system

Abstract

The system according to this embodiment aims to respond to customer inquiries quickly and in real time. [Solution] The system according to this embodiment comprises a response unit, a provision unit, and a support unit. The response unit responds to customer inquiries using natural language processing technology. The provision unit provides answers in real time, 24 hours a day, 365 days a year, based on the inquiries handled by the response unit. The support unit provides a wide range of support, from troubleshooting to usage guidance, based on the answers provided by the provision unit.

Description

Technical Field

[0006] , , , ,

[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, the method 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 was a problem that the response to customer inquiries was not prompt, and it took time for waiting and the effort to reach the window.

[0005] The system according to the embodiment aims to respond promptly and in real time to customer inquiries.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a response unit, a provision unit, and a support unit. The response unit responds to customer inquiries using natural language processing technology. The provision unit provides real-time answers 24 hours a day, 365 days a year based on the inquiries handled by the response unit. The support unit provides a wide range of support, from troubleshooting to usage guidance, based on the answers provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can respond to customer inquiries quickly and in real time. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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), etc.

[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 AI ​​assistant system according to an embodiment of the present invention is a system that instantly responds to inquiries about products and services in a conversational format and provides a wide range of support, from troubleshooting to usage guidance. This AI assistant system utilizes natural language processing technology to respond to customer inquiries and provides real-time answers 24 hours a day, 365 days a year. Furthermore, it provides a wide range of support, from troubleshooting to usage guidance. This eliminates waiting times and the need to visit a service counter, thereby improving customer satisfaction. First, it responds to customer inquiries using natural language processing technology. At this time, it analyzes the content of the customer's inquiry and provides an appropriate answer. For example, in response to a question about how to use a product, it provides specific operating procedures. Next, it provides real-time answers 24 hours a day, 365 days a year. This allows customers to get answers to their inquiries anytime, anywhere. For example, even if a product problem occurs in the middle of the night, it can be addressed immediately. Furthermore, it provides a wide range of support, from troubleshooting to usage guidance. This allows customers to receive appropriate support for any question about products and services. For example, it provides troubleshooting procedures in the event of a product malfunction and provides specific guidance for questions about usage. This mechanism eliminates waiting times and the need to visit a service counter, thereby improving customer satisfaction. Because customers can receive prompt and appropriate support, this is expected to improve repeat customer rates and increase efficiency by streamlining in-store customer service. This allows the AI ​​assistant system to provide quick and appropriate support to customer inquiries.

[0029] The AI ​​assistant system according to this embodiment comprises a response unit, a provision unit, and a support unit. The response unit responds to customer inquiries using natural language processing technology. The response unit analyzes the content of customer inquiries using, for example, morphological analysis and provides appropriate answers. The response unit can also analyze the content of customer inquiries using grammatical analysis. Furthermore, the response unit can also analyze the content of customer inquiries using semantic analysis. For example, the response unit breaks down the content of customer inquiries into word units using morphological analysis and provides appropriate answers. It analyzes the grammatical structure of the content of customer inquiries using grammatical analysis and provides appropriate answers. It analyzes the meaning of the content of customer inquiries using semantic analysis and provides appropriate answers. The provision unit provides answers in real time, 24 hours a day, 365 days a year, based on the inquiries handled by the response unit. The provision unit, for example, sets the system operating time to 24 hours a day, 365 days a year, so that customers can get answers to their inquiries at any time. The provision unit can also set an upper limit on the response time to ensure real-time performance. Furthermore, the provision unit can implement redundancy and load balancing to increase the system's operating rate. For example, the service department ensures 24 / 7 system operation so that customers can get answers to their inquiries at any time. They set limits on response times to ensure real-time performance. Redundancy and load balancing are implemented to increase system availability. The support department provides a wide range of support, from troubleshooting to usage guidance, based on the answers provided by the service department. For example, the support department provides troubleshooting procedures to help customers resolve product malfunctions. They also provide usage guidance to ensure customers use the product correctly. Furthermore, the support department provides technical support to help customers resolve technical issues with the product. For example, the support department provides troubleshooting procedures to help customers resolve product malfunctions. They provide usage guidance to ensure customers use the product correctly. They provide technical support to help customers resolve technical issues with the product.As a result, the AI ​​assistant system according to this embodiment can provide prompt and appropriate support to customer inquiries.

[0030] The customer support team utilizes natural language processing technology to respond to customer inquiries. Specifically, it employs morphological analysis, grammatical analysis, and semantic analysis techniques to analyze customer inquiries in detail. Morphological analysis breaks down customer inquiries into individual words, identifying the part of speech and meaning of each word. This allows for a grasp of the basic structure of the inquiry. Grammatical analysis analyzes the grammatical structure of the inquiry, clarifying the relationships between subjects, predicates, and objects. This allows for an understanding of the grammatical accuracy and semantic flow of the inquiry. Semantic analysis analyzes the context and intent of the inquiry to accurately grasp what the customer is seeking. For example, if a customer inquires, "My product hasn't arrived," the customer support team uses morphological analysis to identify the words "product" and "hasn't arrived," grammatical analysis to analyze the sentence structure of "My product hasn't arrived," and semantic analysis to understand the situation "My product hasn't arrived." This allows the customer support team to provide an appropriate answer. Furthermore, the customer support team can utilize past inquiry data and FAQ databases to quickly search for and provide answers to similar inquiries. This allows the support department to respond to customer inquiries quickly and accurately.

[0031] The service provider will provide real-time answers 24 hours a day, 365 days a year, based on inquiries handled by the support department. Specifically, the system will operate 24 hours a day, 365 days a year, ensuring that customers can receive answers to their inquiries at any time. The service provider will set an upper limit on response time to ensure real-time performance. For example, the service provider will optimize the system's response time, aiming to provide answers to customer inquiries within a few seconds. The service provider will also implement redundancy and load balancing to increase system uptime. Redundancy allows other parts of the system to take over and continue operating even if a part of the system fails. Load balancing distributes inquiries across multiple servers, equalizing the load on the entire system. This ensures the stability and reliability of the system, allowing the service provider to consistently provide high-quality service to customers. Furthermore, the service provider will manage customer inquiry history and refer to past inquiries and answers to provide more appropriate responses. For example, it can customize answers to current inquiries based on past customer inquiries. This allows the service provider to provide personalized services tailored to customer needs.

[0032] The support department provides a wide range of support, from troubleshooting to usage guidance, based on answers provided by the service department. Specifically, it provides troubleshooting procedures to enable customers to resolve product malfunctions. For example, if a product is not working, the support department will provide the customer with specific steps, identify the cause of the problem, and provide a solution. It also provides usage guidance to enable customers to use the product correctly. For example, it provides detailed explanations of how to use and configure new features to help customers get the most out of the product. Furthermore, it provides technical support to enable customers to resolve technical issues with the product. For example, it provides technical support regarding product installation and updates to ensure customers can use the product smoothly. The support department collects customer feedback and uses it to improve the support it provides. For example, it evaluates whether customers are satisfied with the support provided and identifies areas for improvement. This allows the support department to continue providing high-quality support that meets customer needs. In addition, the support department stores customer inquiries and support history in a database and uses it to improve the quality of support in the future. This allows the support department to provide customers with consistently high-quality support.

[0033] The learning unit can learn from and predict based on past customer data using machine learning. For example, the learning unit can learn from past customer data using supervised learning and make predictions. The learning unit can also learn from past customer data using unsupervised learning and make predictions. Furthermore, the learning unit can learn from past customer data and make predictions using reinforcement learning. For example, the learning unit can learn from past customer data using supervised learning and make predictions. It can learn from past customer data using unsupervised learning and make predictions. It can learn from past customer data and make predictions using reinforcement learning. This allows the learning unit to provide more appropriate support by learning from past customer data. Some or all of the above processes in the learning unit may be performed using, for example, generative AI, or not using generative AI. For example, the learning unit can input past customer data into a generative AI and have the generative AI perform learning and predictions.

