System

Abstract

An object of a system according to an embodiment is to quickly and appropriately respond to an inquiry from a customer.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an inquiry from a customer. The analysis unit analyzes the inquiry received by the reception unit. The generation unit generates an appropriate answer on the basis of the information analyzed by the analysis unit. The providing unit provides the answer generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to respond to customer inquiries quickly and appropriately, and there are issues in improving customer satisfaction.

[0005] The system according to the embodiment aims to respond quickly and appropriately to inquiries from customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives inquiries from customers. The analysis unit analyzes the inquiries received by the reception unit. The generation unit generates an appropriate answer based on the information analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can respond quickly and appropriately to inquiries from customers. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A customer support system according to an embodiment of the present invention automatically accepts and analyzes customer inquiries, generates appropriate responses, and provides them to customers. This customer support system is available 24 hours a day, 365 days a year, enabling ultra-low-cost operation with minimal bases and minimal operators. For example, the customer support system accepts customer inquiries. For example, if a customer has a question about product usage or troubleshooting, the customer inputs the question via chat. This information is entered into the customer support system. The customer support system then analyzes the input information and generates an appropriate response. For example, for a question about product usage, the system generates a response that explains specific steps. For a question about troubleshooting, the system generates a response that explains the cause of the problem and how to solve it. The generated response is provided to the customer in real time, allowing the customer to quickly resolve the problem. Furthermore, since the customer support system is available 24 hours a day, 365 days a year, customers can receive support at any time. Furthermore, the system is available with minimal bases and minimal operators, enabling ultra-low-cost operation. For example, since the customer support system handles the majority of inquiries, the number of operators can be minimized. This allows companies to provide high-quality support while reducing costs. Finally, customer support systems are applicable to all companies that provide products and services and target a global market. This allows many companies to use this system and improve customer satisfaction. For example, companies in various industries, such as e-commerce sites and service providers, can implement this system to improve the efficiency of customer support and reduce costs. This allows customer support systems to automatically accept and analyze customer inquiries, generate and provide appropriate answers. For example, customers can receive support at any time, improving satisfaction. This also allows companies to provide high-quality support while reducing costs.Furthermore, many businesses can use this system to improve customer satisfaction.

[0029] A customer support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives inquiries from customers. Customer inquiries include, but are not limited to, questions about product usage and troubleshooting. The reception unit receives inquiries from customers using, for example, a chat interface. The reception unit can also receive inquiries using voice input or text input. The reception unit can also receive inquiries using images or videos. For example, the reception unit analyzes images or videos sent by customers to identify the content of the inquiries. The analysis unit analyzes the inquiries received by the reception unit. The analysis unit analyzes the content of the inquiries using, for example, natural language processing technology. The analysis unit can also classify the content of the inquiries using a machine learning algorithm. The analysis unit can also analyze the content of the current inquiries by referring to past inquiry data. For example, the analysis unit identifies the cause of the current problem based on past similar inquiry data. The generation unit generates an appropriate answer based on the information analyzed by the analysis unit. The generation unit generates the answer using, for example, a text generation AI (e.g., LLM). The generation unit can also use multimodal generation AI to generate answers that include not only text but also images and videos. Furthermore, the generation unit can also quickly generate answers using a template-based generation algorithm. For example, the generation unit generates an answer that explains specific steps in response to a question about how to use a product. The provision unit provides the answer generated by the generation unit. The provision unit provides the answer, for example, through a chat interface. The provision unit can also provide the answer via email or SMS. Furthermore, the provision unit can provide the answer via voice using speech synthesis technology. For example, the provision unit provides the generated answer to the customer in real time. This allows the customer support system according to the embodiment to efficiently accept and analyze customer inquiries and provide appropriate answers.

[0030] The reception unit can receive questions about how to use a product or troubleshooting. Examples of product usage include, but are not limited to, basic operations and advanced operations. For example, the reception unit can receive questions about how to use a product through a chat interface. The reception unit can also receive questions about troubleshooting. Examples of troubleshooting include, but are not limited to, questions about general problems and specific error codes. For example, the reception unit can analyze an error message sent by a customer and identify the cause of the problem. This allows the reception unit to receive questions about how to use a product or troubleshooting. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input a question from a customer into a generation AI and have the generation AI analyze the content of the question.

[0031] The analysis unit can analyze the received information and identify the cause of the problem and a solution. The analysis unit, for example, analyzes error logs. For example, the analysis unit analyzes error logs sent by customers and identifies the cause of the problem. The analysis unit can also check user operation histories. For example, the analysis unit analyzes customer operation histories and identifies the cause of the problem. The analysis unit can also identify the cause of the current problem by referring to past inquiry data. For example, the analysis unit identifies the cause of the current problem based on similar past inquiry data. This makes it possible to identify the cause of the problem and a solution. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer inquiry data into a generation AI and have the generation AI identify the cause of the problem and a solution.

[0032] The generation unit can generate an answer that explains specific procedures. For example, the generation unit generates an answer that includes a step-by-step explanation. For example, the generation unit generates an answer that explains specific procedures regarding how to use a product. The generation unit can also provide illustrations. For example, the generation unit generates an answer that explains how to use a product using illustrations. Furthermore, the generation unit can generate an answer that includes a video. For example, the generation unit generates an answer that includes a video that explains how to use a product. This makes it possible to generate an answer that explains specific procedures. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input a question from a customer to the generation AI and cause the generation AI to generate an answer that explains specific procedures.

[0033] The providing unit can provide the generated answer to the customer in real time. Real-time includes, but is not limited to, an upper limit on the response time and an acceptable range of delays, for example. The providing unit can provide the generated answer in real time, for example, through a chat interface. The providing unit can also provide the answer in real time using email or SMS. Furthermore, the providing unit can provide the answer in real time by voice using speech synthesis technology. For example, the providing unit can immediately provide the generated answer to the customer. This allows the generated answer to be provided to the customer in real time. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the generated answer to a generation AI and have the generation AI provide the answer in real time.

[0034] The provision unit can be available 24 hours a day, 365 days a year. 24 hours a day, 365 days a year includes, but is not limited to, a shift system and a backup system. For example, the provision unit establishes a shift system for 24 hours a day, 365 days a year. The provision unit can also be available 24 hours a day, 365 days a year using a backup system. Furthermore, the provision unit can be available 24 hours a day, 365 days a year using a cloud-based system. For example, the provision unit can be kept in operation at all times using a cloud-based system. This allows for 24 hours a day, 365 days a year. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can cause the generation AI to optimize the shift system for 24 hours a day, 365 days a year.

[0035] The system can achieve low-cost operation through small-scale bases and operation by a small number of operators. Examples of small-scale bases include, but are not limited to, the number of employees and the type of equipment. For example, the system creates a shift system for operation by a small number of operators. The system can also be operated at small-scale bases using a cloud-based system. Furthermore, the system can be operated with a small number of operators by introducing remote work. For example, the system minimizes the number of operators by introducing remote work. This allows low-cost operation through small-scale bases and operation by a small number of operators. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system can have a generation AI perform the optimization of operator shift systems.

