaint handling support system according to this embodiment comprises an analysis unit, a proposal unit, a learning unit, and a response unit. The analysis unit analyzes the customer's emotions in real time. For example, the analysis unit analyzes the customer's statements and tone of voice to determine whether the customer is angry or distressed. The analysis unit can analyze the customer's emotions in real time using generative AI. The proposal unit proposes appropriate countermeasures based on the emotions analyzed by the analysis unit. For example, if the customer is angry, the proposal unit suggests encouraging a calm response, and if the customer is distressed, it proposes specific solutions. The proposal unit can propose appropriate countermeasures based on the analysis results using generative AI. The learning unit learns the countermeasures proposed by the proposal unit. For example, the learning unit analyzes past complaint data and learns the optimal response method. The learning unit can analyze past complaint data and learn the optimal response method using generative AI. The response unit generates automated responses to simple inquiries and complaints based on the countermeasures learned by the learning unit. For example, the response unit automatically answers frequently asked questions and handles simple complaints. The response unit can use generation AI to automatically generate responses to simple inquiries and complaints. As a result, the complaint handling support system according to this embodiment can analyze customer emotions in real time and propose appropriate countermeasures, thereby achieving faster complaint handling and improved customer satisfaction.

[0030] The analysis unit analyzes customer emotions in real time. For example, it analyzes the content of customer statements and tone of voice to determine whether the customer is angry or confused. Specifically, it transcribes customer statements into text and performs emotion analysis using natural language processing technology. For tone of voice, it analyzes the audio data using speech recognition technology and extracts features such as volume, pitch, and tempo. By combining this data, it is possible to determine customer emotions with high accuracy. The analysis unit can also analyze customer emotions in real time using generative AI. Generative AI has learned from a large amount of emotion data and can estimate emotions with high accuracy from customer statements and tone of voice. For example, if a customer speaks quickly in a high-pitched voice, it is likely to be showing anger. Also, if a customer speaks slowly in a low-pitched voice, it is likely to be feeling confused or anxious. This allows the analysis unit to analyze customer emotions in real time and provide basic data for taking appropriate action. Furthermore, the analysis unit can continuously monitor changes in customer emotions and evaluate the effectiveness of the response. For example, if the customer's tone of voice calms down after the response, it can be determined that the response was effective. This allows the analysis unit to analyze customer emotions in real time, provide foundational data for appropriate responses, evaluate the effectiveness of those responses, and continuously improve the system.

[0031] The proposal department proposes appropriate countermeasures based on the emotions analyzed by the analysis department. For example, if a customer is angry, the proposal department will suggest a calm response, and if a customer is distressed, it will propose a specific solution. Specifically, the proposal department selects the optimal countermeasure based on the emotion data provided by the analysis department, referring to past response history and success stories. The proposal department can use generative AI to propose appropriate countermeasures based on the analysis results. The generative AI has learned from a large amount of complaint handling data and can automatically generate the optimal countermeasure according to the customer's emotions. For example, if a customer is angry, it will suggest words of apology and specific steps for resolving the problem. Also, if a customer is distressed, it will identify the cause of the problem and present a solution. This allows the proposal department to quickly propose appropriate countermeasures according to the customer's emotions. Furthermore, the proposal department can evaluate the effectiveness of the proposed countermeasures and continuously improve them. For example, if a proposed countermeasure improves customer satisfaction, it will be reflected in future proposals. Also, if a proposed countermeasure is not effective, the cause will be analyzed and areas for improvement will be identified. This allows the proposal department to not only quickly propose appropriate solutions tailored to customer emotions, but also to improve the quality of complaint handling through continuous improvement.

[0032] The learning unit learns the countermeasures proposed by the proposal unit. For example, the learning unit analyzes past complaint data to learn the optimal response methods. Specifically, the learning unit stores past complaint handling history in a database and analyzes that data using generative AI. The generative AI extracts patterns from past complaint handling data and classifies successful and unsuccessful countermeasures. This allows the learning unit to learn which countermeasures are effective and provide feedback to the proposal unit. The learning unit can analyze past complaint data using generative AI and learn the optimal response methods. By learning from a large amount of complaint data, the generative AI can automatically find the optimal countermeasures according to customer emotions and situations. For example, if there are many complaints about a particular product, it will identify common problems related to that product and learn effective countermeasures. Also, if a particular countermeasure improves customer satisfaction, it will apply that countermeasure to other complaint handling. In this way, the learning unit can not only analyze past complaint data and learn the optimal response methods, but also improve the quality of complaint handling by providing feedback to the proposal unit. Furthermore, the learning unit continues to learn each time new complaint data is added, enabling it to provide countermeasures based on the latest information. This allows the learning unit to consistently provide optimal countermeasures based on the most up-to-date information, continuously improving the quality of complaint handling.

[0033] The response unit generates automated responses to simple inquiries and complaints based on countermeasures learned by the learning unit. For example, the response unit automatically answers frequently asked questions and handles simple complaints. Specifically, the response unit generates automated responses to customer inquiries and complaints based on countermeasures provided by the learning unit. The response unit can generate automated responses to simple inquiries and complaints using a generation AI. The generation AI has learned from a large amount of inquiry data and can automatically generate appropriate answers to customer questions. For example, for questions about product usage, it provides specific operating procedures, and for complaints about product defects, it provides information on repair methods and replacement procedures. This allows the response unit to respond to customer inquiries and complaints quickly and accurately. Furthermore, the response unit can monitor customer reactions and modify its responses as needed. For example, if an automated response does not satisfy the customer, the response unit analyzes the cause and incorporates it into future responses. The response unit can also reliably convey information to customers using multiple communication methods. For example, in addition to providing automated responses via email and chatbots, it can also provide support via telephone and SMS as needed. This allows the response unit to not only respond quickly and accurately to customer inquiries and complaints, but also to improve customer satisfaction through continuous improvement.

[0034] The voice analysis unit can analyze the content of a customer's speech and the tone of their voice. For example, the voice analysis unit can analyze the content of a customer's speech to understand what the customer is saying. The voice analysis unit can use generative AI to analyze the content of a customer's speech. For example, the voice analysis unit can analyze the tone of a customer's voice to determine what emotions the customer is feeling. The voice analysis unit can use generative AI to analyze the tone of a customer's voice. For example, the voice analysis unit can combine the content of a customer's speech and the tone of their voice in its analysis to more accurately grasp the customer's emotions. The voice analysis unit can use generative AI to combine the content of a customer's speech and the tone of their voice in its analysis. This allows for a more accurate understanding of the customer's emotions by analyzing the content of their speech and the tone of their voice. Some or all of the above-described processes in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the content of a customer's speech and the tone of their voice into a generative AI and have the generative AI perform the analysis of the customer's emotions.

[0035] The data collection unit can collect past complaint data. For example, the data collection unit can collect customer complaint details and store them in a database. The data collection unit can collect customer complaint details using generative AI. For example, the data collection unit can collect customer interaction history and store it in a database. The data collection unit can collect customer interaction history using generative AI. For example, the data collection unit can collect customer complaint details and interaction history in combination and store them in a database. The data collection unit can collect customer complaint details and interaction history in combination using generative AI. This allows the unit to learn the optimal response method by collecting past complaint data and improve operational efficiency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer complaint details and interaction history into the generative AI and have the generative AI perform data collection.

