system

Abstract

The system according to this embodiment aims to improve customer satisfaction by proposing appropriate products and services based on customer requests and needs. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a collaboration unit. The collection unit collects customer requests and needs. The analysis unit analyzes the information collected by the collection unit and identifies appropriate products and services. The proposal unit proposes the products and services identified by the analysis unit. The collaboration unit provides detailed explanations and advice to the customer based on the products and services proposed by the proposal unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the process of proposing appropriate products and services based on customer desires and needs is not efficient, and there are problems in improving customer satisfaction.

[0005] The system according to the embodiment aims to propose appropriate products and services based on customer desires and needs and improve customer satisfaction.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a collaboration unit. The data collection unit collects customer requests and needs. The analysis unit analyzes the information collected by the data collection unit and identifies appropriate products and services. The proposal unit proposes the products and services identified by the analysis unit. The collaboration unit provides detailed explanations and advice to the customer based on the products and services proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose appropriate products and services based on customer requests and needs, thereby improving customer satisfaction. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device14. An example of the data processing device 12 is a server.

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The customer service system according to an embodiment of the present invention is a system that provides personalized service to customers visiting a store using a generative AI. In this customer service system, customers input their requests and needs using an online reservation system before visiting the store. This information is collected and analyzed by the generative AI. Next, when the customer visits the store, the generative AI proposes appropriate products and services based on the collected information. Furthermore, the store staff and the generative AI collaborate to propose the optimal plan and device for the customer. This enables efficient and highly satisfying customer service and reduces the burden on store staff. For example, when a customer wants to purchase a new smartphone, they input their desired features, budget, and information about the device they are currently using. This information is collected by the generative AI. Next, the generative AI analyzes the collected information. The generative AI understands the customer's requests and needs and identifies appropriate products and services. For example, it proposes the optimal smartphone model based on the customer's desired features and budget. When the customer visits the store, the generative AI proposes appropriate products and services based on the analysis results. For example, the AI ​​suggests smartphone models and plans that customers desire, explaining their features and benefits. Furthermore, store staff and the generative AI collaborate to suggest the most suitable plans and devices for the customer. Based on the products and services suggested by the generative AI, store staff provide detailed explanations and advice to the customer. This allows customers to select the products and services that best suit their needs and preferences. This system enables personalized service delivery and improves customer satisfaction. Customers can have a more satisfying shopping experience by receiving suggestions based on their own requests and needs. It also reduces the burden on store staff. Because the generative AI analyzes customer requests and needs and makes appropriate suggestions, store staff can focus on customer service. For example, when a customer purchases a new smartphone, the generative AI analyzes the customer's requests and needs and suggests the most suitable model. Based on that suggestion, store staff provide detailed explanations and advice to the customer. This allows customers to select the smartphone that best suits their needs.In this way, personalized responses using generational AI enable efficient and highly satisfying customer service, while also reducing the burden on store staff. As a result, the customer service system can provide personalized responses based on customer requests and needs, achieving efficient and highly satisfying customer service.

[0029] The customer service system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a collaboration unit. The collection unit collects customer requests and needs. The collection unit can collect customer requests and needs, for example, through an online reservation system. For example, if a customer wants to buy a new smartphone, they can input information such as desired functions, budget, and information about the device they are currently using. The analysis unit analyzes the information collected by the collection unit and identifies appropriate products and services. The analysis unit can analyze customer requests and needs, for example, using natural language processing. For example, it can identify the optimal smartphone model based on the functions and budget desired by the customer. The proposal unit proposes the products and services identified by the analysis unit. The proposal unit can propose the optimal products and services based on the customer's budget and desired functions, for example. For example, it can propose the smartphone model and plan desired by the customer and explain its features and advantages. The collaboration unit provides detailed explanations and advice to the customer based on the products and services proposed by the proposal unit. For example, the collaboration unit can provide the content proposed by the generation AI to store staff in real time, allowing store staff to provide detailed explanations and advice to customers based on that information. This allows customers to select the products and services that best suit their requests and needs. As a result, the customer service system according to this embodiment can provide personalized responses based on customer requests and needs, enabling efficient and highly satisfying customer service.

[0030] The data collection department collects customer requests and needs. For example, the data collection department can collect customer requests and needs through online reservation systems. Specifically, online reservation systems collect information entered by customers in real time and store it in a database. If a customer wants to buy a new smartphone, they can enter information such as desired features, budget, and information about the device they are currently using. This allows the data collection department to accurately understand the customer's detailed requests and needs. Furthermore, the data collection department can also collect the customer's purchase history, inquiries, and feedback. This allows for an understanding of customer preferences and trends, enabling more personalized responses. The data collection department can collect information through multiple channels, such as websites, mobile apps, and chatbots. For example, browsing history when a customer searches for products on a website and the content of conversations with chatbots are also collected. This allows for an understanding of customer behavior patterns and interests, enabling more accurate analysis. The data collection department centrally manages the collected data and makes it accessible to the analysis and proposal departments. By adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the data collection unit to identify appropriate products and services. For example, the analysis unit can analyze customer requests and needs using natural language processing. Specifically, it uses natural language processing technology to analyze text data entered by customers and extract important keywords and phrases. For example, if a customer enters requests such as "high-resolution camera," "long battery life," and "budget under 50,000 yen," the analysis unit will identify the optimal smartphone model based on these keywords. The analysis unit can use machine learning algorithms to analyze past data and trends to predict the products and services best suited to customer requests. For example, it can make optimal suggestions based on products chosen by customers with similar requests in the past and subsequent satisfaction data. The analysis unit can also analyze customer behavior data and purchase history to understand customer preferences and trends. This allows it to predict products and services that customers are likely to be interested in and provide them to the suggestion unit. Furthermore, the analysis unit can continuously revise its analysis results based on real-time updated data to respond to the latest situations. As a result, the analysis unit can always perform highly accurate analysis based on the latest information and identify the products and services best suited to customer requests.

[0032] The Proposal Department proposes products and services identified by the Analysis Department. For example, the Proposal Department can propose the most suitable products and services based on the customer's budget and desired features. Specifically, based on information provided by the Analysis Department, it presents the customer with multiple options and explains the features and benefits of each. For example, it might propose a smartphone model and plan desired by the customer and explain its features and benefits in detail. The Proposal Department can provide detailed information such as product specifications, pricing, and available plans, according to the customer's requests. Furthermore, the Proposal Department can also propose options and accessories related to the product or service selected by the customer. For example, when purchasing a smartphone, it might propose protective cases, screen protectors, and additional warranty plans. The Proposal Department collects customer reactions and feedback to continuously improve the accuracy and effectiveness of its proposals. For example, it collects data on how customers reacted to the proposed products and whether they actually purchased them, and uses this information to improve future proposals. The Proposal Department can also make proposals to customers using multiple communication methods. For example, it can make proposals quickly and reliably to customers via email, SMS, phone, and chat. This allows the proposal department to suggest the most suitable products and services to customers, resulting in highly satisfying customer service.

