system

Abstract

The system according to the embodiment aims to solve the problems of labor shortages and labor costs in the service industry. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives customer questions and requests. The generation unit analyzes the information received by the reception unit and generates answers and suggestions. The provision unit provides the answers and suggestions generated by the generation unit to the customer.

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] Conventional technologies have not provided sufficient and effective solutions to the labor shortage and rising labor costs in the service industry, and there is room for improvement.

[0005] The system according to the embodiment aims to solve the problems of labor shortages and labor costs in the service industry. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives questions and requests from customers. The generation unit analyzes the information received by the reception unit and generates answers and suggestions. The provision unit provides the answers and suggestions generated by the generation unit to the customers. [Effects of the Invention]

[0007] The system according to the embodiment can solve the problems of labor shortages and labor costs in the service industry. [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 service support system according to an embodiment of the present invention utilizes a generative AI to accept and analyze customer questions and requests and provide appropriate answers and suggestions. This system aims to address the challenges of labor shortages and labor costs in the service industry. Specifically, when a customer uses a service, a generative AI accepts the customer's questions and requests. This system can be used in a variety of situations, such as ordering at a restaurant or checking in at a hotel. The customer inputs the question or request to the generative AI in natural language. The generative AI then analyzes the received information and generates appropriate answers and suggestions. For example, when ordering at a restaurant, the generative AI understands the menu and makes suggestions tailored to the customer's preferences. The generated answers and suggestions are then provided to the customer. The generative AI provides answers and suggestions to the customer in natural language. For example, when checking in at a hotel, the generative AI provides room information and service information. This system can address the labor shortage in the service industry. Having the generative AI handle customer inquiries reduces the burden on employees and enables more efficient service delivery. Labor costs can also be reduced. By having generative AI take over some tasks, the number of employees can be reduced, resulting in a reduction in labor costs. For example, in a restaurant, generative AI can take orders and suggest dishes, reducing the burden on employees. This allows the restaurant to operate efficiently with fewer staff. In addition, in a hotel, generative AI can handle check-in and service instructions, improving the efficiency of front desk operations. In this way, using generative AI in customer service can solve the problems of labor shortages and labor costs in the service industry. As a result, customer service support systems can make up for the labor shortage in the service industry and reduce labor costs.

[0029] A customer service support system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives customer questions and requests. Customer questions and requests include, but are not limited to, technical questions and product-related requests. The reception unit allows customers to input their questions and requests in natural language, for example. The generation unit uses a generation AI to analyze the information received by the reception unit and generate appropriate answers and suggestions. The generation AI may use models such as GPT-4 (registered trademark) or Gemini. The generation unit understands the customer's questions and requests and generates appropriate answers and suggestions. The generation unit performs analysis using, for example, natural language processing technology or a machine learning algorithm. The provision unit provides the customer with the answers and suggestions generated by the generation unit. The provision unit provides the customer with the answers and suggestions generated by the generation AI in natural language, for example. The provision unit provides the answers and suggestions using, for example, grammatically correct sentences or colloquial expressions. This allows the customer service support system according to an embodiment to efficiently receive and analyze customer questions and requests and provide appropriate answers and suggestions.

[0030] The reception unit can accept customer questions and requests in natural language. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. The reception unit, for example, allows customers to input questions and requests in natural language. For example, a customer can input an order at a restaurant in natural language. The reception unit also allows customers to check in at a hotel in natural language. Furthermore, the reception unit allows customers to input technical questions about products in natural language. This allows customers to input questions and requests in natural language.

[0031] The generation unit can analyze customer questions and requests using generative AI and generate appropriate answers and suggestions. Examples of generative AI include, but are not limited to, models such as GPT-4 and Gemini. The generation unit, for example, understands customer questions and requests and generates appropriate answers and suggestions. The generation unit performs analysis using, for example, natural language processing technology or machine learning algorithms. For example, the generation unit analyzes customer questions and generates appropriate answers. The generation unit can also analyze customer requests and generate appropriate suggestions. Furthermore, the generation unit can collect related information based on the customer questions and requests and reflect it in the answers and suggestions. As a result, the generative AI can be used to generate appropriate answers and suggestions for customer questions and requests.

[0032] The providing unit can provide answers and suggestions generated by the generation AI to customers in natural language. Natural language includes, but is not limited to, grammatically correct sentences and colloquial expressions. The providing unit, for example, provides answers and suggestions generated by the generation AI to customers in natural language. For example, the providing unit provides answers and suggestions using grammatically correct sentences. The providing unit can also provide answers and suggestions using colloquial expressions. Furthermore, the providing unit can select an appropriate expression method depending on the customer's situation. As a result, by using the generation AI, answers and suggestions can be provided to customers in natural language.

