system

Abstract

The system according to the embodiment aims to respond quickly and appropriately to inquiries from customers and improve customer satisfaction. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a notification unit. The reception unit accepts inquiries from customers. The analysis unit analyzes the inquiries accepted by the reception unit and generates appropriate answers. The proposal unit proposes a plan that meets the customer's needs based on the results of the analysis by the analysis unit. The notification unit notifies the customer of information based on the plan proposed by the proposal unit.

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 was difficult to respond to customer inquiries quickly and appropriately, posing a challenge to improving customer satisfaction.

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

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a notification unit. The reception unit accepts inquiries from customers. The analysis unit analyzes the inquiries accepted by the reception unit and generates appropriate answers. The proposal unit proposes a plan that meets the customer's needs based on the results of the analysis by the analysis unit. The notification unit notifies the customer of information based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can respond to customer inquiries quickly and appropriately, thereby improving customer satisfaction. [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 provides customer support through a robot using a generative AI in cooperation with a messaging app (e.g., LINE (registered trademark)). When a customer submits an inquiry via text or voice through the messaging app, the generative AI analyzes the inquiry and provides an appropriate response. The system also proposes a plan tailored to the customer's needs. This system allows customers to receive immediate support 24 hours a day, regardless of language or disability. Furthermore, the system contributes to reducing costs, improving customer spending, and increasing engagement. Furthermore, the system can provide information that customers will enjoy, such as birthday specials or friend-only sales and campaign information, via the messaging app. For example, a customer submits an inquiry via text or voice through the messaging app. For example, the customer can ask about product usage or troubleshooting. This allows the customer to easily submit an inquiry regardless of language or disability. The generative AI then analyzes the input inquiry. The generative AI uses natural language processing technology to understand the customer's inquiry and generate an appropriate response. For example, in response to a question about product usage, the system can generate a response that explains specific steps. The generative AI also proposes a plan tailored to the customer's needs. For example, if a customer shows interest in a particular product, plans and services related to that product can be suggested. This allows the customer to choose the plan that best suits them. This system allows customers to receive support 24 hours a day. For example, even if a problem occurs late at night, support can be received immediately. In addition, anyone can easily receive support regardless of language or disability. For example, visually impaired people can make inquiries by voice. Furthermore, for the company, this contributes to cost reduction and improvement of average customer spending and engagement. For example, using generative AI can reduce costs compared to providing 24-hour customer support manually. In addition, by suggesting appropriate plans to customers, average customer spending can be improved.Furthermore, it is possible to notify customers of information that will delight them, such as birthday specials or information about sales and campaigns exclusive to friends, through messaging apps. For example, sending special coupons on a customer's birthday can improve customer satisfaction. This allows the customer support system to respond to customer inquiries quickly and accurately, improving customer satisfaction. It also contributes to cost reduction and an increase in average customer spending.

[0029] A customer support system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a notification unit. The reception unit receives inquiries from customers. Customer inquiries include, but are not limited to, text, voice, and image. The reception unit receives inquiries via text or voice, for example, through a messaging app (e.g., LINE). The reception unit can also receive inquiries through other messaging apps. For example, inquiries can be received through messaging apps such as WhatsApp (registered trademark) and Messenger. The analysis unit uses a generation AI to analyze the inquiries received by the reception unit and generate an appropriate answer. The analysis unit understands the content of the inquiries and generates an appropriate answer, for example, using natural language processing technology. Examples of natural language processing technology include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit generates an answer that explains specific steps in response to a question about how to use a product. The analysis unit can also generate an answer that explains specific solutions in response to a question about troubleshooting a product. The analysis unit can also generate an answer that provides specific advice in response to a question about how to choose a product. The suggestion unit proposes a plan that meets the customer's needs based on the results of the analysis by the analysis unit. For example, if a customer shows interest in a particular product, the suggestion unit proposes plans and services related to the product. For example, if a customer shows interest in a particular service, the suggestion unit can also propose plans and options related to the service. The suggestion unit can also propose an optimal plan based on the customer's past purchase history and usage history. The notification unit notifies the customer of information based on the plan proposed by the suggestion unit. For example, the notification unit notifies the customer of birthday benefits and friend-only sales and campaign information via a messaging app. The notification unit can also notify the customer of information via email or SMS, for example. The notification unit can also notify the customer of information via app notifications. As a result, the customer support system according to the embodiment can respond to customer inquiries quickly and accurately and improve customer satisfaction.It also contributes to cost reduction and improvement of average customer spending. For example, the reception unit receives inquiries from customers. The analysis unit analyzes the inquiries received by the reception unit and generates appropriate answers. The proposal unit proposes a plan that meets the customer's needs based on the results of the analysis by the analysis unit. The notification unit notifies the customer of information based on the plan proposed by the proposal unit. This enables the customer support system to respond to customer inquiries quickly and accurately and improve customer satisfaction.

[0030] The reception unit can accept inquiries via text or voice through a messaging app. The reception unit, for example, accepts inquiries via text or voice through a messaging app. For example, a customer can ask questions about how to use a product or troubleshooting through a messaging app. The reception unit can also accept inquiries through other messaging apps. For example, inquiries can be accepted through messaging apps such as WhatsApp or Messenger. This allows customers to easily make inquiries. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input an inquiry from a customer into a generation AI and have the generation AI analyze the content of the inquiry.

[0031] The analysis unit can understand the inquiry content using natural language processing technology and generate an appropriate answer. The analysis unit can understand the inquiry content using, for example, natural language processing technology and generate an appropriate answer. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to these examples. The analysis unit can generate an answer that explains specific procedures for, for example, a question about how to use a product. For example, the analysis unit can generate an answer that provides a step-by-step guide for, for a question about how to use a product. The analysis unit can also generate an answer that provides a video tutorial for, for a question about how to use a product. Furthermore, the analysis unit can generate an answer that provides a guide that combines text and images for, for a question about how to use a product. This makes it possible to accurately understand the inquiry content and generate an appropriate answer. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input a customer inquiry into a generation AI and cause the generation AI to generate an appropriate answer.

[0032] The proposal unit can propose a plan according to the customer's request. For example, if a customer shows interest in a particular product, the proposal unit can propose plans and services related to the product. For example, if a customer shows interest in a particular product, the proposal unit can propose plans and services related to the product. Furthermore, if a customer shows interest in a particular service, the proposal unit can propose plans and options related to the service. For example, if a customer shows interest in a particular service, the proposal unit can propose plans and options related to the service. Furthermore, the proposal unit can propose an optimal plan based on the customer's past purchase history or usage history. For example, the proposal unit can propose an optimal plan based on the customer's past purchase history or usage history. This makes it possible to provide the customer with an optimal plan. Some or all of the above-described processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the customer's request into the generation AI and have the generation AI propose an optimal plan.

