system

Abstract

The system according to this embodiment aims to allow customers to deepen their knowledge of the product while proceeding with the purchase process. [Solution] The system according to the embodiment comprises a reception unit, a question unit, an answer reception unit, a judgment unit, and a procedure progress unit. The reception unit receives the customer's selection of a product and initiates the purchase procedure. The question unit presents a quiz about the product selected by the reception unit. The answer reception unit receives the customer's answer to the quiz presented by the question unit. The judgment unit determines whether the answer to the quiz is correct based on the answer received by the answer reception unit. The procedure progress unit proceeds with the purchase procedure if the judgment unit determines the answer is correct.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that when a customer purchases a product, there are few opportunities to deepen knowledge about the product.

[0005] The system according to the embodiment aims to proceed with the purchase procedure while the customer deepens knowledge about the product.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a question unit, an answer reception unit, a judgment unit, and a procedure progress unit. The reception unit receives the customer's selection of a product and initiates the purchase procedure. The question unit presents a quiz about the product selected by the reception unit. The answer reception unit receives the customer's answer to the quiz presented by the question unit. The judgment unit determines whether the answer to the quiz is correct based on the answer received by the answer reception unit. The procedure progress unit proceeds with the purchase procedure if the judgment unit determines the answer is correct. [Effects of the Invention]

[0007] The system according to this embodiment allows customers to proceed with the purchase process while deepening their knowledge of the product. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The product purchase support system according to an embodiment of the present invention is a system that presents customers with a quiz about a product before purchase, and enables purchase if the customer answers correctly. The product purchase support system allows the customer to select a product and start the purchase procedure. Next, the product purchase support system presents a quiz about the product. This quiz is about the product's features, usage, history, etc. If the customer answers the quiz correctly, they can proceed with the purchase procedure. Conversely, if they answer the quiz incorrectly, they can try the quiz again or select a different product. This mechanism allows customers to deepen their knowledge about the product and increase their desire to purchase. Furthermore, the content of the quiz is generated by a generation AI, and the difficulty level can be adjusted based on the customer's answer history. For example, by avoiding questions that have been answered correctly in the past, new knowledge can be provided to the customer. In addition, it is possible to offer rewards and discounts depending on the quiz correct answer rate and answer time. This can improve customer satisfaction and is expected to increase repeat customers. In this way, the product purchase support system can increase customer desire to purchase and improve customer satisfaction.

[0029] The product purchase support system according to this embodiment comprises a reception unit, a question unit, an answer reception unit, a judgment unit, and a procedure progress unit. The reception unit allows the customer to select a product and initiate the purchase procedure. The reception unit provides an interface for the customer to select a product and initiate the purchase procedure, for example, through an online shopping site or a mobile application. The question unit presents a quiz about the product selected by the reception unit. The question unit uses a generation AI to present quizzes about the product's features, usage, history, etc., for example. The generation AI generates quizzes based on product descriptions and catalog information, for example. The answer reception unit receives the customer's answers to the quizzes presented by the question unit. The answer reception unit provides an interface for the customer to answer the quiz, for example, by providing multiple-choice or written answer formats. The judgment unit determines whether the answer is correct based on the answer received by the answer reception unit. The judgment unit analyzes the customer's answer using a generation AI, for example, and determines whether it is correct or incorrect. The procedure progress unit proceeds with the purchase procedure if the judgment unit determines the answer is correct. The procedure management unit provides, for example, an interface for credit card payment and delivery procedures. This allows the product purchase support system according to the embodiment to deepen the customer's knowledge of the product and increase their desire to purchase it.

[0030] The reception desk allows customers to select products and begin the purchase process. The reception desk provides an interface for customers to select products and begin the purchase process, for example, through online shopping sites or mobile applications. Specifically, the reception desk provides a user-friendly interface, enabling customers to easily search, select, add products to their cart, and begin the purchase process. For example, when a customer searches for a specific product, the reception desk displays related and recommended products, expanding the customer's options. Furthermore, the product details page provides information such as product images, descriptions, prices, and reviews, allowing customers to make informed purchasing decisions. The reception desk also features a function to suggest personalized products based on the customer's past purchase and browsing history. This makes it easier for customers to find products that suit them and increases their purchase intent. The reception desk also provides an interface that prompts customers to log in or register when they begin the purchase process. This allows for centralized management of customer information and supports a smooth purchase process.