[0033] The generation unit can make suggestions based on the customer's preferences and past usage history. Examples of the customer's preferences and past usage history include, but are not limited to, purchase history, browsing history, and feedback. The generation unit can make suggestions based on, for example, the customer's purchase history. The generation unit can also make suggestions based on the customer's browsing history. Furthermore, the generation unit can make suggestions based on the customer's feedback. For example, the generation unit can analyze the customer's past purchase history to suggest products that match the customer's preferences. The generation unit can also analyze the customer's browsing history to suggest related information. Furthermore, the generation unit can analyze the customer's feedback to improve the quality of the suggestions. This allows for more appropriate suggestions to be made by taking the customer's preferences and past usage history into consideration.

[0034] The generation unit can receive feedback from customers and improve the quality of answers and proposals based on the feedback. Examples of feedback include, but are not limited to, survey results and review comments. For example, the generation unit can receive survey results from customers and improve the quality of answers and proposals. The generation unit can also receive review comments from customers and improve the quality of answers and proposals. Furthermore, the generation unit can analyze feedback from customers and improve the quality of answers and proposals. For example, the generation unit can improve the content of answers and proposals based on customer feedback. The generation unit can also improve the format of answers and proposals based on customer feedback. In this way, the quality of answers and proposals can be improved based on customer feedback.

[0035] The reception unit can analyze the history of past questions and requests from the customer and select a reception method. The history of past questions and requests includes, but is not limited to, past inquiry content, response history, etc. The reception unit can, for example, automatically display questions that the customer has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception unit can predict and suggest questions and requests that will be used during specific time periods based on the customer's past question history. In this way, the optimal reception method can be selected by analyzing the history of past questions and requests from the customer.

[0036] When receiving questions or requests, the reception unit can filter them based on the customer's current situation and areas of interest. Examples of the current situation and areas of interest include, but are not limited to, the customer's current purchasing situation and topics of interest. For example, when the customer inputs their current situation, the reception unit preferentially receives questions or requests related to that situation. The reception unit can also automatically filter related questions or requests based on the customer's areas of interest. Furthermore, when the customer is in a specific situation, the reception unit can suggest questions or requests that are best suited to that situation. In this way, by filtering based on the customer's current situation and areas of interest, more appropriate questions and requests can be received.

[0037] When accepting questions or requests, the reception unit can prioritize accepting highly relevant questions or requests by taking into account the customer's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, etc. For example, if the customer is in a specific area, the reception unit can prioritize accepting questions or requests related to that area. The reception unit can also prioritize accepting questions or requests regarding nearby services based on the customer's current location. Furthermore, if the customer is traveling, the reception unit can prioritize accepting questions or requests related to the customer's travel destination. In this way, by taking into account the customer's geographical location information, highly relevant questions and requests can be prioritized.

[0038] When accepting a question or request, the reception unit can analyze the customer's social media activity and accept related questions or requests. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit analyzes the content of the customer's social media posts and prioritizes accepting related questions or requests. The reception unit can also suggest related questions or requests by referring to the activity of the customer's social media followers and friends. Furthermore, the reception unit can analyze the customer's social media trends and accept related questions or requests. In this way, it is possible to accept related questions and requests by analyzing the customer's social media activity.

[0039] When generating an answer or a proposal, the generation unit can adjust the level of detail based on the importance of the question or request. Examples of the level of detail include, but are not limited to, the depth and specificity of the information. For example, the generation unit generates a detailed answer or proposal for a question or request with a high level of importance. The generation unit can also generate a concise answer or proposal for a question or request with a low level of importance. Furthermore, the generation unit can adjust the level of detail based on the importance according to the customer's situation. By adjusting the level of detail based on the importance of the question or request, more appropriate answers or proposals can be generated.

[0040] When generating answers or suggestions, the generation unit can apply different generation algorithms depending on the category of the question or request. Examples of generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, the generation unit can apply a generation algorithm that understands the contents of a menu to a restaurant order. For a hotel check-in, the generation unit can also apply a generation algorithm that understands room information and service information. Furthermore, for shopping questions, the generation unit can apply a generation algorithm that understands detailed product information. In this way, by applying different generation algorithms depending on the category of the question or request, more appropriate answers and suggestions can be generated.