[0033] The notification unit can notify a customer of birthday benefits or friend-only sales or campaign information through a messaging app. For example, the notification unit can notify a customer of birthday benefits or friend-only sales or campaign information through a messaging app. For example, the notification unit can improve customer satisfaction by sending a special coupon on a customer's birthday. The notification unit can also improve customer engagement by notifying a customer of friend-only sales or campaign information. For example, the notification unit can improve customer engagement by notifying a customer of friend-only sales or campaign information. Furthermore, the notification unit can notify a customer of personalized information through a messaging app. For example, the notification unit can notify a customer of personalized information based on the customer's past purchase history or usage history. This can improve customer satisfaction. Some or all of the above-described processing by the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input customer information into the generation AI and cause the generation AI to generate notification content.

[0034] The analysis unit can generate answers that explain specific steps in response to questions about how to use a product. For example, the analysis unit generates answers that explain specific steps in response to questions about how to use a product. For example, the analysis unit can generate answers that provide step-by-step guides in response to questions about how to use a product. The analysis unit can also generate answers that provide video tutorials in response to questions about how to use a product. Furthermore, the analysis unit can generate answers that provide guides that combine text and images in response to questions about how to use a product. This enables customers to use the product correctly. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a customer's inquiry into a generation AI and cause the generation AI to generate an appropriate answer.

[0035] When a customer shows interest in a particular product, the suggestion unit can suggest plans and services related to the product. For example, when a customer shows interest in a particular product, the suggestion unit can suggest plans and services related to the product. For example, when a customer shows interest in a particular product, the suggestion unit can suggest plans and services related to the product. Furthermore, when a customer shows interest in a particular service, the suggestion unit can suggest plans and options related to the service. For example, when a customer shows interest in a particular service, the suggestion unit can suggest plans and options related to the service. Furthermore, the suggestion unit can suggest an optimal plan based on the customer's past purchase history or usage history. For example, the suggestion unit can suggest an optimal plan based on the customer's past purchase history or usage history. This can improve customer satisfaction. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input customer requests into the generation AI and cause the generation AI to propose an optimal plan.

[0036] The reception unit can analyze the customer's past inquiry history and select the optimal reception method. For example, the reception unit can automatically recognize the content of inquiries frequently made by the customer in the past and use the associated template to receive the inquiry. For example, the reception unit can automatically recognize the content of inquiries frequently made by the customer in the past and use the associated template to receive the inquiry. The reception unit can also prioritize suggesting an inquiry method (text, voice, etc.) that the customer has used in the past. For example, the reception unit can prioritize suggesting an inquiry method (text, voice, etc.) that the customer has used in the past. Furthermore, the reception unit can predict and suggest a reception method to be used during a specific time period based on the customer's past inquiry history. For example, the reception unit can predict and suggest a reception method to be used during a specific time period based on the customer's past inquiry history. This enables the selection of the optimal reception method and efficient response. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the customer's past inquiry history into the generation AI and have the generation AI select the optimal reception method.

[0037] The reception unit can filter inquiries based on the customer's current situation and areas of interest when receiving an inquiry. For example, when a customer inputs their current situation, the reception unit can prioritize receiving inquiries related to that situation. For example, when a customer inputs their current situation, the reception unit can prioritize receiving inquiries related to that situation. The reception unit can also filter related inquiries based on the customer's areas of interest and prioritize processing them. For example, the reception unit can filter related inquiries based on the customer's areas of interest and prioritize processing them. Furthermore, when a customer is in a specific situation, the reception unit can automatically suggest inquiry content appropriate for that situation. For example, when a customer is in a specific situation, the reception unit can automatically suggest inquiry content appropriate for that situation. This enables more appropriate responses by filtering based on the customer's current situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the customer's current situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0038] When receiving an inquiry, the reception unit can prioritize receiving highly relevant inquiries by taking into account the customer's geographical location information. For example, if the customer is in a specific region, the reception unit can prioritize receiving inquiries related to that region. For example, if the customer is in a specific region, the reception unit can prioritize receiving inquiries related to that region. The reception unit can also prioritize processing inquiries regarding region-specific services and campaigns based on the customer's geographical location information. For example, the reception unit can prioritize processing inquiries regarding region-specific services and campaigns based on the customer's geographical location information. Furthermore, if the customer is traveling, the reception unit can prioritize receiving inquiries related to the customer's travel destination. For example, if the customer is traveling, the reception unit can prioritize receiving inquiries related to the customer's travel destination. In this way, by taking the customer's geographical location information into consideration, highly relevant inquiries can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the customer's geographical location information into the generation AI and cause the generation AI to select highly relevant inquiries.

[0039] The reception unit can analyze the customer's social media activity when receiving an inquiry and receive related inquiries. For example, if a customer mentions a specific product on social media, the reception unit can prioritize receiving inquiries related to that product. For example, if a customer mentions a specific product on social media, the reception unit can prioritize receiving inquiries related to that product. The reception unit can also analyze the customer's social media activity and prioritize processing inquiries related to topics of high interest. For example, the reception unit can analyze the customer's social media activity and prioritize processing inquiries related to topics of high interest. Furthermore, if a customer expresses dissatisfaction on social media, the reception unit can prioritize receiving inquiries related to that dissatisfaction. For example, if a customer expresses dissatisfaction on social media, the reception unit can prioritize receiving inquiries related to that dissatisfaction. In this way, by analyzing the customer's social media activity, related inquiries can be prioritized. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the customer's social media activity into the generation AI and cause the generation AI to select related inquiries.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the query. For example, the analysis unit can perform a detailed analysis for a query of high importance and provide a highly accurate answer. For example, the analysis unit can perform a detailed analysis for a query of high importance and provide a highly accurate answer. The analysis unit can also perform a concise analysis for a query of low importance and provide a quick answer. For example, the analysis unit can perform a concise analysis for a query of low importance and provide a quick answer. Furthermore, the analysis unit can perform an analysis with a moderate level of detail for a query of medium importance and provide a balanced answer. For example, the analysis unit can perform an analysis with a moderate level of detail for a query of medium importance and provide a balanced answer. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the query. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the importance of the inquiry to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, the analysis unit can apply a specialized analysis algorithm to technical inquiries. For example, the analysis unit can apply a specialized analysis algorithm to technical inquiries. Furthermore, the analysis unit can apply an analysis algorithm specialized for product information to product inquiries. For example, the analysis unit can apply an analysis algorithm specialized for product information to product inquiries. Furthermore, the analysis unit can apply an analysis algorithm specialized for service content to service inquiries. For example, the analysis unit can apply an analysis algorithm specialized for service content to service inquiries. This enables more appropriate analysis by applying different analysis algorithms depending on the category of the inquiry. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of the inquiry into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] 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 can prioritize analysis of recently submitted inquiries and provide a quick answer. For example, the analysis unit can prioritize analysis of recently submitted inquiries and provide a quick answer. The analysis unit can also analyze older inquiries with a normal priority. For example, the analysis unit can analyze older inquiries with a normal priority. Furthermore, the analysis unit can analyze inquiries submitted during a specific time period with a priority according to that time period. For example, the analysis unit can analyze inquiries submitted during a specific time period with a priority according to that time period. This enables a quick response by determining the priority of analysis based on the time of submission of the inquiry. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit can input the time of submission of the inquiry into the generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the inquiries. For example, the analysis unit prioritizes analysis of inquiries related to the customer's current situation. For example, the analysis unit can prioritize analysis of inquiries related to the customer's current situation. The analysis unit can also prioritize analysis of inquiries related to the customer's past inquiry history. For example, the analysis unit can prioritize analysis of inquiries related to the customer's past inquiry history. Furthermore, the analysis unit can also prioritize analysis of inquiries related to the customer's areas of interest. For example, the analysis unit can prioritize analysis of inquiries related to the customer's areas of interest. This enables efficient analysis by adjusting the order of analysis based on the relevance of the inquiries. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the inquiries into the generation AI and cause the generation AI to adjust the order of analysis.