[0036] When receiving an inquiry, the reception unit can select the optimal reception method by referring to the user's past inquiry history. For example, the reception unit can prioritize suggesting an inquiry method that the user has frequently used in the past. For example, the reception unit can recommend a frequently used inquiry method by referring to the user's past inquiry history. The reception unit can also display related information in advance based on the content of the user's past inquiry. For example, the reception unit can display related support information in advance based on the content of the past inquiry. The reception unit can also prioritize processing new inquiries related to problems the user has previously solved. For example, the reception unit prioritizes processing inquiries related to problems previously solved. This makes it possible to select the optimal reception method by referring to the user's past inquiry history. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past inquiry history into a generation AI and have the generation AI select the optimal reception method.

[0037] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving an inquiry. For example, the reception unit prioritizes processing inquiries related to products currently used by the user. For example, the reception unit prioritizes processing inquiries related to products currently used by the user. The reception unit can also provide related support information based on the user's areas of interest. For example, the reception unit provides related support information based on the user's areas of interest. The reception unit can also adjust the priority of inquiries according to the user's current situation (e.g., urgency). For example, the reception unit adjusts the priority of inquiries according to the user's current situation. This allows filtering to be performed based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0038] When receiving an inquiry, the reception unit can select the optimal reception means depending on the user's input method. For example, when the user makes an inquiry by voice, the reception unit uses voice recognition technology to receive the inquiry. For example, the reception unit uses voice recognition technology to receive the voice inquiry. Furthermore, when the user makes an inquiry by text, the reception unit can also use a chatbot to receive the inquiry. For example, the reception unit uses a chatbot to receive the text inquiry. Furthermore, when the user sends an image, the reception unit can also identify the problem using image analysis technology. For example, the reception unit uses image analysis technology to analyze the image and identify the problem. This makes it possible to select the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0039] When receiving an inquiry, the reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes processing inquiries related to that area. For example, the reception unit prioritizes processing inquiries related to that area based on the user's geographical location information. The reception unit can also transfer an inquiry to the nearest support center based on the user's location information. For example, the reception unit transfers an inquiry to the nearest support center based on the user's location information. The reception unit can also provide information on region-specific issues based on the user's location information. For example, the reception unit provides information on region-specific issues based on the user's location information. This makes it possible to prioritize receiving highly relevant inquiries by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant inquiries.

[0040] The reception unit can analyze the user's social media activity when receiving an inquiry and receive related inquiries. The reception unit, for example, prioritizes inquiries related to issues mentioned by the user on social media. For example, the reception unit analyzes the user's social media activity and prioritizes inquiries related to the issues mentioned. The reception unit can also provide related support information based on the user's social media activity. For example, the reception unit provides related support information based on the user's social media activity. The reception unit can also adjust the priority of inquiries based on the user's feedback on social media. For example, the reception unit adjusts the priority of inquiries based on the user's feedback on social media. This makes it possible to analyze the user's social media activity and receive related inquiries. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to receive related inquiries.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an inquiry. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. For example, the reception unit refers to the user's past feedback and suggests the optimal reception method. The reception unit can also provide related support information based on the user's past feedback. For example, the reception unit provides related support information based on the user's past feedback. The reception unit can also adjust the priority of inquiries based on the user's past feedback. For example, the reception unit adjusts the priority of inquiries based on the user's past feedback. This makes it possible to customize the reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the reception method.

[0042] During analysis, the analysis unit can identify the cause of the current inquiry by referring to past inquiry data. The analysis unit, for example, identifies the cause of the current problem based on similar past inquiry data. For example, the analysis unit refers to past inquiry data and identifies the cause of similar problems. The analysis unit can also analyze past inquiry data to identify common problems. For example, the analysis unit analyzes past inquiry data and identifies common problems. The analysis unit can also propose a solution to the current problem based on the past inquiry data. For example, the analysis unit proposes a solution to the current problem based on the past inquiry data. In this way, the cause of the current inquiry can be identified by referring to the past inquiry data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past inquiry data to a generation AI and cause the generation AI to identify the cause of the current inquiry.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, the analysis unit applies a specific analysis algorithm to inquiries regarding how to use a product. For example, the analysis unit applies a specific analysis algorithm to inquiries regarding how to use a product. The analysis unit can also apply a different analysis algorithm to inquiries regarding troubleshooting. For example, the analysis unit applies a different analysis algorithm to inquiries regarding troubleshooting. The analysis unit can also apply a dedicated analysis algorithm to inquiries regarding customer support. For example, the analysis unit applies a dedicated analysis algorithm to inquiries regarding customer support. This makes it possible to apply different analysis algorithms depending on the category of the inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input inquiry category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past inquiry results. The analysis unit, for example, identifies the cause of a current problem based on the user's past inquiry results. For example, the analysis unit refers to the user's past inquiry results and identifies the cause of the current problem. The analysis unit can also analyze the user's past inquiry results and identify common problems. For example, the analysis unit can analyze the user's past inquiry results and identify common problems. The analysis unit can also suggest a solution to the current problem based on the user's past inquiry results. For example, the analysis unit suggests a solution to the current problem based on the user's past inquiry results. This allows the accuracy of the analysis to be improved by referring to the user's past inquiry results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past inquiry result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the inquiry. For example, the analysis unit prioritizes analysis of urgent inquiries. For example, the analysis unit prioritizes analysis of urgent inquiries based on the time of submission of the inquiry. The analysis unit can also analyze normal inquiries with normal priority. For example, the analysis unit analyzes normal inquiries with normal priority. The analysis unit can also set priorities according to the time of submission based on past inquiry data. For example, the analysis unit sets priorities according to the time of submission based on past inquiry data. This makes it possible to determine the priority of analysis based on the time of submission of the inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input inquiry submission time data to the generation AI and have the generation AI determine the analysis priority.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the queries. For example, the analysis unit prioritizes analysis of highly relevant queries. For example, the analysis unit prioritizes analysis of highly relevant queries based on the relevance of the queries. The analysis unit can also postpone less relevant queries. For example, the analysis unit postpones less relevant queries. The analysis unit can also set the analysis order according to the relevance based on past query data. For example, the analysis unit sets the analysis order according to the relevance based on past query data. This makes it possible to adjust the analysis order based on the relevance of the queries. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input query relevance data to the generation AI and cause the generation AI to adjust the analysis order.