[0031] The question-generating department creates quizzes about products selected by the reception department. For example, the question-generating department uses a generative AI to create quizzes about product features, usage, history, etc. The generative AI generates quizzes based on product descriptions and catalog information, for example. Specifically, the generative AI analyzes detailed product information and automatically generates relevant quiz questions and answer choices. For instance, the generative AI extracts important keywords from product descriptions and creates quiz questions based on them. It can also generate quizzes that are likely to interest customers based on information about product usage and history. The generative AI uses natural language processing technology to generate quiz questions in natural language and provides them in a format that is easy for customers to understand. Furthermore, the generative AI can adjust the difficulty level and question format according to the product category and characteristics. For example, quizzes about expensive or specialized products are set to a higher difficulty level to allow customers to deepen their knowledge of the product. On the other hand, quizzes about general products are set to a lower difficulty level to allow customers to participate casually. This allows the question-generating department to provide effective quizzes that deepen customers' knowledge of the product and increase their purchase intent.

[0032] The response reception unit receives customer responses to quizzes posed by the question-setting unit. The response reception unit provides an interface for customers to answer the quiz, for example, by offering multiple-choice or open-ended answer formats. Specifically, the response reception unit provides an intuitive interface to allow customers to easily input their answers. In multiple-choice quizzes, multiple options are displayed, and customers can choose the option they believe is correct. In open-ended quizzes, a text box is provided where customers can freely enter their answers. After a customer enters an answer, the response reception unit immediately accepts it and either proceeds to the next question or displays the results. Furthermore, the response reception unit provides help functions and guidelines to support customers in their quiz-taking process. For example, it displays explanations of the quiz rules and answering methods to ensure customers can participate smoothly. The response reception unit can also set time limits for customers to answer the quiz. This allows customers to focus on the quiz and more effectively deepen their knowledge of the product.

[0033] The judgment unit determines the correct answer to a quiz based on the answer received by the answer reception unit. The judgment unit analyzes the customer's answer using, for example, a generative AI to determine whether it is correct or incorrect. Specifically, the generative AI compares the customer's answer with a pre-configured database of correct answers to determine whether it is correct or incorrect. In multiple-choice quizzes, it immediately determines whether the customer's chosen option is correct and displays the result. In written quizzes, the generative AI analyzes the customer's answer using natural language processing technology to determine whether it matches the correct answer. For example, the generative AI tokenizes the customer's answer, extracts important keywords and phrases, and compares them with the database of correct answers. This allows the judgment unit to determine the accuracy of the customer's answer with high precision. Furthermore, the judgment unit also has a function to provide feedback on the customer's answer. For example, if the answer is correct, it displays "That's correct!" and if it is incorrect, it displays "Unfortunately, that's incorrect." In the case of an incorrect answer, it also displays an explanation of the correct answer and related information to allow the customer to deepen their learning. This allows the evaluation unit to support customers in deepening their knowledge of the product through quizzes and increase their willingness to purchase.

[0034] The processing unit proceeds with the purchase process if the judgment unit determines it to be correct. The processing unit provides interfaces for, for example, credit card payments and shipping procedures. Specifically, the processing unit provides an intuitive and user-friendly interface so that customers can complete the purchase process smoothly. For credit card payments, it allows customers to enter their card information and complete the payment in a secure environment. For shipping procedures, it allows customers to enter their shipping address and select the shipping method and date. Furthermore, the processing unit displays the progress of the purchase process in real time, allowing customers to check the current status. For example, it displays "Payment Complete" when payment is complete and "Shipping in Progress" when shipping is in progress. In addition, the processing unit provides a help function and means of contacting customer support to assist customers if they need support while proceeding with the purchase process. In this way, the processing unit can support customers so that they can proceed with the purchase process with peace of mind, thereby improving customer satisfaction.

[0035] The question-generating unit can create quizzes about the product's features, usage, history, etc. For example, the question-generating unit can create quizzes about the product's main functions and features. For example, the question-generating unit can create quizzes about the product's usage and maintenance. For example, the question-generating unit can create quizzes about the product's history and background. This allows customers to deepen their knowledge of the product. Some or all of the above-described processes in the question-generating unit may be performed using a generative AI, or not. For example, the question-generating unit can input product descriptions and catalog information into a generative AI and have the generative AI generate the quizzes.