[0034] The Improvement Department can aim to increase the repeat purchase rate by improving customer satisfaction. For example, the Improvement Department can set evaluation criteria for customer satisfaction and implement measures to improve customer satisfaction. The Improvement Department can also use NPS (Net Promoter Score) to evaluate customer satisfaction and aim to improve the repeat purchase rate. Furthermore, the Improvement Department can conduct surveys and implement measures to improve customer satisfaction. For example, the Improvement Department can set evaluation criteria for customer satisfaction and implement measures to improve customer satisfaction. It can use NPS to evaluate customer satisfaction and aim to improve the repeat purchase rate. It can conduct surveys and implement measures to improve customer satisfaction. As a result, the repeat purchase rate will increase due to the improvement of customer satisfaction. Some or all of the above processes in the Improvement Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Improvement Department can input customer satisfaction evaluation criteria into a generative AI and have the generative AI execute measures to improve customer satisfaction.

[0035] The efficiency department can improve efficiency by streamlining counter services at physical stores. For example, the efficiency department can implement measures to shorten business processes. The efficiency department can also implement measures to optimize resources. Furthermore, the efficiency department can also implement measures to automate operations. For example, the efficiency department can implement measures to shorten business processes. Measures to optimize resources. Measures to automate operations. This will improve the efficiency of counter services at physical stores. Some or all of the above-mentioned processes in the efficiency department may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency department can input business process shortening measures into a generative AI and have the generative AI execute the efficiency measures.

[0036] The response unit can analyze a customer's past inquiry history and select the optimal response method. For example, if a customer has made a similar inquiry in the past, the response unit can refer to that history and respond quickly. The response unit can also automatically extract frequently asked questions from the customer's past inquiry history and provide appropriate answers. Furthermore, the response unit can analyze a customer's past inquiry history and select the optimal response method based on specific patterns. For example, if a customer has made a similar inquiry in the past, the response unit can refer to that history and respond quickly. It can automatically extract frequently asked questions from the customer's past inquiry history and provide appropriate answers. It analyzes the customer's past inquiry history and selects the optimal response method based on specific patterns. This enables quick and appropriate responses based on past inquiry history. Some or all of the above processes in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input the customer's past inquiry history into a generative AI and have the generative AI select the optimal response method.

[0037] The response unit can filter the customer's current situation and areas of interest when analyzing the content of inquiries. For example, if the customer inputs their current situation, the response unit will provide the most suitable answer based on that information. The response unit can also prioritize the analysis of relevant information based on the customer's areas of interest. Furthermore, the response unit can filter out unnecessary information, taking into account the customer's current situation and areas of interest. For example, if the customer inputs their current situation, the response unit will provide the most suitable answer based on that information. It will prioritize the analysis of relevant information based on the customer's areas of interest. It will filter out unnecessary information, taking into account the customer's current situation and areas of interest. This allows the response unit to provide appropriate information according to the customer's situation and areas of interest. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input data on the customer's current situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0038] The support unit can prioritize the analysis of highly relevant information by considering the customer's geographical location when analyzing the content of an inquiry. For example, if the customer is in a specific region, the support unit will prioritize the analysis of information related to that region. The support unit can also provide answers to region-specific problems based on the customer's geographical location. Furthermore, the support unit can select the optimal support method by considering the customer's geographical location. For example, if the customer is in a specific region, the support unit will prioritize the analysis of information related to that region. Based on the customer's geographical location, it will provide answers to region-specific problems. It will select the optimal support method by considering the customer's geographical location. This allows the support unit to provide appropriate information based on the customer's geographical location. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the customer's geographical location information into a generative AI and have the generative AI perform the analysis of highly relevant information.

[0039] The support unit can analyze the customer's social media activity and obtain relevant information when analyzing the content of an inquiry. For example, the support unit can analyze the customer's social media activity and provide answers based on relevant topics. The support unit can also select the most appropriate support method based on the information the customer has shared on social media. Furthermore, the support unit can prioritize the analysis of relevant information by referring to the customer's social media activity. For example, the support unit can analyze the customer's social media activity and provide answers based on relevant topics. It can select the most appropriate support method based on the information the customer has shared on social media. It can prioritize the analysis of relevant information by referring to the customer's social media activity. This allows the support unit to provide appropriate information based on the customer's social media activity. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input customer social media activity data into a generative AI and have the generative AI perform the acquisition of relevant information.

[0040] The service provider can adjust the level of detail in its responses based on the importance of the inquiry. For example, it can provide detailed responses to high-priority inquiries, and concise responses to low-priority inquiries. Furthermore, it can adjust the level of detail in its responses in stages according to the importance of the inquiry. For example, it can provide detailed responses to high-priority inquiries, concise responses to low-priority inquiries, and adjust the level of detail in stages according to the importance of the inquiry. This allows for the provision of responses with an appropriate level of detail according to the importance of the inquiry. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input inquiry importance data into a generation AI and have the generation AI adjust the level of detail in its responses.

[0041] The service provider can apply different response algorithms depending on the category of the inquiry when providing a response. For example, the service provider can apply a specialized response algorithm to technical inquiries. It can also apply a concise response algorithm to general inquiries. Furthermore, the service provider can select the optimal response algorithm depending on the category of the inquiry. For example, the service provider can apply a specialized response algorithm to technical inquiries, a concise response algorithm to general inquiries, and select the optimal response algorithm depending on the category of the inquiry. This allows the service provider to apply the most appropriate response algorithm for each inquiry category. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input inquiry category data into a generative AI and have the generative AI apply the response algorithm.

[0042] The service provider can determine the priority of responses based on when the inquiry was submitted. For example, the service provider will provide a quick response to urgent inquiries. The service provider can also set a lower priority for older inquiries. Furthermore, the service provider can adjust the priority of responses in stages based on the submission date. For example, the service provider will provide a quick response to urgent inquiries. Older inquiries will be given a lower priority. The priority of responses will be adjusted in stages based on the submission date. This allows for the provision of responses with appropriate priority according to when the inquiry was submitted. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input inquiry submission date data into a generating AI and have the generating AI determine the priority of responses.

[0043] The response unit can adjust the order of responses based on the relevance of the inquiries when providing answers. For example, the response unit will prioritize responses to highly relevant inquiries. The response unit can also postpone responses to less relevant inquiries. Furthermore, the response unit can adjust the order of responses in stages based on the relevance of the inquiries. For example, the response unit will prioritize responses to highly relevant inquiries. It will postpone responses to less relevant inquiries. It will adjust the order of responses in stages based on the relevance of the inquiries. This allows responses to be provided in an appropriate order according to the relevance of the inquiries. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input inquiry relevance data into a generative AI and have the generative AI perform the adjustment of the order of responses.

[0044] The support department can select the optimal support method by analyzing the customer's past support history when providing support. For example, the support department can select the optimal support method by referring to the customer's past support history. The support department can also automatically extract support methods for common problems from the customer's past support history. Furthermore, the support department can analyze the customer's past support history and select the optimal support method based on specific patterns. For example, the support department can select the optimal support method by referring to the customer's past support history. It can automatically extract support methods for common problems from the customer's past support history. It can analyze the customer's past support history and select the optimal support method based on specific patterns. This enables quick and appropriate support based on past support history. Some or all of the above processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the customer's past support history into a generative AI and have the generative AI select the optimal support method.

[0045] The support unit can customize the means of support provided based on the customer's current situation. For example, if the customer inputs their current situation, the support unit will provide the most suitable means of support based on that information. The support unit can also prioritize providing relevant means of support based on the customer's current situation. Furthermore, the support unit can filter out unnecessary means of support, taking into account the customer's current situation. For example, if the customer inputs their current situation, the support unit will provide the most suitable means of support based on that information. Based on the customer's current situation, it will prioritize providing relevant means of support. Based on the customer's current situation, it will filter out unnecessary means of support, taking into account the customer's current situation. This allows the support unit to provide appropriate means of support according to the customer's current situation. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input customer's current situation data into a generative AI and have the generative AI perform the customization of the means of support.

[0046] The support department can select the optimal support method when providing support, taking into account the customer's geographical location. For example, if the customer is in a specific region, the support department will prioritize providing support methods relevant to that region. The support department can also provide support methods for region-specific issues based on the customer's geographical location. Furthermore, the support department can select the optimal support method, taking into account the customer's geographical location. For example, if the customer is in a specific region, the support department will prioritize providing support methods relevant to that region. Based on the customer's geographical location, it will provide support methods for region-specific issues. It will select the optimal support method, taking into account the customer's geographical location. This allows for the provision of appropriate support methods based on the customer's geographical location. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the customer's geographical location information into a generative AI and have the generative AI select the optimal support method.