[0047] During analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, the analysis unit provides detailed analysis results to users with high levels of expertise. For example, the analysis unit provides detailed analysis results based on the user's level of expertise. The analysis unit can also provide concise analysis results to users with low levels of expertise. For example, the analysis unit provides concise analysis results to users with low levels of expertise. The analysis unit can also provide analysis results according to the user's level of expertise based on the user's past inquiry history. For example, the analysis unit provides analysis results according to the user's level of expertise based on the user's past inquiry history. This makes it possible to adjust the level of detail of the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0048] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the inquiry. For example, the generation unit generates a detailed answer for an inquiry with a high level of importance. For example, the generation unit generates a detailed answer for an inquiry with a high level of importance based on the importance of the inquiry. The generation unit can also generate a concise answer for an inquiry with a low level of importance. For example, the generation unit generates a concise answer for an inquiry with a low level of importance. The generation unit can also adjust the level of detail of the answer according to the importance of the inquiry. For example, the generation unit adjusts the level of detail of the answer according to the importance of the inquiry. In this way, the level of detail of the answer can be adjusted based on the importance of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input inquiry importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0049] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the inquiry. For example, the generation unit applies a specific generation algorithm to inquiries regarding how to use a product. For example, the generation unit applies a specific generation algorithm to inquiries regarding how to use a product. The generation unit can also apply a different generation algorithm to inquiries regarding troubleshooting. For example, the generation unit applies a different generation algorithm to inquiries regarding troubleshooting. The generation unit can also apply a dedicated generation algorithm to inquiries regarding customer support. For example, the generation unit applies a dedicated generation algorithm to inquiries regarding customer support. This makes it possible to apply different generation algorithms depending on the category of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input inquiry category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0050] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The generation unit, for example, generates an optimal answer to the current problem based on the user's past answer results. For example, the generation unit generates an optimal answer to the current problem by referring to the user's past answer results. The generation unit can also analyze the user's past answer results and identify common problems. For example, the generation unit can analyze the user's past answer results and identify common problems. The generation unit can also suggest a solution to the current problem based on the user's past answer results. For example, the generation unit suggests a solution to the current problem based on the user's past answer results. This allows the accuracy of the answer to be improved by referring to the user's past answer results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past answer result data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0051] When generating answers, the generation unit can determine the priority of the answers based on the time of submission of the inquiry. For example, the generation unit generates answers with priority for urgent inquiries. For example, the generation unit generates answers with priority for urgent inquiries based on the time of submission of the inquiry. The generation unit can also generate answers with normal priority for regular inquiries. For example, the generation unit generates answers with normal priority for regular inquiries. The generation unit can also set priorities according to the time of submission based on past inquiry data. For example, the generation unit sets priorities according to the time of submission based on past inquiry data. This makes it possible to determine the priority of answers based on the time of submission of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input inquiry submission time data into the generation AI and have the generation AI determine the priority of answers.

[0052] When generating answers, the generation unit can adjust the order of answers based on the relevance of the inquiries. For example, the generation unit generates answers preferentially to highly relevant inquiries. For example, the generation unit generates answers preferentially to highly relevant inquiries based on the relevance of the inquiries. The generation unit can also postpone less relevant inquiries. For example, the generation unit postpones less relevant inquiries. The generation unit can also set the order of answers according to relevance based on past inquiry data. For example, the generation unit sets the order of answers according to relevance based on past inquiry data. This makes it possible to adjust the order of answers based on the relevance of the inquiries. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input inquiry relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0053] When generating an answer, the generation unit can adjust the use of technical terms in the answer depending on the user's level of expertise. For example, the generation unit generates an answer that uses a lot of technical terms for a user with high level of expertise. For example, the generation unit generates an answer that uses a lot of technical terms based on the user's level of expertise. The generation unit can also generate a concise and easy-to-understand answer for a user with low level of expertise. For example, the generation unit generates a concise and easy-to-understand answer for a user with low level of expertise. The generation unit can also provide an answer according to the user's level of expertise based on the user's past inquiry history. For example, the generation unit provides an answer according to the user's level of expertise based on the user's past inquiry history. This makes it possible to adjust the use of technical terms in the answer depending on the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms in the answer.

[0054] When providing an answer, the providing unit can select the optimal providing method by referring to the user's past operation history. For example, the providing unit preferentially suggests a providing method that the user has used favorably in the past. For example, the providing unit can refer to the user's past operation history and suggest the preferred providing method. The providing unit can also display related information in advance based on the user's past operation history. For example, the providing unit can display related support information in advance based on the past operation history. The providing unit can also select the optimal providing method based on the user's past operation history. For example, the providing unit selects the optimal providing method based on the past operation history. This makes it possible to select the optimal providing method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past operation history data to the generation AI and cause the generation AI to select the optimal providing method.

[0055] When providing an answer, the providing unit can customize the content to be provided according to the user's current situation. For example, the providing unit prioritizes providing answers related to products currently being used by the user. For example, the providing unit prioritizes providing answers related to products currently being used by the user. The providing unit can also adjust the content to be provided according to the user's current situation (e.g., urgency). For example, the providing unit adjusts the content to be provided according to the user's current situation. The providing unit can also provide related support information based on the user's current situation. For example, the providing unit provides related support information based on the user's current situation. This makes it possible to customize the content to be provided according to the user's current situation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's current situation to the generation AI and cause the generation AI to customize the content to be provided.

[0056] The providing unit can improve the answer providing method by reflecting the user's feedback when providing an answer. The providing unit, for example, proposes an optimal answer providing method based on the feedback provided by the user. For example, the providing unit refers to the user's feedback and proposes an optimal answer providing method. The providing unit can also provide related support information based on the user's feedback. For example, the providing unit provides related support information based on the user's feedback. The providing unit can also improve the answer providing method based on the user's feedback. For example, the providing unit improves the answer providing method based on the user's feedback. This makes it possible to improve the answer providing method by reflecting the user's feedback. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into the generating AI and cause the generating AI to improve the answer providing method.

[0057] When providing an answer, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit prioritizes providing answers related to that area. For example, the providing unit prioritizes providing answers related to that area based on the user's geographical location information. The providing unit can also forward an inquiry to the nearest support center based on the user's location information. For example, the providing unit forwards an inquiry to the nearest support center based on the user's location information. The providing unit can also provide information on area-specific problems based on the user's location information. For example, the providing unit provides information on area-specific problems based on the user's location information. This makes it possible to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal delivery method.

[0058] When providing an answer, the providing unit can customize the provided content by analyzing the user's social media activity. For example, the providing unit can prioritize providing answers related to problems mentioned by the user on social media. For example, the providing unit can analyze the user's social media activity and prioritize providing answers related to the problems mentioned. The providing unit can also provide related support information based on the user's social media activity. For example, the providing unit can provide related support information based on the user's social media activity. The providing unit can also customize the provided content based on the user's feedback on social media. For example, the providing unit customizes the provided content based on the user's feedback on social media. This makes it possible to customize the provided content by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's social media activity data to the generation AI and cause the generation AI to customize the provided content.

[0059] When providing an answer, the providing unit can customize the providing method by reflecting the user's past feedback. The providing unit, for example, suggests an optimal providing method based on feedback provided by the user in the past. For example, the providing unit refers to the user's past feedback and suggests an optimal providing method. The providing unit can also provide related support information based on the user's past feedback. For example, the providing unit provides related support information based on the user's past feedback. The providing unit can also customize the providing method based on the user's past feedback. For example, the providing unit customizes the providing method based on the user's past feedback. This makes it possible to customize the providing method by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the providing method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can refer to the user's past purchase history and prioritize processing related inquiries. For example, the reception unit prioritizes processing inquiries about products the user has purchased in the past. The reception unit can also display related support information in advance based on the user's purchase history. For example, the reception unit displays support information about products the user has purchased in the past in advance. The reception unit can also prioritize processing new inquiries related to products the user has purchased in the past. For example, the reception unit prioritizes processing inquiries related to products the user has purchased in the past. This makes it possible to select the optimal reception method by referring to the user's past purchase history.

[0062] The analysis unit can analyze the user's current network environment and propose an optimal solution. For example, the analysis unit can analyze the user's network speed and connection status to identify the cause of the problem. The analysis unit can also propose an optimal solution based on the user's network environment. For example, if the network speed is slow, the analysis unit can propose specific steps to improve the speed. The analysis unit can also customize troubleshooting steps according to the user's network environment. For example, the analysis unit can propose optimal troubleshooting steps according to the user's network environment. This makes it possible to propose an optimal solution taking the user's network environment into consideration.