[0033] The Collaboration Department provides customers with detailed explanations and advice based on the products and services proposed by the Proposal Department. For example, the Collaboration Department can provide store staff with real-time information generated by the Generative AI, enabling them to provide detailed explanations and advice to customers based on that information. Specifically, the Generative AI generates detailed explanations of the most suitable products and services for customers based on the information provided by the Proposal Department. For example, it generates information such as smartphone functions and specifications, available plans, and related options and accessories, and provides this information to store staff. Store staff can then provide detailed explanations and advice to customers based on the information provided by the Generative AI. This allows customers to select the products and services that best suit their needs and desires. Furthermore, the Collaboration Department can respond quickly to customer questions and inquiries. For example, if a customer asks about a specific function or plan, the Generative AI generates an appropriate answer on the spot and provides it to the store staff. This allows store staff to provide customers with quick and accurate answers. In addition, the Collaboration Department can collect customer feedback and continuously improve the accuracy and effectiveness of the proposals and explanations. For example, data is collected on how customers reacted to proposed products or services, and whether they actually made a purchase, and this data is then used to improve future proposals and explanations. This allows the liaison department to provide customers with detailed explanations and advice, resulting in highly satisfying customer service.

[0034] The data collection unit can collect customer requests and needs through an online reservation system. For example, if a customer wants to buy a new smartphone, they can input information such as desired features, budget, and the device they are currently using. This allows for the efficient collection of customer requests and needs through the online reservation system. The online reservation system includes, but is not limited to, a web-based reservation system or a mobile app. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input customer requests and needs collected through the online reservation system into a generative AI, which can then analyze the information.

[0035] The analysis unit can analyze customer requests and needs using natural language processing. For example, the analysis unit can accurately analyze customer requests and needs using natural language processing. For example, it can identify the optimal smartphone model based on the functions and budget desired by the customer. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. This allows for accurate analysis of customer requests and needs using natural language processing. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input customer requests and needs collected by the collection unit into a generative AI, which can then analyze that information.

[0036] The proposal department can propose the most suitable products and services based on the customer's budget and desired features. For example, the proposal department can propose the most suitable products and services based on the customer's budget and desired features. For example, it can propose the smartphone model and plan that the customer wants and explain its features and benefits. This allows the proposal department to suggest the most suitable products and services based on the customer's budget and desired features. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the products and services identified by the analysis department into a generative AI, and the generative AI can make proposals based on that information.

[0037] The collaboration unit provides store staff with the content suggested by the generation AI in real time, enabling store staff to provide detailed explanations and advice to customers based on that information. For example, the collaboration unit provides store staff with the content suggested by the generation AI in real time, enabling store staff to provide detailed explanations and advice to customers based on that information. This improves customer satisfaction as store staff provide detailed explanations and advice to customers based on the content suggested by the generation AI. Some or all of the above processing in the collaboration unit may be performed using the generation AI, or without the generation AI. For example, the collaboration unit provides store staff with the content suggested by the generation AI in real time, enabling store staff to provide detailed explanations and advice to customers based on that information.

[0038] The data collection unit can analyze a customer's past purchase history and select the optimal timing for data collection. For example, if a customer has made purchases during a specific time period in the past, the data collection unit can collect requests and needs during that time period. Similarly, if a customer has made purchases on a specific day of the week, the data collection unit can collect requests and needs on that day. Furthermore, the data collection unit can collect requests and needs from the customer's purchase history during specific events or sales periods. This allows for the collection of requests and needs at the optimal time by analyzing the customer's past purchase history. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generating AI, or without one. For instance, the data collection unit can input the customer's past purchase history data into a generating AI, which can then select the optimal timing for data collection based on that information.

[0039] The data collection unit can filter data based on the customer's current lifestyle and areas of interest during the collection process. For example, if a customer wants to buy a new smartphone, the data collection unit can collect requests and needs based on their current device usage and areas of interest. Furthermore, if a customer is interested in a particular service, the data collection unit can prioritize collecting requests and needs related to that service. The data collection unit can also collect relevant requests and needs based on the customer's lifestyle (e.g., family structure, occupation). This allows for the collection of more relevant information by filtering based on the customer's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without one. For example, the data collection unit can input data on the customer's lifestyle and areas of interest into a generative AI, which can then filter the data based on that information.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location during the collection process. For example, if a customer lives in a specific region, the data collection unit can prioritize the collection of requests and needs for products and services related to that region. Furthermore, if a customer is traveling, the data collection unit can prioritize the collection of information related to their travel destination. Also, if a customer plans to visit a specific store, the data collection unit can prioritize the collection of information related to that store. This allows for the priority collection of highly relevant information by considering the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's geographical location information into a generative AI, which can then prioritize the collection of highly relevant information based on that information.

[0041] The data collection unit can analyze the customer's social media activity and collect relevant information during the collection process. For example, if the customer mentions a specific product or service on social media, the data collection unit can collect requests and needs based on that information. Furthermore, if the customer participates in a specific event on social media, the data collection unit can collect information related to that event. The data collection unit can also collect information related to the customer's areas of interest and hobbies from their social media activity. This allows for the efficient collection of relevant information by analyzing the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's social media activity data into a generative AI, which can then collect relevant information based on that data.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of customer requests and needs. For example, if customer requests are high, the analysis unit can perform a detailed analysis and provide specific proposals. Conversely, if customer needs are low, the analysis unit can perform a concise analysis and provide basic proposals. The analysis unit can also adjust the depth and scope of its analysis according to the importance of customer requests and needs. By adjusting the level of detail of the analysis according to the importance of customer requests and needs, it can provide appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input customer requests and needs data into a generative AI, and the generative AI can adjust the level of detail of the analysis based on that information.

[0043] The analysis unit can apply different analysis algorithms depending on the customer's category during analysis. For example, if the customer is a business user, the analysis unit can apply a business-oriented analysis algorithm. If the customer is a general consumer, the analysis unit can apply a consumer-oriented analysis algorithm. Furthermore, if the customer belongs to a specific industry, the analysis unit can apply an industry-specific analysis algorithm. This allows for more accurate analysis results by applying the appropriate analysis algorithm according to the customer's category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input customer category data into a generative AI, and the generative AI can apply an appropriate analysis algorithm based on that information.