[0036] The Generative AI Analysis Unit can analyze customer emotions using Generative AI. For example, the Generative AI Analysis Unit inputs customer statements and tone of voice into the Generative AI and analyzes customer emotions. The Generative AI Analysis Unit can analyze customer statements and tone of voice using Generative AI. For example, the Generative AI Analysis Unit analyzes customer emotions in real time and determines whether the customer is angry or troubled. The Generative AI Analysis Unit can analyze customer emotions in real time using Generative AI. For example, the Generative AI Analysis Unit analyzes customer emotions and sends the results to the Proposal Unit. This allows for a more accurate analysis of customer emotions by using Generative AI. Some or all of the above-described processes in the Generative AI Analysis Unit may be performed using AI, or not using AI. For example, the Generative AI Analysis Unit can input customer statements and tone of voice into the Generative AI and have the Generative AI perform the analysis of customer emotions.

[0037] The Generative AI Proposal Unit can propose appropriate countermeasures using the Generative AI. For example, the Generative AI Proposal Unit inputs appropriate countermeasures into the Generative AI based on the results of analyzing the customer's emotions and makes a proposal. The Generative AI Proposal Unit can propose appropriate countermeasures based on the results of analyzing the customer's emotions using the Generative AI. For example, if the customer is angry, the Generative AI Proposal Unit will make a proposal encouraging a calm response, and if the customer is in distress, it will propose a concrete solution. The Generative AI Proposal Unit can use the Generative AI to make a proposal encouraging a calm response if the customer is angry, and propose a concrete solution if the customer is in distress. For example, the Generative AI Proposal Unit will notify the person in charge of the proposal, and the person in charge will take action based on that proposal. The Generative AI Proposal Unit can use the Generative AI to notify the person in charge of the proposal, and the person in charge will take action based on that proposal. This allows for the rapid proposal of appropriate countermeasures by using the Generative AI. Some or all of the above-described processes in the Generative AI Proposal Unit may be performed using AI, for example, or not using AI. For example, the Generative AI Proposal Unit can input the results of analyzing customer emotions into the Generative AI and have the Generative AI execute suggestions for appropriate countermeasures.

[0038] The analysis unit can improve the accuracy of its analysis by referring to the customer's past statement history during the analysis. The analysis unit can, for example, refer to the customer's past complaint content and reflect similar patterns in the analysis. The analysis unit can use generational AI to refer to the customer's past complaint content and reflect similar patterns in the analysis. The analysis unit can, for example, analyze the customer's past statement tone and improve the accuracy of its analysis by comparing it with the current statement tone. The analysis unit can use generational AI to analyze the customer's past statement tone and improve the accuracy of its analysis by comparing it with the current statement tone. The analysis unit can, for example, refer to the customer's past interaction history and select the optimal analysis method. The analysis unit can use generational AI to refer to the customer's past interaction history and select the optimal analysis method. This allows the accuracy of the analysis to be improved by referring to the customer's past statement history. 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 customer's past statement history into generational AI and have generational AI perform the improvement of the analysis accuracy.

[0039] The analysis unit can perform analysis while considering the customer's current situation and background information. For example, the analysis unit can perform analysis while considering the customer's current situation (e.g., time elapsed since product purchase). The analysis unit can perform analysis while considering the customer's current situation using generative AI. For example, the analysis unit can perform analysis by referring to the customer's background information (e.g., past purchase history). The analysis unit can perform analysis by referring to the customer's background information using generative AI. For example, the analysis unit can perform analysis while considering the customer's current environment (e.g., devices being used). The analysis unit can perform analysis while considering the customer's current environment using generative AI. This makes it possible to perform more appropriate analysis by considering the customer's current situation and background information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the customer's current situation and background information into the generative AI and have the generative AI perform the analysis.

[0040] The analysis unit can perform analysis while considering the customer's geographical location information. The analysis unit can, for example, consider the customer's location and reflect region-specific problems in the analysis. The analysis unit can use generative AI to consider the customer's location and reflect region-specific problems in the analysis. The analysis unit can, for example, consider the customer's geographical distance and set priority for responses. The analysis unit can use generative AI to consider the customer's geographical distance and set priority for responses. The analysis unit can, for example, refer to weather information in the customer's region and reflect it in the analysis results. The analysis unit can use generative AI to refer to weather information in the customer's region and reflect it in the analysis results. This allows for addressing region-specific problems by considering the customer's geographical location information. 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 customer's geographical location information into the generative AI and have the generative AI perform the analysis.

[0041] The analysis unit can analyze the customer's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can analyze the content of the customer's social media posts and estimate their current sentiment. The analysis unit can use generative AI to analyze the content of the customer's social media posts and estimate their current sentiment. For example, the analysis unit can refer to the frequency of the customer's social media activity to improve the accuracy of the analysis. The analysis unit can use generative AI to refer to the frequency of the customer's social media activity to improve the accuracy of the analysis. For example, the analysis unit can consider the number of the customer's social media followers and set the importance of the response. The analysis unit can use generative AI to consider the number of the customer's social media followers and set the importance of the response. This makes it possible to perform a more accurate analysis by analyzing the customer's social media activity. 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 customer's social media activity into generative AI and have the generative AI perform the analysis.

[0042] The proposal unit can select the optimal proposal method by referring to past proposal history when making a proposal. For example, the proposal unit can refer to past successful proposal methods and propose a similar method. The proposal unit can use generative AI to refer to past successful proposal methods and propose a similar method. For example, the proposal unit can analyze customer reactions from past proposal history and select the optimal proposal method. The proposal unit can use generative AI to analyze customer reactions from past proposal history and select the optimal proposal method. For example, the proposal unit can select a proposal method tailored to customer preferences based on past proposal history. The proposal unit can use generative AI to select a proposal method tailored to customer preferences based on past proposal history. This allows the optimal proposal method to be selected by referring to past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past proposal history into generative AI and have the generative AI select the optimal proposal method.

[0043] The proposal unit can make proposals considering the customer's current situation and background information. For example, the proposal unit can make proposals considering the customer's current situation (e.g., time elapsed since product purchase). The proposal unit can use generative AI to make proposals considering the customer's current situation. For example, the proposal unit can make proposals by referring to the customer's background information (e.g., past purchase history). The proposal unit can use generative AI to refer to the customer's background information. For example, the proposal unit can make proposals considering the customer's current environment (e.g., devices being used). The proposal unit can use generative AI to make proposals considering the customer's current environment. This makes it possible to make more appropriate proposals by considering the customer's current situation and background information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the customer's current situation and background information into the generative AI and have the generative AI execute the proposal.

[0044] The proposal unit can make optimal proposals by considering the customer's geographical location information when making a proposal. For example, the proposal unit can consider the customer's location and make region-specific proposals. The proposal unit can use generative AI to consider the customer's location and make region-specific proposals. For example, the proposal unit can consider the customer's geographical distance and set priority for responses. The proposal unit can use generative AI to consider the customer's geographical distance and set priority for responses. For example, the proposal unit can refer to weather information in the customer's region and reflect it in the proposal content. The proposal unit can use generative AI to refer to weather information in the customer's region and reflect it in the proposal content. This makes it possible to make region-specific proposals by considering the customer's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the customer's geographical location information into the generative AI and have the generative AI execute the proposal.

[0045] The proposal department can analyze the customer's social media activity and reflect relevant information in the proposal. For example, the proposal department can refer to the customer's social media posts and reflect them in the proposal. The proposal department can use generative AI to refer to the customer's social media posts and reflect them in the proposal. For example, the proposal department can consider the customer's frequency of social media activity and set the priority of proposals. The proposal department can use generative AI to consider the customer's frequency of social media activity and set the priority of proposals. For example, the proposal department can consider the customer's number of social media followers and reflect it in the proposal. The proposal department can use generative AI to consider the customer's number of social media followers and reflect it in the proposal. This makes it possible to make more appropriate proposals by analyzing the customer's social media activity. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the customer's social media activity into generative AI and have the generative AI execute the proposal.