[0041] When generating answers or suggestions, the generation unit can determine the priority based on the time of submission of the question or request. The submission time includes, but is not limited to, for example, the submission date and time, the submission order, etc. The generation unit can determine the priority based on, for example, the time period in which the question or request was submitted. The generation unit can also generate answers or suggestions that require a prompt response, based on the time of submission of the question or request. Furthermore, the generation unit can generate answers or suggestions at an appropriate time based on the time of submission of the question or request. This enables more appropriate responses by determining the priority based on the time of submission of the question or request.

[0042] When generating answers or suggestions, the generation unit can adjust the order based on the relevance of the question or request. Relevance includes, but is not limited to, for example, the degree of topic agreement and related keywords. For example, the generation unit generates answers or suggestions with the highest relevance based on the relevance of the question or request. The generation unit can also adjust the order according to the relevance of the question or request to generate answers or suggestions. Furthermore, the generation unit can generate answers or suggestions in an appropriate order based on the relevance of the question or request. In this way, by adjusting the order based on the relevance of the question or request, more appropriate answers or suggestions can be generated.

[0043] When providing an answer or a suggestion, the providing unit can select a delivery method by referring to the customer's past feedback. Past feedback includes, but is not limited to, customer ratings and comments. The providing unit selects the optimal delivery method, for example, based on feedback provided by the customer on answers or suggestions provided in the past. The providing unit can also analyze the customer's past feedback and select the most effective delivery method. Furthermore, the providing unit can customize the delivery method by referring to the customer's feedback history. In this way, the optimal delivery method can be selected by referring to the customer's past feedback.

[0044] When providing an answer or a suggestion, the providing unit can customize the means of provision based on the customer's current situation. The current situation includes, but is not limited to, for example, a current purchasing situation, a current usage situation, etc. For example, when the customer inputs the current situation, the providing unit selects the means of provision that is optimal for that situation. The providing unit can also customize the optimal means of provision based on the customer's current situation. Furthermore, when the customer is in a specific situation, the providing unit can suggest the means of provision that is optimal for that situation. This allows for more appropriate responses by customizing the means of provision based on the customer's current situation.

[0045] When providing answers or suggestions, the providing unit can select the optimal providing method by taking into account the customer's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the customer is in a specific area, the providing unit can prioritize providing answers or suggestions related to that area. The providing unit can also prioritize providing answers or suggestions related to nearby services based on the customer's current location. Furthermore, if the customer is traveling, the providing unit can prioritize providing answers or suggestions related to the customer's travel destination. In this way, the optimal providing method can be selected by taking into account the customer's geographical location information.

[0046] When providing answers or suggestions, the providing unit can analyze the customer's social media activity and suggest a means of providing the answers or suggestions. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the providing unit can analyze the content of the customer's social media posts and prioritize providing relevant answers or suggestions. The providing unit can also suggest relevant answers or suggestions by referring to the activities of the customer's social media followers and friends. Furthermore, the providing unit can analyze the customer's social media trends and provide relevant answers or suggestions. In this way, relevant answers and suggestions can be provided by analyzing the customer's social media activity.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When accepting a customer's questions or requests, the reception unit can refer to the customer's past purchase history and browsing history and automatically suggest related questions and requests. For example, if a customer has purchased a specific product in the past, questions and requests related to that product will be displayed preferentially. It can also suggest related questions and requests based on products or services that the customer has viewed in the past. Furthermore, it can suggest optimal questions and requests based on the customer's past feedback. This makes it possible to accept more appropriate questions and requests by utilizing the customer's past behavioral history.

[0049] When analyzing customer questions and requests, the generation unit can apply different analysis algorithms depending on the category of the question or request. For example, an analysis algorithm with specialized knowledge can be applied to technical questions. Also, an analysis algorithm with an understanding of product information can be applied to product requests. Furthermore, an analysis algorithm with an understanding of service content can be applied to service-related questions. This allows the system to generate more appropriate answers and suggestions by applying the optimal analysis algorithm depending on the category of question or request.

[0050] When analyzing customer questions and requests, the generation unit can determine the priority of analysis based on the importance of the questions and requests. For example, for questions and requests with high urgency, the generation unit can quickly analyze and generate answers and proposals. For questions and requests with low importance, the generation unit can also perform a detailed analysis and generate more specific answers and proposals. Furthermore, the generation unit can adjust the priority of analysis based on importance depending on the customer's situation. In this way, by determining the priority of analysis based on the importance of the questions and requests, more appropriate answers and proposals can be generated.