[0044] When making a proposal, the proposal unit can analyze the customer's past purchase history and propose an optimal plan. The proposal unit, for example, proposes plans related to products purchased by the customer in the past. For example, the proposal unit can propose plans related to products purchased by the customer in the past. The proposal unit can also propose plans related to products in a specific category based on the customer's purchase history. For example, the proposal unit can propose plans related to products in a specific category based on the customer's purchase history. The proposal unit can also analyze the customer's purchase history and propose the most suitable plan. For example, the proposal unit can analyze the customer's purchase history and propose the most suitable plan. In this way, the optimal plan can be proposed by analyzing the customer's past purchase history. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the customer's past purchase history into the generation AI and cause the generation AI to propose an optimal plan.

[0045] When proposing a plan, the proposal unit can customize the plan based on the customer's current living situation. For example, when a customer inputs their current living situation, the proposal unit can propose a plan suitable for that situation. For example, when a customer inputs their current living situation, the proposal unit can propose a plan suitable for that situation. The proposal unit can also propose a customized plan based on the customer's living situation. For example, the proposal unit can propose a customized plan based on the customer's living situation. Furthermore, when a customer is in a specific living situation, the proposal unit can automatically propose a plan suitable for that situation. For example, when a customer is in a specific living situation, the proposal unit can automatically propose a plan suitable for that situation. This makes it possible to provide an optimal plan to the customer by customizing the plan based on the customer's current living situation. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit may input the customer's current living situation into the generation AI and cause the generation AI to customize the plan.

[0046] When making a proposal, the proposal unit can propose an optimal plan by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the proposal unit can propose a plan related to that area. For example, if the customer is in a specific area, the proposal unit can propose a plan related to that area. The proposal unit can also propose plans related to area-specific services or campaigns based on the customer's geographical location information. For example, the proposal unit can propose plans related to area-specific services or campaigns based on the customer's geographical location information. Furthermore, if the customer is traveling, the proposal unit can also propose a plan related to the travel destination. For example, if the customer is traveling, the proposal unit can propose a plan related to the travel destination. In this way, the optimal plan can be proposed by taking the customer's geographical location information into consideration. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the customer's geographical location information into the generation AI and cause the generation AI to propose an optimal plan.

[0047] When making a proposal, the proposal unit can analyze the customer's social media activity and propose a related plan. For example, if a customer mentions a specific product on social media, the proposal unit can propose a plan related to the product. For example, if a customer mentions a specific product on social media, the proposal unit can propose a plan related to the product. The proposal unit can also analyze the customer's social media activity and propose a plan related to a topic of high interest. For example, the proposal unit can analyze the customer's social media activity and propose a plan related to a topic of high interest. The proposal unit can also propose a plan related to a product in which the customer expressed an interest on social media. For example, the proposal unit can propose a plan related to a product in which the customer expressed an interest on social media. In this way, related plans can be proposed by analyzing the customer's social media activity. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the customer's social media activity into the generation AI and cause the generation AI to propose related plans.

[0048] The notification unit can select the optimal notification method by analyzing the customer's past response history at the time of notification. For example, the notification unit can prioritize the use of a notification method to which the customer has previously responded favorably. For example, the notification unit can prioritize the use of a notification method to which the customer has previously responded favorably. The notification unit can also select a notification method to be sent during a specific time period based on the customer's past response history. For example, the notification unit can select a notification method to be sent during a specific time period based on the customer's past response history. The notification unit can also analyze the customer's past response history and select the most effective notification method. For example, the notification unit can analyze the customer's past response history and select the most effective notification method. This allows the optimal notification method to be selected by analyzing the customer's past response history, enabling efficient notification. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the customer's past response history into the generation AI and have the generation AI select the optimal notification method.

[0049] The notification unit can customize the timing of the notification based on the customer's current situation when sending a notification. For example, when a customer inputs their current situation, the notification unit can send a notification at a timing appropriate for that situation. For example, when a customer inputs their current situation, the notification unit can send a notification at a timing appropriate for that situation. The notification unit can also send a notification at an optimal timing based on the customer's current activity status. For example, the notification unit can send a notification at an optimal timing based on the customer's current activity status. Furthermore, the notification unit can automatically send a notification at a timing appropriate for a specific situation when the customer is in that situation. For example, when a customer is in a specific situation, the notification unit can automatically send a notification at a timing appropriate for that situation. This allows for customizing the timing of the notification based on the customer's current situation, thereby improving customer satisfaction. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input the customer's current situation into the generation AI and cause the generation AI to customize the timing of the notification.

[0050] The notification unit can select the optimal notification method by taking into account the customer's geographical location information when providing a notification. For example, if the customer is in a specific area, the notification unit can prioritize sending notifications related to that area. For example, if the customer is in a specific area, the notification unit can prioritize sending notifications related to that area. The notification unit can also send notifications about area-specific services and campaigns based on the customer's geographical location information. For example, the notification unit can send notifications about area-specific services and campaigns based on the customer's geographical location information. Furthermore, if the customer is traveling, the notification unit can prioritize sending notifications related to the customer's travel destination. For example, if the customer is traveling, the notification unit can prioritize sending notifications related to the customer's travel destination. This allows the optimal notification method to be selected by taking into account the customer's geographical location information, enabling efficient notification. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input the customer's geographical location information into the generation AI and have the generation AI select the optimal notification method.

[0051] The notification unit can analyze the customer's social media activity and notify the customer of related information when providing notification. For example, if a customer mentions a specific product on social media, the notification unit can notify the customer of information related to the product. For example, if a customer mentions a specific product on social media, the notification unit can notify the customer of information related to the product. The notification unit can also analyze the customer's social media activity and notify the customer of information related to topics of high interest. For example, the notification unit can analyze the customer's social media activity and notify the customer of information related to topics of high interest. The notification unit can also notify the customer of information related to products in which the customer has shown interest on social media. For example, the notification unit can notify the customer of information related to products in which the customer has shown interest on social media. In this way, by analyzing the customer's social media activity, the customer can be notified of related information. Some or all of the above-described processing in the notification unit can be performed using, or without, the generation AI. For example, the notification unit can input the customer's social media activity into the generation AI and cause the generation AI to notify the customer of related information.