[0063] The analysis unit can refer to the user's past inquiry history and identify the cause of the current inquiry. For example, the analysis unit can identify the cause of the current problem based on past similar inquiry data. The analysis unit can also analyze past inquiry data and identify common problems. For example, the analysis unit can analyze past inquiry data and identify common problems. The analysis unit can also propose a solution to the current problem based on the past inquiry data. For example, the analysis unit can propose a solution to the current problem based on the past inquiry data. In this way, the cause of the current inquiry can be identified by referring to the past inquiry data.

[0064] The generation unit can generate an optimal answer by referring to the user's past feedback. For example, the generation unit generates an optimal answer based on feedback provided by the user in the past. The generation unit can also analyze the user's past feedback and identify common problems. For example, the generation unit analyzes the user's past feedback and identify common problems. The generation unit can also suggest a solution to the current problem based on the user's past feedback. For example, the generation unit suggests a solution to the current problem based on the user's past feedback. In this way, the optimal answer can be generated by referring to the user's past feedback.

[0065] The providing unit can adjust the answer providing method based on the user's current situation. For example, the providing unit selects the optimal providing method depending on the device the user is currently using. For example, if the user is using a smartphone, the providing unit can provide a mobile-friendly answer. Also, the providing unit can provide detailed documentation if the user is using a personal computer. For example, if the user is using a personal computer, the providing unit can provide detailed documentation. Also, the providing unit can provide an interactive guide if the user is using a tablet. For example, if the user is using a tablet, the providing unit can provide an interactive guide. This makes it possible to adjust the answer providing method based on the user's current situation.

[0066] The reception unit can select the optimal reception method by referring to the user's past inquiry results. For example, the reception unit preferentially suggests reception methods that the user has used favorably in the past. The reception unit can also display related support information in advance based on the user's past inquiry results. For example, the reception unit displays related support information in advance based on the user's past inquiry results. The reception unit can also preferentially process new inquiries related to problems that the user has solved in the past. For example, the reception unit preferentially processes inquiries related to problems that have been solved in the past. This makes it possible to select the optimal reception method by referring to the user's past inquiry results.

[0067] The analysis unit can determine the priority of analysis based on the user's current situation. For example, the analysis unit prioritizes analysis when the user is in an emergency. The analysis unit can also perform analysis with normal priority when the user is in a normal situation. For example, the analysis unit performs analysis with normal priority when the user is in a normal situation. The analysis unit can also customize the analysis procedure according to the user's current situation. For example, the analysis unit suggests an optimal analysis procedure according to the user's current situation. This makes it possible to determine the priority of analysis based on the user's current situation.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit receives inquiries from customers. Customer inquiries include, but are not limited to, questions about product usage and troubleshooting. The reception unit can receive inquiries using a chat interface, voice input, text input, images, or videos. For example, the reception unit can analyze images or videos sent by customers to identify the content of the inquiry. Step 2: The analysis unit analyzes the inquiry received by the reception unit. The analysis unit uses natural language processing technology and machine learning algorithms to analyze and classify the inquiry content. It can also refer to past inquiry data to analyze the current inquiry content and identify the cause of the problem. Step 3: The generator generates an appropriate answer based on the information analyzed by the analyzer. The generator can use text generation AI (e.g., LLM) or multimodal generation AI to generate answers that include not only text but also images and videos. It can also use template-based generation algorithms to generate answers quickly. Step 4: The providing unit provides the answer generated by the generating unit. The providing unit can provide the answer using a chat interface, email, SMS, or voice synthesis technology. For example, the generated answer is provided to the customer in real time.

[0070] (Example 2) A customer support system according to an embodiment of the present invention automatically accepts and analyzes customer inquiries, generates appropriate responses, and provides them to customers. This customer support system is available 24 hours a day, 365 days a year, enabling ultra-low-cost operation with minimal bases and minimal operators. For example, the customer support system accepts customer inquiries. For example, if a customer has a question about product usage or troubleshooting, the customer inputs the question via chat. This information is entered into the customer support system. The customer support system then analyzes the input information and generates an appropriate response. For example, for a question about product usage, the system generates a response that explains specific steps. For a question about troubleshooting, the system generates a response that explains the cause of the problem and how to solve it. The generated response is provided to the customer in real time, allowing the customer to quickly resolve the problem. Furthermore, since the customer support system is available 24 hours a day, 365 days a year, customers can receive support at any time. Furthermore, the system is available with minimal bases and minimal operators, enabling ultra-low-cost operation. For example, since the customer support system handles the majority of inquiries, the number of operators can be minimized. This allows companies to provide high-quality support while reducing costs. Finally, customer support systems are applicable to all companies that provide products and services and target a global market. This allows many companies to use this system and improve customer satisfaction. For example, companies in various industries, such as e-commerce sites and service providers, can implement this system to improve the efficiency of customer support and reduce costs. This allows customer support systems to automatically accept and analyze customer inquiries, generate and provide appropriate answers. For example, customers can receive support at any time, improving satisfaction. This also allows companies to provide high-quality support while reducing costs.Furthermore, many businesses can use this system to improve customer satisfaction.

[0071] A customer support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives inquiries from customers. Customer inquiries include, but are not limited to, questions about product usage and troubleshooting. The reception unit receives inquiries from customers using, for example, a chat interface. The reception unit can also receive inquiries using voice input or text input. The reception unit can also receive inquiries using images or videos. For example, the reception unit analyzes images or videos sent by customers to identify the content of the inquiries. The analysis unit analyzes the inquiries received by the reception unit. The analysis unit analyzes the content of the inquiries using, for example, natural language processing technology. The analysis unit can also classify the content of the inquiries using a machine learning algorithm. The analysis unit can also analyze the content of the current inquiries by referring to past inquiry data. For example, the analysis unit identifies the cause of the current problem based on past similar inquiry data. The generation unit generates an appropriate answer based on the information analyzed by the analysis unit. The generation unit generates the answer using, for example, a text generation AI (e.g., LLM). The generation unit can also use multimodal generation AI to generate answers that include not only text but also images and videos. Furthermore, the generation unit can also quickly generate answers using a template-based generation algorithm. For example, the generation unit generates an answer that explains specific steps in response to a question about how to use a product. The provision unit provides the answer generated by the generation unit. The provision unit provides the answer, for example, through a chat interface. The provision unit can also provide the answer via email or SMS. Furthermore, the provision unit can provide the answer via voice using speech synthesis technology. For example, the provision unit provides the generated answer to the customer in real time. This allows the customer support system according to the embodiment to efficiently accept and analyze customer inquiries and provide appropriate answers.

[0072] The reception unit can receive questions about how to use a product or troubleshooting. Examples of product usage include, but are not limited to, basic operations and advanced operations. For example, the reception unit can receive questions about how to use a product through a chat interface. The reception unit can also receive questions about troubleshooting. Examples of troubleshooting include, but are not limited to, questions about general problems and specific error codes. For example, the reception unit can analyze an error message sent by a customer and identify the cause of the problem. This allows the reception unit to receive questions about how to use a product or troubleshooting. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input a question from a customer into a generation AI and have the generation AI analyze the content of the question.