[0036] The question generation unit can adjust the difficulty level of quizzes based on the customer's answer history. For example, the question generation unit can generate new quizzes while avoiding questions the customer has answered correctly in the past. For example, the question generation unit can also generate quizzes on areas in which the customer excels, based on the customer's past answer history. For example, the question generation unit can analyze the customer's past answer history and generate quizzes on areas in which they struggle. This allows the system to provide quizzes of appropriate difficulty levels to the customer. Some or all of the above-described processes in the question generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the question generation unit can input customer answer history data into a generation AI and have the generation AI perform the quiz difficulty adjustment.

[0037] The judgment unit can avoid quiz content that has been answered correctly in the past. For example, the judgment unit can record the content of quizzes that the customer has answered correctly in the past and avoid the same content when presenting quizzes in the future. The judgment unit can also avoid quizzes from a specific period or quizzes from a specific category. This allows the customer to be provided with new knowledge. Some or all of the above processing in the judgment unit may be performed using a generative AI or not. For example, the judgment unit can input the customer's past quiz answer data into a generative AI and have the generative AI execute the process of avoiding content when presenting quizzes in the future.

[0038] The procedure execution unit can allow the user to try the quiz again or select a different product if they answer incorrectly. For example, the procedure execution unit can provide the customer with the option to try again if they answer the quiz incorrectly. The procedure execution unit can also set, for example, a limit on the number of retries and the timing of retries. The procedure execution unit can also provide the option to select a different product and present a range of selectable products. This allows the customer to try the quiz again or select a different product. Some or all of the above processing in the procedure execution unit may be performed using a generative AI or not. For example, the procedure execution unit can input the customer's quiz answer data into a generative AI and have the generative AI execute the options of trying again or selecting other products.

[0039] The procedure management unit can offer rewards and discounts based on the quiz's accuracy rate and response time. For example, the procedure management unit can offer points or coupons based on the customer's quiz accuracy rate. The procedure management unit can also offer discounts based on the response time. For example, the procedure management unit can set the content and conditions of the rewards and discounts and notify the customer. This can improve customer satisfaction and is expected to increase repeat customers. Some or all of the above processing in the procedure management unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the procedure management unit can input the customer's quiz response data into a generation AI and have the generation AI execute the provision of rewards and discounts.

[0040] The reception desk can analyze a customer's past purchase history and recommend the most suitable products. For example, the reception desk can automatically display products similar to those the customer has previously purchased as candidates. For example, the reception desk can also analyze the frequency of use of products the customer has previously purchased and recommend related products. For example, the reception desk can suggest products related to a specific season or event based on the customer's past purchase history. This allows the reception desk to recommend the most suitable products to the customer. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input customer purchase history data into a generative AI and have the generative AI recommend the most suitable products.

[0041] The reception desk can filter products based on the customer's current interests and preferences when they select a product. For example, the reception desk can prioritize displaying relevant products based on keywords the customer has recently searched for. The reception desk can also analyze what the customer has shared on social media and recommend products of interest. The reception desk can also filter and display relevant products based on the customer's browsing history of product pages. This allows the reception desk to provide products tailored to the customer's interests and preferences. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the customer's search history data into a generative AI and have the generative AI perform the filtering of relevant products.

[0042] The reception desk can prioritize displaying highly relevant products when customers select products, taking into account their geographical location. For example, the reception desk can display products available at nearby stores based on the customer's current location. The reception desk can also recommend region-specific products and services based on the customer's geographical location. The reception desk can also prioritize displaying products that can be delivered, taking into account the customer's location. This allows for the provision of products based on the customer's geographical location. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input customer location data into a generative AI and have the generative AI display highly relevant products.

[0043] The reception desk can analyze a customer's social media activity when they select a product and recommend relevant products. For example, the reception desk can recommend relevant products based on products the customer has "liked" or shared on social media. The reception desk can also analyze posts from brands and influencers the customer follows and display relevant products. The reception desk can also recommend products of interest based on comments and reviews the customer has made on social media. This allows the reception desk to provide products based on the customer's social media activity. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the customer's social media data into a generative AI and have the generative AI perform the recommendation of relevant products.