[0047] The support department can analyze a customer's social media activity and propose support methods when providing support. For example, the support department can analyze a customer's social media activity and propose support methods based on relevant topics. The support department can also select the most suitable support method based on information shared by the customer on social media. Furthermore, the support department can prioritize providing relevant support methods based on the customer's social media activity. For example, the support department can analyze a customer's social media activity and propose support methods based on relevant topics. It can select the most suitable support method based on information shared by the customer on social media. It can prioritize providing relevant support methods based on the customer's social media activity. This allows the support department to provide appropriate support methods based on the customer's social media activity. Some or all of the above processes in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input customer social media activity data into generative AI and have the generative AI propose support methods.

[0048] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and selects the optimal learning algorithm. Based on past learning data, it adjusts the parameters of the learning algorithm. By referring to past learning data, it improves the accuracy of the learning algorithm. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative 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.

[0049] The learning unit can weight the training data based on the submission date of the inquiry history during training. For example, the learning unit can weight the training data by giving more weight to recent inquiry histories. The learning unit can also reduce the weight of older inquiry histories during training. Furthermore, the learning unit can adjust the weighting of the training data in stages based on the submission date. For example, the learning unit can weight the training data by giving more weight to recent inquiry histories, reduce the weight of older inquiry histories during training, and adjust the weighting of the training data in stages based on the submission date. This allows training to be performed with appropriate weighting according to the submission date of the inquiry history. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the inquiry history submission date data into a generative AI and have the generative AI perform the weighting of the training data.

[0050] The improvement unit can analyze past customer feedback to select the optimal improvement method when improving customer satisfaction. For example, the improvement unit selects the optimal improvement method based on past customer feedback. The improvement unit can also automatically extract improvement methods for common problems from past customer feedback. Furthermore, the improvement unit can analyze past customer feedback and select the optimal improvement method based on specific patterns. For example, the improvement unit selects the optimal improvement method based on past customer feedback. It automatically extracts improvement methods for common problems from past customer feedback. It analyzes past customer feedback and selects the optimal improvement method based on specific patterns. This allows the improvement unit to select the optimal improvement method based on past customer feedback. Some or all of the above processes in the improvement unit may be performed using, for example, generative AI, or without generative AI. For example, the improvement unit can input past customer feedback into generative AI and have the generative AI select the optimal improvement method.

[0051] The Improvement Department can select the optimal improvement method when improving customer satisfaction, taking into account the customer's geographical location. For example, if a customer is in a specific region, the Improvement Department will prioritize providing improvement methods relevant to that region. The Improvement Department can also provide improvement methods for region-specific problems based on the customer's geographical location. Furthermore, the Improvement Department can select the optimal improvement method, taking into account the customer's geographical location. For example, if a customer is in a specific region, the Improvement Department will prioritize providing improvement methods relevant to that region. Based on the customer's geographical location, it will provide improvement methods for region-specific problems. It will select the optimal improvement method, taking into account the customer's geographical location. This allows the Improvement Department to provide appropriate improvement methods based on the customer's geographical location. Some or all of the above processing in the Improvement Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Improvement Department can input the customer's geographical location information into a generative AI and have the generative AI select the optimal improvement method.

[0052] The efficiency improvement department can analyze past business data to select the optimal efficiency improvement method when improving the efficiency of counter services. For example, the efficiency improvement department can select the optimal efficiency improvement method based on past business data. The efficiency improvement department can also automatically extract efficiency improvement methods for common problems from past business data. Furthermore, the efficiency improvement department can analyze past business data and select the optimal efficiency improvement method based on specific patterns. For example, the efficiency improvement department can select the optimal efficiency improvement method based on past business data. It can automatically extract efficiency improvement methods for common problems from past business data. It can analyze past business data and select the optimal efficiency improvement method based on specific patterns. This allows the efficiency improvement department to select the optimal efficiency improvement method based on past business data. Some or all of the above processes in the efficiency improvement department may be performed using, for example, a generation AI, or not using a generation AI. For example, the efficiency improvement department can input past business data into a generation AI and have the generation AI select the optimal efficiency improvement method.

[0053] The efficiency improvement unit can select the optimal efficiency improvement method when improving the efficiency of counter services, taking into account the customer's geographical location information. For example, if a customer is in a specific region, the efficiency improvement unit will prioritize providing efficiency improvement methods related to that region. The efficiency improvement unit can also provide efficiency improvement methods for region-specific problems based on the customer's geographical location information. Furthermore, the efficiency improvement unit can select the optimal efficiency improvement method by taking into account the customer's geographical location information. For example, if a customer is in a specific region, the efficiency improvement unit will prioritize providing efficiency improvement methods related to that region. Based on the customer's geographical location information, it will provide efficiency improvement methods for region-specific problems. It will select the optimal efficiency improvement method by taking into account the customer's geographical location information. This allows for the provision of appropriate efficiency improvement methods based on the customer's geographical location information. Some or all of the above processing in the efficiency improvement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the efficiency improvement unit can input the customer's geographical location information into a generative AI and have the generative AI select the optimal efficiency improvement method.

[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 AI ​​assistant system can further analyze customer purchase history and suggest products and services relevant to their inquiries. For example, if a customer inquires about a product they previously purchased, it can suggest related accessories or additional services. If a customer frequently purchases products in a particular category, it can also provide information on new products and promotions related to that category. Furthermore, based on the customer's purchase history, it can predict products and services they might be interested in and provide personalized suggestions. This is expected to improve the customer's purchasing experience and encourage repeat purchases.

[0056] The Improvement Department can collect and analyze customer feedback in real time to improve customer satisfaction. For example, if a customer responds to a questionnaire provided after an inquiry, the feedback can be immediately analyzed to identify areas for improvement. Furthermore, if a customer posts comments or reviews on social media, this information can be collected and used to implement measures to improve customer satisfaction. In addition, the department can evaluate service quality and response speed based on customer feedback, enabling continuous improvement. This allows for the provision of services that reflect customer voices, leading to improved customer satisfaction.

[0057] The response unit can analyze a customer's past inquiry history and select the optimal response method. For example, if a customer has made a similar inquiry in the past, it can refer to that history and respond quickly. It can also automatically extract frequently asked questions from the customer's past inquiry history and provide appropriate answers. Furthermore, it can analyze the customer's past inquiry history and select the optimal response method based on specific patterns. This enables quick and appropriate responses based on past inquiry history. Some or all of the above processing in the response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response unit can input the customer's past inquiry history into a generation AI and have the generation AI select the optimal response method.

[0058] The response unit can filter the customer's current situation and areas of interest when analyzing the content of inquiries. For example, if the customer enters their current situation, the unit will provide the most appropriate answer based on that information. It can also prioritize the analysis of relevant information based on the customer's areas of interest. Furthermore, it can filter out unnecessary information, taking into account the customer's current situation and areas of interest. This allows the unit to provide appropriate information tailored to the customer's situation and areas of interest. Some or all of the above processing in the response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response unit can input data on the customer's current situation and areas of interest into a generation AI and have the generation AI perform the filtering.

[0059] The response unit can adjust the level of detail in the response based on the importance of the inquiry. For example, it can provide a detailed response to a high-importance inquiry, and a concise response to a low-importance inquiry. Furthermore, it can adjust the level of detail in the response in stages according to the importance of the inquiry. This allows for the provision of a response with an appropriate level of detail according to the importance of the inquiry. Some or all of the above processing in the response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response unit can input inquiry importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the response.

[0060] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, it can analyze past learning data and select the optimal learning algorithm. It can also adjust the parameters of the learning algorithm based on past learning data. Furthermore, it can improve the accuracy of the learning algorithm by referring to past learning data. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative 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.