[0044] The analysis unit can determine the priority of analysis based on when customer requests and needs were submitted. For example, if a customer is in a hurry, the analysis unit can prioritize the analysis of the earliest submitted requests and needs. Conversely, if a customer is relaxed, the analysis unit can perform a detailed analysis and consider requests and needs submitted later. The analysis unit can also adjust the order of analysis based on when customer requests and needs were submitted. This allows for a quick response by prioritizing analysis based on when customer requests and needs were submitted. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input customer request and need submission timing data into a generative AI, which can then determine the priority of analysis based on that information.

[0045] The analysis unit can adjust the order of analysis based on customer relevance during the analysis process. For example, if a customer has high demand or needs, the analysis unit can prioritize analyzing the most relevant information. Conversely, if a customer has low demand or needs, the analysis unit can postpone analyzing less relevant information. The analysis unit can also adjust the order of analysis based on customer relevance. This allows for prioritizing the analysis of important information by adjusting the order of analysis based on customer relevance. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input customer relevance data into a generative AI, and the generative AI can adjust the order of analysis based on that information.

[0046] The proposal department can adjust the level of detail in a proposal based on the importance of the product or service. For example, if the customer's demand is high, the proposal department can provide a detailed proposal and offer specific products or services. Conversely, if the customer's need is low, the proposal department can provide a concise proposal and offer basic products or services. The proposal department can also adjust the depth and scope of the proposal according to the importance of the customer's demands and needs. This allows for the provision of appropriate proposals by adjusting the level of detail based on the importance of the product or service. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input customer demand and needs data into a generative AI, which can then adjust the level of detail in the proposal based on that information.

[0047] The proposal unit can apply different proposal algorithms depending on the product or service category when making a proposal. For example, if a customer wants a smartphone, the proposal unit can apply a proposal algorithm for smartphones. If a customer wants internet services, the proposal unit can apply a proposal algorithm for internet services. If a customer wants accessories, the proposal unit can apply a proposal algorithm for accessories. By applying the appropriate proposal algorithm according to the product or service category, more accurate proposals can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input customer category data into a generative AI, and the generative AI can apply an appropriate proposal algorithm based on that information.

[0048] The proposal department can prioritize proposals based on the timing of product and service submissions. For example, if a customer is in a hurry, the proposal department can prioritize the earliest submitted products and services. Conversely, if a customer is relaxed, the proposal department can provide detailed proposals and consider products and services submitted later. The proposal department can also adjust the order of proposals based on the timing of customer requests and needs submissions. This allows for a quick response by prioritizing proposals based on the timing of product and service submissions. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not. For example, the proposal department can input customer request and needs submission timing data into a generative AI, which can then use that information to determine the priority of proposals.

[0049] The proposal department can adjust the order of proposals based on the relevance of products and services during the proposal process. For example, if the customer's demand or needs are high, the proposal department can prioritize proposing the most relevant products and services. Conversely, if the customer's demand or needs are low, the proposal department can postpone proposing less relevant products and services. The proposal department can also adjust the order of proposals based on customer relevance. This allows important information to be prioritized by adjusting the order of proposals based on the relevance of products and services. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input customer relevance data into a generative AI, and the generative AI can adjust the order of proposals based on that information.

[0050] The integration unit can select the optimal integration method by referring to the customer's past interaction history during integration. For example, the integration unit can perform a similar response based on the customer's preferred response method in the past. It can also avoid response methods that the customer was dissatisfied with in the past and select an alternative method. Furthermore, the integration unit can select the most effective integration method from the customer's past interaction history. In this way, the optimal integration method can be selected by referring to the customer's past interaction history. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit can input the customer's past interaction history data into a generating AI, and the generating AI can select the optimal integration method based on that information.

[0051] The integration unit can customize the means of integration based on the customer's current lifestyle during integration. For example, if the customer is busy, the integration unit can select a short integration method. If the customer is relaxed, the integration unit can select an integration method that includes detailed explanations. Furthermore, the integration unit can customize the optimal integration method according to the customer's lifestyle. This allows for a more appropriate response by customizing the integration method according to the customer's lifestyle. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input customer lifestyle data into a generative AI, and the generative AI can customize the means of integration based on that information.

[0052] The integration unit can select the optimal integration method by considering the customer's geographical location information during integration. For example, if the customer is in a specific region, the integration unit can provide information related to that region. Also, if the customer is traveling, the integration unit can provide information related to their travel destination. Furthermore, the integration unit can select the optimal integration method based on the customer's geographical location information. In this way, the optimal integration method can be selected by considering the customer's geographical location information. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the integration unit can input the customer's geographical location information data into a generating AI, and the generating AI can select the optimal integration method based on that information.

[0053] The integration unit can analyze the customer's social media activity and propose integration methods during integration. For example, if the customer mentions a specific product or service on social media, the integration unit can propose integration methods based on that information. Furthermore, if the customer participates in a specific event on social media, the integration unit can propose integration methods related to that event. The integration unit can also propose integration methods related to the customer's areas of interest or hobbies based on their social media activity. In this way, by analyzing the customer's social media activity, appropriate integration methods can be proposed. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input customer social media activity data into a generative AI, which can then propose integration methods based on that information.

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

[0055] The customer service system can also include a history analysis unit that analyzes the customer's purchase history. This unit can analyze the customer's past purchase history to understand their preferences and trends. For example, if a customer has frequently purchased products from a particular brand in the past, the system can prioritize suggesting new or related products from that brand. Similarly, if a customer has previously purchased products within a specific price range, the system can suggest products within that price range. Furthermore, by analyzing the usage and evaluation of products the customer has purchased in the past, the system can suggest the most suitable products and services for the customer. This enables more personalized suggestions by leveraging the customer's purchase history.

[0056] The customer service system can also include a social media analytics unit that analyzes customers' social media activity. This unit collects information that customers share on social media, allowing it to understand their interests and preferences. For example, if a customer frequently mentions a particular brand or product, the system can prioritize providing information related to that brand or product. Furthermore, it can infer customer interests from events they participate in and accounts they follow, and make suggestions based on that. This makes it possible to leverage customers' social media activity to provide more relevant information.

[0057] The customer service system can also include a location information analysis unit that utilizes the customer's geographical location. This unit can provide optimal suggestions based on the customer's current location and past visit history. For example, if a customer is in a specific region, it can suggest products and services related to that region. If a customer is traveling, it can provide information and services related to their travel destination. Furthermore, if a customer plans to visit a specific store, it can inform them about campaigns and offers related to that store. This allows for more appropriate suggestions by leveraging the customer's geographical location.