[0044] The question-generating unit can select quiz content based on the product's key features when creating a quiz. For example, the question-generating unit can create quizzes about the product's main functions and features. For example, the question-generating unit can create quizzes about the product's usage and maintenance. For example, the question-generating unit can create quizzes about the product's history and background. This allows the system to provide quizzes based on the product's key features. Some or all of the above-described processes in the question-generating unit may be performed using a generative AI, or not. For example, the question-generating unit can input product descriptions and catalog information into a generative AI and have the generative AI select the quiz content.

[0045] The question-generating unit can apply different question-generating algorithms depending on the product category when generating quizzes. For example, in the case of electronic products, the question-generating unit may generate quizzes about technical features. For example, in the case of food products, the question-generating unit may generate quizzes about ingredients and nutritional value. For example, in the case of fashion items, the question-generating unit may generate quizzes about design and materials. This allows for the provision of quizzes tailored to the product category. Some or all of the above-described processing in the question-generating unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the question-generating unit can input product category data into a generative AI and have the generative AI execute the application of the question-generating algorithm.

[0046] The question generation unit can generate new quizzes based on the customer's past answer history when presenting quizzes. For example, the question generation unit can generate new quizzes while avoiding questions the customer has answered correctly in the past. For example, the question generation unit can also present quizzes on areas in which the customer excels based on their past answer history. For example, the question generation unit can analyze the customer's past answer history and present quizzes on areas in which they struggle. This allows the system to provide new quizzes based on the customer's past answer history. Some or all of the above processing in the question generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the question generation unit can input customer answer history data into a generation AI and have the generation AI generate new quizzes.

[0047] The question-generating unit can enrich the quiz content by referring to product-related information when creating quiz questions. For example, the question-generating unit can obtain information from the product's official website or catalog to enrich the quiz content. For example, the question-generating unit can refer to product reviews and ratings to make the quiz content more comprehensive. For example, the question-generating unit can update the quiz content with the latest information based on product-related news and articles. This allows the system to provide quizzes based on product-related information. Some or all of the above processing in the question-generating unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the question-generating unit can input product-related information data into a generation AI and have the generation AI perform the task of enriching the quiz content.

[0048] The response reception unit can, upon receiving a response, refer to the customer's past response history to suggest the most suitable response method. For example, the response reception unit can prioritize suggesting response methods (multiple choice, open-ended, etc.) that the customer has used in the past. For example, the response reception unit can also suggest response formats that the customer is good at based on their past response history. For example, the response reception unit can analyze the customer's past response history and avoid response formats that the customer is not good at. This allows the response reception unit to provide the most suitable response method based on the customer's past response history. Some or all of the above processing in the response reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response reception unit can input the customer's response history data into a generation AI and have the generation AI perform the task of suggesting the most suitable response method.

[0049] The response reception unit can provide hints for the answer based on the customer's current situation when a response is received. For example, if the customer is having trouble answering, the response reception unit can provide relevant information and hints. For example, if the customer is in a hurry, the response reception unit can provide concise hints. For example, if the customer is relaxed, the response reception unit can provide detailed hints. This allows the response reception unit to provide hints for the answer that are appropriate to the customer's current situation. Some or all of the above processing in the response reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the response reception unit can input customer situation data into a generative AI and have the generative AI perform the task of providing hints for the answer.

[0050] The response reception unit can, upon receiving a response, present relevant response methods while considering the customer's geographical location. For example, the response reception unit can present region-specific response methods based on the customer's current location. The response reception unit can also, for example, suggest region-related response methods based on the customer's geographical location. The response reception unit can also, for example, present the optimal response method while considering the customer's location. This allows for the provision of response methods based on the customer's geographical location. Some or all of the above processing in the response reception unit may be performed using a generative AI, or not. For example, the response reception unit can input customer location data into a generative AI and have the generative AI perform the task of presenting relevant response methods.

[0051] The response reception unit can analyze the customer's social media activity and suggest relevant response methods when receiving responses. For example, the response reception unit can suggest relevant response methods based on what the customer has shared on social media. For example, the response reception unit can also analyze posts from brands and influencers that the customer follows and suggest relevant response methods. For example, the response reception unit can suggest the most suitable response method based on the customer's comments and reviews on social media. This allows the system to provide response methods based on the customer's social media activity. Some or all of the above processing in the response reception unit may be performed using or without generative AI. For example, the response reception unit can input the customer's social media data into a generative AI and have the generative AI perform the task of suggesting relevant response methods.