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

[0062] Step 1: The support unit uses natural language processing technology to respond to customer inquiries. For example, it analyzes customer inquiries using morphological analysis, grammatical analysis, and semantic analysis, and provides appropriate answers. Morphological analysis is used to break down customer inquiries into individual words, grammatical analysis is used to analyze the grammatical structure, and semantic analysis is used to analyze the meaning of the inquiry. Step 2: The service provider will provide real-time responses 24 hours a day, 365 days a year, based on inquiries handled by the support team. For example, the system will be operational 24 hours a day, 365 days a year, so that customers can get answers to their inquiries at any time. A maximum response time will be set, and redundancy and load balancing will be implemented to ensure real-time performance. Step 3: The support department provides a wide range of support, from troubleshooting to usage guidance, based on the answers provided by the service department. For example, they provide troubleshooting steps to help customers resolve product malfunctions, usage guidance to help customers use the product correctly, and technical support to help customers resolve technical issues with the product.

[0063] (Example of form 2) The AI ​​assistant system according to an embodiment of the present invention is a system that instantly responds to inquiries about products and services in a conversational format and provides a wide range of support, from troubleshooting to usage guidance. This AI assistant system utilizes natural language processing technology to respond to customer inquiries and provides real-time answers 24 hours a day, 365 days a year. Furthermore, it provides a wide range of support, from troubleshooting to usage guidance. This eliminates waiting times and the need to visit a service counter, thereby improving customer satisfaction. First, it responds to customer inquiries using natural language processing technology. At this time, it analyzes the content of the customer's inquiry and provides an appropriate answer. For example, in response to a question about how to use a product, it provides specific operating procedures. Next, it provides real-time answers 24 hours a day, 365 days a year. This allows customers to get answers to their inquiries anytime, anywhere. For example, even if a product problem occurs in the middle of the night, it can be addressed immediately. Furthermore, it provides a wide range of support, from troubleshooting to usage guidance. This allows customers to receive appropriate support for any question about products and services. For example, it provides troubleshooting procedures in the event of a product malfunction and provides specific guidance for questions about usage. This mechanism eliminates waiting times and the need to visit a service counter, thereby improving customer satisfaction. Because customers can receive prompt and appropriate support, this is expected to improve repeat customer rates and increase efficiency by streamlining in-store customer service. This allows the AI ​​assistant system to provide quick and appropriate support to customer inquiries.

[0064] The AI ​​assistant system according to this embodiment comprises a response unit, a provision unit, and a support unit. The response unit responds to customer inquiries using natural language processing technology. The response unit analyzes the content of customer inquiries using, for example, morphological analysis and provides appropriate answers. The response unit can also analyze the content of customer inquiries using grammatical analysis. Furthermore, the response unit can also analyze the content of customer inquiries using semantic analysis. For example, the response unit breaks down the content of customer inquiries into word units using morphological analysis and provides appropriate answers. It analyzes the grammatical structure of the content of customer inquiries using grammatical analysis and provides appropriate answers. It analyzes the meaning of the content of customer inquiries using semantic analysis and provides appropriate answers. The provision unit provides answers in real time, 24 hours a day, 365 days a year, based on the inquiries handled by the response unit. The provision unit, for example, sets the system operating time to 24 hours a day, 365 days a year, so that customers can get answers to their inquiries at any time. The provision unit can also set an upper limit on the response time to ensure real-time performance. Furthermore, the provision unit can implement redundancy and load balancing to increase the system's operating rate. For example, the service department ensures 24 / 7 system operation so that customers can get answers to their inquiries at any time. They set limits on response times to ensure real-time performance. Redundancy and load balancing are implemented to increase system availability. The support department provides a wide range of support, from troubleshooting to usage guidance, based on the answers provided by the service department. For example, the support department provides troubleshooting procedures to help customers resolve product malfunctions. They also provide usage guidance to ensure customers use the product correctly. Furthermore, the support department provides technical support to help customers resolve technical issues with the product. For example, the support department provides troubleshooting procedures to help customers resolve product malfunctions. They provide usage guidance to ensure customers use the product correctly. They provide technical support to help customers resolve technical issues with the product.As a result, the AI ​​assistant system according to this embodiment can provide prompt and appropriate support to customer inquiries.

[0065] The customer support team utilizes natural language processing technology to respond to customer inquiries. Specifically, it employs morphological analysis, grammatical analysis, and semantic analysis techniques to analyze customer inquiries in detail. Morphological analysis breaks down customer inquiries into individual words, identifying the part of speech and meaning of each word. This allows for a grasp of the basic structure of the inquiry. Grammatical analysis analyzes the grammatical structure of the inquiry, clarifying the relationships between subjects, predicates, and objects. This allows for an understanding of the grammatical accuracy and semantic flow of the inquiry. Semantic analysis analyzes the context and intent of the inquiry to accurately grasp what the customer is seeking. For example, if a customer inquires, "My product hasn't arrived," the customer support team uses morphological analysis to identify the words "product" and "hasn't arrived," grammatical analysis to analyze the sentence structure of "My product hasn't arrived," and semantic analysis to understand the situation "My product hasn't arrived." This allows the customer support team to provide an appropriate answer. Furthermore, the customer support team can utilize past inquiry data and FAQ databases to quickly search for and provide answers to similar inquiries. This allows the support department to respond to customer inquiries quickly and accurately.

[0066] The service provider will provide real-time answers 24 hours a day, 365 days a year, based on inquiries handled by the support department. Specifically, the system will operate 24 hours a day, 365 days a year, ensuring that customers can receive answers to their inquiries at any time. The service provider will set an upper limit on response time to ensure real-time performance. For example, the service provider will optimize the system's response time, aiming to provide answers to customer inquiries within a few seconds. The service provider will also implement redundancy and load balancing to increase system uptime. Redundancy allows other parts of the system to take over and continue operating even if a part of the system fails. Load balancing distributes inquiries across multiple servers, equalizing the load on the entire system. This ensures the stability and reliability of the system, allowing the service provider to consistently provide high-quality service to customers. Furthermore, the service provider will manage customer inquiry history and refer to past inquiries and answers to provide more appropriate responses. For example, it can customize answers to current inquiries based on past customer inquiries. This allows the service provider to provide personalized services tailored to customer needs.

[0067] The support department provides a wide range of support, from troubleshooting to usage guidance, based on answers provided by the service department. Specifically, it provides troubleshooting procedures to enable customers to resolve product malfunctions. For example, if a product is not working, the support department will provide the customer with specific steps, identify the cause of the problem, and provide a solution. It also provides usage guidance to enable customers to use the product correctly. For example, it provides detailed explanations of how to use and configure new features to help customers get the most out of the product. Furthermore, it provides technical support to enable customers to resolve technical issues with the product. For example, it provides technical support regarding product installation and updates to ensure customers can use the product smoothly. The support department collects customer feedback and uses it to improve the support it provides. For example, it evaluates whether customers are satisfied with the support provided and identifies areas for improvement. This allows the support department to continue providing high-quality support that meets customer needs. In addition, the support department stores customer inquiries and support history in a database and uses it to improve the quality of support in the future. This allows the support department to provide customers with consistently high-quality support.

[0068] The learning unit can learn from and predict based on past customer data using machine learning. For example, the learning unit can learn from past customer data using supervised learning and make predictions. The learning unit can also learn from past customer data using unsupervised learning and make predictions. Furthermore, the learning unit can learn from past customer data and make predictions using reinforcement learning. For example, the learning unit can learn from past customer data using supervised learning and make predictions. It can learn from past customer data using unsupervised learning and make predictions. It can learn from past customer data and make predictions using reinforcement learning. This allows the learning unit to provide more appropriate support by learning from past customer data. Some or all of the above processes in the learning unit may be performed using, for example, generative AI, or not using generative AI. For example, the learning unit can input past customer data into a generative AI and have the generative AI perform learning and predictions.

[0069] The Improvement Department can aim to increase the repeat purchase rate by improving customer satisfaction. For example, the Improvement Department can set evaluation criteria for customer satisfaction and implement measures to improve customer satisfaction. The Improvement Department can also use NPS (Net Promoter Score) to evaluate customer satisfaction and aim to improve the repeat purchase rate. Furthermore, the Improvement Department can conduct surveys and implement measures to improve customer satisfaction. For example, the Improvement Department can set evaluation criteria for customer satisfaction and implement measures to improve customer satisfaction. It can use NPS to evaluate customer satisfaction and aim to improve the repeat purchase rate. It can conduct surveys and implement measures to improve customer satisfaction. As a result, the repeat purchase rate will increase due to the improvement of customer satisfaction. Some or all of the above processes in the Improvement Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Improvement Department can input customer satisfaction evaluation criteria into a generative AI and have the generative AI execute measures to improve customer satisfaction.