[0073] The analysis unit can analyze the received information and identify the cause of the problem and a solution. The analysis unit, for example, analyzes error logs. For example, the analysis unit analyzes error logs sent by customers and identifies the cause of the problem. The analysis unit can also check user operation histories. For example, the analysis unit analyzes customer operation histories and identifies the cause of the problem. The analysis unit can also identify the cause of the current problem by referring to past inquiry data. For example, the analysis unit identifies the cause of the current problem based on similar past inquiry data. This makes it possible to identify the cause of the problem and a solution. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer inquiry data into a generation AI and have the generation AI identify the cause of the problem and a solution.

[0074] The generation unit can generate an answer that explains specific procedures. For example, the generation unit generates an answer that includes a step-by-step explanation. For example, the generation unit generates an answer that explains specific procedures regarding how to use a product. The generation unit can also provide illustrations. For example, the generation unit generates an answer that explains how to use a product using illustrations. Furthermore, the generation unit can generate an answer that includes a video. For example, the generation unit generates an answer that includes a video that explains how to use a product. This makes it possible to generate an answer that explains specific procedures. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input a question from a customer to the generation AI and cause the generation AI to generate an answer that explains specific procedures.

[0075] The providing unit can provide the generated answer to the customer in real time. Real-time includes, but is not limited to, an upper limit on the response time and an acceptable range of delays, for example. The providing unit can provide the generated answer in real time, for example, through a chat interface. The providing unit can also provide the answer in real time using email or SMS. Furthermore, the providing unit can provide the answer in real time by voice using speech synthesis technology. For example, the providing unit can immediately provide the generated answer to the customer. This allows the generated answer to be provided to the customer in real time. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the generated answer to a generation AI and have the generation AI provide the answer in real time.

[0076] The provision unit can be available 24 hours a day, 365 days a year. 24 hours a day, 365 days a year includes, but is not limited to, a shift system and a backup system. For example, the provision unit establishes a shift system for 24 hours a day, 365 days a year. The provision unit can also be available 24 hours a day, 365 days a year using a backup system. Furthermore, the provision unit can be available 24 hours a day, 365 days a year using a cloud-based system. For example, the provision unit can be kept in operation at all times using a cloud-based system. This allows for 24 hours a day, 365 days a year. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can cause the generation AI to optimize the shift system for 24 hours a day, 365 days a year.

[0077] The system can achieve low-cost operation through small-scale bases and operation by a small number of operators. Examples of small-scale bases include, but are not limited to, the number of employees and the type of equipment. For example, the system creates a shift system for operation by a small number of operators. The system can also be operated at small-scale bases using a cloud-based system. Furthermore, the system can be operated with a small number of operators by introducing remote work. For example, the system minimizes the number of operators by introducing remote work. This allows low-cost operation through small-scale bases and operation by a small number of operators. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system can have a generation AI perform the optimization of operator shift systems.

[0078] The reception unit can estimate the user's emotions and determine the priority of inquiries based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit processes the inquiry with the highest priority. For example, the reception unit estimates the user's emotions and prioritizes inquiries from users who are feeling stressed. The reception unit can also process inquiries with normal priority if the user is relaxed. For example, the reception unit processes inquiries from relaxed users with normal priority. The reception unit can also increase the priority to respond quickly if the user is in a hurry. For example, the reception unit quickly processes inquiries from users who are in a hurry. This makes it possible to determine the priority of inquiries based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of inquiries.

[0079] When receiving an inquiry, the reception unit can select the optimal reception method by referring to the user's past inquiry history. For example, the reception unit can prioritize suggesting an inquiry method that the user has frequently used in the past. For example, the reception unit can recommend a frequently used inquiry method by referring to the user's past inquiry history. The reception unit can also display related information in advance based on the content of the user's past inquiry. For example, the reception unit can display related support information in advance based on the content of the past inquiry. The reception unit can also prioritize processing new inquiries related to problems the user has previously solved. For example, the reception unit prioritizes processing inquiries related to problems previously solved. This makes it possible to select the optimal reception method by referring to the user's past inquiry history. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past inquiry history into a generation AI and have the generation AI select the optimal reception method.

[0080] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving an inquiry. For example, the reception unit prioritizes processing inquiries related to products currently used by the user. For example, the reception unit prioritizes processing inquiries related to products currently used by the user. The reception unit can also provide related support information based on the user's areas of interest. For example, the reception unit provides related support information based on the user's areas of interest. The reception unit can also adjust the priority of inquiries according to the user's current situation (e.g., urgency). For example, the reception unit adjusts the priority of inquiries according to the user's current situation. This allows filtering to be performed based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0081] When receiving an inquiry, the reception unit can select the optimal reception means depending on the user's input method. For example, when the user makes an inquiry by voice, the reception unit uses voice recognition technology to receive the inquiry. For example, the reception unit uses voice recognition technology to receive the voice inquiry. Furthermore, when the user makes an inquiry by text, the reception unit can also use a chatbot to receive the inquiry. For example, the reception unit uses a chatbot to receive the text inquiry. Furthermore, when the user sends an image, the reception unit can also identify the problem using image analysis technology. For example, the reception unit uses image analysis technology to analyze the image and identify the problem. This makes it possible to select the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0082] The reception unit can estimate the user's emotions and adjust the timing of reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit immediately starts responding. For example, the reception unit estimates the user's emotions and immediately responds to inquiries from stressed users. The reception unit can also start responding at a normal timing if the user is relaxed. For example, the reception unit responds to inquiries from relaxed users at a normal timing. The reception unit can also start responding quickly if the user is in a hurry. For example, the reception unit responds quickly to inquiries from rushed users. This makes it possible to adjust the timing of reception based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of reception.

[0083] When receiving an inquiry, the reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes processing inquiries related to that area. For example, the reception unit prioritizes processing inquiries related to that area based on the user's geographical location information. The reception unit can also transfer an inquiry to the nearest support center based on the user's location information. For example, the reception unit transfers an inquiry to the nearest support center based on the user's location information. The reception unit can also provide information on region-specific issues based on the user's location information. For example, the reception unit provides information on region-specific issues based on the user's location information. This makes it possible to prioritize receiving highly relevant inquiries by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant inquiries.

[0084] The reception unit can analyze the user's social media activity when receiving an inquiry and receive related inquiries. The reception unit, for example, prioritizes inquiries related to issues mentioned by the user on social media. For example, the reception unit analyzes the user's social media activity and prioritizes inquiries related to the issues mentioned. The reception unit can also provide related support information based on the user's social media activity. For example, the reception unit provides related support information based on the user's social media activity. The reception unit can also adjust the priority of inquiries based on the user's feedback on social media. For example, the reception unit adjusts the priority of inquiries based on the user's feedback on social media. This makes it possible to analyze the user's social media activity and receive related inquiries. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to receive related inquiries.

[0085] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an inquiry. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. For example, the reception unit refers to the user's past feedback and suggests the optimal reception method. The reception unit can also provide related support information based on the user's past feedback. For example, the reception unit provides related support information based on the user's past feedback. The reception unit can also adjust the priority of inquiries based on the user's past feedback. For example, the reception unit adjusts the priority of inquiries based on the user's past feedback. This makes it possible to customize the reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the reception method.