[0058] The customer service system can also include a lifestyle analysis unit that takes into account the customer's living situation. This unit collects information such as the customer's family structure, occupation, and lifestyle, and based on this information, can provide optimal suggestions. For example, if the customer is raising children, it can suggest products and services for children. If the customer is a busy business person, it can suggest products and services that can be used efficiently. Furthermore, it can suggest relevant products and services based on the customer's hobbies and interests. This enables personalized suggestions that take into account the customer's living situation.

[0059] The customer service system can also include a history-based suggestion unit that makes optimal suggestions based on the customer's purchase history. This unit analyzes the customer's past purchase history to understand their preferences and trends. For example, if a customer has frequently purchased products from a particular brand in the past, it can prioritize suggesting new or related products from that brand. Similarly, if a customer has previously purchased products within a specific price range, it can suggest products within that price range. Furthermore, it can analyze the usage and evaluation of products the customer has purchased in the past to suggest the most suitable products and services. This allows for more personalized suggestions by leveraging the customer's purchase history.

[0060] The customer service system can also include a lifestyle suggestion section that takes into account the customer's living situation. This section can collect information such as the customer's family structure, occupation, and lifestyle, and make optimal suggestions based on that information. For example, if the customer is raising children, it can suggest products and services for children. If the customer is a busy business person, it can suggest products and services that can be used efficiently. Furthermore, it can suggest relevant products and services based on the customer's hobbies and interests. This enables personalized suggestions that take into account the customer's living situation.

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

[0062] Step 1: The data collection department gathers customer requests and needs. For example, customer requests and needs can be collected through an online reservation system. If a customer wants to purchase a new smartphone, they can input information such as desired features, budget, and information about the device they are currently using. Step 2: The analysis unit analyzes the information collected by the collection unit to identify appropriate products and services. For example, it uses natural language processing to analyze customer requests and needs, and identifies the optimal smartphone model based on desired features and budget. Step 3: The proposal department proposes the products and services identified by the analysis department. For example, they propose the most suitable products and services based on the customer's budget and desired features, suggest the desired smartphone model and plan, and explain their features and benefits. Step 4: The Collaboration Department provides customers with detailed explanations and advice based on the products and services proposed by the Proposal Department. For example, the content proposed by the Generating AI can be provided to store staff in real time, and the store staff can then provide customers with detailed explanations and advice based on that information.

[0063] (Example of form 2) The customer service system according to an embodiment of the present invention is a system that provides personalized service to customers visiting a store using a generative AI. In this customer service system, customers input their requests and needs using an online reservation system before visiting the store. This information is collected and analyzed by the generative AI. Next, when the customer visits the store, the generative AI proposes appropriate products and services based on the collected information. Furthermore, the store staff and the generative AI collaborate to propose the optimal plan and device for the customer. This enables efficient and highly satisfying customer service and reduces the burden on store staff. For example, when a customer wants to purchase a new smartphone, they input their desired features, budget, and information about the device they are currently using. This information is collected by the generative AI. Next, the generative AI analyzes the collected information. The generative AI understands the customer's requests and needs and identifies appropriate products and services. For example, it proposes the optimal smartphone model based on the customer's desired features and budget. When the customer visits the store, the generative AI proposes appropriate products and services based on the analysis results. For example, the AI ​​suggests smartphone models and plans that customers desire, explaining their features and benefits. Furthermore, store staff and the generative AI collaborate to suggest the most suitable plans and devices for the customer. Based on the products and services suggested by the generative AI, store staff provide detailed explanations and advice to the customer. This allows customers to select the products and services that best suit their needs and preferences. This system enables personalized service delivery and improves customer satisfaction. Customers can have a more satisfying shopping experience by receiving suggestions based on their own requests and needs. It also reduces the burden on store staff. Because the generative AI analyzes customer requests and needs and makes appropriate suggestions, store staff can focus on customer service. For example, when a customer purchases a new smartphone, the generative AI analyzes the customer's requests and needs and suggests the most suitable model. Based on that suggestion, store staff provide detailed explanations and advice to the customer. This allows customers to select the smartphone that best suits their needs.In this way, personalized responses using generational AI enable efficient and highly satisfying customer service, while also reducing the burden on store staff. As a result, the customer service system can provide personalized responses based on customer requests and needs, achieving efficient and highly satisfying customer service.

[0064] The customer service system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a collaboration unit. The collection unit collects customer requests and needs. The collection unit can collect customer requests and needs, for example, through an online reservation system. For example, if a customer wants to buy a new smartphone, they can input information such as desired functions, budget, and information about the device they are currently using. The analysis unit analyzes the information collected by the collection unit and identifies appropriate products and services. The analysis unit can analyze customer requests and needs, for example, using natural language processing. For example, it can identify the optimal smartphone model based on the functions and budget desired by the customer. The proposal unit proposes the products and services identified by the analysis unit. The proposal unit can propose the optimal products and services based on the customer's budget and desired functions, for example. For example, it can propose the smartphone model and plan desired by the customer and explain its features and advantages. The collaboration unit provides detailed explanations and advice to the customer based on the products and services proposed by the proposal unit. For example, the collaboration unit can provide the content proposed by the generation AI to store staff in real time, allowing store staff to provide detailed explanations and advice to customers based on that information. This allows customers to select the products and services that best suit their requests and needs. As a result, the customer service system according to this embodiment can provide personalized responses based on customer requests and needs, enabling efficient and highly satisfying customer service.

[0065] The data collection department collects customer requests and needs. For example, the data collection department can collect customer requests and needs through online reservation systems. Specifically, online reservation systems collect information entered by customers in real time and store it in a database. If a customer wants to buy a new smartphone, they can enter information such as desired features, budget, and information about the device they are currently using. This allows the data collection department to accurately understand the customer's detailed requests and needs. Furthermore, the data collection department can also collect the customer's purchase history, inquiries, and feedback. This allows for an understanding of customer preferences and trends, enabling more personalized responses. The data collection department can collect information through multiple channels, such as websites, mobile apps, and chatbots. For example, browsing history when a customer searches for products on a website and the content of conversations with chatbots are also collected. This allows for an understanding of customer behavior patterns and interests, enabling more accurate analysis. The data collection department centrally manages the collected data and makes it accessible to the analysis and proposal departments. By adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes the information collected by the data collection unit to identify appropriate products and services. For example, the analysis unit can analyze customer requests and needs using natural language processing. Specifically, it uses natural language processing technology to analyze text data entered by customers and extract important keywords and phrases. For example, if a customer enters requests such as "high-resolution camera," "long battery life," and "budget under 50,000 yen," the analysis unit will identify the optimal smartphone model based on these keywords. The analysis unit can use machine learning algorithms to analyze past data and trends to predict the products and services best suited to customer requests. For example, it can make optimal suggestions based on products chosen by customers with similar requests in the past and subsequent satisfaction data. The analysis unit can also analyze customer behavior data and purchase history to understand customer preferences and trends. This allows it to predict products and services that customers are likely to be interested in and provide them to the suggestion unit. Furthermore, the analysis unit can continuously revise its analysis results based on real-time updated data to respond to the latest situations. As a result, the analysis unit can always perform highly accurate analysis based on the latest information and identify the products and services best suited to customer requests.