[0070] The efficiency department can improve efficiency by streamlining counter services at physical stores. For example, the efficiency department can implement measures to shorten business processes. The efficiency department can also implement measures to optimize resources. Furthermore, the efficiency department can also implement measures to automate operations. For example, the efficiency department can implement measures to shorten business processes. Measures to optimize resources. Measures to automate operations. This will improve the efficiency of counter services at physical stores. Some or all of the above-mentioned processes in the efficiency department may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency department can input business process shortening measures into a generative AI and have the generative AI execute the efficiency measures.

[0071] The response unit can estimate the customer's emotions and adjust the analysis method of the inquiry based on the estimated customer emotions. For example, if the customer is angry, the response unit will carefully adjust the analysis method to provide a calm and courteous response. The response unit can also select a concise and easy-to-understand analysis method if the customer is confused. Furthermore, if the customer is in a hurry, the response unit can streamline the analysis method to provide a quick response. For example, if the customer is angry, the response unit will carefully adjust the analysis method to provide a calm and courteous response. If the customer is confused, it will select a concise and easy-to-understand analysis method. If the customer is in a hurry, it will streamline the analysis method to provide a quick response. This allows for the provision of an appropriate analysis method tailored to the customer's emotions. Estimation of customer emotions is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes 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 perform emotion estimation.

[0072] The response unit can analyze a customer's past inquiry history and select the optimal response method. For example, if a customer has made a similar inquiry in the past, the response unit can refer to that history and respond quickly. The response unit can also automatically extract frequently asked questions from the customer's past inquiry history and provide appropriate answers. Furthermore, the response unit can analyze a customer's past inquiry history and select the optimal response method based on specific patterns. For example, if a customer has made a similar inquiry in the past, the response unit can refer to that history and respond quickly. It can automatically extract frequently asked questions from the customer's past inquiry history and provide appropriate answers. It analyzes the customer's past inquiry history and selects the optimal response method based on specific patterns. This enables quick and appropriate responses based on past inquiry history. Some or all of the above processes in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input the customer's past inquiry history into a generative AI and have the generative AI select the optimal response method.

[0073] The response unit can filter the customer's current situation and areas of interest when analyzing the content of inquiries. For example, if the customer inputs their current situation, the response unit will provide the most suitable answer based on that information. The response unit can also prioritize the analysis of relevant information based on the customer's areas of interest. Furthermore, the response unit can filter out unnecessary information, taking into account the customer's current situation and areas of interest. For example, if the customer inputs their current situation, the response unit will provide the most suitable answer based on that information. It will prioritize the analysis of relevant information based on the customer's areas of interest. It will filter out unnecessary information, taking into account the customer's current situation and areas of interest. This allows the response unit to provide appropriate information according to the customer's situation and areas of interest. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input data on the customer's current situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0074] The response unit can estimate the customer's emotions and determine the priority of inquiries to respond to based on the estimated emotions. For example, if the customer is angry, the response unit may set a high priority for a quick response. If the customer is confused, the response unit may set a medium priority as a detailed explanation is needed. Furthermore, if the customer is in a hurry, the response unit may set a high priority as a quick response is required. For example, if the customer is angry, the response unit may set a high priority for a quick response. If the customer is confused, the response unit may set a medium priority as a detailed explanation is needed. If the customer is in a hurry, the response unit may set a high priority as a quick response is required. This allows for a quick response with priorities tailored to the customer's emotions. The estimation of customer emotions 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 perform emotion estimation.

[0075] The support unit can prioritize the analysis of highly relevant information by considering the customer's geographical location when analyzing the content of an inquiry. For example, if the customer is in a specific region, the support unit will prioritize the analysis of information related to that region. The support unit can also provide answers to region-specific problems based on the customer's geographical location. Furthermore, the support unit can select the optimal support method by considering the customer's geographical location. For example, if the customer is in a specific region, the support unit will prioritize the analysis of information related to that region. Based on the customer's geographical location, it will provide answers to region-specific problems. It will select the optimal support method by considering the customer's geographical location. This allows the support unit to provide appropriate information based on the customer's geographical location. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the customer's geographical location information into a generative AI and have the generative AI perform the analysis of highly relevant information.

[0076] The support unit can analyze the customer's social media activity and obtain relevant information when analyzing the content of an inquiry. For example, the support unit can analyze the customer's social media activity and provide answers based on relevant topics. The support unit can also select the most appropriate support method based on the information the customer has shared on social media. Furthermore, the support unit can prioritize the analysis of relevant information by referring to the customer's social media activity. For example, the support unit can analyze the customer's social media activity and provide answers based on relevant topics. It can select the most appropriate support method based on the information the customer has shared on social media. It can prioritize the analysis of relevant information by referring to the customer's social media activity. This allows the support unit to provide appropriate information based on the customer's social media activity. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input customer social media activity data into a generative AI and have the generative AI perform the acquisition of relevant information.

[0077] The service provider can estimate the customer's emotions and adjust the way it expresses its response based on those emotions. For example, if the customer is angry, the service provider will use a calm and polite expression. If the customer is confused, the service provider may use a concise and easy-to-understand expression. Furthermore, if the customer is in a hurry, the service provider may use an expression that can be quickly understood. For example, if the customer is angry, the service provider will use a calm and polite expression. If the customer is confused, the service provider will use a concise and easy-to-understand expression. If the customer is in a hurry, the service provider will use an expression that can be quickly understood. This allows the service provider to provide a response using an appropriate expression that matches the customer's emotions. The estimation of customer emotions 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input customer emotional data into a generating AI and have the AI ​​adjust the way the response is expressed.

[0078] The service provider can adjust the level of detail in its responses based on the importance of the inquiry. For example, it can provide detailed responses to high-priority inquiries, and concise responses to low-priority inquiries. Furthermore, it can adjust the level of detail in its responses in stages according to the importance of the inquiry. For example, it can provide detailed responses to high-priority inquiries, concise responses to low-priority inquiries, and adjust the level of detail in stages according to the importance of the inquiry. This allows for the provision of responses with an appropriate level of detail according to the importance of the inquiry. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input inquiry importance data into a generation AI and have the generation AI adjust the level of detail in its responses.

[0079] The service provider can apply different response algorithms depending on the category of the inquiry when providing a response. For example, the service provider can apply a specialized response algorithm to technical inquiries. It can also apply a concise response algorithm to general inquiries. Furthermore, the service provider can select the optimal response algorithm depending on the category of the inquiry. For example, the service provider can apply a specialized response algorithm to technical inquiries, a concise response algorithm to general inquiries, and select the optimal response algorithm depending on the category of the inquiry. This allows the service provider to apply the most appropriate response algorithm for each inquiry category. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input inquiry category data into a generative AI and have the generative AI apply the response algorithm.

[0080] The service provider can estimate the customer's emotions and adjust the length of the response based on the estimated emotions. For example, if the customer is angry, the service provider will provide a concise and to-the-point response. If the customer is confused, the service provider can also provide a response that includes a detailed explanation. Furthermore, if the customer is in a hurry, the service provider can provide a short response that can be quickly understood. For example, if the customer is angry, the service provider will provide a concise and to-the-point response. If the customer is confused, the service provider will provide a response that includes a detailed explanation. If the customer is in a hurry, the service provider will provide a short response that can be quickly understood. This allows the service provider to provide a response of an appropriate length according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input customer emotion data into a generating AI and have the AI ​​adjust the length of the response.

[0081] The service provider can determine the priority of responses based on when the inquiry was submitted. For example, the service provider will provide a quick response to urgent inquiries. The service provider can also set a lower priority for older inquiries. Furthermore, the service provider can adjust the priority of responses in stages based on the submission date. For example, the service provider will provide a quick response to urgent inquiries. Older inquiries will be given a lower priority. The priority of responses will be adjusted in stages based on the submission date. This allows for the provision of responses with appropriate priority according to when the inquiry was submitted. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input inquiry submission date data into a generating AI and have the generating AI determine the priority of responses.