[0046] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can select the optimal learning algorithm based on past learning data using generative AI. For example, the learning unit can analyze customer responses from past learning data and optimize the learning algorithm. The learning unit can analyze customer responses from past learning data and optimize the learning algorithm using generative AI. For example, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. The learning unit can adjust the parameters of the learning algorithm by referring to past learning data using generative AI. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0047] The learning unit can perform learning while considering the customer's current situation and background information. For example, the learning unit can perform learning while considering the customer's current situation (e.g., the time elapsed since the product purchase). The learning unit can perform learning while considering the customer's current situation using generative AI. For example, the learning unit can perform learning by referring to the customer's background information (e.g., past purchase history). The learning unit can perform learning by referring to the customer's background information using generative AI. For example, the learning unit can perform learning while considering the customer's current environment (e.g., the device being used). The learning unit can perform learning while considering the customer's current environment using generative AI. This makes it possible to perform more appropriate learning by considering the customer's current situation and background information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the customer's current situation and background information into the generative AI and have the generative AI perform the learning.

[0048] The learning unit can weight the training data while considering the customer's geographical location information during training. For example, the learning unit can weight the training data for region-specific problems while considering the customer's location. The learning unit can use generative AI to weight the training data for region-specific problems while considering the customer's location. For example, the learning unit can set priority for responses while considering the customer's geographical distance. The learning unit can use generative AI to set priority for responses while considering the customer's geographical distance. For example, the learning unit can refer to weather information in the customer's region to weight the training data. The learning unit can use generative AI to refer to weather information in the customer's region to weight the training data. This allows the learning unit to respond to region-specific problems by considering the customer's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the customer's geographical location information into the generative AI and have the generative AI perform the weighting of the training data.

[0049] The learning unit can analyze the customer's social media activity during training and reflect relevant information in the learning process. For example, the learning unit can refer to the customer's social media posts and reflect them in the training data. The learning unit can use generative AI to refer to the customer's social media posts and reflect them in the training data. For example, the learning unit can consider the customer's frequency of social media activity and weight the training data. The learning unit can use generative AI to consider the customer's frequency of social media activity and weight the training data. For example, the learning unit can consider the customer's number of social media followers and reflect them in the training data. The learning unit can use generative AI to consider the customer's number of social media followers and reflect them in the training data. This enables more appropriate learning by analyzing the customer's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the customer's social media activity into a generative AI and have the generative AI perform the learning.

[0050] The response unit can select the optimal response method by referring to past response history when responding. For example, the response unit can refer to a previously successful response method and respond in a similar manner. The response unit can use generational AI to refer to a previously successful response method and respond in a similar manner. For example, the response unit can analyze customer reactions from past response history and select the optimal response method. The response unit can use generational AI to analyze customer reactions from past response history and select the optimal response method. For example, the response unit can select a response method tailored to customer preferences based on past response history. The response unit can use generational AI to select a response method tailored to customer preferences based on past response history. This allows the response unit to select the optimal response method by referring to past response history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input past response history into generational AI and have the generational AI select the optimal response method.

[0051] The response unit can respond while considering the customer's current situation and background information. For example, the response unit can respond while considering the customer's current situation (e.g., the time elapsed since the product purchase). The response unit can respond while considering the customer's current situation using generative AI. For example, the response unit can respond by referring to the customer's background information (e.g., past purchase history). The response unit can respond by referring to the customer's background information using generative AI. For example, the response unit can respond while considering the customer's current environment (e.g., the device being used). The response unit can respond while considering the customer's current environment using generative AI. This makes it possible to provide a more appropriate response by considering the customer's current situation and background information. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's current situation and background information into the generative AI and have the generative AI execute the response.

[0052] The response unit can provide the optimal response by considering the customer's geographical location information when responding. For example, the response unit can consider the customer's location and respond to region-specific issues. The response unit can use generative AI to consider the customer's location and respond to region-specific issues. For example, the response unit can consider the customer's geographical distance and set priority for responses. The response unit can use generative AI to consider the customer's geographical distance and set priority for responses. For example, the response unit can refer to weather information in the customer's region and reflect it in the response. The response unit can use generative AI to refer to weather information in the customer's region and reflect it in the response. This allows the response unit to address region-specific issues by considering the customer's geographical location information. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's geographical location information into generative AI and have the generative AI execute the response.

[0053] The response unit can analyze the customer's social media activity and reflect relevant information in the response. For example, the response unit can refer to the customer's social media posts and reflect them in the response. The response unit can use generative AI to refer to the customer's social media posts and reflect them in the response. For example, the response unit can consider the customer's frequency of social media activity and set response priorities. The response unit can use generative AI to consider the customer's frequency of social media activity and set response priorities. For example, the response unit can consider the customer's number of social media followers and reflect them in the response. The response unit can use generative AI to consider the customer's number of social media followers and reflect them in the response. This allows for more appropriate responses by analyzing the customer's social media activity. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's social media activity into generative AI and have the generative AI execute the response.

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

[0055] The analysis unit can analyze customer statements while taking into account the customer's language and dialect. For example, if a customer uses a specific regional dialect, it can accurately understand expressions unique to that dialect. If a customer uses a foreign language, it performs analysis corresponding to that language. Furthermore, if a customer uses multiple languages, it appropriately analyzes each language to grasp the overall meaning. This allows for more accurate analysis by considering the customer's language and dialect.

[0056] The data collection unit can collect relevant data by referring to customers' purchase and usage history when gathering customer complaints. For example, it can collect complaints about products and services that customers have purchased in the past and store them in a database. It can also collect complaints about services that customers frequently used during a specific period and use them for analysis. Furthermore, it can collect complaints related to customers' participation in specific campaigns and promotions and use them for future improvements. In this way, more relevant data can be collected by referring to customers' purchase and usage history.

[0057] The AI-generated proposal system can adjust its proposals by considering past customer feedback. For example, if a customer was satisfied with a previously proposed solution, it will make a similar proposal. If a customer expressed dissatisfaction with a previously proposed solution, it will propose a different approach. Furthermore, it analyzes how customers have reacted to information previously provided and optimizes the proposals accordingly. This allows for more effective proposals by taking past customer feedback into consideration.

[0058] The analysis unit can evaluate the frequency and consistency of customer statements when referring to their past statements. For example, if a customer frequently repeats the same complaint, the analysis unit will prioritize that complaint and respond promptly. If a customer's statements are consistent, their reliability will be highly valued. Furthermore, if a customer's statements contradict past statements, the analysis unit will identify the inconsistencies and take appropriate action. By evaluating the frequency and consistency of customer statements, more accurate analysis becomes possible.

[0059] The analysis unit can incorporate customer life events and significant occurrences into its analysis, taking into account the customer's current situation and background information. For example, if a customer has recently moved, the analysis will take that stress into account. If a customer has started a new job, the analysis will take their workload into account. Furthermore, if a customer has a family health issue, the analysis will take that situation into account. By considering the customer's life events and significant occurrences, a more appropriate analysis becomes possible.

[0060] The analysis unit can incorporate the customer's local culture and customs into its analysis when considering the customer's geographical location. For example, if a customer uses language based on a specific local culture, the analysis unit will understand and respond to that cultural background. If a customer is making a complaint based on a specific local custom, the analysis unit will take that custom into consideration. Furthermore, if a customer is making a complaint related to a specific local event or holiday, the analysis unit will take that event or holiday into consideration. This allows for more accurate analysis by considering the customer's local culture and customs.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis unit analyzes the customer's emotions in real time. The analysis unit analyzes the customer's statements and tone of voice to determine whether the customer is angry or distressed. Generative AI can be used to analyze the customer's emotions in real time. Step 2: The proposal unit proposes appropriate countermeasures based on the emotions analyzed by the analysis unit. For example, if the customer is angry, it will suggest a calm response; if the customer is troubled, it will propose specific solutions. Generative AI can be used to propose appropriate countermeasures based on the analysis results. Step 3: The learning unit learns the countermeasures proposed by the proposal unit. It analyzes past complaint data and learns the optimal response method. Using generation AI, it is possible to analyze past complaint data and learn the optimal response method. Step 4: The response unit generates automated responses to simple inquiries and complaints based on the countermeasures learned by the learning unit. For example, it automatically answers frequently asked questions and handles simple complaints. The generation AI can be used to generate automated responses to simple inquiries and complaints.