[0052] The judgment unit can improve the accuracy of its judgment by referring to the customer's past answer history during the judgment process. For example, the judgment unit can analyze patterns of quizzes that the customer has answered correctly in the past to improve the accuracy of its judgment. For example, the judgment unit can also reflect the customer's strengths and weaknesses in its judgment based on the customer's past answer history. For example, the judgment unit can evaluate the consistency of answers based on the customer's past answer history to improve the accuracy of its judgment. This allows the judgment unit to provide accuracy based on the customer's past answer history. Some or all of the above processing in the judgment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the judgment unit can input the customer's answer history data into a generative AI and have the generative AI perform the improvement of the judgment accuracy.

[0053] The judgment unit can apply different judgment algorithms depending on the product category during the judgment process. For example, in the case of electronic products, the judgment unit may apply a judgment algorithm that emphasizes technical knowledge. For example, in the case of food products, the judgment unit may also apply a judgment algorithm that emphasizes knowledge of ingredients and nutritional value. For example, in the case of fashion items, the judgment unit may also apply a judgment algorithm that emphasizes knowledge of design and materials. This allows the judgment unit to provide judgment algorithms appropriate to the product category. Some or all of the above processing in the judgment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the judgment unit can input product category data into a generation AI and have the generation AI execute the application of the judgment algorithm.

[0054] The determination unit can improve the accuracy of its determination by considering the customer's geographical location information during the determination process. For example, the determination unit can reflect region-specific knowledge in its determination based on the customer's current location. For example, the determination unit can also perform determinations that emphasize region-related knowledge based on the customer's geographical location information. For example, the determination unit can improve the accuracy of its determination by considering the customer's location information. This allows for the provision of determination accuracy based on the customer's geographical location information. Some or all of the above-described processes in the determination unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the determination unit can input customer location data into a generative AI and have the generative AI perform the task of improving the accuracy of the determination.

[0055] The judgment unit can analyze the customer's social media activity during the judgment process to improve the accuracy of the judgment. For example, the judgment unit can reflect relevant knowledge in the judgment based on what the customer has shared on social media. For example, the judgment unit can also analyze posts from brands and influencers that the customer follows and reflect relevant knowledge in the judgment. For example, the judgment unit can improve the accuracy of the judgment based on the customer's comments and reviews on social media. This allows for the provision of judgment accuracy based on the customer's social media activity. Some or all of the above processing in the judgment unit may be performed using generative AI, or not. For example, the judgment unit can input the customer's social media data into a generative AI and have the generative AI perform the task of improving the accuracy of the judgment.

[0056] The procedure execution unit can select the optimal procedure execution method by referring to the customer's past purchase history during the execution of a procedure. For example, the procedure execution unit may prioritize suggesting procedure execution methods that the customer has used in the past. For example, the procedure execution unit may also suggest procedure execution methods that it is good at based on the customer's past purchase history. For example, the procedure execution unit may select the optimal procedure execution method based on the customer's past purchase history. This allows the system to provide the optimal procedure execution method based on the customer's past purchase history. Some or all of the above processing in the procedure execution unit may be performed using a generation AI, or not. For example, the procedure execution unit can input customer purchase history data into a generation AI and have the generation AI select the optimal procedure execution method.

[0057] The procedure progress unit can customize the means of proceeding with the procedure based on the customer's current situation during the procedure. For example, if the customer is in a hurry, the procedure progress unit can provide a fast procedure progress method. For example, if the customer is relaxed, the procedure progress unit can provide a procedure progress method that includes detailed explanations. For example, if the customer is stressed, the procedure progress unit can provide a simple and intuitive procedure progress method. This allows the procedure progress unit to provide means of proceeding with the procedure that are appropriate to the customer's current situation. Some or all of the above processing in the procedure progress unit may be performed using a generative AI, or not. For example, the procedure progress unit can input customer situation data into a generative AI and have the generative AI perform the customization of the means of proceeding with the procedure.

[0058] The procedure progress unit can select the optimal procedure progress method while considering the customer's geographical location information. For example, the procedure progress unit can provide a region-specific procedure progress method based on the customer's current location. For example, the procedure progress unit can also propose a region-related procedure progress method based on the customer's geographical location information. For example, the procedure progress unit can select the optimal procedure progress method while considering the customer's location information. This allows the system to provide the optimal procedure progress method based on the customer's geographical location information. Some or all of the above processing in the procedure progress unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the procedure progress unit can input customer location data into a generation AI and have the generation AI select the optimal procedure progress method.