[0086] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit increases the accuracy of the analysis to quickly identify the problem. For example, the analysis unit estimates the user's emotions and increases the accuracy of the analysis for inquiries from users who are feeling stressed. The analysis unit can also perform analysis with normal accuracy when the user is relaxed. For example, the analysis unit performs analysis with normal accuracy for inquiries from relaxed users. The analysis unit can also increase the accuracy of the analysis for inquiries from users who are in a hurry to quickly identify the problem. For example, the analysis unit increases the accuracy of the analysis for inquiries from users who are in a hurry. This allows the analysis accuracy to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the accuracy of the analysis.

[0087] During analysis, the analysis unit can identify the cause of the current inquiry by referring to past inquiry data. The analysis unit, for example, identifies the cause of the current problem based on similar past inquiry data. For example, the analysis unit refers to past inquiry data and identifies the cause of similar problems. The analysis unit can also analyze past inquiry data to identify common problems. For example, the analysis unit analyzes past inquiry data and identifies common problems. The analysis unit can also propose a solution to the current problem based on the past inquiry data. For example, the analysis unit proposes a solution to the current problem based on the past inquiry data. In this way, the cause of the current inquiry can be identified by referring to the past inquiry data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past inquiry data to a generation AI and cause the generation AI to identify the cause of the current inquiry.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, the analysis unit applies a specific analysis algorithm to inquiries regarding how to use a product. For example, the analysis unit applies a specific analysis algorithm to inquiries regarding how to use a product. The analysis unit can also apply a different analysis algorithm to inquiries regarding troubleshooting. For example, the analysis unit applies a different analysis algorithm to inquiries regarding troubleshooting. The analysis unit can also apply a dedicated analysis algorithm to inquiries regarding customer support. For example, the analysis unit applies a dedicated analysis algorithm to inquiries regarding customer support. This makes it possible to apply different analysis algorithms depending on the category of the inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input inquiry category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past inquiry results. The analysis unit, for example, identifies the cause of a current problem based on the user's past inquiry results. For example, the analysis unit refers to the user's past inquiry results and identifies the cause of the current problem. The analysis unit can also analyze the user's past inquiry results and identify common problems. For example, the analysis unit can analyze the user's past inquiry results and identify common problems. The analysis unit can also suggest a solution to the current problem based on the user's past inquiry results. For example, the analysis unit suggests a solution to the current problem based on the user's past inquiry results. This allows the accuracy of the analysis to be improved by referring to the user's past inquiry results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past inquiry result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0090] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, the analysis unit increases the analysis priority when the user is stressed. For example, the analysis unit estimates the user's emotions and increases the analysis priority for inquiries from stressed users. The analysis unit can also perform analysis at a normal priority when the user is relaxed. For example, the analysis unit performs analysis at a normal priority for inquiries from relaxed users. The analysis unit can also increase the analysis priority when the user is in a hurry. For example, the analysis unit increases the analysis priority for inquiries from rushed users. This makes it possible to determine the analysis priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI determine the analysis priorities.

[0091] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the inquiry. For example, the analysis unit prioritizes analysis of urgent inquiries. For example, the analysis unit prioritizes analysis of urgent inquiries based on the time of submission of the inquiry. The analysis unit can also analyze normal inquiries with normal priority. For example, the analysis unit analyzes normal inquiries with normal priority. The analysis unit can also set priorities according to the time of submission based on past inquiry data. For example, the analysis unit sets priorities according to the time of submission based on past inquiry data. This makes it possible to determine the priority of analysis based on the time of submission of the inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input inquiry submission time data to the generation AI and have the generation AI determine the analysis priority.

[0092] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the queries. For example, the analysis unit prioritizes analysis of highly relevant queries. For example, the analysis unit prioritizes analysis of highly relevant queries based on the relevance of the queries. The analysis unit can also postpone less relevant queries. For example, the analysis unit postpones less relevant queries. The analysis unit can also set the analysis order according to the relevance based on past query data. For example, the analysis unit sets the analysis order according to the relevance based on past query data. This makes it possible to adjust the analysis order based on the relevance of the queries. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input query relevance data to the generation AI and cause the generation AI to adjust the analysis order.

[0093] During analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, the analysis unit provides detailed analysis results to users with high levels of expertise. For example, the analysis unit provides detailed analysis results based on the user's level of expertise. The analysis unit can also provide concise analysis results to users with low levels of expertise. For example, the analysis unit provides concise analysis results to users with low levels of expertise. The analysis unit can also provide analysis results according to the user's level of expertise based on the user's past inquiry history. For example, the analysis unit provides analysis results according to the user's level of expertise based on the user's past inquiry history. This makes it possible to adjust the level of detail of the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0094] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates a concise and clear answer. For example, the generation unit estimates the user's emotions and generates a concise and clear answer for a user who is feeling stressed. The generation unit can also generate an answer including a detailed explanation if the user is relaxed. For example, the generation unit generates an answer including a detailed explanation for a relaxed user. The generation unit can also generate an answer that can be quickly understood if the user is in a hurry. For example, the generation unit generates an answer that can be quickly understood for a user who is in a hurry. This makes it possible to adjust the way the answer is expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0095] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the inquiry. For example, the generation unit generates a detailed answer for an inquiry with a high level of importance. For example, the generation unit generates a detailed answer for an inquiry with a high level of importance based on the importance of the inquiry. The generation unit can also generate a concise answer for an inquiry with a low level of importance. For example, the generation unit generates a concise answer for an inquiry with a low level of importance. The generation unit can also adjust the level of detail of the answer according to the importance of the inquiry. For example, the generation unit adjusts the level of detail of the answer according to the importance of the inquiry. In this way, the level of detail of the answer can be adjusted based on the importance of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input inquiry importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0096] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the inquiry. For example, the generation unit applies a specific generation algorithm to inquiries regarding how to use a product. For example, the generation unit applies a specific generation algorithm to inquiries regarding how to use a product. The generation unit can also apply a different generation algorithm to inquiries regarding troubleshooting. For example, the generation unit applies a different generation algorithm to inquiries regarding troubleshooting. The generation unit can also apply a dedicated generation algorithm to inquiries regarding customer support. For example, the generation unit applies a dedicated generation algorithm to inquiries regarding customer support. This makes it possible to apply different generation algorithms depending on the category of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input inquiry category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0097] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The generation unit, for example, generates an optimal answer to the current problem based on the user's past answer results. For example, the generation unit generates an optimal answer to the current problem by referring to the user's past answer results. The generation unit can also analyze the user's past answer results and identify common problems. For example, the generation unit can analyze the user's past answer results and identify common problems. The generation unit can also suggest a solution to the current problem based on the user's past answer results. For example, the generation unit suggests a solution to the current problem based on the user's past answer results. This allows the accuracy of the answer to be improved by referring to the user's past answer results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past answer result data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0098] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates a short and to-the-point answer. For example, the generation unit estimates the user's emotions and generates a short and to-the-point answer for a user who is feeling stressed. The generation unit can also generate a longer answer including detailed explanations for a user who is relaxed. For example, the generation unit generates a longer answer including detailed explanations for a relaxed user. The generation unit can also generate a short answer that can be quickly understood for a user who is in a hurry. For example, the generation unit generates a short answer that can be quickly understood for a user who is in a hurry. This allows the length of the answer to be adjusted based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the answer.