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

[0053] The reception unit can analyze a customer's past inquiry history and select the optimal reception method. For example, it can automatically recognize the content of inquiries that the customer has frequently made in the past and use related templates to receive inquiries. It can also prioritize and suggest inquiry methods (text, voice, etc.) that the customer has used in the past. Furthermore, it can predict and suggest the reception method to use during a specific time period based on the customer's past inquiry history. This allows for the selection of the optimal reception method and efficient response.

[0054] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry. For example, for inquiries of high importance, a detailed analysis can be performed to provide a highly accurate answer. For inquiries of low importance, a concise analysis can be performed to provide a quick answer. Furthermore, for inquiries of medium importance, an analysis can be performed with an appropriate level of detail to provide a balanced answer. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the inquiry.

[0055] When proposing a plan, the proposal unit can customize the plan based on the customer's current living situation. For example, when the customer inputs their current living situation, a plan suitable for that situation can be proposed. The proposal unit can also propose a customized plan based on the customer's living situation. Furthermore, if the customer is in a specific living situation, a plan suitable for that situation can be automatically proposed. In this way, by customizing the plan based on the customer's current living situation, it is possible to provide the customer with the optimal plan.

[0056] When sending a notification, the notification unit can analyze the customer's past response history and select the optimal notification method. For example, it can prioritize the use of notification methods to which the customer has responded favorably in the past. It can also select a notification method to send at a specific time period based on the customer's past response history. It can also analyze the customer's past response history and select the most effective notification method. This makes it possible to select the optimal notification method and provide efficient notifications by analyzing the customer's past response history.

[0057] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, a specialized analysis algorithm can be applied to technical inquiries. Furthermore, an analysis algorithm specialized for product information can be applied to product inquiries. Furthermore, an analysis algorithm specialized for service content can be applied to service inquiries. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the inquiry.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception department accepts inquiries from customers. Inquiries from customers can include text, voice, images, etc. For example, inquiries can be accepted through messaging apps such as WhatsApp and Messenger. Step 2: The analysis unit uses the generation AI to analyze the inquiry received by the reception unit and generate an appropriate response. The analysis unit uses natural language processing technology to understand the content of the inquiry and generate a response that provides specific procedures, solutions, and advice. Step 3: The proposal unit proposes plans that meet the customer's needs based on the results of the analysis by the analysis unit. For example, if a customer shows interest in a particular product or service, the proposal unit will propose related plans and options. It can also propose optimal plans based on past purchase history and usage history. Step 4: The notification unit notifies the user of information based on the plan proposed by the proposal unit. For example, the notification unit notifies the user of birthday benefits, sales, and campaign information via a messaging app, email, SMS, or app notification.

[0060] (Example 2) A customer support system according to an embodiment of the present invention provides customer support through a robot using a generative AI in conjunction with a messaging app (e.g., LINE). When a customer submits a text or voice inquiry through a messaging app, the generative AI analyzes the inquiry and provides an appropriate response. The system also proposes a plan tailored to the customer's needs. This system allows customers to receive immediate support 24 hours a day, regardless of language or disability. Furthermore, the system contributes to reducing costs, improving customer spending, and increasing engagement. Furthermore, the system can provide information that customers will enjoy, such as birthday specials or friend-only sales and campaign information, through messaging apps. For example, a customer submits an inquiry through a messaging app via text or voice. For example, the customer can ask about product usage or troubleshooting. This allows the customer to easily submit an inquiry regardless of language or disability. The generative AI then analyzes the input inquiry. The generative AI uses natural language processing technology to understand the customer's inquiry and generate an appropriate response. For example, in response to a question about product usage, the system can generate a response that explains specific steps. The generative AI also proposes a plan tailored to the customer's needs. For example, if a customer shows interest in a particular product, plans and services related to that product can be suggested. This allows the customer to choose the plan that best suits them. This system allows customers to receive support 24 hours a day. For example, even if a problem occurs late at night, support can be received immediately. In addition, anyone can easily receive support regardless of language or disability. For example, visually impaired people can make inquiries by voice. Furthermore, for the company, this contributes to cost reduction and improvement of average customer spending and engagement. For example, using generative AI can reduce costs compared to providing 24-hour customer support manually. In addition, by suggesting appropriate plans to customers, average customer spending can be improved.Furthermore, it is possible to notify customers of information that will delight them, such as birthday specials or information about sales and campaigns exclusive to friends, through messaging apps. For example, sending special coupons on a customer's birthday can improve customer satisfaction. This allows the customer support system to respond to customer inquiries quickly and accurately, improving customer satisfaction. It also contributes to cost reduction and an increase in average customer spending.

[0061] A customer support system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a notification unit. The reception unit receives inquiries from customers. Customer inquiries include, but are not limited to, text, voice, and image. The reception unit receives inquiries via text or voice, for example, through a messaging app (e.g., LINE). The reception unit can also receive inquiries through other messaging apps. For example, inquiries can be received through messaging apps such as WhatsApp and Messenger. The analysis unit uses a generative AI to analyze the inquiries received by the reception unit and generate an appropriate answer. The analysis unit understands the content of the inquiries and generates an appropriate answer using, for example, natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit generates an answer that explains specific steps in response to a question about how to use a product. The analysis unit can also generate an answer that explains specific solutions in response to a question about troubleshooting a product. The analysis unit can also generate an answer that provides specific advice in response to a question about how to choose a product. The suggestion unit proposes a plan that meets the customer's needs based on the results of the analysis by the analysis unit. For example, if a customer shows interest in a particular product, the suggestion unit proposes plans and services related to the product. For example, if a customer shows interest in a particular service, the suggestion unit can also propose plans and options related to the service. The suggestion unit can also propose an optimal plan based on the customer's past purchase history and usage history. The notification unit notifies the customer of information based on the plan proposed by the suggestion unit. For example, the notification unit notifies the customer of birthday benefits and friend-only sales and campaign information via a messaging app. The notification unit can also notify the customer of information via email or SMS, for example. The notification unit can also notify the customer of information via app notifications. As a result, the customer support system according to the embodiment can respond to customer inquiries quickly and accurately and improve customer satisfaction. Furthermore, the system can contribute to cost reduction and an increase in average customer spending.For example, the reception unit receives inquiries from customers. The analysis unit analyzes the inquiries received by the reception unit and generates appropriate answers. The proposal unit proposes a plan that meets the customer's needs based on the results of the analysis by the analysis unit. The notification unit notifies the customer of information based on the plan proposed by the proposal unit. This allows the customer support system to respond to customer inquiries quickly and accurately and improve customer satisfaction.

[0062] The reception unit can accept inquiries via text or voice through a messaging app. The reception unit, for example, accepts inquiries via text or voice through a messaging app. For example, a customer can ask questions about how to use a product or troubleshooting through a messaging app. The reception unit can also accept inquiries through other messaging apps. For example, inquiries can be accepted through messaging apps such as WhatsApp or Messenger. This allows customers to easily make inquiries. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input an inquiry from a customer into a generation AI and have the generation AI analyze the content of the inquiry.