[0067] The Proposal Department proposes products and services identified by the Analysis Department. For example, the Proposal Department can propose the most suitable products and services based on the customer's budget and desired features. Specifically, based on information provided by the Analysis Department, it presents the customer with multiple options and explains the features and benefits of each. For example, it might propose a smartphone model and plan desired by the customer and explain its features and benefits in detail. The Proposal Department can provide detailed information such as product specifications, pricing, and available plans, according to the customer's requests. Furthermore, the Proposal Department can also propose options and accessories related to the product or service selected by the customer. For example, when purchasing a smartphone, it might propose protective cases, screen protectors, and additional warranty plans. The Proposal Department collects customer reactions and feedback to continuously improve the accuracy and effectiveness of its proposals. For example, it collects data on how customers reacted to the proposed products and whether they actually purchased them, and uses this information to improve future proposals. The Proposal Department can also make proposals to customers using multiple communication methods. For example, it can make proposals quickly and reliably to customers via email, SMS, phone, and chat. This allows the proposal department to suggest the most suitable products and services to customers, resulting in highly satisfying customer service.

[0068] The Collaboration Department provides customers with detailed explanations and advice based on the products and services proposed by the Proposal Department. For example, the Collaboration Department can provide store staff with real-time information generated by the Generative AI, enabling them to provide detailed explanations and advice to customers based on that information. Specifically, the Generative AI generates detailed explanations of the most suitable products and services for customers based on the information provided by the Proposal Department. For example, it generates information such as smartphone functions and specifications, available plans, and related options and accessories, and provides this information to store staff. Store staff can then provide detailed explanations and advice to customers based on the information provided by the Generative AI. This allows customers to select the products and services that best suit their needs and desires. Furthermore, the Collaboration Department can respond quickly to customer questions and inquiries. For example, if a customer asks about a specific function or plan, the Generative AI generates an appropriate answer on the spot and provides it to the store staff. This allows store staff to provide customers with quick and accurate answers. In addition, the Collaboration Department can collect customer feedback and continuously improve the accuracy and effectiveness of the proposals and explanations. For example, data is collected on how customers reacted to proposed products or services, and whether they actually made a purchase, and this data is then used to improve future proposals and explanations. This allows the liaison department to provide customers with detailed explanations and advice, resulting in highly satisfying customer service.

[0069] The data collection unit can collect customer requests and needs through an online reservation system. For example, if a customer wants to buy a new smartphone, they can input information such as desired features, budget, and the device they are currently using. This allows for the efficient collection of customer requests and needs through the online reservation system. The online reservation system includes, but is not limited to, a web-based reservation system or a mobile app. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input customer requests and needs collected through the online reservation system into a generative AI, which can then analyze the information.

[0070] The analysis unit can analyze customer requests and needs using natural language processing. For example, the analysis unit can accurately analyze customer requests and needs using natural language processing. For example, it can identify the optimal smartphone model based on the functions and budget desired by the customer. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. This allows for accurate analysis of customer requests and needs using natural language processing. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input customer requests and needs collected by the collection unit into a generative AI, which can then analyze that information.

[0071] The proposal department can propose the most suitable products and services based on the customer's budget and desired features. For example, the proposal department can propose the most suitable products and services based on the customer's budget and desired features. For example, it can propose the smartphone model and plan that the customer wants and explain its features and benefits. This allows the proposal department to suggest the most suitable products and services based on the customer's budget and desired features. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the products and services identified by the analysis department into a generative AI, and the generative AI can make proposals based on that information.

[0072] The collaboration unit provides store staff with the content suggested by the generation AI in real time, enabling store staff to provide detailed explanations and advice to customers based on that information. For example, the collaboration unit provides store staff with the content suggested by the generation AI in real time, enabling store staff to provide detailed explanations and advice to customers based on that information. This improves customer satisfaction as store staff provide detailed explanations and advice to customers based on the content suggested by the generation AI. Some or all of the above processing in the collaboration unit may be performed using the generation AI, or without the generation AI. For example, the collaboration unit provides store staff with the content suggested by the generation AI in real time, enabling store staff to provide detailed explanations and advice to customers based on that information.

[0073] The data collection unit can estimate the customer's emotions and adjust the method of collecting requests and needs based on the estimated emotions. For example, if the customer is stressed, the data collection unit can provide a simple interface and minimize the input steps. If the customer is relaxed, the data collection unit can provide detailed input options and suggest a customizable input method. If the customer is in a hurry, the data collection unit can prioritize voice input to quickly collect requests and needs. This allows for the collection of more relevant information by adjusting the method of collecting requests and needs 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using or without generative AI. For example, the data collection unit can input customer emotion data into a generative AI, which can then adjust the collection method based on that information.

[0074] The data collection unit can analyze a customer's past purchase history and select the optimal timing for data collection. For example, if a customer has made purchases during a specific time period in the past, the data collection unit can collect requests and needs during that time period. Similarly, if a customer has made purchases on a specific day of the week, the data collection unit can collect requests and needs on that day. Furthermore, the data collection unit can collect requests and needs from the customer's purchase history during specific events or sales periods. This allows for the collection of requests and needs at the optimal time by analyzing the customer's past purchase history. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generating AI, or without one. For instance, the data collection unit can input the customer's past purchase history data into a generating AI, which can then select the optimal timing for data collection based on that information.

[0075] The data collection unit can filter data based on the customer's current lifestyle and areas of interest during the collection process. For example, if a customer wants to buy a new smartphone, the data collection unit can collect requests and needs based on their current device usage and areas of interest. Furthermore, if a customer is interested in a particular service, the data collection unit can prioritize collecting requests and needs related to that service. The data collection unit can also collect relevant requests and needs based on the customer's lifestyle (e.g., family structure, occupation). This allows for the collection of more relevant information by filtering based on the customer's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without one. For example, the data collection unit can input data on the customer's lifestyle and areas of interest into a generative AI, which can then filter the data based on that information.