[0099] When generating answers, the generation unit can determine the priority of the answers based on the time of submission of the inquiry. For example, the generation unit generates answers with priority for urgent inquiries. For example, the generation unit generates answers with priority for urgent inquiries based on the time of submission of the inquiry. The generation unit can also generate answers with normal priority for regular inquiries. For example, the generation unit generates answers with normal priority for regular inquiries. The generation unit can also set priorities according to the time of submission based on past inquiry data. For example, the generation unit sets priorities according to the time of submission based on past inquiry data. This makes it possible to determine the priority of answers based on the time of submission of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input inquiry submission time data into the generation AI and have the generation AI determine the priority of answers.

[0100] When generating answers, the generation unit can adjust the order of answers based on the relevance of the inquiries. For example, the generation unit generates answers preferentially to highly relevant inquiries. For example, the generation unit generates answers preferentially to highly relevant inquiries based on the relevance of the inquiries. The generation unit can also postpone less relevant inquiries. For example, the generation unit postpones less relevant inquiries. The generation unit can also set the order of answers according to relevance based on past inquiry data. For example, the generation unit sets the order of answers according to relevance based on past inquiry data. This makes it possible to adjust the order of answers based on the relevance of the inquiries. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input inquiry relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0101] When generating an answer, the generation unit can adjust the use of technical terms in the answer depending on the user's level of expertise. For example, the generation unit generates an answer that uses a lot of technical terms for a user with high level of expertise. For example, the generation unit generates an answer that uses a lot of technical terms based on the user's level of expertise. The generation unit can also generate a concise and easy-to-understand answer for a user with low level of expertise. For example, the generation unit generates a concise and easy-to-understand answer for a user with low level of expertise. The generation unit can also provide an answer according to the user's level of expertise based on the user's past inquiry history. For example, the generation unit provides an answer according to the user's level of expertise based on the user's past inquiry history. This makes it possible to adjust the use of technical terms in the answer depending on the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms in the answer.

[0102] The providing unit can estimate the user's emotions and adjust the method of providing an answer based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides a concise and clear answer. For example, the providing unit estimates the user's emotions and provides a concise and clear answer to a user who is feeling stressed. The providing unit can also provide an answer including a detailed explanation if the user is relaxed. For example, the providing unit provides an answer including a detailed explanation to a relaxed user. The providing unit can also provide an answer that can be quickly understood if the user is in a hurry. For example, the providing unit provides an answer that can be quickly understood to a user who is in a hurry. This makes it possible to adjust the method of providing an answer based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the way in which the answer is provided.

[0103] When providing an answer, the providing unit can select the optimal providing method by referring to the user's past operation history. For example, the providing unit preferentially suggests a providing method that the user has used favorably in the past. For example, the providing unit can refer to the user's past operation history and suggest the preferred providing method. The providing unit can also display related information in advance based on the user's past operation history. For example, the providing unit can display related support information in advance based on the past operation history. The providing unit can also select the optimal providing method based on the user's past operation history. For example, the providing unit selects the optimal providing method based on the past operation history. This makes it possible to select the optimal providing method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past operation history data to the generation AI and cause the generation AI to select the optimal providing method.

[0104] When providing an answer, the providing unit can customize the content to be provided according to the user's current situation. For example, the providing unit prioritizes providing answers related to products currently being used by the user. For example, the providing unit prioritizes providing answers related to products currently being used by the user. The providing unit can also adjust the content to be provided according to the user's current situation (e.g., urgency). For example, the providing unit adjusts the content to be provided according to the user's current situation. The providing unit can also provide related support information based on the user's current situation. For example, the providing unit provides related support information based on the user's current situation. This makes it possible to customize the content to be provided according to the user's current situation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's current situation to the generation AI and cause the generation AI to customize the content to be provided.

[0105] The providing unit can improve the answer providing method by reflecting the user's feedback when providing an answer. The providing unit, for example, proposes an optimal answer providing method based on the feedback provided by the user. For example, the providing unit refers to the user's feedback and proposes an optimal answer providing method. The providing unit can also provide related support information based on the user's feedback. For example, the providing unit provides related support information based on the user's feedback. The providing unit can also improve the answer providing method based on the user's feedback. For example, the providing unit improves the answer providing method based on the user's feedback. This makes it possible to improve the answer providing method by reflecting the user's feedback. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into the generating AI and cause the generating AI to improve the answer providing method.

[0106] The providing unit can estimate the user's emotions and adjust the timing of providing an answer based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides an answer immediately. For example, the providing unit estimates the user's emotions and provides an answer immediately to a user who is feeling stressed. The providing unit can also provide an answer at a normal timing if the user is relaxed. For example, the providing unit provides an answer at a normal timing to a relaxed user. The providing unit can also provide an answer quickly if the user is in a hurry. For example, the providing unit provides an answer quickly to a user who is in a hurry. This makes it possible to adjust the timing of providing an answer based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of providing an answer.

[0107] When providing an answer, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit prioritizes providing answers related to that area. For example, the providing unit prioritizes providing answers related to that area based on the user's geographical location information. The providing unit can also forward an inquiry to the nearest support center based on the user's location information. For example, the providing unit forwards an inquiry to the nearest support center based on the user's location information. The providing unit can also provide information on area-specific problems based on the user's location information. For example, the providing unit provides information on area-specific problems based on the user's location information. This makes it possible to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal delivery method.

[0108] When providing an answer, the providing unit can customize the provided content by analyzing the user's social media activity. For example, the providing unit can prioritize providing answers related to problems mentioned by the user on social media. For example, the providing unit can analyze the user's social media activity and prioritize providing answers related to the problems mentioned. The providing unit can also provide related support information based on the user's social media activity. For example, the providing unit can provide related support information based on the user's social media activity. The providing unit can also customize the provided content based on the user's feedback on social media. For example, the providing unit customizes the provided content based on the user's feedback on social media. This makes it possible to customize the provided content by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's social media activity data to the generation AI and cause the generation AI to customize the provided content.

[0109] When providing an answer, the providing unit can customize the providing method by reflecting the user's past feedback. The providing unit, for example, suggests an optimal providing method based on feedback provided by the user in the past. For example, the providing unit refers to the user's past feedback and suggests an optimal providing method. The providing unit can also provide related support information based on the user's past feedback. For example, the providing unit provides related support information based on the user's past feedback. The providing unit can also customize the providing method based on the user's past feedback. For example, the providing unit customizes the providing method based on the user's past feedback. This makes it possible to customize the providing method by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the providing method. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives inquiries from customers using the reception device 38 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing technology. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer using a text generation AI. For example, the provision unit provides the generated answer to the customer using the output device 40 of the smart device 14. For example, the reception unit estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14 and determines the priority of the inquiry. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives an inquiry from a customer using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing technology. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer using a text generation AI. For example, the provision unit provides the generated answer to the customer using the speaker 240 of the smart glasses 214. For example, the reception unit estimates the user's emotions using the camera 42 and microphone 238 of the smart glasses 214 and determines the priority of the inquiry. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives an inquiry from a customer using the microphone 238 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing technology. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer using a text generation AI. For example, the provision unit provides the generated answer to the customer using the speaker 240 of the headset-type terminal 314. For example, the reception unit estimates the user's emotions using the camera 42 and microphone 238 of the headset-type terminal 314 and determines the priority of the inquiry. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives inquiries from customers using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the inquiry using natural language processing technology. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer using a text generation AI. For example, the provision unit provides the answer generated by the speaker 240 of the robot 414 to the customer. For example, the reception unit estimates the user's emotions using the camera 42 and microphone 238 of the robot 414 and determines the priority of the inquiry.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The reception unit can refer to the user's past purchase history and prioritize processing related inquiries. For example, the reception unit prioritizes processing inquiries about products the user has purchased in the past. The reception unit can also display related support information in advance based on the user's purchase history. For example, the reception unit displays support information about products the user has purchased in the past in advance. The reception unit can also prioritize processing new inquiries related to products the user has purchased in the past. For example, the reception unit prioritizes processing inquiries related to products the user has purchased in the past. This makes it possible to select the optimal reception method by referring to the user's past purchase history.