[0051] When analyzing customer questions and requests, the generation unit can apply different analysis algorithms depending on the category of the question or request. For example, an analysis algorithm with specialized knowledge can be applied to technical questions. Also, an analysis algorithm with an understanding of product information can be applied to product requests. Furthermore, an analysis algorithm with an understanding of service content can be applied to service-related questions. This allows the system to generate more appropriate answers and suggestions by applying the optimal analysis algorithm depending on the category of question or request.

[0052] When analyzing customer questions and requests, the generation unit can determine the priority of analysis based on the importance of the questions and requests. For example, for questions and requests with high urgency, the generation unit can quickly analyze and generate answers and proposals. For questions and requests with low importance, the generation unit can also perform a detailed analysis and generate more specific answers and proposals. Furthermore, the generation unit can adjust the priority of analysis based on importance depending on the customer's situation. In this way, by determining the priority of analysis based on the importance of the questions and requests, more appropriate answers and proposals can be generated.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit accepts customer questions and requests. Customer questions and requests include technical questions and product-related requests. The reception unit allows customers to input their questions and requests in natural language. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate appropriate answers and suggestions. The generation AI can use models such as GPT-4 and Gemini. The generation unit performs analysis using natural language processing technology and machine learning algorithms. Step 3: The provision unit provides the answers and suggestions generated by the generation unit to the customer. The provision unit provides the answers and suggestions generated by the generation AI to the customer in natural language, using grammatically correct sentences and colloquial expressions to provide answers and suggestions.

[0055] (Example 2) A customer service support system according to an embodiment of the present invention utilizes a generative AI to accept and analyze customer questions and requests and provide appropriate answers and suggestions. This system aims to address the challenges of labor shortages and labor costs in the service industry. Specifically, when a customer uses a service, a generative AI accepts the customer's questions and requests. This system can be used in a variety of situations, such as ordering at a restaurant or checking in at a hotel. The customer inputs the question or request to the generative AI in natural language. The generative AI then analyzes the received information and generates appropriate answers and suggestions. For example, when ordering at a restaurant, the generative AI understands the menu and makes suggestions tailored to the customer's preferences. The generated answers and suggestions are then provided to the customer. The generative AI provides answers and suggestions to the customer in natural language. For example, when checking in at a hotel, the generative AI provides room information and service information. This system can address the labor shortage in the service industry. Having the generative AI handle customer inquiries reduces the burden on employees and enables more efficient service delivery. Labor costs can also be reduced. By having generative AI take over some tasks, the number of employees can be reduced, resulting in a reduction in labor costs. For example, in a restaurant, generative AI can take orders and suggest dishes, reducing the burden on employees. This allows the restaurant to operate efficiently with fewer staff. In addition, in a hotel, generative AI can handle check-in and service instructions, improving the efficiency of front desk operations. In this way, using generative AI in customer service can solve the problems of labor shortages and labor costs in the service industry. As a result, customer service support systems can make up for the labor shortage in the service industry and reduce labor costs.

[0056] A customer service support system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives customer questions and requests. Customer questions and requests include, but are not limited to, technical questions and product-related requests. The reception unit allows customers to input their questions and requests in natural language, for example. The generation unit uses a generation AI to analyze the information received by the reception unit and generate appropriate answers and suggestions. The generation AI may use a model such as GPT-4 or Gemini. The generation unit understands the customer's questions and requests and generates appropriate answers and suggestions. The generation unit performs analysis using, for example, natural language processing technology or a machine learning algorithm. The provision unit provides the customer with the answers and suggestions generated by the generation unit. The provision unit provides the customer with the answers and suggestions generated by the generation AI in natural language, for example. The provision unit provides the answers and suggestions using, for example, grammatically correct sentences or colloquial expressions. This allows the customer service support system according to an embodiment to efficiently receive and analyze customer questions and requests and provide appropriate answers and suggestions.

[0057] The reception unit can accept customer questions and requests in natural language. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. The reception unit, for example, allows customers to input questions and requests in natural language. For example, a customer can input an order at a restaurant in natural language. The reception unit also allows customers to check in at a hotel in natural language. Furthermore, the reception unit allows customers to input technical questions about products in natural language. This allows customers to input questions and requests in natural language.

