System

Abstract

An object of the system according to the embodiment is to provide a consistent and high-quality customer service to visiting customers.SOLUTION: A system according to an embodiment includes a recognition unit, an answer unit, an explanation unit, and a recommendation unit. The recognition unit performs face recognition or voice recognition of the visiting customer. The response unit provides a response to the question of the visiting customer recognized by the recognition unit. The explanation unit explains the product based on the answer provided by the answer unit. The recommendation unit recommends the product based on the description provided by the description unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology can result in inconsistent customer service, making it difficult to improve customer satisfaction.

[0005] The system according to the embodiment aims to provide consistent, high-quality customer service to customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, an answering unit, an explanation unit, and a recommendation unit. The recognition unit performs face recognition or voice recognition of customers. The answering unit provides answers to questions from customers recognized by the recognition unit. The explanation unit provides product explanations based on answers provided by the answering unit. The recommendation unit recommends products based on explanations provided by the explanation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide consistent, high-quality customer service to customers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A customer service system according to an embodiment of the present invention performs facial and voice recognition on customers, and a generation AI provides appropriate answers, product explanations, and recommendations. The customer service system recognizes customers, greets them, and guides them. It then provides appropriate answers to their questions. It also provides product explanations and recommendations, and supports the purchase process. For example, when a customer asks, "Tell me about this product," the generation AI provides detailed product information. When a customer asks, "What products do you recommend?", the generation AI suggests recommended products based on the customer's preferences. Furthermore, the customer service system supports the purchase process by guiding the customer through the purchase process and helping them enter the necessary information. This allows customers to complete the purchase process smoothly. The customer service system not only improves customer satisfaction but also contributes to improving store operational efficiency. For example, the customer service system uses facial and voice recognition technology to enable more natural conversations. When the generation AI explains and recommends products, it bases its decisions on the customer's past purchase history and preference data. This allows the customer service system to improve customer satisfaction and streamline store operations. For example, by serving customers quickly and accurately, the store's workload is reduced. Also, by suggesting products that match customers' preferences, customers are more likely to buy.

[0029] The customer service system according to the embodiment includes a recognition unit, a response unit, an explanation unit, and a recommendation unit. The recognition unit performs facial or voice recognition of customers. For example, the recognition unit recognizes the faces of customers using a facial recognition algorithm based on deep learning. The recognition unit can also recognize the voices of customers using a voice recognition engine. The recognition unit can also analyze questions from customers using natural language processing technology. For example, the recognition unit photographs the faces of customers with a camera and identifies the customers using a facial recognition algorithm. The recognition unit can also collect the voices of customers using a microphone and convert the voices into text using a voice recognition engine. The response unit provides answers to questions from customers recognized by the recognition unit. For example, the response unit generates appropriate answers to the questions from customers using a generation AI. The response unit can also provide answers based on the customers' past purchase history and preference data. For example, the response unit provides detailed information about a product in response to a question from the generation AI such as, "Tell me about this product." The answering unit can also suggest recommended products based on the customer's preferences in response to the question "What product do you recommend?" from the generation AI. The explanation unit explains the product based on the answer provided by the answering unit. For example, the explanation unit uses the generation AI to explain the product's features and usage. The explanation unit can also compare the product's advantages and with other products. For example, the explanation unit uses the generation AI to briefly explain the product's features. The explanation unit can also use the generation AI to explain in detail how to use the product. The recommendation unit recommends products based on the explanation provided by the explanation unit. For example, the recommendation unit uses the generation AI to suggest recommended products based on the customer's preferences. The recommendation unit can also make recommendations based on the customer's past purchase history and preference data. For example, the recommendation unit uses the generation AI to suggest products that match the customer's preferences. The recommendation unit can also use the generation AI to suggest related products based on the customer's past purchase history. This allows the customer service system according to the embodiment to improve customer satisfaction. For example, a customer's motivation to purchase is increased when products that match their preferences are suggested. In addition, the operational burden on stores will be reduced, improving operational efficiency.

[0030] The customer service system includes a support unit that supports the purchase process. The support unit supports the purchase process. For example, the support unit uses a generation AI to guide customers through the purchase process. The support unit can also help customers enter the necessary information. For example, the support unit uses a generation AI to explain each step of the purchase process. The support unit can also use a generation AI to support customers in entering information. This allows customers to complete the purchase process smoothly. For example, it becomes easier for customers to understand the purchase process and enter the necessary information accurately. This allows the customer service system to improve customer satisfaction. For example, by completing the purchase process smoothly, customers can enjoy shopping without feeling stressed. Furthermore, the workload on the store is reduced, improving work efficiency.

[0031] The recognition unit can analyze a customer's past visit history and take this into consideration when recognizing the customer. For example, if a customer has visited the store frequently in the past, the recognition unit can prioritize recognizing that customer. The recognition unit can also analyze a customer's tendency to visit during specific time periods based on the customer's past visit history, and improve recognition accuracy during those time periods. The recognition unit can also prioritize providing specific services based on the customer's past visit history. This makes it possible to provide more appropriate services to regular customers by taking past visit history into consideration. Some or all of the above-described processing in the recognition unit can be performed using, for example, AI, or without AI. For example, the recognition unit can input the customer's visit history data into the generation AI and have the generation AI analyze the visit history.

[0032] The recognition unit can analyze the clothing and belongings of customers and provide individualized support. For example, if a customer is wearing clothing from a particular brand, the recognition unit can prioritize the introduction of products related to that brand. The recognition unit can also analyze the bags and accessories the customer carries and suggest products that match them. The recognition unit can also infer the season and weather from the customer's clothing and suggest appropriate products. This allows for more personalized service by providing individual support based on the customer's clothing and belongings. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on the customer's clothing and belongings into a generation AI and have the generation AI execute individualized support.

[0033] The recognition unit can estimate the age and gender of a customer and select an appropriate response. The recognition unit can, for example, estimate the customer's age and suggest products appropriate for that age. The recognition unit can also estimate the customer's gender and suggest products appropriate for that gender. The recognition unit can also select an appropriate customer service style based on the customer's age and gender. This allows for more appropriate service to be provided by responding to the customer according to their age and gender. Some or all of the above-described processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input data on the customer's age and gender into the generation AI and have the generation AI select an appropriate response.

[0034] The recognition unit can prioritize acquiring highly relevant information based on the geographical location information of the customer. For example, if the customer is from a specific area, the recognition unit can prioritize introducing products and services related to that area. The recognition unit can also suggest nearby stores and services based on the customer's geographical location information. The recognition unit can also provide area-specific campaign information by taking the customer's geographical location information into consideration. In this way, by taking the geographical location information into consideration, it is possible to provide information related to the area. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the customer's geographical location data into the generation AI and cause the generation AI to acquire highly relevant information.