[0063] (Example of form 2) The complaint handling support system according to an embodiment of the present invention is a system that reduces the mental stress on employees in a company's customer service department and enables efficient complaint handling. This system utilizes generative AI to analyze customer emotions in real time and propose appropriate countermeasures. This reduces the burden on staff and enables faster complaint handling and improved customer satisfaction. For example, when the complaint handling support system receives a complaint from a customer, the generative AI analyzes the customer's emotions in real time. For example, it analyzes the content of the customer's statements and tone of voice to determine whether the customer is angry or distressed. Next, the generative AI proposes appropriate countermeasures based on the analysis results. For example, if the customer is angry, it suggests encouraging a calm response, and if the customer is distressed, it proposes specific solutions. Furthermore, the generative AI analyzes past complaint data and learns the optimal response method. For example, if a similar complaint has occurred in the past, it learns the response method so that it can respond quickly the next time a similar complaint occurs. This improves the efficiency of complaint handling and reduces the burden on staff. The generative AI can also generate automated responses to simple inquiries and complaints. For example, it reduces the burden on staff by automatically providing answers to frequently asked questions and responses to simple complaints. This allows staff to focus on handling more complex complaints. The system reduces mental stress for customer service employees, leading to faster complaint resolution and improved customer satisfaction. It also significantly improves operational efficiency and contributes to cost reduction. For example, it is particularly effective for companies with large customer bases or those employing more than 100 customer service representatives. The complaint handling support system analyzes customer emotions in real time and proposes appropriate solutions, thereby enabling faster complaint resolution and improved customer satisfaction.

[0064] The complaint handling support system according to this embodiment comprises an analysis unit, a proposal unit, a learning unit, and a response unit. The analysis unit analyzes the customer's emotions in real time. For example, the analysis unit analyzes the customer's statements and tone of voice to determine whether the customer is angry or distressed. The analysis unit can analyze the customer's emotions in real time using generative AI. The proposal unit proposes appropriate countermeasures based on the emotions analyzed by the analysis unit. For example, if the customer is angry, the proposal unit suggests encouraging a calm response, and if the customer is distressed, it proposes specific solutions. The proposal unit can propose appropriate countermeasures based on the analysis results using generative AI. The learning unit learns the countermeasures proposed by the proposal unit. For example, the learning unit analyzes past complaint data and learns the optimal response method. The learning unit can analyze past complaint data and learn the optimal response method using generative AI. The response unit generates automated responses to simple inquiries and complaints based on the countermeasures learned by the learning unit. For example, the response unit automatically answers frequently asked questions and handles simple complaints. The response unit can use generation AI to automatically generate responses to simple inquiries and complaints. As a result, the complaint handling support system according to this embodiment can analyze customer emotions in real time and propose appropriate countermeasures, thereby achieving faster complaint handling and improved customer satisfaction.

[0065] The analysis unit analyzes customer emotions in real time. For example, it analyzes the content of customer statements and tone of voice to determine whether the customer is angry or confused. Specifically, it transcribes customer statements into text and performs emotion analysis using natural language processing technology. For tone of voice, it analyzes the audio data using speech recognition technology and extracts features such as volume, pitch, and tempo. By combining this data, it is possible to determine customer emotions with high accuracy. The analysis unit can also analyze customer emotions in real time using generative AI. Generative AI has learned from a large amount of emotion data and can estimate emotions with high accuracy from customer statements and tone of voice. For example, if a customer speaks quickly in a high-pitched voice, it is likely to be showing anger. Also, if a customer speaks slowly in a low-pitched voice, it is likely to be feeling confused or anxious. This allows the analysis unit to analyze customer emotions in real time and provide basic data for taking appropriate action. Furthermore, the analysis unit can continuously monitor changes in customer emotions and evaluate the effectiveness of the response. For example, if the customer's tone of voice calms down after the response, it can be determined that the response was effective. This allows the analysis unit to analyze customer emotions in real time, provide foundational data for appropriate responses, evaluate the effectiveness of those responses, and continuously improve the system.

[0066] The proposal department proposes appropriate countermeasures based on the emotions analyzed by the analysis department. For example, if a customer is angry, the proposal department will suggest a calm response, and if a customer is distressed, it will propose a specific solution. Specifically, the proposal department selects the optimal countermeasure based on the emotion data provided by the analysis department, referring to past response history and success stories. The proposal department can use generative AI to propose appropriate countermeasures based on the analysis results. The generative AI has learned from a large amount of complaint handling data and can automatically generate the optimal countermeasure according to the customer's emotions. For example, if a customer is angry, it will suggest words of apology and specific steps for resolving the problem. Also, if a customer is distressed, it will identify the cause of the problem and present a solution. This allows the proposal department to quickly propose appropriate countermeasures according to the customer's emotions. Furthermore, the proposal department can evaluate the effectiveness of the proposed countermeasures and continuously improve them. For example, if a proposed countermeasure improves customer satisfaction, it will be reflected in future proposals. Also, if a proposed countermeasure is not effective, the cause will be analyzed and areas for improvement will be identified. This allows the proposal department to not only quickly propose appropriate solutions tailored to customer emotions, but also to improve the quality of complaint handling through continuous improvement.

[0067] The learning unit learns the countermeasures proposed by the proposal unit. For example, the learning unit analyzes past complaint data to learn the optimal response methods. Specifically, the learning unit stores past complaint handling history in a database and analyzes that data using generative AI. The generative AI extracts patterns from past complaint handling data and classifies successful and unsuccessful countermeasures. This allows the learning unit to learn which countermeasures are effective and provide feedback to the proposal unit. The learning unit can analyze past complaint data using generative AI and learn the optimal response methods. By learning from a large amount of complaint data, the generative AI can automatically find the optimal countermeasures according to customer emotions and situations. For example, if there are many complaints about a particular product, it will identify common problems related to that product and learn effective countermeasures. Also, if a particular countermeasure improves customer satisfaction, it will apply that countermeasure to other complaint handling. In this way, the learning unit can not only analyze past complaint data and learn the optimal response methods, but also improve the quality of complaint handling by providing feedback to the proposal unit. Furthermore, the learning unit continues to learn each time new complaint data is added, enabling it to provide countermeasures based on the latest information. This allows the learning unit to consistently provide optimal countermeasures based on the most up-to-date information, continuously improving the quality of complaint handling.

[0068] The response unit generates automated responses to simple inquiries and complaints based on countermeasures learned by the learning unit. For example, the response unit automatically answers frequently asked questions and handles simple complaints. Specifically, the response unit generates automated responses to customer inquiries and complaints based on countermeasures provided by the learning unit. The response unit can generate automated responses to simple inquiries and complaints using a generation AI. The generation AI has learned from a large amount of inquiry data and can automatically generate appropriate answers to customer questions. For example, for questions about product usage, it provides specific operating procedures, and for complaints about product defects, it provides information on repair methods and replacement procedures. This allows the response unit to respond to customer inquiries and complaints quickly and accurately. Furthermore, the response unit can monitor customer reactions and modify its responses as needed. For example, if an automated response does not satisfy the customer, the response unit analyzes the cause and incorporates it into future responses. The response unit can also reliably convey information to customers using multiple communication methods. For example, in addition to providing automated responses via email and chatbots, it can also provide support via telephone and SMS as needed. This allows the response unit to not only respond quickly and accurately to customer inquiries and complaints, but also to improve customer satisfaction through continuous improvement.