[0058] The generation unit can analyze customer questions and requests using generative AI and generate appropriate answers and suggestions. Examples of generative AI include, but are not limited to, models such as GPT-4 and Gemini. The generation unit, for example, understands customer questions and requests and generates appropriate answers and suggestions. The generation unit performs analysis using, for example, natural language processing technology or machine learning algorithms. For example, the generation unit analyzes customer questions and generates appropriate answers. The generation unit can also analyze customer requests and generate appropriate suggestions. Furthermore, the generation unit can collect related information based on the customer questions and requests and reflect it in the answers and suggestions. As a result, the generative AI can be used to generate appropriate answers and suggestions for customer questions and requests.

[0059] The providing unit can provide answers and suggestions generated by the generation AI to customers in natural language. Natural language includes, but is not limited to, grammatically correct sentences and colloquial expressions. The providing unit, for example, provides answers and suggestions generated by the generation AI to customers in natural language. For example, the providing unit provides answers and suggestions using grammatically correct sentences. The providing unit can also provide answers and suggestions using colloquial expressions. Furthermore, the providing unit can select an appropriate expression method depending on the customer's situation. As a result, by using the generation AI, answers and suggestions can be provided to customers in natural language.

[0060] The generation unit can make suggestions based on the customer's preferences and past usage history. Examples of the customer's preferences and past usage history include, but are not limited to, purchase history, browsing history, and feedback. The generation unit can make suggestions based on, for example, the customer's purchase history. The generation unit can also make suggestions based on the customer's browsing history. Furthermore, the generation unit can make suggestions based on the customer's feedback. For example, the generation unit can analyze the customer's past purchase history to suggest products that match the customer's preferences. The generation unit can also analyze the customer's browsing history to suggest related information. Furthermore, the generation unit can analyze the customer's feedback to improve the quality of the suggestions. This allows for more appropriate suggestions to be made by taking the customer's preferences and past usage history into consideration.

[0061] The generation unit can receive feedback from customers and improve the quality of answers and proposals based on the feedback. Examples of feedback include, but are not limited to, survey results and review comments. For example, the generation unit can receive survey results from customers and improve the quality of answers and proposals. The generation unit can also receive review comments from customers and improve the quality of answers and proposals. Furthermore, the generation unit can analyze feedback from customers and improve the quality of answers and proposals. For example, the generation unit can improve the content of answers and proposals based on customer feedback. The generation unit can also improve the format of answers and proposals based on customer feedback. In this way, the quality of answers and proposals can be improved based on customer feedback.

[0062] The reception unit can estimate a customer's emotions and adjust the method for accepting questions and requests based on the estimated customer emotions. For example, if a customer is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if a customer is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if a customer is in a hurry, the reception unit can prioritize voice input and quickly accept questions and requests. This allows for more appropriate responses by adjusting the method for accepting questions and requests according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0063] The reception unit can analyze the history of past questions and requests from the customer and select a reception method. The history of past questions and requests includes, but is not limited to, past inquiry content, response history, etc. The reception unit can, for example, automatically display questions that the customer has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception unit can predict and suggest questions and requests that will be used during specific time periods based on the customer's past question history. In this way, the optimal reception method can be selected by analyzing the history of past questions and requests from the customer.

[0064] When receiving questions or requests, the reception unit can filter them based on the customer's current situation and areas of interest. Examples of the current situation and areas of interest include, but are not limited to, the customer's current purchasing situation and topics of interest. For example, when the customer inputs their current situation, the reception unit preferentially receives questions or requests related to that situation. The reception unit can also automatically filter related questions or requests based on the customer's areas of interest. Furthermore, when the customer is in a specific situation, the reception unit can suggest questions or requests that are best suited to that situation. In this way, by filtering based on the customer's current situation and areas of interest, more appropriate questions and requests can be received.

[0065] The reception unit can estimate the customer's emotions and determine the priority of questions and requests to be received based on the estimated customer emotions. Priorities include, but are not limited to, urgency, importance, and the customer's emotional state. For example, if the customer is nervous, the reception unit can prioritize urgent questions and requests. Furthermore, if the customer is relaxed, the reception unit can prioritize detailed questions and requests. Furthermore, if the customer is in a hurry, the reception unit can prioritize questions and requests that require a quick response. This allows for more appropriate responses by prioritizing questions and requests according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] When accepting questions or requests, the reception unit can prioritize accepting highly relevant questions or requests by taking into account the customer's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, etc. For example, if the customer is in a specific area, the reception unit can prioritize accepting questions or requests related to that area. The reception unit can also prioritize accepting questions or requests regarding nearby services based on the customer's current location. Furthermore, if the customer is traveling, the reception unit can prioritize accepting questions or requests related to the customer's travel destination. In this way, by taking into account the customer's geographical location information, highly relevant questions and requests can be prioritized.