[0063] The analysis unit can understand the inquiry content using natural language processing technology and generate an appropriate answer. The analysis unit can understand the inquiry content using, for example, natural language processing technology and generate an appropriate answer. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to these examples. The analysis unit can generate an answer that explains specific procedures for, for example, a question about how to use a product. For example, the analysis unit can generate an answer that provides a step-by-step guide for, for a question about how to use a product. The analysis unit can also generate an answer that provides a video tutorial for, for a question about how to use a product. Furthermore, the analysis unit can generate an answer that provides a guide that combines text and images for, for a question about how to use a product. This makes it possible to accurately understand the inquiry content and generate an appropriate answer. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input a customer inquiry into a generation AI and cause the generation AI to generate an appropriate answer.

[0064] The proposal unit can propose a plan according to the customer's request. For example, if a customer shows interest in a particular product, the proposal unit can propose plans and services related to the product. For example, if a customer shows interest in a particular product, the proposal unit can propose plans and services related to the product. Furthermore, if a customer shows interest in a particular service, the proposal unit can propose plans and options related to the service. For example, if a customer shows interest in a particular service, the proposal unit can propose plans and options related to the service. Furthermore, the proposal unit can propose an optimal plan based on the customer's past purchase history or usage history. For example, the proposal unit can propose an optimal plan based on the customer's past purchase history or usage history. This makes it possible to provide the customer with an optimal plan. Some or all of the above-described processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the customer's request into the generation AI and have the generation AI propose an optimal plan.

[0065] The notification unit can notify a customer of birthday benefits or friend-only sales or campaign information through a messaging app. For example, the notification unit can notify a customer of birthday benefits or friend-only sales or campaign information through a messaging app. For example, the notification unit can improve customer satisfaction by sending a special coupon on a customer's birthday. The notification unit can also improve customer engagement by notifying a customer of friend-only sales or campaign information. For example, the notification unit can improve customer engagement by notifying a customer of friend-only sales or campaign information. Furthermore, the notification unit can notify a customer of personalized information through a messaging app. For example, the notification unit can notify a customer of personalized information based on the customer's past purchase history or usage history. This can improve customer satisfaction. Some or all of the above-described processing by the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input customer information into the generation AI and cause the generation AI to generate notification content.

[0066] The analysis unit can generate answers that explain specific steps in response to questions about how to use a product. For example, the analysis unit generates answers that explain specific steps in response to questions about how to use a product. For example, the analysis unit can generate answers that provide step-by-step guides in response to questions about how to use a product. The analysis unit can also generate answers that provide video tutorials in response to questions about how to use a product. Furthermore, the analysis unit can generate answers that provide guides that combine text and images in response to questions about how to use a product. This enables customers to use the product correctly. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a customer's inquiry into a generation AI and cause the generation AI to generate an appropriate answer.

[0067] When a customer shows interest in a particular product, the suggestion unit can suggest plans and services related to the product. For example, when a customer shows interest in a particular product, the suggestion unit can suggest plans and services related to the product. For example, when a customer shows interest in a particular product, the suggestion unit can suggest plans and services related to the product. Furthermore, when a customer shows interest in a particular service, the suggestion unit can suggest plans and options related to the service. For example, when a customer shows interest in a particular service, the suggestion unit can suggest plans and options related to the service. Furthermore, the suggestion unit can suggest an optimal plan based on the customer's past purchase history or usage history. For example, the suggestion unit can suggest an optimal plan based on the customer's past purchase history or usage history. This can improve customer satisfaction. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input customer requests into the generation AI and cause the generation AI to propose an optimal plan.

[0068] The reception unit can estimate the customer's emotions and prioritize inquiries based on the estimated customer emotions. For example, if a customer is feeling stressed, the reception unit can prioritize and respond to inquiries quickly. For example, if a customer is feeling stressed, the reception unit can prioritize and respond to inquiries quickly. Furthermore, if a customer is relaxed, the reception unit can process inquiries with normal priority. For example, if a customer is relaxed, the reception unit can process inquiries with normal priority. Furthermore, if a customer is angry, the reception unit can process inquiries with the highest priority and quickly resolve the problem. For example, if a customer is angry, the reception unit can process inquiries with the highest priority and quickly resolve the problem. This allows customer satisfaction to be improved by prioritizing inquiries based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input customer emotion data into the generation AI and have the generation AI estimate the emotion.

[0069] The reception unit can analyze the customer's past inquiry history and select the optimal reception method. For example, the reception unit can automatically recognize the content of inquiries frequently made by the customer in the past and use the associated template to receive the inquiry. For example, the reception unit can automatically recognize the content of inquiries frequently made by the customer in the past and use the associated template to receive the inquiry. The reception unit can also prioritize suggesting an inquiry method (text, voice, etc.) that the customer has used in the past. For example, the reception unit can prioritize suggesting an inquiry method (text, voice, etc.) that the customer has used in the past. Furthermore, the reception unit can predict and suggest a reception method to be used during a specific time period based on the customer's past inquiry history. For example, the reception unit can predict and suggest a reception method to be used during a specific time period based on the customer's past inquiry history. This enables the selection of the optimal reception method and efficient response. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the customer's past inquiry history into the generation AI and have the generation AI select the optimal reception method.

[0070] The reception unit can filter inquiries based on the customer's current situation and areas of interest when receiving an inquiry. For example, when a customer inputs their current situation, the reception unit can prioritize receiving inquiries related to that situation. For example, when a customer inputs their current situation, the reception unit can prioritize receiving inquiries related to that situation. The reception unit can also filter related inquiries based on the customer's areas of interest and prioritize processing them. For example, the reception unit can filter related inquiries based on the customer's areas of interest and prioritize processing them. Furthermore, when a customer is in a specific situation, the reception unit can automatically suggest inquiry content appropriate for that situation. For example, when a customer is in a specific situation, the reception unit can automatically suggest inquiry content appropriate for that situation. This enables more appropriate responses by filtering based on the customer's current situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the customer's current situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0071] The reception unit can estimate the customer's emotions and adjust the timing of reception based on the estimated customer emotions. For example, if a customer is feeling stressed, the reception unit can quickly receive the customer and minimize waiting time. For example, if a customer is feeling stressed, the reception unit can quickly receive the customer and minimize waiting time. Furthermore, if a customer is relaxed, the reception unit can respond at a normal reception timing. For example, if a customer is relaxed, the reception unit can respond at a normal reception timing. Furthermore, if a customer is in a hurry, the reception unit can give top priority to receiving the customer and respond quickly. For example, if a customer is in a hurry, the reception unit can give top priority to receiving the customer and respond quickly. This allows for adjusting the timing of reception based on the customer's emotions to improve customer satisfaction. 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-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input customer emotion data into the generation AI and have the generation AI estimate the emotion.

[0072] When receiving an inquiry, the reception unit can prioritize receiving highly relevant inquiries by taking into account the customer's geographical location information. For example, if the customer is in a specific region, the reception unit can prioritize receiving inquiries related to that region. For example, if the customer is in a specific region, the reception unit can prioritize receiving inquiries related to that region. The reception unit can also prioritize processing inquiries regarding region-specific services and campaigns based on the customer's geographical location information. For example, the reception unit can prioritize processing inquiries regarding region-specific services and campaigns based on the customer's geographical location information. Furthermore, if the customer is traveling, the reception unit can prioritize receiving inquiries related to the customer's travel destination. For example, if the customer is traveling, the reception unit can prioritize receiving inquiries related to the customer's travel destination. In this way, by taking the customer's geographical location information into consideration, highly relevant inquiries can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the customer's geographical location information into the generation AI and cause the generation AI to select highly relevant inquiries.