[0069] The voice analysis unit can analyze the content of a customer's speech and the tone of their voice. For example, the voice analysis unit can analyze the content of a customer's speech to understand what the customer is saying. The voice analysis unit can use generative AI to analyze the content of a customer's speech. For example, the voice analysis unit can analyze the tone of a customer's voice to determine what emotions the customer is feeling. The voice analysis unit can use generative AI to analyze the tone of a customer's voice. For example, the voice analysis unit can combine the content of a customer's speech and the tone of their voice in its analysis to more accurately grasp the customer's emotions. The voice analysis unit can use generative AI to combine the content of a customer's speech and the tone of their voice in its analysis. This allows for a more accurate understanding of the customer's emotions by analyzing the content of their speech and the tone of their voice. Some or all of the above-described processes in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the content of a customer's speech and the tone of their voice into a generative AI and have the generative AI perform the analysis of the customer's emotions.

[0070] The data collection unit can collect past complaint data. For example, the data collection unit can collect customer complaint details and store them in a database. The data collection unit can collect customer complaint details using generative AI. For example, the data collection unit can collect customer interaction history and store it in a database. The data collection unit can collect customer interaction history using generative AI. For example, the data collection unit can collect customer complaint details and interaction history in combination and store them in a database. The data collection unit can collect customer complaint details and interaction history in combination using generative AI. This allows the unit to learn the optimal response method by collecting past complaint data and improve operational efficiency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer complaint details and interaction history into the generative AI and have the generative AI perform data collection.

[0071] The Generative AI Analysis Unit can analyze customer emotions using Generative AI. For example, the Generative AI Analysis Unit inputs customer statements and tone of voice into the Generative AI and analyzes customer emotions. The Generative AI Analysis Unit can analyze customer statements and tone of voice using Generative AI. For example, the Generative AI Analysis Unit analyzes customer emotions in real time and determines whether the customer is angry or troubled. The Generative AI Analysis Unit can analyze customer emotions in real time using Generative AI. For example, the Generative AI Analysis Unit analyzes customer emotions and sends the results to the Proposal Unit. This allows for a more accurate analysis of customer emotions by using Generative AI. Some or all of the above-described processes in the Generative AI Analysis Unit may be performed using AI, or not using AI. For example, the Generative AI Analysis Unit can input customer statements and tone of voice into the Generative AI and have the Generative AI perform the analysis of customer emotions.

[0072] The Generative AI Proposal Unit can propose appropriate countermeasures using the Generative AI. For example, the Generative AI Proposal Unit inputs appropriate countermeasures into the Generative AI based on the results of analyzing the customer's emotions and makes a proposal. The Generative AI Proposal Unit can propose appropriate countermeasures based on the results of analyzing the customer's emotions using the Generative AI. For example, if the customer is angry, the Generative AI Proposal Unit will make a proposal encouraging a calm response, and if the customer is in distress, it will propose a concrete solution. The Generative AI Proposal Unit can use the Generative AI to make a proposal encouraging a calm response if the customer is angry, and propose a concrete solution if the customer is in distress. For example, the Generative AI Proposal Unit will notify the person in charge of the proposal, and the person in charge will take action based on that proposal. The Generative AI Proposal Unit can use the Generative AI to notify the person in charge of the proposal, and the person in charge will take action based on that proposal. This allows for the rapid proposal of appropriate countermeasures by using the Generative AI. Some or all of the above-described processes in the Generative AI Proposal Unit may be performed using AI, for example, or not using AI. For example, the Generative AI Proposal Unit can input the results of analyzing customer emotions into the Generative AI and have the Generative AI execute suggestions for appropriate countermeasures.

[0073] The analysis unit can estimate the customer's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the customer is angry, the analysis unit can use the generative AI to set the intensity of the emotion high and perform a detailed analysis. The analysis unit can use the generative AI to set the intensity of the emotion high and perform a detailed analysis when the customer is angry. For example, if the customer is distressed, the analysis unit can use the generative AI to set the intensity of the emotion medium and perform a moderate analysis. The analysis unit can use the generative AI to set the intensity of the emotion medium and perform a moderate analysis when the customer is distressed. For example, if the customer is calm, the analysis unit can use the generative AI to set the intensity of the emotion low and perform a simplified analysis. The analysis unit can use the generative AI to set the intensity of the emotion low and perform a simplified analysis when the customer is calm. This allows for more accurate analysis by adjusting the accuracy of the analysis based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit may input customer sentiment data into the generation AI and have the generation AI adjust the accuracy of the analysis.

[0074] The analysis unit can improve the accuracy of its analysis by referring to the customer's past statement history during the analysis. The analysis unit can, for example, refer to the customer's past complaint content and reflect similar patterns in the analysis. The analysis unit can use generational AI to refer to the customer's past complaint content and reflect similar patterns in the analysis. The analysis unit can, for example, analyze the customer's past statement tone and improve the accuracy of its analysis by comparing it with the current statement tone. The analysis unit can use generational AI to analyze the customer's past statement tone and improve the accuracy of its analysis by comparing it with the current statement tone. The analysis unit can, for example, refer to the customer's past interaction history and select the optimal analysis method. The analysis unit can use generational AI to refer to the customer's past interaction history and select the optimal analysis method. This allows the accuracy of the analysis to be improved by referring to the customer's past statement history. 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 customer's past statement history into generational AI and have generational AI perform the improvement of the analysis accuracy.

[0075] The analysis unit can perform analysis while considering the customer's current situation and background information. For example, the analysis unit can perform analysis while considering the customer's current situation (e.g., time elapsed since product purchase). The analysis unit can perform analysis while considering the customer's current situation using generative AI. For example, the analysis unit can perform analysis by referring to the customer's background information (e.g., past purchase history). The analysis unit can perform analysis by referring to the customer's background information using generative AI. For example, the analysis unit can perform analysis while considering the customer's current environment (e.g., devices being used). The analysis unit can perform analysis while considering the customer's current environment using generative AI. This makes it possible to perform more appropriate analysis by considering the customer's current situation and background information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the customer's current situation and background information into the generative AI and have the generative AI perform the analysis.

[0076] The analysis unit can estimate the customer's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the customer is angry, the generation AI will display the analysis results concisely to encourage a quick response. The analysis unit can use generation AI to display the analysis results concisely when the customer is angry to encourage a quick response. For example, if the customer is troubled, the generation AI will display the analysis results in detail and suggest specific solutions. The analysis unit can use generation AI to display the analysis results in detail when the customer is troubled and suggest specific solutions. For example, if the customer is calm, the generation AI will display the analysis results in a standard manner to encourage a normal response. The analysis unit can use generation AI to display the analysis results in a standard manner when the customer is calm to encourage a normal response. This allows for a more appropriate response by adjusting how the analysis results are displayed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit may input customer sentiment data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0077] The analysis unit can perform analysis while considering the customer's geographical location information. The analysis unit can, for example, consider the customer's location and reflect region-specific problems in the analysis. The analysis unit can use generative AI to consider the customer's location and reflect region-specific problems in the analysis. The analysis unit can, for example, consider the customer's geographical distance and set priority for responses. The analysis unit can use generative AI to consider the customer's geographical distance and set priority for responses. The analysis unit can, for example, refer to weather information in the customer's region and reflect it in the analysis results. The analysis unit can use generative AI to refer to weather information in the customer's region and reflect it in the analysis results. This allows for addressing region-specific problems by considering the customer's geographical location information. 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 customer's geographical location information into the generative AI and have the generative AI perform the analysis.