[0067] When accepting a question or request, the reception unit can analyze the customer's social media activity and accept related questions or requests. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit analyzes the content of the customer's social media posts and prioritizes accepting related questions or requests. The reception unit can also suggest related questions or requests by referring to the activity of the customer's social media followers and friends. Furthermore, the reception unit can analyze the customer's social media trends and accept related questions or requests. In this way, it is possible to accept related questions and requests by analyzing the customer's social media activity.

[0068] The generation unit can estimate the customer's emotions and adjust the way answers and suggestions are expressed based on the estimated customer emotions. Examples of expression methods include, but are not limited to, formal and casual expressions. For example, if the customer is relaxed, the generation unit can provide answers and suggestions using soft expressions. Furthermore, if the customer is nervous, the generation unit can provide answers and suggestions using concise and clear expressions. Furthermore, if the customer is excited, the generation unit can provide answers and suggestions using energetic expressions. This allows for more appropriate responses by adjusting the way answers and suggestions are expressed according to the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] When generating an answer or a proposal, the generation unit can adjust the level of detail based on the importance of the question or request. Examples of the level of detail include, but are not limited to, the depth and specificity of the information. For example, the generation unit generates a detailed answer or proposal for a question or request with a high level of importance. The generation unit can also generate a concise answer or proposal for a question or request with a low level of importance. Furthermore, the generation unit can adjust the level of detail based on the importance according to the customer's situation. By adjusting the level of detail based on the importance of the question or request, more appropriate answers or proposals can be generated.

[0070] When generating answers or suggestions, the generation unit can apply different generation algorithms depending on the category of the question or request. Examples of generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, the generation unit can apply a generation algorithm that understands the contents of a menu to a restaurant order. For a hotel check-in, the generation unit can also apply a generation algorithm that understands room information and service information. Furthermore, for shopping questions, the generation unit can apply a generation algorithm that understands detailed product information. In this way, by applying different generation algorithms depending on the category of the question or request, more appropriate answers and suggestions can be generated.

[0071] The generation unit can estimate the customer's emotions and adjust the length of the answer or suggestion based on the estimated customer emotions. Examples of length include, but are not limited to, the number of characters and the number of paragraphs. For example, if the customer is in a hurry, the generation unit can generate a short, to-the-point answer or suggestion. If the customer is relaxed, the generation unit can also generate a longer answer or suggestion with detailed explanations. Furthermore, if the customer is excited, the generation unit can also generate an answer or suggestion with visually stimulating effects. This allows for more appropriate responses by adjusting the length of the answer or suggestion according to the customer's emotions. The 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.

[0072] When generating answers or suggestions, the generation unit can determine the priority based on the time of submission of the question or request. The submission time includes, but is not limited to, for example, the submission date and time, the submission order, etc. The generation unit can determine the priority based on, for example, the time period in which the question or request was submitted. The generation unit can also generate answers or suggestions that require a prompt response, based on the time of submission of the question or request. Furthermore, the generation unit can generate answers or suggestions at an appropriate time based on the time of submission of the question or request. This enables more appropriate responses by determining the priority based on the time of submission of the question or request.

[0073] When generating answers or suggestions, the generation unit can adjust the order based on the relevance of the question or request. Relevance includes, but is not limited to, for example, the degree of topic agreement and related keywords. For example, the generation unit generates answers or suggestions with the highest relevance based on the relevance of the question or request. The generation unit can also adjust the order according to the relevance of the question or request to generate answers or suggestions. Furthermore, the generation unit can generate answers or suggestions in an appropriate order based on the relevance of the question or request. In this way, by adjusting the order based on the relevance of the question or request, more appropriate answers or suggestions can be generated.

[0074] The providing unit can estimate the customer's emotions and adjust the method of providing answers and suggestions based on the estimated customer emotions. Examples of the providing method include, but are not limited to, email, chat, and telephone. For example, if the customer is relaxed, the providing unit can provide answers and suggestions using soft language. Furthermore, if the customer is nervous, the providing unit can provide answers and suggestions using concise and clear language. Furthermore, if the customer is excited, the providing unit can provide answers and suggestions using energetic language. This allows for more appropriate responses by adjusting the method of providing answers and suggestions according to the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When providing an answer or a suggestion, the providing unit can select a delivery method by referring to the customer's past feedback. Past feedback includes, but is not limited to, customer ratings and comments. The providing unit selects the optimal delivery method, for example, based on feedback provided by the customer on answers or suggestions provided in the past. The providing unit can also analyze the customer's past feedback and select the most effective delivery method. Furthermore, the providing unit can customize the delivery method by referring to the customer's feedback history. In this way, the optimal delivery method can be selected by referring to the customer's past feedback.