[0035] The recognition unit can analyze the social media activity of customers and acquire related information. For example, the recognition unit can acquire information about places where customers have checked in on social media. The recognition unit can also analyze the content of customers' social media posts and suggest related products and services. The recognition unit can also provide related information by referring to the activities of customers' friends on social media. In this way, by analyzing social media activity, it is possible to provide related information to customers. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input the customer's social media data into the generation AI and cause the generation AI to acquire related information.

[0036] The recognition unit can customize the recognition method by reflecting the customer's past feedback. The recognition unit can adjust the recognition method based on, for example, feedback provided by the customer in the past. The recognition unit can also preferentially use a specific recognition method based on the customer's past feedback. The recognition unit can also improve recognition accuracy by reflecting the customer's feedback. In this way, recognition accuracy is improved by reflecting past feedback. Some or all of the above-mentioned processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input the customer's feedback data into the generation AI and have the generation AI customize the recognition method.

[0037] The answering unit can adjust the level of detail of the answer based on the importance of the question. For example, the answering unit provides a detailed answer to an important question. The answering unit can also provide a concise answer to a general question. The answering unit can also gradually adjust the level of detail of the answer according to the importance of the question. This allows a more appropriate answer to be provided by adjusting the level of detail of the answer according to the importance of the question. Some or all of the above-described processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input question importance data to a generating AI and have the generating AI adjust the level of detail of the answer.

[0038] The answering unit can apply different answering algorithms depending on the category of the question. For example, the answering unit can apply an algorithm that provides detailed product information to a question about a product. The answering unit can also apply an algorithm that provides detailed service information to a question about a service. The answering unit can also select the optimal answering algorithm depending on the category of the question. This makes it possible to provide the optimal answer depending on the category of the question. Some or all of the above-mentioned processing in the answering unit can be performed using AI, for example, or can be performed without using AI. For example, the answering unit can input question category data into the generation AI and have the generation AI apply the answering algorithm.

[0039] The answering unit can improve the accuracy of the answer by referring to the customer's past question history. The answering unit, for example, provides related information based on the content of questions asked by the customer in the past. The answering unit can also provide the optimal answer to a specific question from the customer's past question history. The answering unit can also analyze the customer's question history and improve the accuracy of the answer. In this way, the accuracy of the answer is improved by referring to the past question history. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the customer's question history data into the generation AI and have the generation AI improve the accuracy of the answer.

[0040] The answering unit can determine the priority of answers based on the time when the question was submitted. For example, if the question is urgent, the answering unit can provide answers preferentially. The answering unit can also adjust the priority of answers based on the time when the question was submitted. The answering unit can also provide answers at the optimal timing, taking into account the time when the question was submitted. This enables a prompt response by determining the priority based on the time when the question was submitted. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input question submission time data into the generating AI and have the generating AI determine the priority of answers.

[0041] The answering unit can adjust the order of answers based on the relevance of the question. For example, if the relevance of the question is high, the answering unit provides answers preferentially. The answering unit can also adjust the order of answers based on the relevance of the question. The answering unit can also provide answers in an optimal order taking into account the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, more appropriate answers can be provided. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input question relevance data to a generating AI and cause the generating AI to adjust the order of the answers.

[0042] The answering unit can adjust the use of technical terminology in the answer depending on the customer's level of expertise. For example, if the customer has technical expertise, the answering unit can provide a detailed answer using technical terminology. Alternatively, if the customer does not have technical expertise, the answering unit can provide an answer in simple language that is easy to understand. The answering unit can also adjust the use of technical terminology in the answer depending on the customer's level of expertise. This allows for the provision of a more appropriate answer by adjusting the use of technical terminology depending on the customer's level of expertise. Some or all of the above-described processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input the customer's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0043] The explanation unit can adjust the level of detail of the explanation based on the importance of the product. For example, the explanation unit provides a detailed explanation for an important product. The explanation unit can also provide a concise explanation for a general product. The explanation unit can also adjust the level of detail of the explanation in stages according to the importance of the product. This makes it possible to provide a more appropriate explanation by adjusting the level of detail of the explanation according to the importance of the product. Some or all of the above-mentioned processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit can input product importance data to the generation AI and cause the generation AI to adjust the level of detail of the explanation.

[0044] The explanation unit can apply different explanation algorithms depending on the product category. For example, the explanation unit can apply an algorithm that provides detailed technical information to explanations about electronic products. The explanation unit can also apply an algorithm that provides information about design and materials to explanations about fashion products. The explanation unit can also select the optimal explanation algorithm depending on the product category. This makes it possible to provide the optimal explanation depending on the product category. Some or all of the above-mentioned processing in the explanation unit can be performed using AI, for example, or can be performed without using AI. For example, the explanation unit can input product category data into the generation AI and cause the generation AI to apply the explanation algorithm.

[0045] The explanation unit can improve the accuracy of the explanation by referring to the customer's past purchase history. The explanation unit, for example, provides information related to products previously purchased by the customer. The explanation unit can also provide the optimal explanation for a specific product based on the customer's past purchase history. The explanation unit can also analyze the customer's purchase history and improve the accuracy of the explanation. In this way, the accuracy of the explanation is improved by referring to the past purchase history. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input the customer's purchase history data into the generation AI and cause the generation AI to improve the accuracy of the explanation.

[0046] The explanation unit can determine the priority of explanations based on the submission time of the product. For example, the explanation unit provides explanations preferentially for new products. The explanation unit can also adjust the priority of explanations based on the submission time of the product. The explanation unit can also provide explanations at the optimal timing, taking into account the submission time of the product. This enables a quick response by determining the priority based on the submission time of the product. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input product submission time data into the generation AI and have the generation AI determine the priority of explanations.

[0047] The explanation unit can adjust the order of explanations based on the relevance of the products. For example, if the relevance of the products is high, the explanation unit provides the explanation preferentially. The explanation unit can also adjust the order of explanations based on the relevance of the products. The explanation unit can also provide the explanations in an optimal order taking the relevance of the products into consideration. In this way, by adjusting the order of explanations based on the relevance of the products, more appropriate explanations can be provided. Some or all of the above-described processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of explanations.

[0048] The explanation unit can adjust the use of technical terms in the explanation depending on the customer's level of expertise. For example, if the customer has technical expertise, the explanation unit can provide a detailed explanation using technical terms. If the customer does not have technical expertise, the explanation unit can also provide an easy-to-understand explanation in simple terms. The explanation unit can also adjust the use of technical terms in the explanation depending on the customer's level of expertise. This makes it possible to provide a more appropriate explanation by adjusting the use of technical terms depending on the customer's level of expertise. Some or all of the above-mentioned processing in the explanation unit can be performed using AI, for example, or without AI. For example, the explanation unit can input the customer's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0049] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the product. For example, the recommendation unit provides detailed recommendations for important products. The recommendation unit can also provide concise recommendations for general products. The recommendation unit can also gradually adjust the level of detail of the recommendation according to the importance of the product. This makes it possible to provide more appropriate recommendations by adjusting the level of detail of the recommendation according to the importance of the product. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input product importance data into the generation AI and have the generation AI adjust the level of detail of the recommendation.