[0078] The analysis unit can analyze the customer's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can analyze the content of the customer's social media posts and estimate their current sentiment. The analysis unit can use generative AI to analyze the content of the customer's social media posts and estimate their current sentiment. For example, the analysis unit can refer to the frequency of the customer's social media activity to improve the accuracy of the analysis. The analysis unit can use generative AI to refer to the frequency of the customer's social media activity to improve the accuracy of the analysis. For example, the analysis unit can consider the number of the customer's social media followers and set the importance of the response. The analysis unit can use generative AI to consider the number of the customer's social media followers and set the importance of the response. This makes it possible to perform a more accurate analysis by analyzing the customer's social media activity. 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 customer's social media activity into generative AI and have the generative AI perform the analysis.

[0079] The proposal unit can estimate the customer's emotions and adjust the way it expresses its proposals based on those emotions. For example, if the customer is angry, the proposal unit can make a calm proposal. The proposal unit can use generative AI to make a calm proposal when the customer is angry. For example, if the customer is troubled, the proposal unit can make a specific and helpful proposal. The proposal unit can use generative AI to make a specific and helpful proposal when the customer is troubled. For example, if the customer is calm, the proposal unit can make a standard proposal. The proposal unit can use generative AI to make a standard proposal when the customer is calm. By adjusting the way it expresses its proposals based on the customer's emotions, more appropriate proposals become possible. Emotion estimation is achieved using an emotion estimation function, for example, 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 proposal unit may be performed using AI, for example, or without AI. For example, the proposal department can input customer emotional data into a generation AI and have the AI ​​adjust the way the proposal is expressed.

[0080] The proposal unit can select the optimal proposal method by referring to past proposal history when making a proposal. For example, the proposal unit can refer to past successful proposal methods and propose a similar method. The proposal unit can use generative AI to refer to past successful proposal methods and propose a similar method. For example, the proposal unit can analyze customer reactions from past proposal history and select the optimal proposal method. The proposal unit can use generative AI to analyze customer reactions from past proposal history and select the optimal proposal method. For example, the proposal unit can select a proposal method tailored to customer preferences based on past proposal history. The proposal unit can use generative AI to select a proposal method tailored to customer preferences based on past proposal history. This allows the optimal proposal method to be selected by referring to past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past proposal history into generative AI and have the generative AI select the optimal proposal method.

[0081] The proposal unit can make proposals considering the customer's current situation and background information. For example, the proposal unit can make proposals considering the customer's current situation (e.g., time elapsed since product purchase). The proposal unit can use generative AI to make proposals considering the customer's current situation. For example, the proposal unit can make proposals by referring to the customer's background information (e.g., past purchase history). The proposal unit can use generative AI to refer to the customer's background information. For example, the proposal unit can make proposals considering the customer's current environment (e.g., devices being used). The proposal unit can use generative AI to make proposals considering the customer's current environment. This makes it possible to make more appropriate proposals by considering the customer's current situation and background information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the customer's current situation and background information into the generative AI and have the generative AI execute the proposal.

[0082] The proposal department can estimate the customer's emotions and determine the priority of proposals based on the estimated emotions. For example, if the customer is angry, the proposal department will make a proposal that prioritizes addressing the customer's needs. The proposal department can use generative AI to make proposals that prioritize addressing the customer's needs when the customer is angry. For example, if the customer is in distress, the proposal department will make a proposal that responds quickly. The proposal department can use generative AI to make proposals that respond quickly when the customer is in distress. For example, if the customer is calm, the proposal department will make proposals with normal priority. The proposal department can use generative AI to make proposals with normal priority when the customer is calm. This allows for a quicker response by determining the priority of proposals based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input customer sentiment data into a generation AI and have the generation AI determine the priority of proposals.

[0083] The proposal unit can make optimal proposals by considering the customer's geographical location information when making a proposal. For example, the proposal unit can consider the customer's location and make region-specific proposals. The proposal unit can use generative AI to consider the customer's location and make region-specific proposals. For example, the proposal unit can consider the customer's geographical distance and set priority for responses. The proposal unit can use generative AI to consider the customer's geographical distance and set priority for responses. For example, the proposal unit can refer to weather information in the customer's region and reflect it in the proposal content. The proposal unit can use generative AI to refer to weather information in the customer's region and reflect it in the proposal content. This makes it possible to make region-specific proposals by considering the customer's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the customer's geographical location information into the generative AI and have the generative AI execute the proposal.

[0084] The proposal department can analyze the customer's social media activity and reflect relevant information in the proposal. For example, the proposal department can refer to the customer's social media posts and reflect them in the proposal. The proposal department can use generative AI to refer to the customer's social media posts and reflect them in the proposal. For example, the proposal department can consider the customer's frequency of social media activity and set the priority of proposals. The proposal department can use generative AI to consider the customer's frequency of social media activity and set the priority of proposals. For example, the proposal department can consider the customer's number of social media followers and reflect it in the proposal. The proposal department can use generative AI to consider the customer's number of social media followers and reflect it in the proposal. This makes it possible to make more appropriate proposals by analyzing the customer's social media activity. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the customer's social media activity into generative AI and have the generative AI execute the proposal.

[0085] The learning unit can estimate the customer's emotions and select training data based on the estimated emotions. For example, if the customer is angry, the learning unit can use the generative AI to set the emotion intensity high and select detailed training data. The learning unit can use the generative AI to set the emotion intensity high and select detailed training data when the customer is angry. For example, if the customer is distressed, the learning unit can use the generative AI to set the emotion intensity moderate and select appropriate training data. The learning unit can use the generative AI to set the emotion intensity moderate and select appropriate training data when the customer is distressed. For example, if the customer is calm, the learning unit can use the generative AI to set the emotion intensity low and select simple training data. The learning unit can use the generative AI to set the emotion intensity low and select simple training data when the customer is calm. This allows for more appropriate learning by selecting training data based on the customer's emotions. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input customer emotion data into the generating AI and have the generating AI select the learning data.

[0086] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can select the optimal learning algorithm based on past learning data using generative AI. For example, the learning unit can analyze customer responses from past learning data and optimize the learning algorithm. The learning unit can analyze customer responses from past learning data and optimize the learning algorithm using generative AI. For example, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. The learning unit can adjust the parameters of the learning algorithm by referring to past learning data using generative AI. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0087] The learning unit can perform learning while considering the customer's current situation and background information. For example, the learning unit can perform learning while considering the customer's current situation (e.g., the time elapsed since the product purchase). The learning unit can perform learning while considering the customer's current situation using generative AI. For example, the learning unit can perform learning by referring to the customer's background information (e.g., past purchase history). The learning unit can perform learning by referring to the customer's background information using generative AI. For example, the learning unit can perform learning while considering the customer's current environment (e.g., the device being used). The learning unit can perform learning while considering the customer's current environment using generative AI. This makes it possible to perform more appropriate learning by considering the customer's current situation and background information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the customer's current situation and background information into the generative AI and have the generative AI perform the learning.

[0088] The learning unit can estimate the customer's emotions and adjust the frequency of learning based on the estimated emotions. For example, if the customer is angry, the learning unit will learn frequently to aim for a quick response. The learning unit can use generative AI to learn frequently when the customer is angry to aim for a quick response. For example, if the customer is troubled, the learning unit will learn at a moderate frequency to aim for an appropriate response. The learning unit can use generative AI to learn at a moderate frequency when the customer is troubled to aim for an appropriate response. For example, if the customer is calm, the learning unit will learn at a normal frequency to aim for a standard response. The learning unit can use generative AI to learn at a normal frequency when the customer is calm to aim for a standard response. This allows for a quicker response by adjusting the frequency of learning based on 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-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input customer sentiment data into a generating AI and have the generating AI adjust the learning frequency.