[0059] The procedure progress unit can analyze the customer's social media activity and propose methods for proceeding with the procedure during the process. For example, the procedure progress unit can propose relevant methods of proceeding based on the content the customer has shared on social media. For example, the procedure progress unit can also analyze posts from brands and influencers the customer follows and propose relevant methods of proceeding. For example, the procedure progress unit can propose the optimal method of proceeding based on the customer's comments and reviews on social media. This allows the system to provide methods of proceeding with the procedure based on the customer's social media activity. Some or all of the above processing in the procedure progress unit may be performed using or without a generative AI. For example, the procedure progress unit can input the customer's social media data into a generative AI and have the generative AI propose methods of proceeding with the procedure.

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

[0061] The product purchase support system can further analyze customer purchase history and customize quiz content based on past purchase patterns. For example, it can attract customer interest by presenting quizzes related to products the customer has purchased in the past. It can also deepen customer knowledge by presenting quizzes on the usage and maintenance of products the customer has purchased in the past. Furthermore, it can increase customer interest by generating quizzes based on reviews and ratings of products the customer has purchased in the past. In this way, it is possible to provide quizzes based on the customer's purchase history and improve customer satisfaction.

[0062] The product purchase support system can further customize quiz content by taking into account the customer's geographical location. For example, if a customer is in a specific region, the system can present quizzes related to products in that region to increase their interest. Similarly, if a customer is traveling, the system can present quizzes related to products in their travel destination to enhance their engagement. Furthermore, if a customer is participating in a specific event, the system can present quizzes related to products in that event to further increase their interest. This allows the system to provide quizzes based on the customer's geographical location, thereby improving customer satisfaction.

[0063] The product purchase support system can further analyze customers' social media activity and generate relevant quizzes. For example, it can generate relevant quizzes based on products that customers have "liked" or shared on social media. It can also analyze posts from brands and influencers that customers follow and generate relevant quizzes. Furthermore, it can generate quizzes on products that customers are interested in based on their comments and reviews on social media. This allows the system to provide quizzes based on customers' social media activity, thereby improving customer satisfaction.

[0064] The product purchase support system can further analyze customer purchase history and adjust the frequency of quizzes based on past purchase patterns. For example, regularly presenting quizzes about products that customers frequently purchase can increase their interest. Similarly, presenting seasonal quizzes about products that customers purchase during specific seasons can enhance customer engagement. Furthermore, if a customer purchases products related to a particular event, presenting quizzes related to that event can further increase their interest. This allows the system to provide quiz frequencies tailored to customer purchase history, thereby improving customer satisfaction.

[0065] The product purchase support system can further adjust the timing of quiz questions based on the customer's geographical location. For example, if a customer is in a specific region, the timing of quizzes related to products in that region can be adjusted. Similarly, if a customer is traveling, the timing of quizzes related to products in their travel destination can be adjusted. Furthermore, if a customer is participating in a specific event, the timing of quizzes related to products in that event can be adjusted. This allows for quiz timing based on the customer's geographical location, thereby improving customer satisfaction.

[0066] The product purchase support system can further analyze customers' social media activity and adjust the timing of relevant quizzes. For example, it can adjust the timing of relevant quizzes based on products that customers have "liked" or shared on social media. It can also analyze posts from brands and influencers that customers follow and adjust the timing of relevant quizzes. Furthermore, it can adjust the timing of quizzes on products of interest based on comments and reviews on customers' social media. This allows the system to provide quizzes tailored to customers' social media activity, thereby improving customer satisfaction.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk allows the customer to select products and begin the purchase process. The reception desk provides an interface that allows customers to select products and begin the purchase process, for example, through an online shopping site or mobile application. Step 2: The question-generating unit creates quizzes about the products selected by the reception unit. The question-generating unit uses a generation AI to create quizzes about, for example, the product's features, usage, and history. The generation AI generates quizzes based on the product's description and catalog information. Step 3: The response reception unit receives customer responses to the quiz questions posed by the question generation unit. The response reception unit provides an interface for customers to answer the quiz, for example, by offering multiple-choice or written response formats. Step 4: The judgment unit determines the correct answer to the quiz based on the answer received by the answer reception unit. The judgment unit analyzes the customer's answer using, for example, a generation AI to determine whether it is correct or incorrect. Step 5: The procedure progress unit proceeds with the purchase process if the judgment unit determines it to be correct. The procedure progress unit provides interfaces for, for example, credit card payment and delivery procedures.