[0073] The reception unit can analyze the customer's social media activity when receiving an inquiry and receive related inquiries. For example, if a customer mentions a specific product on social media, the reception unit can prioritize receiving inquiries related to that product. For example, if a customer mentions a specific product on social media, the reception unit can prioritize receiving inquiries related to that product. The reception unit can also analyze the customer's social media activity and prioritize processing inquiries related to topics of high interest. For example, the reception unit can analyze the customer's social media activity and prioritize processing inquiries related to topics of high interest. Furthermore, if a customer expresses dissatisfaction on social media, the reception unit can prioritize receiving inquiries related to that dissatisfaction. For example, if a customer expresses dissatisfaction on social media, the reception unit can prioritize receiving inquiries related to that dissatisfaction. In this way, by analyzing the customer's social media activity, related inquiries can be prioritized. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the customer's social media activity into the generation AI and cause the generation AI to select related inquiries.

[0074] The analysis unit can estimate the customer's emotions and adjust the way a response is expressed based on the estimated customer's emotions. For example, if the customer is feeling stressed, the analysis unit can provide a concise and clear response. For example, if the customer is feeling stressed, the analysis unit can provide a concise and clear response. Furthermore, if the customer is relaxed, the analysis unit can provide a response including a detailed explanation. For example, if the customer is relaxed, the analysis unit can provide a response including a detailed explanation. Furthermore, if the customer is angry, the analysis unit can provide a response using polite and calm language. For example, if the customer is angry, the analysis unit can provide a response using polite and calm language. This allows for adjusting the way a response is expressed based on the customer's emotions, thereby improving customer satisfaction. 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 such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input customer emotional data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the query. For example, the analysis unit can perform a detailed analysis for a query of high importance and provide a highly accurate answer. For example, the analysis unit can perform a detailed analysis for a query of high importance and provide a highly accurate answer. The analysis unit can also perform a concise analysis for a query of low importance and provide a quick answer. For example, the analysis unit can perform a concise analysis for a query of low importance and provide a quick answer. Furthermore, the analysis unit can perform an analysis with a moderate level of detail for a query of medium importance and provide a balanced answer. For example, the analysis unit can perform an analysis with a moderate level of detail for a query of medium importance and provide a balanced answer. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the query. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the importance of the inquiry to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, the analysis unit can apply a specialized analysis algorithm to technical inquiries. For example, the analysis unit can apply a specialized analysis algorithm to technical inquiries. Furthermore, the analysis unit can apply an analysis algorithm specialized for product information to product inquiries. For example, the analysis unit can apply an analysis algorithm specialized for product information to product inquiries. Furthermore, the analysis unit can apply an analysis algorithm specialized for service content to service inquiries. For example, the analysis unit can apply an analysis algorithm specialized for service content to service inquiries. This enables more appropriate analysis by applying different analysis algorithms depending on the category of the inquiry. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of the inquiry into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0077] The analysis unit can estimate the customer's emotions and adjust the length of the response based on the estimated customer emotions. For example, if the customer is in a hurry, the analysis unit can provide a short, to-the-point response. For example, if the customer is in a hurry, the analysis unit can provide a short, to-the-point response. Furthermore, if the customer is relaxed, the analysis unit can provide a longer response with detailed explanations. For example, if the customer is relaxed, the analysis unit can provide a longer response with detailed explanations. Furthermore, if the customer is excited, the analysis unit can provide an response with visually stimulating effects. For example, if the customer is excited, the analysis unit can provide an response with visually stimulating effects. This allows for adjusting the length of the response based on the customer's emotions, thereby improving customer satisfaction. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input customer emotional data into the generation AI and have the generation AI adjust the length of the response.

[0078] 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 can prioritize analysis of recently submitted inquiries and provide a quick answer. For example, the analysis unit can prioritize analysis of recently submitted inquiries and provide a quick answer. The analysis unit can also analyze older inquiries with a normal priority. For example, the analysis unit can analyze older inquiries with a normal priority. Furthermore, the analysis unit can analyze inquiries submitted during a specific time period with a priority according to that time period. For example, the analysis unit can analyze inquiries submitted during a specific time period with a priority according to that time period. This enables a quick response by determining the priority of analysis based on the time of submission of the inquiry. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit can input the time of submission of the inquiry into the generation AI and have the generation AI determine the analysis priority.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the inquiries. For example, the analysis unit prioritizes analysis of inquiries related to the customer's current situation. For example, the analysis unit can prioritize analysis of inquiries related to the customer's current situation. The analysis unit can also prioritize analysis of inquiries related to the customer's past inquiry history. For example, the analysis unit can prioritize analysis of inquiries related to the customer's past inquiry history. Furthermore, the analysis unit can also prioritize analysis of inquiries related to the customer's areas of interest. For example, the analysis unit can prioritize analysis of inquiries related to the customer's areas of interest. This enables efficient analysis by adjusting the order of analysis based on the relevance of the inquiries. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the inquiries into the generation AI and cause the generation AI to adjust the order of analysis.

[0080] The suggestion unit can estimate the customer's emotions and adjust the way the suggestion is expressed based on the estimated customer's emotions. For example, if the customer is feeling stressed, the suggestion unit can provide a concise and clear suggestion. For example, if the customer is feeling stressed, the suggestion unit can provide a concise and clear suggestion. Furthermore, if the customer is relaxed, the suggestion unit can provide a suggestion including a detailed explanation. For example, if the customer is relaxed, the suggestion unit can provide a suggestion including a detailed explanation. Furthermore, if the customer is angry, the suggestion unit can make a suggestion using polite and calm language. For example, if the customer is angry, the suggestion unit can make a suggestion using polite and calm language. This allows customer satisfaction to be improved by adjusting the way the suggestion is expressed based on the customer'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 such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input customer emotional data into the generation AI and have the generation AI adjust the way the proposal is expressed.

[0081] When making a proposal, the proposal unit can analyze the customer's past purchase history and propose an optimal plan. The proposal unit, for example, proposes plans related to products purchased by the customer in the past. For example, the proposal unit can propose plans related to products purchased by the customer in the past. The proposal unit can also propose plans related to products in a specific category based on the customer's purchase history. For example, the proposal unit can propose plans related to products in a specific category based on the customer's purchase history. The proposal unit can also analyze the customer's purchase history and propose the most suitable plan. For example, the proposal unit can analyze the customer's purchase history and propose the most suitable plan. In this way, the optimal plan can be proposed by analyzing the customer's past purchase history. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the customer's past purchase history into the generation AI and cause the generation AI to propose an optimal plan.