[0082] The response unit can adjust the order of responses based on the relevance of the inquiries when providing answers. For example, the response unit will prioritize responses to highly relevant inquiries. The response unit can also postpone responses to less relevant inquiries. Furthermore, the response unit can adjust the order of responses in stages based on the relevance of the inquiries. For example, the response unit will prioritize responses to highly relevant inquiries. It will postpone responses to less relevant inquiries. It will adjust the order of responses in stages based on the relevance of the inquiries. This allows responses to be provided in an appropriate order according to the relevance of the inquiries. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input inquiry relevance data into a generative AI and have the generative AI perform the adjustment of the order of responses.

[0083] The support department can estimate the customer's emotions and adjust its support methods based on those estimates. For example, if a customer is angry, the support department can provide calm and courteous support. If a customer is confused, the support department can provide concise and easy-to-understand support. Furthermore, if a customer is in a hurry, the support department can provide quick support. For example, if a customer is angry, the support department can provide calm and courteous support. If a customer is confused, it can provide concise and easy-to-understand support. If a customer is in a hurry, it can provide quick support. This allows the support department to provide appropriate support methods according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input customer emotion data into a generative AI and have the generative AI adjust the support method.

[0084] The support department can select the optimal support method by analyzing the customer's past support history when providing support. For example, the support department can select the optimal support method by referring to the customer's past support history. The support department can also automatically extract support methods for common problems from the customer's past support history. Furthermore, the support department can analyze the customer's past support history and select the optimal support method based on specific patterns. For example, the support department can select the optimal support method by referring to the customer's past support history. It can automatically extract support methods for common problems from the customer's past support history. It can analyze the customer's past support history and select the optimal support method based on specific patterns. This enables quick and appropriate support based on past support history. Some or all of the above processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the customer's past support history into a generative AI and have the generative AI select the optimal support method.

[0085] The support unit can customize the means of support provided based on the customer's current situation. For example, if the customer inputs their current situation, the support unit will provide the most suitable means of support based on that information. The support unit can also prioritize providing relevant means of support based on the customer's current situation. Furthermore, the support unit can filter out unnecessary means of support, taking into account the customer's current situation. For example, if the customer inputs their current situation, the support unit will provide the most suitable means of support based on that information. Based on the customer's current situation, it will prioritize providing relevant means of support. Based on the customer's current situation, it will filter out unnecessary means of support, taking into account the customer's current situation. This allows the support unit to provide appropriate means of support according to the customer's current situation. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input customer's current situation data into a generative AI and have the generative AI perform the customization of the means of support.

[0086] The support department can estimate the customer's emotions and determine the priority of support based on the estimated emotions. For example, if the customer is angry, the support department may set a high priority to respond quickly. If the customer is confused, the support department may set a medium priority as detailed explanations are needed. Furthermore, if the customer is in a hurry, the support department may set a high priority as a quick response is required. For example, if the customer is angry, the support department may set a high priority to respond quickly. If the customer is confused, the support department may set a medium priority as detailed explanations are needed. If the customer is in a hurry, the support department may set a high priority as a quick response is required. This allows for the rapid provision of support with priorities tailored to the customer's emotions. The estimation of customer emotions is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input customer sentiment data into a generating AI and have the AI ​​determine the priority of support requests.

[0087] The support department can select the optimal support method when providing support, taking into account the customer's geographical location. For example, if the customer is in a specific region, the support department will prioritize providing support methods relevant to that region. The support department can also provide support methods for region-specific issues based on the customer's geographical location. Furthermore, the support department can select the optimal support method, taking into account the customer's geographical location. For example, if the customer is in a specific region, the support department will prioritize providing support methods relevant to that region. Based on the customer's geographical location, it will provide support methods for region-specific issues. It will select the optimal support method, taking into account the customer's geographical location. This allows for the provision of appropriate support methods based on the customer's geographical location. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the customer's geographical location information into a generative AI and have the generative AI select the optimal support method.

[0088] The support department can analyze a customer's social media activity and propose support methods when providing support. For example, the support department can analyze a customer's social media activity and propose support methods based on relevant topics. The support department can also select the most suitable support method based on information shared by the customer on social media. Furthermore, the support department can prioritize providing relevant support methods based on the customer's social media activity. For example, the support department can analyze a customer's social media activity and propose support methods based on relevant topics. It can select the most suitable support method based on information shared by the customer on social media. It can prioritize providing relevant support methods based on the customer's social media activity. This allows the support department to provide appropriate support methods based on the customer's social media activity. Some or all of the above processes in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input customer social media activity data into generative AI and have the generative AI propose support methods.

[0089] 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 will prioritize learning data related to that emotion. The learning unit can also prioritize learning data related to the customer's confused emotions. Furthermore, if the customer is in a hurry, the learning unit can also prioritize learning data related to that emotion. For example, if the customer is angry, the learning unit will prioritize learning data related to that emotion. If the customer is confused, it will prioritize learning data related to that emotion. If the customer is in a hurry, it will prioritize learning data related to that emotion. This allows for the selection of appropriate training data according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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.

[0090] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and selects the optimal learning algorithm. Based on past learning data, it adjusts the parameters of the learning algorithm. By referring to past learning data, it improves the accuracy of the learning algorithm. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative 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.

[0091] 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 can learn more frequently to enable a quick response. If the customer is confused, the learning unit can learn at a moderate frequency to provide an appropriate response. Furthermore, if the customer is in a hurry, the learning unit can increase the frequency of learning to enable a quick response. For example, if the customer is angry, the learning unit can learn more frequently to enable a quick response. If the customer is confused, it can learn at a moderate frequency to provide an appropriate response. If the customer is in a hurry, it can increase the frequency of learning to enable a quick response. This allows learning to be performed at an appropriate frequency according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 adjust the frequency of learning.

[0092] The learning unit can weight the training data based on the submission date of the inquiry history during training. For example, the learning unit can weight the training data by giving more weight to recent inquiry histories. The learning unit can also reduce the weight of older inquiry histories during training. Furthermore, the learning unit can adjust the weighting of the training data in stages based on the submission date. For example, the learning unit can weight the training data by giving more weight to recent inquiry histories, reduce the weight of older inquiry histories during training, and adjust the weighting of the training data in stages based on the submission date. This allows training to be performed with appropriate weighting according to the submission date of the inquiry history. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the inquiry history submission date data into a generative AI and have the generative AI perform the weighting of the training data.

[0093] The improvement unit can estimate customer emotions and adjust methods for improving customer satisfaction based on the estimated emotions. For example, if a customer is angry, the improvement unit can respond quickly and courteously to improve satisfaction. It can also provide clear explanations if a customer is confused to improve satisfaction. Furthermore, if a customer is in a hurry, the improvement unit can respond quickly to improve satisfaction. For example, if a customer is angry, the improvement unit can respond quickly and courteously to improve satisfaction. If a customer is confused, it can provide clear explanations to improve satisfaction. If a customer is in a hurry, it can respond quickly to improve satisfaction. This allows for improving customer satisfaction in an appropriate way according to the customer's emotions. Estimation of customer emotions is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input customer emotion data into a generating AI and have the AI ​​adjust methods to improve customer satisfaction.

[0094] The improvement unit can analyze past customer feedback to select the optimal improvement method when improving customer satisfaction. For example, the improvement unit selects the optimal improvement method based on past customer feedback. The improvement unit can also automatically extract improvement methods for common problems from past customer feedback. Furthermore, the improvement unit can analyze past customer feedback and select the optimal improvement method based on specific patterns. For example, the improvement unit selects the optimal improvement method based on past customer feedback. It automatically extracts improvement methods for common problems from past customer feedback. It analyzes past customer feedback and selects the optimal improvement method based on specific patterns. This allows the improvement unit to select the optimal improvement method based on past customer feedback. Some or all of the above processes in the improvement unit may be performed using, for example, generative AI, or without generative AI. For example, the improvement unit can input past customer feedback into generative AI and have the generative AI select the optimal improvement method.

[0095] The improvement department can estimate customer emotions and determine priorities for improving customer satisfaction based on the estimated emotions. For example, if a customer is angry, the improvement department may set a high priority to address the customer quickly. If a customer is confused, the improvement department may set a medium priority as detailed explanations are needed. Furthermore, if a customer is in a hurry, the improvement department may set a high priority as a quick response is required. For example, if a customer is angry, the improvement department may set a high priority to address the customer quickly. If a customer is confused, the improvement department may set a medium priority as detailed explanations are needed. If a customer is in a hurry, the improvement department may set a high priority as a quick response is required. This allows for rapid improvement of customer satisfaction with priorities tailored to customer emotions. Estimation of customer emotions is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement department may be performed using AI or not using AI. For example, the improvement unit can input customer emotion data into a generating AI and have the AI ​​determine the priorities for improving customer satisfaction.