[0076] The data collection unit can estimate the customer's emotions and prioritize the information to collect based on those emotions. For example, if the customer is stressed, the data collection unit can prioritize collecting important information and postpone collecting detailed information. If the customer is relaxed, the data collection unit can prioritize collecting detailed information and provide customizable information. If the customer is in a hurry, the data collection unit can quickly collect the most important information and collect other information later. This allows for the priority collection of important information by prioritizing the information to be collected 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input customer emotion data into a generative AI, which can then use that information to prioritize the information to collect.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location during the collection process. For example, if a customer lives in a specific region, the data collection unit can prioritize the collection of requests and needs for products and services related to that region. Furthermore, if a customer is traveling, the data collection unit can prioritize the collection of information related to their travel destination. Also, if a customer plans to visit a specific store, the data collection unit can prioritize the collection of information related to that store. This allows for the priority collection of highly relevant information by considering the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's geographical location information into a generative AI, which can then prioritize the collection of highly relevant information based on that information.

[0078] The data collection unit can analyze the customer's social media activity and collect relevant information during the collection process. For example, if the customer mentions a specific product or service on social media, the data collection unit can collect requests and needs based on that information. Furthermore, if the customer participates in a specific event on social media, the data collection unit can collect information related to that event. The data collection unit can also collect information related to the customer's areas of interest and hobbies from their social media activity. This allows for the efficient collection of relevant information by analyzing the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's social media activity data into a generative AI, which can then collect relevant information based on that data.

[0079] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can provide detailed analysis results and display them in an easy-to-understand format. If the customer is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. If the customer is excited, the analysis unit can provide analysis results in a visually appealing format. In this way, by adjusting the presentation of the analysis according to the customer's emotions, more easily understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input customer emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis based on that information.

[0080] The analysis unit can adjust the level of detail of its analysis based on the importance of customer requests and needs. For example, if customer requests are high, the analysis unit can perform a detailed analysis and provide specific proposals. Conversely, if customer needs are low, the analysis unit can perform a concise analysis and provide basic proposals. The analysis unit can also adjust the depth and scope of its analysis according to the importance of customer requests and needs. By adjusting the level of detail of the analysis according to the importance of customer requests and needs, it can provide appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input customer requests and needs data into a generative AI, and the generative AI can adjust the level of detail of the analysis based on that information.

[0081] The analysis unit can apply different analysis algorithms depending on the customer's category during analysis. For example, if the customer is a business user, the analysis unit can apply a business-oriented analysis algorithm. If the customer is a general consumer, the analysis unit can apply a consumer-oriented analysis algorithm. Furthermore, if the customer belongs to a specific industry, the analysis unit can apply an industry-specific analysis algorithm. This allows for more accurate analysis results by applying the appropriate analysis algorithm according to the customer's category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input customer category data into a generative AI, and the generative AI can apply an appropriate analysis algorithm based on that information.

[0082] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is in a hurry, the analysis unit can provide a short, concise analysis. If the customer is relaxed, the analysis unit can provide a detailed analysis. If the customer is excited, the analysis unit can provide the analysis in a visually appealing format. By adjusting the length of the analysis according to the customer's emotions, the analysis unit can provide an analysis of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input customer emotion data into a generative AI, and the generative AI can adjust the length of the analysis based on that information.

[0083] The analysis unit can determine the priority of analysis based on when customer requests and needs were submitted. For example, if a customer is in a hurry, the analysis unit can prioritize the analysis of the earliest submitted requests and needs. Conversely, if a customer is relaxed, the analysis unit can perform a detailed analysis and consider requests and needs submitted later. The analysis unit can also adjust the order of analysis based on when customer requests and needs were submitted. This allows for a quick response by prioritizing analysis based on when customer requests and needs were submitted. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input customer request and need submission timing data into a generative AI, which can then determine the priority of analysis based on that information.

[0084] The analysis unit can adjust the order of analysis based on customer relevance during the analysis process. For example, if a customer has high demand or needs, the analysis unit can prioritize analyzing the most relevant information. Conversely, if a customer has low demand or needs, the analysis unit can postpone analyzing less relevant information. The analysis unit can also adjust the order of analysis based on customer relevance. This allows for prioritizing the analysis of important information by adjusting the order of analysis based on customer relevance. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input customer relevance data into a generative AI, and the generative AI can adjust the order of analysis based on that information.

[0085] The proposal unit can estimate the customer's emotions and adjust the presentation of the proposal based on those emotions. For example, if the customer is relaxed, the proposal unit can provide a detailed proposal in an easy-to-understand format. If the customer is in a hurry, the proposal unit can provide a concise proposal that gets straight to the point. If the customer is excited, the proposal unit can provide a visually appealing proposal. By adjusting the presentation of the proposal according to the customer's emotions, it is possible to provide a more easily understandable proposal. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using or without generative AI. For example, the proposal unit can input customer emotion data into a generative AI, which can then adjust the presentation of the proposal based on that information.

[0086] The proposal department can adjust the level of detail in a proposal based on the importance of the product or service. For example, if the customer's demand is high, the proposal department can provide a detailed proposal and offer specific products or services. Conversely, if the customer's need is low, the proposal department can provide a concise proposal and offer basic products or services. The proposal department can also adjust the depth and scope of the proposal according to the importance of the customer's demands and needs. This allows for the provision of appropriate proposals by adjusting the level of detail based on the importance of the product or service. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input customer demand and needs data into a generative AI, which can then adjust the level of detail in the proposal based on that information.

[0087] The proposal unit can apply different proposal algorithms depending on the product or service category when making a proposal. For example, if a customer wants a smartphone, the proposal unit can apply a proposal algorithm for smartphones. If a customer wants internet services, the proposal unit can apply a proposal algorithm for internet services. If a customer wants accessories, the proposal unit can apply a proposal algorithm for accessories. By applying the appropriate proposal algorithm according to the product or service category, more accurate proposals can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input customer category data into a generative AI, and the generative AI can apply an appropriate proposal algorithm based on that information.

[0088] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the customer is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the customer is relaxed, the suggestion unit can provide a detailed suggestion. If the customer is excited, the suggestion unit can provide a suggestion in a visually appealing format. By adjusting the length of the suggestion according to the customer's emotions, the suggestion unit can provide a suggestion of appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input customer emotion data into a generative AI, which can then adjust the length of the suggestion based on that information.