[0050] The recommendation unit can apply different recommendation algorithms depending on the product category. For example, the recommendation unit can apply an algorithm that provides detailed technical information to recommendations related to electronic products. The recommendation unit can also apply an algorithm that provides information about design and materials to recommendations related to fashion products. The recommendation unit can also select the optimal recommendation algorithm depending on the product category. This makes it possible to provide optimal recommendations depending on the product category. Some or all of the above-mentioned processing in the recommendation unit can be performed using AI, for example, or can be performed without using AI. For example, the recommendation unit can input product category data into the generation AI and cause the generation AI to apply the recommendation algorithm.

[0051] The recommendation unit can improve the accuracy of recommendations by referring to the customer's past purchase history. The recommendation unit, for example, provides information related to products previously purchased by the customer. The recommendation unit can also provide optimal recommendations for specific products based on the customer's past purchase history. The recommendation unit can also analyze the customer's purchase history and improve the accuracy of recommendations. This improves the accuracy of recommendations by referring to the past purchase history. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the customer's purchase history data into the generation AI and cause the generation AI to improve the accuracy of recommendations.

[0052] The recommendation unit can determine the priority of recommendations based on the time of product submission. For example, the recommendation unit can prioritize recommendations for new products. The recommendation unit can also adjust the priority of recommendations based on the time of product submission. The recommendation unit can also provide recommendations at the optimal timing, taking into account the time of product submission. This enables a quick response by determining the priority based on the time of product submission. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input product submission time data into the generation AI and have the generation AI determine the priority of recommendations.

[0053] The recommendation unit can adjust the order of recommendations based on the relevance of products. For example, if the relevance of products is high, the recommendation unit provides recommendations preferentially. The recommendation unit can also adjust the order of recommendations based on the relevance of products. The recommendation unit can also provide recommendations in an optimal order taking into account the relevance of products. In this way, by adjusting the order of recommendations based on the relevance of products, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input product relevance data into a generation AI and cause the generation AI to adjust the order of recommendations.

[0054] The recommendation unit can adjust the use of recommended terminology according to the customer's level of expertise. For example, if the customer has specialized knowledge, the recommendation unit can provide detailed recommendations using specialized terminology. Furthermore, if the customer does not have specialized knowledge, the recommendation unit can provide easy-to-understand recommendations in simple language. Furthermore, the recommendation unit can adjust the use of recommended terminology according to the customer's level of expertise. This makes it possible to provide more appropriate recommendations by adjusting the use of terminology according to the customer's level of expertise. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the customer's level of expertise data into the generation AI and cause the generation AI to adjust the use of terminology.

[0055] The support unit can analyze the customer's past purchase history and select the optimal support method. For example, the support unit provides support related to products that the customer has previously purchased. The support unit can also provide optimal support for specific products based on the customer's past purchase history. The support unit can also analyze the customer's purchase history and improve the accuracy of support. In this way, the optimal support method can be provided by analyzing the past purchase history. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the customer's purchase history data into the generation AI and have the generation AI select the optimal support method.

[0056] The support unit can customize the means of support based on the customer's current living situation. For example, if the customer is busy, the support unit can provide quick and concise support. Also, if the customer is relaxed, the support unit can provide detailed information. The support unit can also select the optimal means of support according to the customer's living situation. In this way, by customizing the means of support based on the customer's current living situation, more appropriate support can be provided. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the customer's living situation data into the generation AI and have the generation AI customize the means of support.

[0057] The support unit can improve the support method by reflecting customer feedback. For example, the support unit adjusts the support method based on the feedback provided by the customer. The support unit can also prioritize the use of a specific support method based on the customer feedback. The support unit can also improve the accuracy of support by reflecting customer feedback. In this way, the accuracy of support is improved by reflecting the feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input customer feedback data into a generation AI and have the generation AI improve the support method.

[0058] The support unit can select the optimal support method by taking into account the geographical location information of the customer. For example, if the customer is from a specific area, the support unit can provide support related to that area. The support unit can also suggest nearby stores and services based on the customer's geographical location information. The support unit can also provide support information specific to the area by taking into account the customer's geographical location information. In this way, support related to the area can be provided by taking into account the geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the customer's geographical location data into the generation AI and have the generation AI select the optimal support method.

[0059] The support unit can analyze the social media activity of the customer and suggest means of support. For example, the support unit can provide support related to the places where the customer has checked in on social media. The support unit can also analyze the content of the customer's social media posts and provide relevant support. The support unit can also provide relevant support by referring to the activities of the customer's friends on social media. In this way, by analyzing social media activity, relevant support can be provided to the customer. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the customer's social media data into a generation AI and have the generation AI suggest means of support.

[0060] The support unit can customize the support method by reflecting the customer's past feedback. For example, the support unit adjusts the support method based on feedback provided by the customer in the past. The support unit can also prioritize the use of a specific support method based on the customer's past feedback. The support unit can also improve the accuracy of support by reflecting the customer's feedback. In this way, the accuracy of support is improved by reflecting past feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the customer's feedback data into the generation AI and have the generation AI customize the support method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The recognition unit can analyze the walking patterns of customers and provide individualized support. For example, if a customer is in a hurry, the recognition unit can provide quick support. On the other hand, if a customer is relaxed, the recognition unit can provide slow support. The recognition unit can also infer a customer's health condition from their walking patterns and suggest appropriate products. This allows for more personalized service by providing individual support based on the customer's walking patterns.

[0063] The support department can provide maintenance information for products that customers have previously purchased based on the customer's purchase history. For example, the support department can provide maintenance methods for home appliances that customers have previously purchased. The support department can also help customers check the warranty period for products they have purchased. The support department can also provide upgrade information for products that customers have purchased. In this way, providing maintenance information based on the customer's purchase history can improve customer satisfaction.

[0064] The recognition unit can provide event information that may interest a customer based on the customer's past visit history. For example, the recognition unit can suggest new related events based on information about events that the customer has previously attended. The recognition unit can also prioritize introducing events held in a specific season based on the customer's visit history. The recognition unit can also suggest events related to a specific theme based on the customer's visit history. This makes it possible to provide more appropriate event information to customers by taking past visit history into consideration.

[0065] The recognition unit can analyze the clothing and belongings of customers and suggest products that suit the customers' hobbies and lifestyles. For example, if a customer is wearing sportswear, the recognition unit can prioritize sports goods. If a customer is wearing a business suit, the recognition unit can suggest business-related products. If a customer is wearing casual clothing, the recognition unit can suggest casual wear and accessories. This allows for more personalized service by responding individually to customers based on their clothing and belongings.