[0096] The Improvement Department can select the optimal improvement method when improving customer satisfaction, taking into account the customer's geographical location. For example, if a customer is in a specific region, the Improvement Department will prioritize providing improvement methods relevant to that region. The Improvement Department can also provide improvement methods for region-specific problems based on the customer's geographical location. Furthermore, the Improvement Department can select the optimal improvement method, taking into account the customer's geographical location. For example, if a customer is in a specific region, the Improvement Department will prioritize providing improvement methods relevant to that region. Based on the customer's geographical location, it will provide improvement methods for region-specific problems. It will select the optimal improvement method, taking into account the customer's geographical location. This allows the Improvement Department to provide appropriate improvement methods based on the customer's geographical location. Some or all of the above processing in the Improvement Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Improvement Department can input the customer's geographical location information into a generative AI and have the generative AI select the optimal improvement method.

[0097] The efficiency unit can estimate customer emotions and adjust the efficiency of counter services based on the estimated emotions. For example, if a customer is angry, the efficiency unit can provide a quick and courteous response to improve the efficiency of counter services. It can also provide clear explanations to improve the efficiency of counter services if a customer is confused. Furthermore, if a customer is in a hurry, the efficiency unit can provide a quick response to improve the efficiency of counter services. For example, if a customer is angry, the efficiency unit can provide a quick and courteous response to improve the efficiency of counter services. If a customer is confused, it can provide clear explanations to improve the efficiency of counter services. If a customer is in a hurry, it can provide a quick response to improve the efficiency of counter services. This allows for the efficiency of counter services to be improved in an appropriate manner according to the customer's emotions. Estimation of customer emotions is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency department can input customer emotion data into a generating AI and have the AI ​​adjust methods to improve the efficiency of counter services.

[0098] The efficiency improvement department can analyze past business data to select the optimal efficiency improvement method when improving the efficiency of counter services. For example, the efficiency improvement department can select the optimal efficiency improvement method based on past business data. The efficiency improvement department can also automatically extract efficiency improvement methods for common problems from past business data. Furthermore, the efficiency improvement department can analyze past business data and select the optimal efficiency improvement method based on specific patterns. For example, the efficiency improvement department can select the optimal efficiency improvement method based on past business data. It can automatically extract efficiency improvement methods for common problems from past business data. It can analyze past business data and select the optimal efficiency improvement method based on specific patterns. This allows the efficiency improvement department to select the optimal efficiency improvement method based on past business data. Some or all of the above processes in the efficiency improvement department may be performed using, for example, a generation AI, or not using a generation AI. For example, the efficiency improvement department can input past business data into a generation AI and have the generation AI select the optimal efficiency improvement method.

[0099] The efficiency unit can estimate customer emotions and determine priorities for streamlining counter services based on the estimated emotions. For example, if a customer is angry, the efficiency unit may set a high priority to ensure a quick response. If a customer is confused, the efficiency unit may set a medium priority because a detailed explanation is needed. Furthermore, if a customer is in a hurry, the efficiency unit may set a high priority because a quick response is required. For example, if a customer is angry, the efficiency unit may set a high priority to ensure a quick response. If a customer is confused, the efficiency unit may set a medium priority because a detailed explanation is needed. If a customer is in a hurry, the efficiency unit may set a high priority because a quick response is required. This allows for the rapid streamlining of counter services with priorities tailored to customer emotions. Customer emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency department can input customer sentiment data into a generating AI and have the AI ​​determine the priorities for improving the efficiency of counter services.

[0100] The efficiency improvement unit can select the optimal efficiency improvement method when improving the efficiency of counter services, taking into account the customer's geographical location information. For example, if a customer is in a specific region, the efficiency improvement unit will prioritize providing efficiency improvement methods related to that region. The efficiency improvement unit can also provide efficiency improvement methods for region-specific problems based on the customer's geographical location information. Furthermore, the efficiency improvement unit can select the optimal efficiency improvement method by taking into account the customer's geographical location information. For example, if a customer is in a specific region, the efficiency improvement unit will prioritize providing efficiency improvement methods related to that region. Based on the customer's geographical location information, it will provide efficiency improvement methods for region-specific problems. It will select the optimal efficiency improvement method by taking into account the customer's geographical location information. This allows for the provision of appropriate efficiency improvement methods based on the customer's geographical location information. Some or all of the above processing in the efficiency improvement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the efficiency improvement unit can input the customer's geographical location information into a generative AI and have the generative AI select the optimal efficiency improvement method.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The AI ​​assistant system can further analyze customer purchase history and suggest products and services relevant to their inquiries. For example, if a customer inquires about a product they previously purchased, it can suggest related accessories or additional services. If a customer frequently purchases products in a particular category, it can also provide information on new products and promotions related to that category. Furthermore, based on the customer's purchase history, it can predict products and services they might be interested in and provide personalized suggestions. This is expected to improve the customer's purchasing experience and encourage repeat purchases.

[0103] 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, it can prioritize learning data related to that emotion. Similarly, if the customer is confused, it can prioritize learning data related to that emotion. Furthermore, if the customer is in a hurry, it can prioritize learning data related to that emotion. This allows for the selection of appropriate training data according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input customer emotion data into a generative AI and have the generative AI perform the selection of training data.

[0104] The Improvement Department can collect and analyze customer feedback in real time to improve customer satisfaction. For example, if a customer responds to a questionnaire provided after an inquiry, the feedback can be immediately analyzed to identify areas for improvement. Furthermore, if a customer posts comments or reviews on social media, this information can be collected and used to implement measures to improve customer satisfaction. In addition, the department can evaluate service quality and response speed based on customer feedback, enabling continuous improvement. This allows for the provision of services that reflect customer voices, leading to improved customer satisfaction.

[0105] The efficiency unit can estimate customer emotions and adjust the efficiency of counter services based on the estimated emotions. For example, if a customer is angry, it can provide quick and courteous service to improve the efficiency of counter services. If a customer is confused, it can provide clear explanations to improve the efficiency of counter services. Furthermore, if a customer is in a hurry, it can provide quick service to improve the efficiency of counter services. In this way, the efficiency of counter services can be improved in an appropriate manner according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, such as 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 efficiency unit may be performed using AI or not. For example, the efficiency unit can input customer emotion data into a generative AI and have the generative AI adjust the efficiency of counter services.

[0106] The response unit can analyze a customer's past inquiry history and select the optimal response method. For example, if a customer has made a similar inquiry in the past, it can refer to that history and respond quickly. It can also automatically extract frequently asked questions from the customer's past inquiry history and provide appropriate answers. Furthermore, it can analyze the customer's past inquiry history and select the optimal response method based on specific patterns. This enables quick and appropriate responses based on past inquiry history. Some or all of the above processing in the response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response unit can input the customer's past inquiry history into a generation AI and have the generation AI select the optimal response method.

[0107] The service provider can estimate the customer's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the customer is angry, a calm and polite expression can be used. If the customer is confused, a concise and easy-to-understand expression can be used. Furthermore, if the customer is in a hurry, an expression that can be quickly understood can be used. This allows the service provider to provide a response using an appropriate expression according to the customer's emotions. The estimation of the customer's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input customer emotion data into the generative AI and have the generative AI adjust the way the response is expressed.

[0108] The response unit can filter the customer's current situation and areas of interest when analyzing the content of inquiries. For example, if the customer enters their current situation, the unit will provide the most appropriate answer based on that information. It can also prioritize the analysis of relevant information based on the customer's areas of interest. Furthermore, it can filter out unnecessary information, taking into account the customer's current situation and areas of interest. This allows the unit to provide appropriate information tailored to the customer's situation and areas of interest. Some or all of the above processing in the response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response unit can input data on the customer's current situation and areas of interest into a generation AI and have the generation AI perform the filtering.