[0089] The proposal department can prioritize proposals based on the timing of product and service submissions. For example, if a customer is in a hurry, the proposal department can prioritize the earliest submitted products and services. Conversely, if a customer is relaxed, the proposal department can provide detailed proposals and consider products and services submitted later. The proposal department can also adjust the order of proposals based on the timing of customer requests and needs submissions. This allows for a quick response by prioritizing proposals based on the timing of product and service submissions. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not. For example, the proposal department can input customer request and needs submission timing data into a generative AI, which can then use that information to determine the priority of proposals.

[0090] The proposal department can adjust the order of proposals based on the relevance of products and services during the proposal process. For example, if the customer's demand or needs are high, the proposal department can prioritize proposing the most relevant products and services. Conversely, if the customer's demand or needs are low, the proposal department can postpone proposing less relevant products and services. The proposal department can also adjust the order of proposals based on customer relevance. This allows important information to be prioritized by adjusting the order of proposals based on the relevance of products and services. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input customer relevance data into a generative AI, and the generative AI can adjust the order of proposals based on that information.

[0091] The interaction unit can estimate the customer's emotions and adjust its interaction method based on the estimated emotions. For example, if the customer is nervous, the interaction unit can respond calmly and provide a sense of security. If the customer is relaxed, the interaction unit can respond in a friendly manner and emphasize approachability. If the customer is in a hurry, the interaction unit can respond quickly and efficiently, saving time. By adjusting the interaction method according to the customer's emotions, a more appropriate response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using, for example, generative AI, or not using generative AI. For example, the interaction unit can input customer emotion data into a generative AI, and the generative AI can adjust its interaction method based on that information.

[0092] The integration unit can select the optimal integration method by referring to the customer's past interaction history during integration. For example, the integration unit can perform a similar response based on the customer's preferred response method in the past. It can also avoid response methods that the customer was dissatisfied with in the past and select an alternative method. Furthermore, the integration unit can select the most effective integration method from the customer's past interaction history. In this way, the optimal integration method can be selected by referring to the customer's past interaction history. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit can input the customer's past interaction history data into a generating AI, and the generating AI can select the optimal integration method based on that information.

[0093] The integration unit can customize the means of integration based on the customer's current lifestyle during integration. For example, if the customer is busy, the integration unit can select a short integration method. If the customer is relaxed, the integration unit can select an integration method that includes detailed explanations. Furthermore, the integration unit can customize the optimal integration method according to the customer's lifestyle. This allows for a more appropriate response by customizing the integration method according to the customer's lifestyle. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input customer lifestyle data into a generative AI, and the generative AI can customize the means of integration based on that information.

[0094] The collaboration unit can estimate customer emotions and determine collaboration priorities based on the estimated emotions. For example, if a customer is stressed, the collaboration unit can prioritize their response and facilitate a quick collaboration. Conversely, if a customer is relaxed, the collaboration unit can prioritize other customers and collaborate with them later. The collaboration unit can also adjust collaboration priorities based on customer emotions. This allows for a quick response by determining collaboration priorities according to customer emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using generative AI, or not. For example, the collaboration unit can input customer emotion data into a generative AI, which can then determine collaboration priorities based on that information.

[0095] The integration unit can select the optimal integration method by considering the customer's geographical location information during integration. For example, if the customer is in a specific region, the integration unit can provide information related to that region. Also, if the customer is traveling, the integration unit can provide information related to their travel destination. Furthermore, the integration unit can select the optimal integration method based on the customer's geographical location information. In this way, the optimal integration method can be selected by considering the customer's geographical location information. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the integration unit can input the customer's geographical location information data into a generating AI, and the generating AI can select the optimal integration method based on that information.

[0096] The integration unit can analyze the customer's social media activity and propose integration methods during integration. For example, if the customer mentions a specific product or service on social media, the integration unit can propose integration methods based on that information. Furthermore, if the customer participates in a specific event on social media, the integration unit can propose integration methods related to that event. The integration unit can also propose integration methods related to the customer's areas of interest or hobbies based on their social media activity. In this way, by analyzing the customer's social media activity, appropriate integration methods can be proposed. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input customer social media activity data into a generative AI, which can then propose integration methods based on that information.

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

[0098] The customer service system can also include a history analysis unit that analyzes the customer's purchase history. This unit can analyze the customer's past purchase history to understand their preferences and trends. For example, if a customer has frequently purchased products from a particular brand in the past, the system can prioritize suggesting new or related products from that brand. Similarly, if a customer has previously purchased products within a specific price range, the system can suggest products within that price range. Furthermore, by analyzing the usage and evaluation of products the customer has purchased in the past, the system can suggest the most suitable products and services for the customer. This enables more personalized suggestions by leveraging the customer's purchase history.

[0099] The customer service system may also include an emotion analysis unit that estimates the customer's emotions and adjusts the response based on those estimates. The emotion analysis unit estimates emotions from the customer's facial expressions, tone of voice, and text content, and can provide a relaxing response if the customer is stressed. For example, if the customer is tense, the system can explain things in a gentle tone; if the customer is relaxed, it can provide detailed information. Furthermore, if the customer is excited, it can offer visually appealing suggestions. This enables flexible responses tailored to the customer's emotions, leading to improved customer satisfaction.

[0100] The customer service system can also include a social media analytics unit that analyzes customers' social media activity. This unit collects information that customers share on social media, allowing it to understand their interests and preferences. For example, if a customer frequently mentions a particular brand or product, the system can prioritize providing information related to that brand or product. Furthermore, it can infer customer interests from events they participate in and accounts they follow, and make suggestions based on that. This makes it possible to leverage customers' social media activity to provide more relevant information.

[0101] The customer service system can also include a location information analysis unit that utilizes the customer's geographical location. This unit can provide optimal suggestions based on the customer's current location and past visit history. For example, if a customer is in a specific region, it can suggest products and services related to that region. If a customer is traveling, it can provide information and services related to their travel destination. Furthermore, if a customer plans to visit a specific store, it can inform them about campaigns and offers related to that store. This allows for more appropriate suggestions by leveraging the customer's geographical location.

[0102] The customer service system can also include a lifestyle analysis unit that takes into account the customer's living situation. This unit collects information such as the customer's family structure, occupation, and lifestyle, and based on this information, can provide optimal suggestions. For example, if the customer is raising children, it can suggest products and services for children. If the customer is a busy business person, it can suggest products and services that can be used efficiently. Furthermore, it can suggest relevant products and services based on the customer's hobbies and interests. This enables personalized suggestions that take into account the customer's living situation.