[0089] The learning unit can weight the training data while considering the customer's geographical location information during training. For example, the learning unit can weight the training data for region-specific problems while considering the customer's location. The learning unit can use generative AI to weight the training data for region-specific problems while considering the customer's location. For example, the learning unit can set priority for responses while considering the customer's geographical distance. The learning unit can use generative AI to set priority for responses while considering the customer's geographical distance. For example, the learning unit can refer to weather information in the customer's region to weight the training data. The learning unit can use generative AI to refer to weather information in the customer's region to weight the training data. This allows the learning unit to respond to region-specific problems by considering the customer's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the customer's geographical location information into the generative AI and have the generative AI perform the weighting of the training data.

[0090] The learning unit can analyze the customer's social media activity during training and reflect relevant information in the learning process. For example, the learning unit can refer to the customer's social media posts and reflect them in the training data. The learning unit can use generative AI to refer to the customer's social media posts and reflect them in the training data. For example, the learning unit can consider the customer's frequency of social media activity and weight the training data. The learning unit can use generative AI to consider the customer's frequency of social media activity and weight the training data. For example, the learning unit can consider the customer's number of social media followers and reflect them in the training data. The learning unit can use generative AI to consider the customer's number of social media followers and reflect them in the training data. This enables more appropriate learning by analyzing the customer's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the customer's social media activity into a generative AI and have the generative AI perform the learning.

[0091] The response unit can estimate the customer's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the customer is angry, the response unit will respond in a calm manner. The response unit can use generative AI to respond in a calm manner when the customer is angry. For example, if the customer is troubled, the response unit will respond in a kind and specific manner. The response unit can use generative AI to respond in a kind and specific manner when the customer is troubled. For example, if the customer is calm, the response unit will respond in a standard manner. The response unit can use generative AI to respond in a standard manner when the customer is calm. This allows for more appropriate responses by adjusting the way the response is expressed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 response unit may be performed using AI, for example, or without AI. For example, the response unit can input customer emotion data into a generating AI and have the generating AI adjust the way the response is expressed.

[0092] The response unit can select the optimal response method by referring to past response history when responding. For example, the response unit can refer to a previously successful response method and respond in a similar manner. The response unit can use generational AI to refer to a previously successful response method and respond in a similar manner. For example, the response unit can analyze customer reactions from past response history and select the optimal response method. The response unit can use generational AI to analyze customer reactions from past response history and select the optimal response method. For example, the response unit can select a response method tailored to customer preferences based on past response history. The response unit can use generational AI to select a response method tailored to customer preferences based on past response history. This allows the response unit to select the optimal response method by referring to past response history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input past response history into generational AI and have the generational AI select the optimal response method.

[0093] The response unit can respond while considering the customer's current situation and background information. For example, the response unit can respond while considering the customer's current situation (e.g., the time elapsed since the product purchase). The response unit can respond while considering the customer's current situation using generative AI. For example, the response unit can respond by referring to the customer's background information (e.g., past purchase history). The response unit can respond by referring to the customer's background information using generative AI. For example, the response unit can respond while considering the customer's current environment (e.g., the device being used). The response unit can respond while considering the customer's current environment using generative AI. This makes it possible to provide a more appropriate response by considering the customer's current situation and background information. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's current situation and background information into the generative AI and have the generative AI execute the response.

[0094] The response unit can estimate the customer's emotions and determine the priority of responses based on the estimated emotions. For example, if the customer is angry, the response unit will respond with the highest priority. The response unit can use generative AI to respond with the highest priority when the customer is angry. For example, if the customer is in distress, the response unit will respond quickly. The response unit can use generative AI to respond quickly when the customer is in distress. For example, if the customer is calm, the response unit will respond with the normal priority. The response unit can use generative AI to respond with the normal priority when the customer is calm. This allows for faster responses by determining the priority of responses based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 response unit may be performed using AI, for example, or without AI. For example, the response unit can input customer emotion data into a generating AI and have the generating AI determine the priority of responses.

[0095] The response unit can provide the optimal response by considering the customer's geographical location information when responding. For example, the response unit can consider the customer's location and respond to region-specific issues. The response unit can use generative AI to consider the customer's location and respond to region-specific issues. For example, the response unit can consider the customer's geographical distance and set priority for responses. The response unit can use generative AI to consider the customer's geographical distance and set priority for responses. For example, the response unit can refer to weather information in the customer's region and reflect it in the response. The response unit can use generative AI to refer to weather information in the customer's region and reflect it in the response. This allows the response unit to address region-specific issues by considering the customer's geographical location information. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's geographical location information into generative AI and have the generative AI execute the response.

[0096] The response unit can analyze the customer's social media activity and reflect relevant information in the response. For example, the response unit can refer to the customer's social media posts and reflect them in the response. The response unit can use generative AI to refer to the customer's social media posts and reflect them in the response. For example, the response unit can consider the customer's frequency of social media activity and set response priorities. The response unit can use generative AI to consider the customer's frequency of social media activity and set response priorities. For example, the response unit can consider the customer's number of social media followers and reflect them in the response. The response unit can use generative AI to consider the customer's number of social media followers and reflect them in the response. This allows for more appropriate responses by analyzing the customer's social media activity. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's social media activity into generative AI and have the generative AI execute the response.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate the customer's emotions and, based on those emotions, evaluate the importance of what the customer says. For example, if the customer is angry, it will prioritize the parts of the statement that are particularly emphasized and respond quickly. If the customer is troubled, it will extract the specific problems from the statement and prioritize presenting solutions. If the customer is calm, it will evaluate the entire statement equally and provide a standard response. In this way, evaluating the importance of what the customer says based on their emotions enables more appropriate responses.

[0099] The analysis unit can analyze customer statements while taking into account the customer's language and dialect. For example, if a customer uses a specific regional dialect, it can accurately understand expressions unique to that dialect. If a customer uses a foreign language, it performs analysis corresponding to that language. Furthermore, if a customer uses multiple languages, it appropriately analyzes each language to grasp the overall meaning. This allows for more accurate analysis by considering the customer's language and dialect.

[0100] The data collection unit can collect relevant data by referring to customers' purchase and usage history when gathering customer complaints. For example, it can collect complaints about products and services that customers have purchased in the past and store them in a database. It can also collect complaints about services that customers frequently used during a specific period and use them for analysis. Furthermore, it can collect complaints related to customers' participation in specific campaigns and promotions and use them for future improvements. In this way, more relevant data can be collected by referring to customers' purchase and usage history.

[0101] The generation AI analysis unit can include customer facial expressions and gestures in its analysis of customer emotions. For example, if a customer is making a complaint via video call, the system analyzes their facial expressions and gestures to more accurately grasp their emotions. If a customer is making a complaint in person, the system analyzes their body movements and posture to evaluate the intensity of their emotions. Furthermore, if a customer provides photos or videos, the system includes this visual information in its analysis to make a comprehensive judgment about their emotions. By including customer facial expressions and gestures in the analysis, the accuracy of emotion analysis is improved.

[0102] The AI-generated proposal system can adjust its proposals by considering past customer feedback. For example, if a customer was satisfied with a previously proposed solution, it will make a similar proposal. If a customer expressed dissatisfaction with a previously proposed solution, it will propose a different approach. Furthermore, it analyzes how customers have reacted to information previously provided and optimizes the proposals accordingly. This allows for more effective proposals by taking past customer feedback into consideration.

[0103] The analysis unit can estimate the customer's emotions and, based on those emotions, evaluate the reliability of the customer's statements. For example, if the customer is angry, emotional expressions are excluded from the statement, and factual content is emphasized. If the customer is troubled, specific problems are extracted from the statement, and their reliability is evaluated. If the customer is calm, the entire statement is evaluated equally to determine its reliability. This allows for more accurate responses by evaluating the reliability of statements based on the customer's emotions.