[0076] When providing an answer or a suggestion, the providing unit can customize the means of provision based on the customer's current situation. The current situation includes, but is not limited to, for example, a current purchasing situation, a current usage situation, etc. For example, when the customer inputs the current situation, the providing unit selects the means of provision that is optimal for that situation. The providing unit can also customize the optimal means of provision based on the customer's current situation. Furthermore, when the customer is in a specific situation, the providing unit can suggest the means of provision that is optimal for that situation. This allows for more appropriate responses by customizing the means of provision based on the customer's current situation.

[0077] The providing unit can estimate the customer's emotions and determine the order in which answers and suggestions are provided based on the estimated customer emotions. Examples of the order of providing answers and suggestions include, but are not limited to, importance, urgency, and the customer's emotional state. For example, if the customer is nervous, the providing unit can prioritize providing answers and suggestions with high urgency. Furthermore, if the customer is relaxed, the providing unit can prioritize providing detailed answers and suggestions. Furthermore, if the customer is in a hurry, the providing unit can prioritize providing answers and suggestions that require a quick response. This allows for more appropriate responses by determining the order in which answers and suggestions are provided based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0078] When providing answers or suggestions, the providing unit can select the optimal providing method by taking into account the customer's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the customer is in a specific area, the providing unit can prioritize providing answers or suggestions related to that area. The providing unit can also prioritize providing answers or suggestions related to nearby services based on the customer's current location. Furthermore, if the customer is traveling, the providing unit can prioritize providing answers or suggestions related to the customer's travel destination. In this way, the optimal providing method can be selected by taking into account the customer's geographical location information.

[0079] When providing answers or suggestions, the providing unit can analyze the customer's social media activity and suggest a means of providing the answers or suggestions. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the providing unit can analyze the content of the customer's social media posts and prioritize providing relevant answers or suggestions. The providing unit can also suggest relevant answers or suggestions by referring to the activities of the customer's social media followers and friends. Furthermore, the providing unit can analyze the customer's social media trends and provide relevant answers or suggestions. In this way, relevant answers and suggestions can be provided by analyzing the customer's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives customer questions and requests. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a generation AI to generate appropriate answers and suggestions. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated answers and suggestions to the customer. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives customer questions and requests. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a generation AI to generate appropriate answers and suggestions. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated answers and suggestions to the customer. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives customer questions and requests. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a generation AI to generate appropriate answers and suggestions. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the generated answers and suggestions to the customer. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives questions and requests from customers. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a generation AI to generate appropriate answers and suggestions. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated answers and suggestions to customers.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When accepting a customer's questions or requests, the reception unit can refer to the customer's past purchase history and browsing history and automatically suggest related questions and requests. For example, if a customer has purchased a specific product in the past, questions and requests related to that product will be displayed preferentially. It can also suggest related questions and requests based on products or services that the customer has viewed in the past. Furthermore, it can suggest optimal questions and requests based on the customer's past feedback. This makes it possible to accept more appropriate questions and requests by utilizing the customer's past behavioral history.

[0082] The reception unit can estimate the customer's emotions and adjust the method of accepting questions and requests based on the estimated customer emotions. For example, if the customer is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the customer is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the customer is in a hurry, voice input can be prioritized to quickly accept questions and requests. This allows for more appropriate responses by adjusting the method of accepting questions and requests according to the customer's emotions.

[0083] When analyzing customer questions and requests, the generation unit can apply different analysis algorithms depending on the category of the question or request. For example, an analysis algorithm with specialized knowledge can be applied to technical questions. Also, an analysis algorithm with an understanding of product information can be applied to product requests. Furthermore, an analysis algorithm with an understanding of service content can be applied to service-related questions. This allows the system to generate more appropriate answers and suggestions by applying the optimal analysis algorithm depending on the category of question or request.

[0084] When providing answers or suggestions generated by the generation AI to a customer, the provision unit can estimate the customer's emotions and adjust the method of providing them based on the estimated customer emotions. For example, if the customer is relaxed, the provision unit can provide answers or suggestions using gentle language. If the customer is nervous, the provision unit can provide answers or suggestions using concise and clear language. Furthermore, if the customer is excited, the provision unit can provide answers or suggestions using energetic language. This makes it possible to respond more appropriately by adjusting the method of providing answers or suggestions according to the customer's emotions.