[0103] The customer service system may also include an emotion-prioritizing unit that estimates the customer's emotions and prioritizes suggestions based on those emotions. This unit estimates the customer's emotions in real time and can prioritize providing important information if the customer is stressed. For example, if the customer is in a hurry, the most important information can be provided quickly, with detailed information being postponed. Conversely, if the customer is relaxed, detailed information can be provided, and explanations can be given until the customer is satisfied. This allows for flexible responses tailored to the customer's emotions, which is expected to improve customer satisfaction.

[0104] The customer service system can also include a history-based suggestion unit that makes optimal suggestions based on the customer's purchase history. This unit analyzes the customer's past purchase history to understand their preferences and trends. For example, if a customer has frequently purchased products from a particular brand in the past, it can prioritize suggesting new or related products from that brand. Similarly, if a customer has previously purchased products within a specific price range, it can suggest products within that price range. Furthermore, it can analyze the usage and evaluation of products the customer has purchased in the past to suggest the most suitable products and services. This allows for more personalized suggestions by leveraging the customer's purchase history.

[0105] The customer service system may also include an emotion expression unit that estimates the customer's emotions and adjusts the way suggestions are presented based on those estimated emotions. The emotion expression unit estimates the customer's emotions in real time and, if the customer is relaxed, can provide detailed suggestions in an easy-to-understand format. For example, if the customer is in a hurry, it can provide concise suggestions that get straight to the point. If the customer is excited, it can provide suggestions in a visually appealing format. This enables flexible suggestions tailored to the customer's emotions, which is expected to improve customer satisfaction.

[0106] The customer service system can also include a lifestyle suggestion section that takes into account the customer's living situation. This section can collect information such as the customer's family structure, occupation, and lifestyle, and make optimal suggestions based on that information. For example, if the customer is raising children, it can suggest products and services for children. If the customer is a busy business person, it can suggest products and services that can be used efficiently. Furthermore, it can suggest relevant products and services based on the customer's hobbies and interests. This enables personalized suggestions that take into account the customer's living situation.

[0107] The customer service system may also include an emotion-based interaction unit that estimates the customer's emotions and adjusts the interaction method based on the estimated emotions. The emotion-based interaction unit can estimate the customer's emotions in real time and, if the customer is tense, provide a calm and reassuring response. For example, if the customer is relaxed, it can provide a friendly response and emphasize approachability. Furthermore, if the customer is in a hurry, it can provide a quick and efficient response, saving time. This enables flexible responses tailored to the customer's emotions, which is expected to improve customer satisfaction.

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

[0109] Step 1: The data collection department gathers customer requests and needs. For example, customer requests and needs can be collected through an online reservation system. If a customer wants to purchase a new smartphone, they can input information such as desired features, budget, and information about the device they are currently using. Step 2: The analysis unit analyzes the information collected by the collection unit to identify appropriate products and services. For example, it uses natural language processing to analyze customer requests and needs, and identifies the optimal smartphone model based on desired features and budget. Step 3: The proposal department proposes the products and services identified by the analysis department. For example, they propose the most suitable products and services based on the customer's budget and desired features, suggest the desired smartphone model and plan, and explain their features and benefits. Step 4: The Collaboration Department provides customers with detailed explanations and advice based on the products and services proposed by the Proposal Department. For example, the content proposed by the Generating AI can be provided to store staff in real time, and the store staff can then provide customers with detailed explanations and advice based on that information.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer requests and needs using the reception device 38 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes appropriate products and services based on the analysis results. The collaboration unit is implemented in the control unit 46A of the smart device 14 and provides the proposed content to store staff. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects customer requests and needs using the microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes appropriate products and services based on the analysis results. The collaboration unit is implemented in the control unit 46A of the smart glasses 214 and provides the proposed content to store staff. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects customer requests and needs using the microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes appropriate products and services based on the analysis results. The collaboration unit is implemented in the control unit 46A of the headset terminal 314 and provides the proposed content to store staff. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and collaboration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects customer requests and needs using the microphone 238 of the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes appropriate products and services based on the analysis results. The collaboration unit is implemented by, for example, the control unit 46A of the robot 414 and provides the proposed content to store staff. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The collection department gathers customer requests and needs, An analysis unit analyzes the information collected by the aforementioned collection unit to identify appropriate products and services, A proposal unit that proposes products and services identified by the aforementioned analysis unit, The system includes a collaboration unit that provides detailed explanations and advice to customers based on the products and services proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect customer requests and needs through our online reservation system. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze customer requests and needs using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the most suitable products and services based on the customer's budget and desired features. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, The AI ​​generates suggestions which are then provided to store staff in real time, allowing staff to use that information to provide detailed explanations and advice to customers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate customer emotions and adjust the methods for collecting requests and needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the customer's past purchase history to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the customer's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate customer emotions and prioritize the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During collection, we analyze customers' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate customer emotions and adjust the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of customer requests and needs. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the customer category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates customer emotions and adjusts the length of the analysis based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, we prioritize the analysis based on when the customer's requests and needs were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on customer relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the customer's emotions and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the product or service. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the product or service category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the customer's emotions and adjust the length of the suggestion based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of product and service submissions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products and services. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, We estimate customer emotions and adjust our approach to customer interaction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, During integration, the system selects the optimal integration method by referring to the customer's past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, During integration, the integration method is customized based on the customer's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, Estimate customer emotions and prioritize collaborations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, When integrating, the optimal integration method is selected considering the customer's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, During the collaboration process, we analyze the customer's social media activity and propose methods for collaboration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department gathers customer requests and needs, An analysis unit analyzes the information collected by the aforementioned collection unit to identify appropriate products and services, A proposal unit that proposes products and services identified by the aforementioned analysis unit, The system includes a collaboration unit that provides detailed explanations and advice to customers based on the products and services proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect customer requests and needs through our online reservation system. The system according to feature 1.

3. The aforementioned analysis unit, Analyze customer requests and needs using natural language processing. The system according to feature 1.

4. The aforementioned proposal section is, We propose the most suitable products and services based on the customer's budget and desired features. The system according to feature 1.

5. The aforementioned linkage unit is, The AI ​​generates suggestions which are then provided to store staff in real time, allowing staff to use that information to provide detailed explanations and advice to customers. The system according to feature 1.

6. The aforementioned collection unit is We estimate customer emotions and adjust the methods for collecting requests and needs based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the customer's past purchase history to select the optimal timing for data collection. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the customer's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is We estimate customer emotions and prioritize the information to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the customer's geographical location. The system according to feature 1.

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