[0082] When proposing a plan, the proposal unit can customize the plan based on the customer's current living situation. For example, when a customer inputs their current living situation, the proposal unit can propose a plan suitable for that situation. For example, when a customer inputs their current living situation, the proposal unit can propose a plan suitable for that situation. The proposal unit can also propose a customized plan based on the customer's living situation. For example, the proposal unit can propose a customized plan based on the customer's living situation. Furthermore, when a customer is in a specific living situation, the proposal unit can automatically propose a plan suitable for that situation. For example, when a customer is in a specific living situation, the proposal unit can automatically propose a plan suitable for that situation. This makes it possible to provide an optimal plan to the customer by customizing the plan based on the customer's current living situation. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit may input the customer's current living situation into the generation AI and cause the generation AI to customize the plan.

[0083] The suggestion unit can estimate the customer's emotions and determine the priority of suggestions based on the estimated customer emotions. For example, if the customer is feeling stressed, the suggestion unit can give the highest priority to suggestions. For example, if the customer is feeling stressed, the suggestion unit can give the highest priority to suggestions. Furthermore, if the customer is relaxed, the suggestion unit can give the normal priority to suggestions. For example, if the customer is relaxed, the suggestion unit can give the normal priority to suggestions. Furthermore, if the customer is in a hurry, the suggestion unit can give the quickest suggestions. For example, if the customer is in a hurry, the suggestion unit can give the quickest suggestions. This allows for improving customer satisfaction by determining the priority of suggestions based on the customer's emotions. The suggestion of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input customer emotion data into the generation AI and have the generation AI determine the priority of proposals.

[0084] When making a proposal, the proposal unit can propose an optimal plan by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the proposal unit can propose a plan related to that area. For example, if the customer is in a specific area, the proposal unit can propose a plan related to that area. The proposal unit can also propose plans related to area-specific services or campaigns based on the customer's geographical location information. For example, the proposal unit can propose plans related to area-specific services or campaigns based on the customer's geographical location information. Furthermore, if the customer is traveling, the proposal unit can also propose a plan related to the travel destination. For example, if the customer is traveling, the proposal unit can propose a plan related to the travel destination. In this way, the optimal plan can be proposed by taking the customer's geographical location information into consideration. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the customer's geographical location information into the generation AI and cause the generation AI to propose an optimal plan.

[0085] When making a proposal, the proposal unit can analyze the customer's social media activity and propose a related plan. For example, if a customer mentions a specific product on social media, the proposal unit can propose a plan related to the product. For example, if a customer mentions a specific product on social media, the proposal unit can propose a plan related to the product. The proposal unit can also analyze the customer's social media activity and propose a plan related to a topic of high interest. For example, the proposal unit can analyze the customer's social media activity and propose a plan related to a topic of high interest. The proposal unit can also propose a plan related to a product in which the customer expressed an interest on social media. For example, the proposal unit can propose a plan related to a product in which the customer expressed an interest on social media. In this way, related plans can be proposed by analyzing the customer's social media activity. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the customer's social media activity into the generation AI and cause the generation AI to propose related plans.

[0086] The notification unit can estimate the customer's emotions and adjust the content of the notification based on the estimated customer emotions. For example, if the customer is feeling stressed, the notification unit can send a concise and clear notification. For example, if the customer is feeling stressed, the notification unit can send a concise and clear notification. Furthermore, if the customer is relaxed, the notification unit can send a notification including detailed information. For example, if the customer is relaxed, the notification unit can send a notification including detailed information. Furthermore, if the customer is angry, the notification unit can send a notification using polite and calm language. For example, if the customer is angry, the notification unit can send a notification using polite and calm language. This allows for adjusting the content of the notification based on the customer's emotions, thereby improving customer satisfaction. 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 such examples. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit can input customer emotion data into the generation AI and have the generation AI adjust the content of the notification.

[0087] The notification unit can select the optimal notification method by analyzing the customer's past response history at the time of notification. For example, the notification unit can prioritize the use of a notification method to which the customer has previously responded favorably. For example, the notification unit can prioritize the use of a notification method to which the customer has previously responded favorably. The notification unit can also select a notification method to be sent during a specific time period based on the customer's past response history. For example, the notification unit can select a notification method to be sent during a specific time period based on the customer's past response history. The notification unit can also analyze the customer's past response history and select the most effective notification method. For example, the notification unit can analyze the customer's past response history and select the most effective notification method. This allows the optimal notification method to be selected by analyzing the customer's past response history, enabling efficient notification. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the customer's past response history into the generation AI and have the generation AI select the optimal notification method.

[0088] The notification unit can customize the timing of the notification based on the customer's current situation when sending a notification. For example, when a customer inputs their current situation, the notification unit can send a notification at a timing appropriate for that situation. For example, when a customer inputs their current situation, the notification unit can send a notification at a timing appropriate for that situation. The notification unit can also send a notification at an optimal timing based on the customer's current activity status. For example, the notification unit can send a notification at an optimal timing based on the customer's current activity status. Furthermore, the notification unit can automatically send a notification at a timing appropriate for a specific situation when the customer is in that situation. For example, when a customer is in a specific situation, the notification unit can automatically send a notification at a timing appropriate for that situation. This allows for customizing the timing of the notification based on the customer's current situation, thereby improving customer satisfaction. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input the customer's current situation into the generation AI and cause the generation AI to customize the timing of the notification.

[0089] The notification unit can estimate the customer's emotions and determine the priority of notifications based on the estimated customer emotions. For example, if the customer is feeling stressed, the notification unit can prioritize sending important notifications. For example, if the customer is feeling stressed, the notification unit can prioritize sending important notifications. Furthermore, if the customer is relaxed, the notification unit can send notifications with normal priority. For example, if the customer is relaxed, the notification unit can send notifications with normal priority. Furthermore, if the customer is in a hurry, the notification unit can send important notifications with highest priority. For example, if the customer is in a hurry, the notification unit can send important notifications with highest priority. This allows customer satisfaction to be improved by determining the priority of notifications based on the customer'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 notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit can input customer emotion data into the generation AI and have the generation AI determine the priority of notifications.

[0090] The notification unit can select the optimal notification method by taking into account the customer's geographical location information when providing a notification. For example, if the customer is in a specific area, the notification unit can prioritize sending notifications related to that area. For example, if the customer is in a specific area, the notification unit can prioritize sending notifications related to that area. The notification unit can also send notifications about area-specific services and campaigns based on the customer's geographical location information. For example, the notification unit can send notifications about area-specific services and campaigns based on the customer's geographical location information. Furthermore, if the customer is traveling, the notification unit can prioritize sending notifications related to the customer's travel destination. For example, if the customer is traveling, the notification unit can prioritize sending notifications related to the customer's travel destination. This allows the optimal notification method to be selected by taking into account the customer's geographical location information, enabling efficient notification. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input the customer's geographical location information into the generation AI and have the generation AI select the optimal notification method.