[0066] The recognition unit can estimate the age and gender of a customer and select an appropriate service style based on the estimated age and gender. For example, the recognition unit can adopt a casual service style if the customer is young. Alternatively, the recognition unit can adopt a polite and relaxed service style if the customer is elderly. The recognition unit can also suggest gender-specific products depending on the customer's gender. This allows for more appropriate service to be provided by responding to the customer's age and gender.

[0067] The recognition unit can provide tourist information that may be of interest to a customer based on the customer's geographic location information. For example, if the customer comes from a specific region, the recognition unit can introduce tourist spots related to that region. The recognition unit can also suggest nearby tourist spots and events based on the customer's geographic location information. The recognition unit can also provide information about tourist campaigns that are limited to that region, taking into account the customer's geographic location information. In this way, tourist information related to the region can be provided by taking into account the geographic location information.

[0068] The recognition unit can analyze the social media activity of store customers and provide trend information that may be of interest to the store customers. For example, the recognition unit can provide information about brands and influencers that the store customers follow on social media. The recognition unit can also analyze the content of the store customers' social media posts and suggest related trend products. The recognition unit can also provide related trend information by referring to the activities of the store customers' friends on social media. In this way, by analyzing social media activity, it is possible to provide trend information relevant to the store customers.

[0069] The recognition unit can provide new product information that is likely to interest a customer by reflecting the customer's past feedback. For example, the recognition unit can suggest related new products based on the customer's past feedback. The recognition unit can also preferentially introduce products in a specific category based on the customer's past feedback. The recognition unit can also customize the provision of new product information by reflecting the customer's feedback. In this way, more appropriate new product information can be provided to the customer by reflecting the customer's past feedback.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The recognition unit performs facial or voice recognition of customers. For example, it uses a facial recognition algorithm based on deep learning or a voice recognition engine to analyze faces captured by a camera or voices collected by a microphone. It can also analyze customers' questions using natural language processing technology. Step 2: The answering unit provides an answer to the customer's question recognized by the recognition unit. For example, an appropriate answer can be generated using a generation AI, and the answer can be provided based on the customer's past purchase history and preference data. Step 3: The explanation section explains the product based on the answers provided by the answering section. For example, it uses a generative AI to explain the product's features, usage, benefits, and comparisons with other products. Step 4: The recommendation unit recommends products based on the descriptions provided by the explanation unit. For example, it can use generation AI to suggest recommended products based on the customer's preferences, and also suggest related products based on past purchase history and preference data.

[0072] (Example 2) A customer service system according to an embodiment of the present invention performs facial and voice recognition on customers, and a generation AI provides appropriate answers, product explanations, and recommendations. The customer service system recognizes customers, greets them, and guides them. It then provides appropriate answers to their questions. It also provides product explanations and recommendations, and supports the purchase process. For example, when a customer asks, "Tell me about this product," the generation AI provides detailed product information. When a customer asks, "What products do you recommend?", the generation AI suggests recommended products based on the customer's preferences. Furthermore, the customer service system supports the purchase process by guiding the customer through the purchase process and helping them enter the necessary information. This allows customers to complete the purchase process smoothly. The customer service system not only improves customer satisfaction but also contributes to improving store operational efficiency. For example, the customer service system uses facial and voice recognition technology to enable more natural conversations. When the generation AI explains and recommends products, it bases its decisions on the customer's past purchase history and preference data. This allows the customer service system to improve customer satisfaction and streamline store operations. For example, by serving customers quickly and accurately, the store's workload is reduced. Also, by suggesting products that match customers' preferences, customers are more likely to buy.

[0073] The customer service system according to the embodiment includes a recognition unit, a response unit, an explanation unit, and a recommendation unit. The recognition unit performs facial or voice recognition of customers. For example, the recognition unit recognizes the faces of customers using a facial recognition algorithm based on deep learning. The recognition unit can also recognize the voices of customers using a voice recognition engine. The recognition unit can also analyze questions from customers using natural language processing technology. For example, the recognition unit photographs the faces of customers with a camera and identifies the customers using a facial recognition algorithm. The recognition unit can also collect the voices of customers using a microphone and convert the voices into text using a voice recognition engine. The response unit provides answers to questions from customers recognized by the recognition unit. For example, the response unit generates appropriate answers to the questions from customers using a generation AI. The response unit can also provide answers based on the customers' past purchase history and preference data. For example, the response unit provides detailed information about a product in response to a question from the generation AI such as, "Tell me about this product." The answering unit can also suggest recommended products based on the customer's preferences in response to the question "What product do you recommend?" from the generation AI. The explanation unit explains the product based on the answer provided by the answering unit. For example, the explanation unit uses the generation AI to explain the product's features and usage. The explanation unit can also compare the product's advantages and with other products. For example, the explanation unit uses the generation AI to briefly explain the product's features. The explanation unit can also use the generation AI to explain in detail how to use the product. The recommendation unit recommends products based on the explanation provided by the explanation unit. For example, the recommendation unit uses the generation AI to suggest recommended products based on the customer's preferences. The recommendation unit can also make recommendations based on the customer's past purchase history and preference data. For example, the recommendation unit uses the generation AI to suggest products that match the customer's preferences. The recommendation unit can also use the generation AI to suggest related products based on the customer's past purchase history. This allows the customer service system according to the embodiment to improve customer satisfaction. For example, a customer's motivation to purchase is increased when products that match their preferences are suggested. In addition, the operational burden on stores will be reduced, improving operational efficiency.

[0074] The customer service system includes a support unit that supports the purchase process. The support unit supports the purchase process. For example, the support unit uses a generation AI to guide customers through the purchase process. The support unit can also help customers enter the necessary information. For example, the support unit uses a generation AI to explain each step of the purchase process. The support unit can also use a generation AI to support customers in entering information. This allows customers to complete the purchase process smoothly. For example, it becomes easier for customers to understand the purchase process and enter the necessary information accurately. This allows the customer service system to improve customer satisfaction. For example, by completing the purchase process smoothly, customers can enjoy shopping without feeling stressed. Furthermore, the workload on the store is reduced, improving work efficiency.

[0075] The recognition unit can estimate the customer's emotions and adjust the accuracy of recognition based on the estimated customer's emotions. For example, if the customer is nervous, the recognition unit can increase the accuracy of facial recognition to more accurately identify the customer. Furthermore, if the customer is relaxed, the recognition unit can increase the accuracy of voice recognition to promote natural conversation. Furthermore, if the customer is in a hurry, the recognition unit can quickly recognize the customer's face and immediately begin responding. This allows for more natural conversation by adjusting the recognition accuracy according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using AI, or without AI. For example, the recognition unit can input facial expression data of the customer into the generation AI and have the generation AI perform emotion estimation.