[0112] The analysis unit can analyze the user's current network environment and propose an optimal solution. For example, the analysis unit can analyze the user's network speed and connection status to identify the cause of the problem. The analysis unit can also propose an optimal solution based on the user's network environment. For example, if the network speed is slow, the analysis unit can propose specific steps to improve the speed. The analysis unit can also customize troubleshooting steps according to the user's network environment. For example, the analysis unit can propose optimal troubleshooting steps according to the user's network environment. This makes it possible to propose an optimal solution taking the user's network environment into consideration.

[0113] The generation unit can estimate the user's emotions and adjust the tone of the answer based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates an answer in a gentle tone. For example, the generation unit estimates the user's emotions and generates an answer in a gentle tone for a user who is feeling stressed. The generation unit can also generate an answer in a friendly tone for a user who is relaxed. For example, the generation unit generates an answer in a friendly tone for a relaxed user. The generation unit can also generate an answer in a quick and concise tone for a user who is in a hurry. For example, the generation unit generates an answer in a quick and concise tone for a user who is in a hurry. In this way, the tone of the answer can be adjusted based on the user's emotions.

[0114] The providing unit can estimate the user's emotions and select an answer providing channel based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can instantly provide an answer via chat. For example, the providing unit can estimate the user's emotions and instantly provide an answer via chat to a user who is feeling stressed. The providing unit can also provide a detailed answer via email if the user is relaxed. For example, the providing unit can provide a detailed answer via email to a relaxed user. The providing unit can also provide a quick answer via SMS if the user is in a hurry. For example, the providing unit can provide a quick answer via SMS to a user who is in a hurry. In this way, it is possible to select an answer providing channel based on the user's emotions.

[0115] The reception unit can estimate the user's emotions and adjust the method of receiving inquiries based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit preferentially receives voice inquiries. For example, the reception unit estimates the user's emotions and preferentially receives voice inquiries from users who are feeling stressed. The reception unit can also accept inquiries via chat when the user is relaxed. For example, the reception unit accepts chat inquiries from relaxed users. The reception unit can also accept inquiries via SMS when the user is in a hurry in order to respond quickly. For example, the reception unit accepts SMS inquiries from users who are in a hurry. This makes it possible to adjust the method of receiving inquiries based on the user's emotions.

[0116] The analysis unit can refer to the user's past inquiry history and identify the cause of the current inquiry. For example, the analysis unit can identify the cause of the current problem based on past similar inquiry data. The analysis unit can also analyze past inquiry data and identify common problems. For example, the analysis unit can analyze past inquiry data and identify common problems. The analysis unit can also propose a solution to the current problem based on the past inquiry data. For example, the analysis unit can propose a solution to the current problem based on the past inquiry data. In this way, the cause of the current inquiry can be identified by referring to the past inquiry data.

[0117] The generation unit can generate an optimal answer by referring to the user's past feedback. For example, the generation unit generates an optimal answer based on feedback provided by the user in the past. The generation unit can also analyze the user's past feedback and identify common problems. For example, the generation unit analyzes the user's past feedback and identify common problems. The generation unit can also suggest a solution to the current problem based on the user's past feedback. For example, the generation unit suggests a solution to the current problem based on the user's past feedback. In this way, the optimal answer can be generated by referring to the user's past feedback.

[0118] The providing unit can adjust the answer providing method based on the user's current situation. For example, the providing unit selects the optimal providing method depending on the device the user is currently using. For example, if the user is using a smartphone, the providing unit can provide a mobile-friendly answer. Also, the providing unit can provide detailed documentation if the user is using a personal computer. For example, if the user is using a personal computer, the providing unit can provide detailed documentation. Also, the providing unit can provide an interactive guide if the user is using a tablet. For example, if the user is using a tablet, the providing unit can provide an interactive guide. This makes it possible to adjust the answer providing method based on the user's current situation.

[0119] The reception unit can select the optimal reception method by referring to the user's past inquiry results. For example, the reception unit preferentially suggests reception methods that the user has used favorably in the past. The reception unit can also display related support information in advance based on the user's past inquiry results. For example, the reception unit displays related support information in advance based on the user's past inquiry results. The reception unit can also preferentially process new inquiries related to problems that the user has solved in the past. For example, the reception unit preferentially processes inquiries related to problems that have been solved in the past. This makes it possible to select the optimal reception method by referring to the user's past inquiry results.

[0120] The analysis unit can determine the priority of analysis based on the user's current situation. For example, the analysis unit prioritizes analysis when the user is in an emergency. The analysis unit can also perform analysis with normal priority when the user is in a normal situation. For example, the analysis unit performs analysis with normal priority when the user is in a normal situation. The analysis unit can also customize the analysis procedure according to the user's current situation. For example, the analysis unit suggests an optimal analysis procedure according to the user's current situation. This makes it possible to determine the priority of analysis based on the user's current situation.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit receives inquiries from customers. Customer inquiries include, but are not limited to, questions about product usage and troubleshooting. The reception unit can receive inquiries using a chat interface, voice input, text input, images, or videos. For example, the reception unit can analyze images or videos sent by customers to identify the content of the inquiry. Step 2: The analysis unit analyzes the inquiry received by the reception unit. The analysis unit uses natural language processing technology and machine learning algorithms to analyze and classify the inquiry content. It can also refer to past inquiry data to analyze the current inquiry content and identify the cause of the problem. Step 3: The generator generates an appropriate answer based on the information analyzed by the analyzer. The generator can use text generation AI (e.g., LLM) or multimodal generation AI to generate answers that include not only text but also images and videos. It can also use template-based generation algorithms to generate answers quickly. Step 4: The providing unit provides the answer generated by the generating unit. The providing unit can provide the answer using a chat interface, email, SMS, or voice synthesis technology. For example, the generated answer is provided to the customer in real time.

[0123] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

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

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0139] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0176] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for receiving inquiries from customers; an analysis unit that analyzes the inquiry received by the reception unit; a generation unit that generates an appropriate answer based on the information analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit. A system characterized by:

2. The reception unit Accept questions about product usage or troubleshooting 2. The system of claim 1.

3. The analysis unit Analyze the information received to determine the cause of the problem and how to resolve it 2. The system of claim 1.

4. The generation unit Generate step-by-step answers 2. The system of claim 1.

5. The providing unit Provide generated answers to customers in real time 2. The system of claim 1.

6. The providing unit Available 24 hours a day, 365 days a year 2. The system of claim 1.

7. Achieve low-cost operations through small-scale bases and a small number of operators 2. The system of claim 1.

8. The reception unit Estimate user sentiment and prioritize inquiries based on the estimated sentiment 2. The system of claim 1.

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