[0091] The notification unit can analyze the customer's social media activity and notify the customer of related information when providing notification. For example, if a customer mentions a specific product on social media, the notification unit can notify the customer of information related to the product. For example, if a customer mentions a specific product on social media, the notification unit can notify the customer of information related to the product. The notification unit can also analyze the customer's social media activity and notify the customer of information related to topics of high interest. For example, the notification unit can analyze the customer's social media activity and notify the customer of information related to topics of high interest. The notification unit can also notify the customer of information related to products in which the customer has shown interest on social media. For example, the notification unit can notify the customer of information related to products in which the customer has shown interest on social media. In this way, by analyzing the customer's social media activity, the customer can be notified of related information. Some or all of the above-described processing in the notification unit can be performed using, or without, the generation AI. For example, the notification unit can input the customer's social media activity into the generation AI and cause the generation AI to notify the customer of related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and notification 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 is realized by the control unit 46A of the smart device 14 and receives inquiries by text or voice through a messaging app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry using a generation AI and generates an appropriate answer. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a plan according to the customer's request. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies information through a messaging app. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, proposal unit, and notification 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 is realized by the control unit 46A of the smart glasses 214 and accepts inquiries by text or voice through a messaging app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry using a generation AI and generates an appropriate answer. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a plan according to the customer's request. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies information through a messaging app. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and notification 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 is realized by the control unit 46A of the headset type terminal 314 and accepts inquiries by text or voice through a messaging app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry using a generation AI and generates an appropriate answer. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a plan according to the customer's request. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314 and notifies information through a messaging app. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts inquiries by text or voice through a messaging app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry using a generation AI and generates an appropriate answer. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a plan according to the customer's request. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies information through a messaging app.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can analyze a customer's past inquiry history and select the optimal reception method. For example, it can automatically recognize the content of inquiries that the customer has frequently made in the past and use related templates to receive inquiries. It can also prioritize and suggest inquiry methods (text, voice, etc.) that the customer has used in the past. Furthermore, it can predict and suggest the reception method to use during a specific time period based on the customer's past inquiry history. This allows for the selection of the optimal reception method and efficient response.

[0094] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry. For example, for inquiries of high importance, a detailed analysis can be performed to provide a highly accurate answer. For inquiries of low importance, a concise analysis can be performed to provide a quick answer. Furthermore, for inquiries of medium importance, an analysis can be performed with an appropriate level of detail to provide a balanced answer. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the inquiry.

[0095] When proposing a plan, the proposal unit can customize the plan based on the customer's current living situation. For example, when the customer inputs their current living situation, a plan suitable for that situation can be proposed. The proposal unit can also propose a customized plan based on the customer's living situation. Furthermore, if the customer is in a specific living situation, a plan suitable for that situation can be automatically proposed. In this way, by customizing the plan based on the customer's current living situation, it is possible to provide the customer with the optimal plan.

[0096] When sending a notification, the notification unit can analyze the customer's past response history and select the optimal notification method. For example, it can prioritize the use of notification methods to which the customer has responded favorably in the past. It can also select a notification method to send at a specific time period based on the customer's past response history. It can also analyze the customer's past response history and select the most effective notification method. This makes it possible to select the optimal notification method and provide efficient notifications by analyzing the customer's past response history.

[0097] The reception unit can estimate the customer's emotions and determine the priority of inquiries based on the estimated customer emotions. For example, if the customer is feeling stressed, the inquiry can be processed with priority and responded to quickly. If the customer is relaxed, the inquiry can be processed with normal priority. Furthermore, if the customer is angry, the inquiry can be processed with the highest priority and the problem can be resolved quickly. In this way, by determining the priority of inquiries based on the customer's emotions, customer satisfaction can be improved.

[0098] The analysis unit can estimate the customer's emotions and adjust the way in which a response is expressed based on the estimated customer's emotions. For example, if the customer is feeling stressed, a concise and clear response can be provided. If the customer is relaxed, a response including detailed explanations can be provided. Furthermore, if the customer is angry, a response can be provided using polite and calm language. In this way, customer satisfaction can be improved by adjusting the way in which a response is expressed based on the customer's emotions.

[0099] The suggestion unit can estimate the customer's emotions and adjust the way suggestions are expressed based on the estimated customer emotions. For example, if the customer is feeling stressed, a concise and clear suggestion can be provided. If the customer is relaxed, a suggestion including detailed explanations can be provided. Furthermore, if the customer is angry, a suggestion can be made using polite and calm language. As a result, customer satisfaction can be improved by adjusting the way suggestions are expressed based on the customer's emotions.

[0100] The notification unit can estimate the customer's emotions and adjust the content of the notification based on the estimated customer's emotions. For example, if the customer is feeling stressed, a concise and clear notification can be sent. If the customer is relaxed, a notification containing detailed information can be sent. Furthermore, if the customer is angry, a notification can be sent using polite and calm language. As a result, customer satisfaction can be improved by adjusting the content of the notification based on the customer's emotions.

[0101] The notification unit can estimate the customer's emotions and determine the priority of notifications based on the estimated customer's emotions. For example, if the customer is feeling stressed, important notifications can be sent with priority. If the customer is relaxed, notifications can be sent with normal priority. Furthermore, if the customer is in a hurry, important notifications can be sent with top priority. In this way, by determining the priority of notifications based on the customer's emotions, customer satisfaction can be improved.

[0102] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, a specialized analysis algorithm can be applied to technical inquiries. Furthermore, an analysis algorithm specialized for product information can be applied to product inquiries. Furthermore, an analysis algorithm specialized for service content can be applied to service inquiries. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the inquiry.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception department accepts inquiries from customers. Inquiries from customers can include text, voice, images, etc. For example, inquiries can be accepted through messaging apps such as WhatsApp and Messenger. Step 2: The analysis unit uses the generation AI to analyze the inquiry received by the reception unit and generate an appropriate response. The analysis unit uses natural language processing technology to understand the content of the inquiry and generate a response that provides specific procedures, solutions, and advice. Step 3: The proposal unit proposes plans that meet the customer's needs based on the results of the analysis by the analysis unit. For example, if a customer shows interest in a particular product or service, the proposal unit will propose related plans and options. It can also propose optimal plans based on past purchase history and usage history. Step 4: The notification unit notifies the user of information based on the plan proposed by the proposal unit. For example, the notification unit notifies the user of birthday benefits, sales, and campaign information via a messaging app, email, SMS, or app notification.

[0105] 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.

[0106] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 and generates an appropriate response; a proposal unit that proposes a plan according to the customer's request based on the results of the analysis by the analysis unit; a notification unit that notifies information based on the plan proposed by the proposal unit; Equipped with A system characterized by:

2. The reception unit Accept inquiries via text or voice through messaging apps 2. The system of claim 1.

3. The analysis unit Uses natural language processing technology to understand inquiries and generate appropriate answers 2. The system of claim 1.

4. The proposal unit Propose plans that meet customer needs 2. The system of claim 1.

5. The notification unit Send birthday specials or exclusive sales or campaign information to friends via messaging apps 2. The system of claim 1.

6. The analysis unit Generate step-by-step answers to questions about how to use a product 2. The system of claim 1.

7. The proposal unit If a customer expresses interest in a particular product, offer them plans or services related to that product.

2. The system of claim 1.

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

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