[0085] When analyzing customer questions and requests, the generation unit can determine the priority of analysis based on the importance of the questions and requests. For example, for questions and requests with high urgency, the generation unit can quickly analyze and generate answers and proposals. For questions and requests with low importance, the generation unit can also perform a detailed analysis and generate more specific answers and proposals. Furthermore, the generation unit can adjust the priority of analysis based on importance depending on the customer's situation. In this way, by determining the priority of analysis based on the importance of the questions and requests, more appropriate answers and proposals can be generated.

[0086] The reception unit can estimate the customer's emotions and adjust the method of accepting questions and requests based on the estimated customer emotions. For example, if the customer is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the customer is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the customer is in a hurry, voice input can be prioritized to quickly accept questions and requests. This allows for more appropriate responses by adjusting the method of accepting questions and requests according to the customer's emotions.

[0087] When analyzing customer questions and requests, the generation unit can apply different analysis algorithms depending on the category of the question or request. For example, an analysis algorithm with specialized knowledge can be applied to technical questions. Also, an analysis algorithm with an understanding of product information can be applied to product requests. Furthermore, an analysis algorithm with an understanding of service content can be applied to service-related questions. This allows the system to generate more appropriate answers and suggestions by applying the optimal analysis algorithm depending on the category of question or request.

[0088] When providing answers or suggestions generated by the generation AI to a customer, the provision unit can estimate the customer's emotions and adjust the method of providing them based on the estimated customer emotions. For example, if the customer is relaxed, the provision unit can provide answers or suggestions using gentle language. If the customer is nervous, the provision unit can provide answers or suggestions using concise and clear language. Furthermore, if the customer is excited, the provision unit can provide answers or suggestions using energetic language. This makes it possible to respond more appropriately by adjusting the method of providing answers or suggestions according to the customer's emotions.

[0089] When analyzing customer questions and requests, the generation unit can determine the priority of analysis based on the importance of the questions and requests. For example, for questions and requests with high urgency, the generation unit can quickly analyze and generate answers and proposals. For questions and requests with low importance, the generation unit can also perform a detailed analysis and generate more specific answers and proposals. Furthermore, the generation unit can adjust the priority of analysis based on importance depending on the customer's situation. In this way, by determining the priority of analysis based on the importance of the questions and requests, more appropriate answers and proposals can be generated.

[0090] When providing answers or suggestions generated by the generation AI to a customer, the provision unit can estimate the customer's emotions and adjust the method of providing them based on the estimated customer emotions. For example, if the customer is relaxed, the provision unit can provide answers or suggestions using gentle language. If the customer is nervous, the provision unit can provide answers or suggestions using concise and clear language. Furthermore, if the customer is excited, the provision unit can provide answers or suggestions using energetic language. This makes it possible to respond more appropriately by adjusting the method of providing answers or suggestions according to the customer's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit accepts customer questions and requests. Customer questions and requests include technical questions and product-related requests. The reception unit allows customers to input their questions and requests in natural language. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate appropriate answers and suggestions. The generation AI can use models such as GPT-4 and Gemini. The generation unit performs analysis using natural language processing technology and machine learning algorithms. Step 3: The provision unit provides the answers and suggestions generated by the generation unit to the customer. The provision unit provides the answers and suggestions generated by the generation AI to the customer in natural language, using grammatically correct sentences and colloquial expressions to provide answers and suggestions.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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, to avoid confusion and 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 desk that accepts customer questions and requests; a generation unit that analyzes the information received by the reception unit and generates answers and suggestions; a providing unit that provides the answer or suggestion generated by the generating unit to the customer. A system characterized by:

2. The reception unit Accept customer questions and requests in natural language The system of claim 1 .

3. The generation unit Generative AI analyzes customer questions and requests and generates appropriate answers and suggestions. The system of claim 1 .

4. The providing unit Generative AI provides answers and suggestions to customers in natural language The system of claim 1 .

5. The generation unit Make suggestions based on customer preferences and past usage history The system of claim 1 .

6. The generation unit Receive customer feedback and use it to improve your answers and recommendations The system of claim 1 .

7. The reception unit Estimate customer sentiment and adjust how you respond to questions or requests based on that sentiment The system of claim 1 .

8. The reception unit Analyze the customer's past questions and requests and select the method of acceptance The system of claim 1 .

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