[0076] The recognition unit can analyze a customer's past visit history and take this into consideration when recognizing the customer. For example, if a customer has visited the store frequently in the past, the recognition unit can prioritize recognizing that customer. The recognition unit can also analyze a customer's tendency to visit during specific time periods based on the customer's past visit history, and improve recognition accuracy during those time periods. The recognition unit can also prioritize providing specific services based on the customer's past visit history. This makes it possible to provide more appropriate services to regular customers by taking past visit history into consideration. Some or all of the above-described processing in the recognition unit can be performed using, for example, AI, or without AI. For example, the recognition unit can input the customer's visit history data into the generation AI and have the generation AI analyze the visit history.

[0077] The recognition unit can analyze the clothing and belongings of customers and provide individualized support. For example, if a customer is wearing clothing from a particular brand, the recognition unit can prioritize the introduction of products related to that brand. The recognition unit can also analyze the bags and accessories the customer carries and suggest products that match them. The recognition unit can also infer the season and weather from the customer's clothing and suggest appropriate products. This allows for more personalized service by providing individual support based on the customer's clothing and belongings. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on the customer's clothing and belongings into a generation AI and have the generation AI execute individualized support.

[0078] The recognition unit can estimate the age and gender of a customer and select an appropriate response. The recognition unit can, for example, estimate the customer's age and suggest products appropriate for that age. The recognition unit can also estimate the customer's gender and suggest products appropriate for that gender. The recognition unit can also select an appropriate customer service style based on the customer's age and gender. This allows for more appropriate service to be provided by responding to the customer according to their age and gender. Some or all of the above-described processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input data on the customer's age and gender into the generation AI and have the generation AI select an appropriate response.

[0079] The recognition unit can estimate the customer's emotions and adjust the display method of the recognition results based on the estimated customer's emotions. For example, if the customer is nervous, the recognition unit can display the recognition results simply to reduce visual stress. Furthermore, if the customer is relaxed, the recognition unit can display detailed recognition results to provide more information. Furthermore, if the customer is in a hurry, the recognition unit can quickly display the recognition results and immediately begin responding. This reduces visual stress by adjusting the display method of the recognition results according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit can be performed using, for example, an AI. For example, the recognition unit can input customer emotion data into the generation AI and have the generation AI adjust the display method of the recognition results.

[0080] The recognition unit can prioritize acquiring highly relevant information based on the geographical location information of the customer. For example, if the customer is from a specific area, the recognition unit can prioritize introducing products and services related to that area. The recognition unit can also suggest nearby stores and services based on the customer's geographical location information. The recognition unit can also provide area-specific campaign information by taking the customer's geographical location information into consideration. In this way, by taking the geographical location information into consideration, it is possible to provide information related to the area. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the customer's geographical location data into the generation AI and cause the generation AI to acquire highly relevant information.

[0081] The recognition unit can analyze the social media activity of customers and acquire related information. For example, the recognition unit can acquire information about places where customers have checked in on social media. The recognition unit can also analyze the content of customers' social media posts and suggest related products and services. The recognition unit can also provide related information by referring to the activities of customers' friends on social media. In this way, by analyzing social media activity, it is possible to provide related information to customers. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input the customer's social media data into the generation AI and cause the generation AI to acquire related information.

[0082] The recognition unit can customize the recognition method by reflecting the customer's past feedback. The recognition unit can adjust the recognition method based on, for example, feedback provided by the customer in the past. The recognition unit can also preferentially use a specific recognition method based on the customer's past feedback. The recognition unit can also improve recognition accuracy by reflecting the customer's feedback. In this way, recognition accuracy is improved by reflecting past feedback. Some or all of the above-mentioned processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input the customer's feedback data into the generation AI and have the generation AI customize the recognition method.

[0083] The answering unit can estimate the customer's emotions and adjust the way the answer is expressed based on the estimated customer's emotions. For example, if the customer is nervous, the answering unit can use simple, easy-to-understand expressions. If the customer is relaxed, the answering unit can provide detailed information. If the customer is in a hurry, the answering unit can provide a quick, concise answer. This allows the answering unit to adjust the way the answer is expressed based on the customer's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the answering unit can be performed using, for example, an AI, or without an AI. For example, the answering unit can input the customer's emotion data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0084] The answering unit can adjust the level of detail of the answer based on the importance of the question. For example, the answering unit provides a detailed answer to an important question. The answering unit can also provide a concise answer to a general question. The answering unit can also gradually adjust the level of detail of the answer according to the importance of the question. This allows a more appropriate answer to be provided by adjusting the level of detail of the answer according to the importance of the question. Some or all of the above-described processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input question importance data to a generating AI and have the generating AI adjust the level of detail of the answer.

[0085] The answering unit can apply different answering algorithms depending on the category of the question. For example, the answering unit can apply an algorithm that provides detailed product information to a question about a product. The answering unit can also apply an algorithm that provides detailed service information to a question about a service. The answering unit can also select the optimal answering algorithm depending on the category of the question. This makes it possible to provide the optimal answer depending on the category of the question. Some or all of the above-mentioned processing in the answering unit can be performed using AI, for example, or can be performed without using AI. For example, the answering unit can input question category data into the generation AI and have the generation AI apply the answering algorithm.

[0086] The answering unit can improve the accuracy of the answer by referring to the customer's past question history. The answering unit, for example, provides related information based on the content of questions asked by the customer in the past. The answering unit can also provide the optimal answer to a specific question from the customer's past question history. The answering unit can also analyze the customer's question history and improve the accuracy of the answer. In this way, the accuracy of the answer is improved by referring to the past question history. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the customer's question history data into the generation AI and have the generation AI improve the accuracy of the answer.

[0087] The answering unit can estimate the customer's emotions and adjust the length of the answer based on the estimated customer's emotions. For example, if the customer is nervous, the answering unit can provide a short, to-the-point answer. If the customer is relaxed, the answering unit can provide a longer answer with detailed explanations. If the customer is in a hurry, the answering unit can provide a quick, concise answer. This allows for adjusting the length of the answer according to the customer's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answering unit can be performed using, for example, an AI, or without an AI. For example, the answering unit can input the customer's emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0088] The answering unit can determine the priority of answers based on the time when the question was submitted. For example, if the question is urgent, the answering unit can provide answers preferentially. The answering unit can also adjust the priority of answers based on the time when the question was submitted. The answering unit can also provide answers at the optimal timing, taking into account the time when the question was submitted. This enables a prompt response by determining the priority based on the time when the question was submitted. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input question submission time data into the generating AI and have the generating AI determine the priority of answers.

[0089] The answering unit can adjust the order of answers based on the relevance of the question. For example, if the relevance of the question is high, the answering unit provides answers preferentially. The answering unit can also adjust the order of answers based on the relevance of the question. The answering unit can also provide answers in an optimal order taking into account the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, more appropriate answers can be provided. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input question relevance data to a generating AI and cause the generating AI to adjust the order of the answers.