[0109] The support department can estimate the customer's emotions and adjust its support methods based on those estimates. For example, if a customer is angry, it can provide calm and courteous support. If a customer is confused, it can provide concise and easy-to-understand support. Furthermore, if a customer is in a hurry, it can provide support that responds quickly. This allows for the provision of appropriate support methods tailored to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support department may be performed using AI or not. For example, the support department can input customer emotion data into a generative AI and have the generative AI adjust the support methods.

[0110] The response unit can adjust the level of detail in the response based on the importance of the inquiry. For example, it can provide a detailed response to a high-importance inquiry, and a concise response to a low-importance inquiry. Furthermore, it can adjust the level of detail in the response in stages according to the importance of the inquiry. This allows for the provision of a response with an appropriate level of detail according to the importance of the inquiry. Some or all of the above processing in the response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response unit can input inquiry importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the response.

[0111] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, it can analyze past learning data and select the optimal learning algorithm. It can also adjust the parameters of the learning algorithm based on past learning data. Furthermore, it can improve the accuracy of the learning algorithm by referring to past learning data. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative 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.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The support unit uses natural language processing technology to respond to customer inquiries. For example, it analyzes customer inquiries using morphological analysis, grammatical analysis, and semantic analysis, and provides appropriate answers. Morphological analysis is used to break down customer inquiries into individual words, grammatical analysis is used to analyze the grammatical structure, and semantic analysis is used to analyze the meaning of the inquiry. Step 2: The service provider will provide real-time responses 24 hours a day, 365 days a year, based on inquiries handled by the support team. For example, the system will be operational 24 hours a day, 365 days a year, so that customers can get answers to their inquiries at any time. A maximum response time will be set, and redundancy and load balancing will be implemented to ensure real-time performance. Step 3: The support department provides a wide range of support, from troubleshooting to usage guidance, based on the answers provided by the service department. For example, they provide troubleshooting steps to help customers resolve product malfunctions, usage guidance to help customers use the product correctly, and technical support to help customers resolve technical issues with the product.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the response unit, provision unit, support unit, learning unit, improvement unit, and efficiency unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the response unit is implemented by the control unit 46A of the smart device 14, which analyzes customer inquiries and provides appropriate answers. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides answers in real time, 24 hours a day, 365 days a year. The support unit is implemented by the control unit 46A of the smart device 14, which provides a wide range of support, from troubleshooting to usage guidance. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns past customer data and makes predictions. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12, which aims to improve customer satisfaction. The efficiency unit is implemented by the control unit 46A of the smart device 14, which aims to improve efficiency by compressing counter services at physical stores. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the response unit, provision unit, support unit, learning unit, improvement unit, and efficiency unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the response unit is implemented by the control unit 46A of the smart glasses 214, which analyzes customer inquiries and provides appropriate answers. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which provides answers in real time, 24 hours a day, 365 days a year. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides a wide range of support, from troubleshooting to usage guidance. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which learns past customer data and makes predictions. The improvement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which aims to improve customer satisfaction. The efficiency unit is implemented, for example, by the control unit 46A of the smart glasses 214, which aims to improve efficiency by compressing counter services at physical stores. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the response unit, provision unit, support unit, learning unit, improvement unit, and efficiency unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the response unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the content of customer inquiries and provides appropriate answers. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides answers in real time 24 hours a day, 365 days a year. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides a wide range of support, from troubleshooting to guidance on how to use the device. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which learns past customer data and makes predictions. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which aims to improve customer satisfaction. The efficiency unit is implemented by, for example, the control unit 46A of the headset terminal 314, which aims to improve efficiency by compressing counter services at physical stores. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the response unit, provision unit, support unit, learning unit, improvement unit, and efficiency unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the response unit is implemented by the control unit 46A of the robot 414, which analyzes customer inquiries and provides appropriate answers. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides answers in real time 24 hours a day, 365 days a year. The support unit is implemented by, for example, the control unit 46A of the robot 414, which provides a wide range of support, from troubleshooting to guidance on how to use the device. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which learns past customer data and makes predictions. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which aims to improve customer satisfaction. The efficiency unit is implemented by, for example, the control unit 46A of the robot 414, which aims to improve efficiency by compressing counter services at physical stores. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A customer support department that uses natural language processing technology to handle customer inquiries, A service unit that provides real-time responses 24 hours a day, 365 days a year based on inquiries handled by the aforementioned response unit, The system includes a support unit that provides a wide range of support, from troubleshooting to usage guidance, based on the answers provided by the aforementioned supply unit. A system characterized by the following features. (Note 2) It features a learning unit that uses machine learning to learn from and predict based on customers' historical data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an improvement section designed to increase repeat customer rates by improving customer satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an efficiency department that aims to improve efficiency by streamlining counter services at physical stores. The system described in Appendix 1, characterized by the features described herein. (Note 5) The corresponding part is, We estimate customer emotions and adjust the method of analyzing inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The corresponding part is, Analyze the customer's past inquiry history and select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The corresponding part is, When analyzing inquiries, filtering is performed based on the customer's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The corresponding part is, Estimate customer emotions and prioritize inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The corresponding part is, When analyzing customer inquiries, the system prioritizes analyzing highly relevant information by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The corresponding part is, When analyzing customer inquiries, we analyze their social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, We estimate the customer's emotions and adjust the way we express our responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing a response, adjust the level of detail in the response based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When providing an answer, a different answer algorithm is applied depending on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, The system estimates customer emotions and adjusts the length of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing a response, we will prioritize the responses based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing responses, we adjust the order of responses based on the relevance of the inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is We estimate customer emotions and adjust our support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is When providing support, we analyze the customer's past support history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is When providing support, customize the support methods based on the customer's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is We estimate customer emotions and prioritize support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is When providing support, the optimal support method will be selected considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit is When providing support, we analyze the customer's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, The system estimates customer emotions and selects training data based on the estimated customer emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned learning unit, It estimates customer emotions and adjusts the learning frequency based on the estimated customer emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the training data is weighted based on when the inquiry history was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned improvement section is, We estimate customer emotions and adjust methods for improving customer satisfaction based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned improvement section is, When improving customer satisfaction, analyze past customer feedback to select the most effective improvement methods. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned improvement section is, We estimate customer emotions and determine priorities for improving customer satisfaction based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned improvement section is, When improving customer satisfaction, consider the customer's geographical location to select the most suitable improvement method. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned efficiency improvement unit is We estimate customer emotions and adjust the efficiency of counter services based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned efficiency improvement unit is When streamlining counter services, we analyze past business data to select the most optimal efficiency method. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned efficiency improvement unit is Estimate customer emotions and prioritize improvements to customer service efficiency based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned efficiency improvement unit is When streamlining counter services, the most efficient method is selected by considering the customer's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0186] 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. A customer support department that uses natural language processing technology to handle customer inquiries, A service unit that provides real-time responses 24 hours a day, 365 days a year based on inquiries handled by the aforementioned response unit, The system includes a support unit that provides a wide range of support, from troubleshooting to usage guidance, based on the answers provided by the aforementioned supply unit. A system characterized by the following features.

2. It features a learning unit that uses machine learning to learn from and predict based on customers' historical data. The system according to feature 1.

3. It includes an improvement section designed to increase repeat customer rates by improving customer satisfaction. The system according to feature 1.

4. It includes an efficiency department that aims to improve efficiency by streamlining counter services at physical stores. The system according to feature 1.

5. The corresponding part is, We estimate customer emotions and adjust the method of analyzing inquiries based on those estimated emotions. The system according to feature 1.

6. The corresponding part is, Analyze the customer's past inquiry history and select the most appropriate response method. The system according to feature 1.

7. The corresponding part is, When analyzing inquiries, filtering is performed based on the customer's current situation and areas of interest. The system according to feature 1.

8. The corresponding part is, Estimate customer emotions and prioritize inquiries based on those estimated emotions. The system according to feature 1.

9. The corresponding part is, When analyzing customer inquiries, the system prioritizes analyzing highly relevant information by considering the customer's geographical location. The system according to feature 1.

10. The corresponding part is, When analyzing customer inquiries, we analyze their social media activity and obtain relevant information. The system according to feature 1.

11. The aforementioned supply unit is, We estimate the customer's emotions and adjust the way we express our responses based on those estimated emotions. The system according to feature 1.

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