[0104] The analysis unit can evaluate the frequency and consistency of customer statements when referring to their past statements. For example, if a customer frequently repeats the same complaint, the analysis unit will prioritize that complaint and respond promptly. If a customer's statements are consistent, their reliability will be highly valued. Furthermore, if a customer's statements contradict past statements, the analysis unit will identify the inconsistencies and take appropriate action. By evaluating the frequency and consistency of customer statements, more accurate analysis becomes possible.

[0105] The analysis unit can incorporate customer life events and significant occurrences into its analysis, taking into account the customer's current situation and background information. For example, if a customer has recently moved, the analysis will take that stress into account. If a customer has started a new job, the analysis will take their workload into account. Furthermore, if a customer has a family health issue, the analysis will take that situation into account. By considering the customer's life events and significant occurrences, a more appropriate analysis becomes possible.

[0106] The analysis unit can estimate the customer's emotions and, based on those emotions, prioritize the customer's statements. For example, if the customer is angry, it will prioritize addressing the most urgent parts of their statement. If the customer is distressed, it will prioritize resolving the specific problems they are facing. If the customer is calm, it will evaluate all of their statements equally and address them with normal priorities. By prioritizing statements based on customer emotions, a faster response becomes possible.

[0107] The analysis unit can incorporate the customer's local culture and customs into its analysis when considering the customer's geographical location. For example, if a customer uses language based on a specific local culture, the analysis unit will understand and respond to that cultural background. If a customer is making a complaint based on a specific local custom, the analysis unit will take that custom into consideration. Furthermore, if a customer is making a complaint related to a specific local event or holiday, the analysis unit will take that event or holiday into consideration. This allows for more accurate analysis by considering the customer's local culture and customs.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The analysis unit analyzes the customer's emotions in real time. The analysis unit analyzes the customer's statements and tone of voice to determine whether the customer is angry or distressed. Generative AI can be used to analyze the customer's emotions in real time. Step 2: The proposal unit proposes appropriate countermeasures based on the emotions analyzed by the analysis unit. For example, if the customer is angry, it will suggest a calm response; if the customer is troubled, it will propose specific solutions. Generative AI can be used to propose appropriate countermeasures based on the analysis results. Step 3: The learning unit learns the countermeasures proposed by the proposal unit. It analyzes past complaint data and learns the optimal response method. Using generation AI, it is possible to analyze past complaint data and learn the optimal response method. Step 4: The response unit generates automated responses to simple inquiries and complaints based on the countermeasures learned by the learning unit. For example, it automatically answers frequently asked questions and handles simple complaints. The generation AI can be used to generate automated responses to simple inquiries and complaints.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the analysis unit, proposal unit, learning unit, response unit, voice analysis unit, data collection unit, generation AI analysis unit, and generation AI proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the content of the customer's speech and the tone of their voice. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate countermeasures based on the analysis results. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the optimal response method by analyzing past claim data. The response unit is implemented by the control unit 46A of the smart device 14 and generates automatic responses to simple inquiries and claims. The voice analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the content of the customer's speech and the tone of their voice. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects past claim data. The generation AI analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the customer's emotions. The generation AI proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes appropriate countermeasures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the analysis unit, proposal unit, learning unit, response unit, voice analysis unit, data collection unit, generation AI analysis unit, and generation AI proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the content of the customer's speech and the tone of their voice. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate countermeasures based on the analysis results. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the optimal response method by analyzing past complaint data. The response unit is implemented by the control unit 46A of the smart glasses 214 and generates automatic responses to simple inquiries and complaints. The voice analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the content of the customer's speech and the tone of their voice. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects past complaint data. The generation AI analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the customer's emotions. The generation AI proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes appropriate countermeasures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the analysis unit, proposal unit, learning unit, response unit, voice analysis unit, data collection unit, generation AI analysis unit, and generation AI proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the content of the customer's speech and the tone of their voice. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate countermeasures based on the analysis results. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the optimal response method by analyzing past claim data. The response unit is implemented by the control unit 46A of the headset terminal 314 and generates automatic responses to simple inquiries and claims. The voice analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the content of the customer's speech and the tone of their voice. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects past claim data. The generation AI analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the customer's emotions. The generation AI proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes appropriate countermeasures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the analysis unit, proposal unit, learning unit, response unit, voice analysis unit, data collection unit, generation AI analysis unit, and generation AI proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the content of the customer's statements and the tone of their voice. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate countermeasures based on the analysis results. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the optimal response method by analyzing past complaint data. The response unit is implemented by the control unit 46A of the robot 414 and generates automatic responses to simple inquiries and complaints. The voice analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the content of the customer's statements and the tone of their voice. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects past complaint data. The generation AI analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the customer's emotions. The generation AI proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes appropriate countermeasures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) An analysis unit that analyzes customer emotions in real time, A proposal unit proposes appropriate countermeasures based on the emotions analyzed by the analysis unit, A learning unit that learns the countermeasures proposed by the aforementioned proposal unit, The system includes a response unit that generates automated responses to simple inquiries and complaints based on the countermeasures learned by the learning unit. A system characterized by the following features. (Note 2) It is equipped with a voice analysis unit that analyzes the content of what the customer says and the tone of their voice. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a data collection unit that collects past customer complaint data. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a generative AI analysis unit that uses generative AI to analyze customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a generation AI proposal unit that uses generation AI to propose appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We estimate customer emotions and adjust the accuracy of the analysis based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, During analysis, we refer to the customer's past statement history to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During the analysis, the customer's current situation and background information are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates customer emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During the analysis, the customer's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, we analyze customers' social media activity and incorporate relevant information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, We estimate the customer's emotions and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, refer to past proposal history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, we take into account the customer's current situation and background information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, Estimate customer emotions and prioritize proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we take the customer's geographical location into consideration to provide the most suitable solution. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, analyze the client's social media activity and incorporate relevant information into the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, The system estimates customer emotions and selects training data based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During the learning process, the system takes into account the customer's current situation and background information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, It estimates customer emotions and adjusts the learning frequency based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, During training, the training data is weighted considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, During the learning process, analyze customers' social media activity and incorporate relevant information into the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 24) The response unit is It estimates the customer's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The response unit is When responding, the system will refer to past response history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The response unit is When responding, take into account the customer's current situation and background information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The response unit is It estimates the customer's emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The response unit is When responding, the system takes into account the customer's geographical location to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 29) The response unit is When responding, analyze the customer's social media activity and incorporate relevant information into the response. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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. An analysis unit that analyzes customer emotions in real time, A proposal unit proposes appropriate countermeasures based on the emotions analyzed by the analysis unit, A learning unit that learns the countermeasures proposed by the aforementioned proposal unit, The system includes a response unit that generates automated responses to simple inquiries and complaints based on countermeasures learned by the learning unit. A system characterized by the following features.

2. It is equipped with a voice analysis unit that analyzes the content of what the customer says and the tone of their voice. The system according to feature 1.

3. It includes a data collection unit that collects past customer complaint data. The system according to feature 1.

4. It includes a generative AI analysis unit that uses generative AI to analyze customer emotions. The system according to feature 1.

5. It includes a generation AI proposal unit that uses generation AI to propose appropriate countermeasures. The system according to feature 1.

6. The aforementioned analysis unit, We estimate customer emotions and adjust the accuracy of the analysis based on the estimated customer emotions. The system according to feature 1.

7. The aforementioned analysis unit, During analysis, we refer to the customer's past statement history to improve the accuracy of the analysis. The system according to feature 1.

8. The aforementioned analysis unit, During the analysis, the customer's current situation and background information are taken into consideration. The system according to feature 1.

9. The aforementioned analysis unit, It estimates customer emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

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