[0090] The answering unit can adjust the use of technical terminology in the answer depending on the customer's level of expertise. For example, if the customer has technical expertise, the answering unit can provide a detailed answer using technical terminology. Alternatively, if the customer does not have technical expertise, the answering unit can provide an answer in simple language that is easy to understand. The answering unit can also adjust the use of technical terminology in the answer depending on the customer's level of expertise. This allows for the provision of a more appropriate answer by adjusting the use of technical terminology depending on the customer's level of expertise. Some or all of the above-described processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input the customer's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0091] The explanation unit can estimate the customer's emotions and adjust the way the explanation is presented based on the estimated customer's emotions. For example, if the customer is nervous, the explanation unit can use simple, easy-to-understand expressions. If the customer is relaxed, the explanation unit can also provide detailed information. If the customer is in a hurry, the explanation unit can also provide a quick, concise explanation. This allows for adjusting the way the explanation is presented according to the customer's emotions, thereby providing a more appropriate explanation. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the explanation unit can be performed using, for example, AI, or without AI. For example, the explanation unit can input the customer's emotion data into the generation AI and have the generation AI adjust the way the explanation is presented.

[0092] The explanation unit can adjust the level of detail of the explanation based on the importance of the product. For example, the explanation unit provides a detailed explanation for an important product. The explanation unit can also provide a concise explanation for a general product. The explanation unit can also adjust the level of detail of the explanation in stages according to the importance of the product. This makes it possible to provide a more appropriate explanation by adjusting the level of detail of the explanation according to the importance of the product. Some or all of the above-mentioned processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit can input product importance data to the generation AI and cause the generation AI to adjust the level of detail of the explanation.

[0093] The explanation unit can apply different explanation algorithms depending on the product category. For example, the explanation unit can apply an algorithm that provides detailed technical information to explanations about electronic products. The explanation unit can also apply an algorithm that provides information about design and materials to explanations about fashion products. The explanation unit can also select the optimal explanation algorithm depending on the product category. This makes it possible to provide the optimal explanation depending on the product category. Some or all of the above-mentioned processing in the explanation unit can be performed using AI, for example, or can be performed without using AI. For example, the explanation unit can input product category data into the generation AI and cause the generation AI to apply the explanation algorithm.

[0094] The explanation unit can improve the accuracy of the explanation by referring to the customer's past purchase history. The explanation unit, for example, provides information related to products previously purchased by the customer. The explanation unit can also provide the optimal explanation for a specific product based on the customer's past purchase history. The explanation unit can also analyze the customer's purchase history and improve the accuracy of the explanation. In this way, the accuracy of the explanation is improved by referring to the past purchase history. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input the customer's purchase history data into the generation AI and cause the generation AI to improve the accuracy of the explanation.

[0095] The explanation unit can estimate the customer's emotions and adjust the length of the explanation based on the estimated customer's emotions. For example, if the customer is nervous, the explanation unit can provide a short, concise explanation. If the customer is relaxed, the explanation unit can provide a longer explanation with detailed information. If the customer is in a hurry, the explanation unit can provide a quick, concise explanation. This allows the length of the explanation to be adjusted according to the customer's emotions, thereby providing a more appropriate explanation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the explanation unit can be performed using AI, for example, or without AI. For example, the explanation unit can input the customer's emotion data into the generation AI and have the generation AI adjust the length of the explanation.

[0096] The explanation unit can determine the priority of explanations based on the submission time of the product. For example, the explanation unit provides explanations preferentially for new products. The explanation unit can also adjust the priority of explanations based on the submission time of the product. The explanation unit can also provide explanations at the optimal timing, taking into account the submission time of the product. This enables a quick response by determining the priority based on the submission time of the product. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input product submission time data into the generation AI and have the generation AI determine the priority of explanations.

[0097] The explanation unit can adjust the order of explanations based on the relevance of the products. For example, if the relevance of the products is high, the explanation unit provides the explanation preferentially. The explanation unit can also adjust the order of explanations based on the relevance of the products. The explanation unit can also provide the explanations in an optimal order taking the relevance of the products into consideration. In this way, by adjusting the order of explanations based on the relevance of the products, more appropriate explanations can be provided. Some or all of the above-described processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of explanations.

[0098] The explanation unit can adjust the use of technical terms in the explanation depending on the customer's level of expertise. For example, if the customer has technical expertise, the explanation unit can provide a detailed explanation using technical terms. If the customer does not have technical expertise, the explanation unit can also provide an easy-to-understand explanation in simple terms. The explanation unit can also adjust the use of technical terms in the explanation depending on the customer's level of expertise. This makes it possible to provide a more appropriate explanation by adjusting the use of technical terms depending on the customer's level of expertise. Some or all of the above-mentioned processing in the explanation unit can be performed using AI, for example, or without AI. For example, the explanation unit can input the customer's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0099] The recommendation unit can estimate the customer's emotions and adjust the way recommendations are presented based on the estimated customer's emotions. For example, if the customer is nervous, the recommendation unit can use simple, easy-to-understand expressions. If the customer is relaxed, the recommendation unit can also provide detailed information. If the customer is in a hurry, the recommendation unit can also provide quick, concise recommendations. This allows for more appropriate recommendations to be provided by adjusting the way recommendations are presented based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the customer's emotion data into the generation AI and have the generation AI adjust the way recommendations are presented.

[0100] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the product. For example, the recommendation unit provides detailed recommendations for important products. The recommendation unit can also provide concise recommendations for general products. The recommendation unit can also gradually adjust the level of detail of the recommendation according to the importance of the product. This makes it possible to provide more appropriate recommendations by adjusting the level of detail of the recommendation according to the importance of the product. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input product importance data into the generation AI and have the generation AI adjust the level of detail of the recommendation.

[0101] The recommendation unit can apply different recommendation algorithms depending on the product category. For example, the recommendation unit can apply an algorithm that provides detailed technical information to recommendations related to electronic products. The recommendation unit can also apply an algorithm that provides information about design and materials to recommendations related to fashion products. The recommendation unit can also select the optimal recommendation algorithm depending on the product category. This makes it possible to provide optimal recommendations depending on the product category. Some or all of the above-mentioned processing in the recommendation unit can be performed using AI, for example, or can be performed without using AI. For example, the recommendation unit can input product category data into the generation AI and cause the generation AI to apply the recommendation algorithm.

[0102] The recommendation unit can improve the accuracy of recommendations by referring to the customer's past purchase history. The recommendation unit, for example, provides information related to products previously purchased by the customer. The recommendation unit can also provide optimal recommendations for specific products based on the customer's past purchase history. The recommendation unit can also analyze the customer's purchase history and improve the accuracy of recommendations. This improves the accuracy of recommendations by referring to the past purchase history. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the customer's purchase history data into the generation AI and cause the generation AI to improve the accuracy of recommendations.

[0103] The recommendation unit can estimate the customer's emotions and adjust the length of recommendations based on the estimated customer's emotions. For example, if the customer is nervous, the recommendation unit can provide short, to-the-point recommendations. If the customer is relaxed, the recommendation unit can provide longer recommendations with detailed explanations. If the customer is in a hurry, the recommendation unit can provide quick, concise recommendations. This allows for adjusting the length of recommendations according to the customer's emotions, thereby providing more appropriate recommendations. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or without an AI. For example, the recommendation unit can input the customer's emotion data into the generation AI and have the generation AI adjust the length of the recommendations.

[0104] The recommendation unit can determine the priority of recommendations based on the time of product submission. For example, the recommendation unit can prioritize recommendations for new products. The recommendation unit can also adjust the priority of recommendations based on the time of product submission. The recommendation unit can also provide recommendations at the optimal timing, taking into account the time of product submission. This enables a quick response by determining the priority based on the time of product submission. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input product submission time data into the generation AI and have the generation AI determine the priority of recommendations.

[0105] The recommendation unit can adjust the order of recommendations based on the relevance of products. For example, if the relevance of products is high, the recommendation unit provides recommendations preferentially. The recommendation unit can also adjust the order of recommendations based on the relevance of products. The recommendation unit can also provide recommendations in an optimal order taking into account the relevance of products. In this way, by adjusting the order of recommendations based on the relevance of products, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input product relevance data into a generation AI and cause the generation AI to adjust the order of recommendations.

[0106] The recommendation unit can adjust the use of recommended terminology according to the customer's level of expertise. For example, if the customer has specialized knowledge, the recommendation unit can provide detailed recommendations using specialized terminology. Furthermore, if the customer does not have specialized knowledge, the recommendation unit can provide easy-to-understand recommendations in simple language. Furthermore, the recommendation unit can adjust the use of recommended terminology according to the customer's level of expertise. This makes it possible to provide more appropriate recommendations by adjusting the use of terminology according to the customer's level of expertise. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the customer's level of expertise data into the generation AI and cause the generation AI to adjust the use of terminology.

[0107] The support unit can estimate the customer's emotions and adjust the support method based on the estimated customer's emotions. For example, if the customer is nervous, the support unit can provide simple and easy-to-understand support. If the customer is relaxed, the support unit can also provide detailed information. If the customer is in a hurry, the support unit can also provide quick and concise support. This allows the support method to be adjusted according to the customer's emotions, thereby providing more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the support unit can be performed using AI, for example, or without AI. For example, the support unit can input the customer's emotion data into the generation AI and have the generation AI adjust the support method.

[0108] The support unit can analyze the customer's past purchase history and select the optimal support method. For example, the support unit provides support related to products that the customer has previously purchased. The support unit can also provide optimal support for specific products based on the customer's past purchase history. The support unit can also analyze the customer's purchase history and improve the accuracy of support. In this way, the optimal support method can be provided by analyzing the past purchase history. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the customer's purchase history data into the generation AI and have the generation AI select the optimal support method.

[0109] The support unit can customize the means of support based on the customer's current living situation. For example, if the customer is busy, the support unit can provide quick and concise support. Also, if the customer is relaxed, the support unit can provide detailed information. The support unit can also select the optimal means of support according to the customer's living situation. In this way, by customizing the means of support based on the customer's current living situation, more appropriate support can be provided. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the customer's living situation data into the generation AI and have the generation AI customize the means of support.

[0110] The support unit can improve the support method by reflecting customer feedback. For example, the support unit adjusts the support method based on the feedback provided by the customer. The support unit can also prioritize the use of a specific support method based on the customer feedback. The support unit can also improve the accuracy of support by reflecting customer feedback. In this way, the accuracy of support is improved by reflecting the feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input customer feedback data into a generation AI and have the generation AI improve the support method.

[0111] The support unit can estimate the emotions of customers and determine support priorities based on the estimated emotions. For example, if a customer is nervous, the support unit can prioritize support. The support unit can also provide detailed information if the customer is relaxed. The support unit can also provide quick and concise support if the customer is in a hurry. This allows for more appropriate support by determining support priorities based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input customer emotion data into the generation AI and have the generation AI determine support priorities.

[0112] The support unit can select the optimal support method by taking into account the geographical location information of the customer. For example, if the customer is from a specific area, the support unit can provide support related to that area. The support unit can also suggest nearby stores and services based on the customer's geographical location information. The support unit can also provide support information specific to the area by taking into account the customer's geographical location information. In this way, support related to the area can be provided by taking into account the geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the customer's geographical location data into the generation AI and have the generation AI select the optimal support method.

[0113] The support unit can analyze the social media activity of the customer and suggest means of support. For example, the support unit can provide support related to the places where the customer has checked in on social media. The support unit can also analyze the content of the customer's social media posts and provide relevant support. The support unit can also provide relevant support by referring to the activities of the customer's friends on social media. In this way, by analyzing social media activity, relevant support can be provided to the customer. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the customer's social media data into a generation AI and have the generation AI suggest means of support.

[0114] The support unit can customize the support method by reflecting the customer's past feedback. For example, the support unit adjusts the support method based on feedback provided by the customer in the past. The support unit can also prioritize the use of a specific support method based on the customer's past feedback. The support unit can also improve the accuracy of support by reflecting the customer's feedback. In this way, the accuracy of support is improved by reflecting past feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the customer's feedback data into the generation AI and have the generation AI customize the support method. === Hard Collateral 1-1 === Each of the multiple elements, including the recognition unit, answer unit, explanation unit, recommendation unit, and support unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit recognizes the face and voice of a customer using the camera 42 and microphone 38B of the smart device 14, and the recognition unit analyzes the face and voice of the customer using the control unit 46A. The answer unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate answers to the customer's questions using a generation AI. The explanation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and explains the features and usage of products. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests recommended products based on the customer's preferences. The support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and guides the customer through the purchase process and supports the customer in entering necessary information. === Hard Collateral 1-2 === Each of the multiple elements, including the recognition unit, answer unit, explanation unit, recommendation unit, and support unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit recognizes the face and voice of a customer using the camera 42 and microphone 238 of the smart glasses 214, and the recognition unit analyzes the face and voice of the customer using the control unit 46A. The answer unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate answers to the customer's questions using a generation AI. The explanation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and explains the features and usage of products. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests recommended products based on the customer's preferences. The support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and guides the customer through the purchase procedure and supports the customer in entering necessary information. === Hard Collateral 1-3 === Each of the multiple elements, including the recognition unit, answer unit, explanation unit, recommendation unit, and support unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the recognition unit recognizes the face and voice of a customer using the camera 42 and microphone 238 of the headset terminal 314, and the recognition unit analyzes the face and voice of the customer using the control unit 46A. The answer unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate answers to the customer's questions using a generation AI. The explanation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and explains the features and usage of products. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests recommended products based on the customer's preferences. The support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and guides the customer through the purchase process and supports the customer in entering necessary information. === Hard Collateral 1-4 === Each of the multiple elements, including the recognition unit, answering unit, explanation unit, recommendation unit, and support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit recognizes the face and voice of a customer using the camera 42 and microphone 238 of the robot 414, and the recognition unit analyzes the face and voice of the customer using the control unit 46A. The answering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate answers to the customer's questions using a generation AI. The explanation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and explains the features and usage of products. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests recommended products based on the customer's preferences. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and guides the customer through the purchase procedure and supports the customer in entering necessary information.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The recognition unit can analyze the walking patterns of customers and provide individualized support. For example, if a customer is in a hurry, the recognition unit can provide quick support. On the other hand, if a customer is relaxed, the recognition unit can provide slow support. The recognition unit can also infer a customer's health condition from their walking patterns and suggest appropriate products. This allows for more personalized service by providing individual support based on the customer's walking patterns.

[0117] The support department can provide maintenance information for products that customers have previously purchased based on the customer's purchase history. For example, the support department can provide maintenance methods for home appliances that customers have previously purchased. The support department can also help customers check the warranty period for products they have purchased. The support department can also provide upgrade information for products that customers have purchased. In this way, providing maintenance information based on the customer's purchase history can improve customer satisfaction.

[0118] The recognition unit can estimate the customer's emotions and adjust the tone of the voice based on the estimated customer's emotions. For example, if the customer is nervous, the recognition unit can speak to them in a calm tone. If the customer is relaxed, the recognition unit can speak to them in a cheerful tone. If the customer is in a hurry, the recognition unit can speak to them in a quick and concise tone. This allows for more natural conversation by adjusting the tone of the voice according to the customer's emotions.

[0119] The recognition unit can provide event information that may interest a customer based on the customer's past visit history. For example, the recognition unit can suggest new related events based on information about events that the customer has previously attended. The recognition unit can also prioritize introducing events held in a specific season based on the customer's visit history. The recognition unit can also suggest events related to a specific theme based on the customer's visit history. This makes it possible to provide more appropriate event information to customers by taking past visit history into consideration.

[0120] The recognition unit can analyze the clothing and belongings of customers and suggest products that suit the customers' hobbies and lifestyles. For example, if a customer is wearing sportswear, the recognition unit can prioritize sports goods. If a customer is wearing a business suit, the recognition unit can suggest business-related products. If a customer is wearing casual clothing, the recognition unit can suggest casual wear and accessories. This allows for more personalized service by responding individually to customers based on their clothing and belongings.

[0121] The recognition unit can estimate the age and gender of a customer and select an appropriate service style based on the estimated age and gender. For example, the recognition unit can adopt a casual service style if the customer is young. Alternatively, the recognition unit can adopt a polite and relaxed service style if the customer is elderly. The recognition unit can also suggest gender-specific products depending on the customer's gender. This allows for more appropriate service to be provided by responding to the customer's age and gender.

[0122] The recognition unit can estimate the customer's emotions and adjust the feedback method of the recognition results based on the estimated customer's emotions. For example, if the customer is nervous, the feedback can be simplified to reduce visual stress. If the customer is relaxed, detailed feedback can be provided to enhance the information provided. If the customer is in a hurry, feedback can be provided quickly to initiate an immediate response. In this way, visual stress can be reduced by adjusting the feedback method according to the customer's emotions.

[0123] The recognition unit can provide tourist information that may be of interest to a customer based on the customer's geographic location information. For example, if the customer comes from a specific region, the recognition unit can introduce tourist spots related to that region. The recognition unit can also suggest nearby tourist spots and events based on the customer's geographic location information. The recognition unit can also provide information about tourist campaigns that are limited to that region, taking into account the customer's geographic location information. In this way, tourist information related to the region can be provided by taking into account the geographic location information.

[0124] The recognition unit can analyze the social media activity of store customers and provide trend information that may be of interest to the store customers. For example, the recognition unit can provide information about brands and influencers that the store customers follow on social media. The recognition unit can also analyze the content of the store customers' social media posts and suggest related trend products. The recognition unit can also provide related trend information by referring to the activities of the store customers' friends on social media. In this way, by analyzing social media activity, it is possible to provide trend information relevant to the store customers.

[0125] The recognition unit can provide new product information that is likely to interest a customer by reflecting the customer's past feedback. For example, the recognition unit can suggest related new products based on the customer's past feedback. The recognition unit can also preferentially introduce products in a specific category based on the customer's past feedback. The recognition unit can also customize the provision of new product information by reflecting the customer's feedback. In this way, more appropriate new product information can be provided to the customer by reflecting the customer's past feedback.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The recognition unit performs facial or voice recognition of customers. For example, it uses a facial recognition algorithm based on deep learning or a voice recognition engine to analyze faces captured by a camera or voices collected by a microphone. It can also analyze customers' questions using natural language processing technology. Step 2: The answering unit provides an answer to the customer's question recognized by the recognition unit. For example, an appropriate answer can be generated using a generation AI, and the answer can be provided based on the customer's past purchase history and preference data. Step 3: The explanation section explains the product based on the answers provided by the answering section. For example, it uses a generative AI to explain the product's features, usage, benefits, and comparisons with other products. Step 4: The recommendation unit recommends products based on the descriptions provided by the explanation unit. For example, it can use generation AI to suggest recommended products based on the customer's preferences, and also suggest related products based on past purchase history and preference data.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0172] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0175] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0184] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0185] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0189] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0190] 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.

[0191] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0192] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0193] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0194] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0197] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recognition unit that performs face or voice recognition of customers; an answering unit that provides an answer to the question of the customer recognized by the recognition unit; an explanation unit that explains the product based on the answer provided by the answer unit; a recommendation unit that recommends products based on the explanation provided by the explanation unit; Equipped with A system characterized by:

2. Equipped with a support department to assist with purchasing procedures 2. The system of claim 1.

3. The recognition unit Estimate the customer's emotions and adjust the recognition accuracy based on the estimated emotions.

2. The system of claim 1.

4. The recognition unit Analyze the customer's past visit history and take it into account when recognizing them 2. The system of claim 1.

5. The recognition unit Analyze customers' clothing and belongings and provide individual support 2. The system of claim 1.

6. The recognition unit Estimate the age and gender of customers and choose the appropriate response 2. The system of claim 1.

7. The recognition unit Estimate the customer's emotions and adjust the display method of the recognition results based on the estimated customer emotions.

2. The system of claim 1.

8. The recognition unit Prioritize relevant information based on a customer's geographic location 2. The system of claim 1.

9. The recognition unit Analyze customers' social media activity and obtain relevant information 2. The system of claim 1.

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