[0069] (Example of form 2) The product purchase support system according to an embodiment of the present invention is a system that presents customers with a quiz about a product before purchase, and enables purchase if the customer answers correctly. The product purchase support system allows the customer to select a product and start the purchase procedure. Next, the product purchase support system presents a quiz about the product. This quiz is about the product's features, usage, history, etc. If the customer answers the quiz correctly, they can proceed with the purchase procedure. Conversely, if they answer the quiz incorrectly, they can try the quiz again or select a different product. This mechanism allows customers to deepen their knowledge about the product and increase their desire to purchase. Furthermore, the content of the quiz is generated by a generation AI, and the difficulty level can be adjusted based on the customer's answer history. For example, by avoiding questions that have been answered correctly in the past, new knowledge can be provided to the customer. In addition, it is possible to offer rewards and discounts depending on the quiz correct answer rate and answer time. This can improve customer satisfaction and is expected to increase repeat customers. In this way, the product purchase support system can increase customer desire to purchase and improve customer satisfaction.

[0070] The product purchase support system according to this embodiment comprises a reception unit, a question unit, an answer reception unit, a judgment unit, and a procedure progress unit. The reception unit allows the customer to select a product and initiate the purchase procedure. The reception unit provides an interface for the customer to select a product and initiate the purchase procedure, for example, through an online shopping site or a mobile application. The question unit presents a quiz about the product selected by the reception unit. The question unit uses a generation AI to present quizzes about the product's features, usage, history, etc., for example. The generation AI generates quizzes based on product descriptions and catalog information, for example. The answer reception unit receives the customer's answers to the quizzes presented by the question unit. The answer reception unit provides an interface for the customer to answer the quiz, for example, by providing multiple-choice or written answer formats. The judgment unit determines whether the answer is correct based on the answer received by the answer reception unit. The judgment unit analyzes the customer's answer using a generation AI, for example, and determines whether it is correct or incorrect. The procedure progress unit proceeds with the purchase procedure if the judgment unit determines the answer is correct. The procedure management unit provides, for example, an interface for credit card payment and delivery procedures. This allows the product purchase support system according to the embodiment to deepen the customer's knowledge of the product and increase their desire to purchase it.

[0071] The reception desk allows customers to select products and begin the purchase process. The reception desk provides an interface for customers to select products and begin the purchase process, for example, through online shopping sites or mobile applications. Specifically, the reception desk provides a user-friendly interface, enabling customers to easily search, select, add products to their cart, and begin the purchase process. For example, when a customer searches for a specific product, the reception desk displays related and recommended products, expanding the customer's options. Furthermore, the product details page provides information such as product images, descriptions, prices, and reviews, allowing customers to make informed purchasing decisions. The reception desk also features a function to suggest personalized products based on the customer's past purchase and browsing history. This makes it easier for customers to find products that suit them and increases their purchase intent. The reception desk also provides an interface that prompts customers to log in or register when they begin the purchase process. This allows for centralized management of customer information and supports a smooth purchase process.

[0072] The question-generating department creates quizzes about products selected by the reception department. For example, the question-generating department uses a generative AI to create quizzes about product features, usage, history, etc. The generative AI generates quizzes based on product descriptions and catalog information, for example. Specifically, the generative AI analyzes detailed product information and automatically generates relevant quiz questions and answer choices. For instance, the generative AI extracts important keywords from product descriptions and creates quiz questions based on them. It can also generate quizzes that are likely to interest customers based on information about product usage and history. The generative AI uses natural language processing technology to generate quiz questions in natural language and provides them in a format that is easy for customers to understand. Furthermore, the generative AI can adjust the difficulty level and question format according to the product category and characteristics. For example, quizzes about expensive or specialized products are set to a higher difficulty level to allow customers to deepen their knowledge of the product. On the other hand, quizzes about general products are set to a lower difficulty level to allow customers to participate casually. This allows the question-generating department to provide effective quizzes that deepen customers' knowledge of the product and increase their purchase intent.

[0073] The response reception unit receives customer responses to quizzes posed by the question-setting unit. The response reception unit provides an interface for customers to answer the quiz, for example, by offering multiple-choice or open-ended answer formats. Specifically, the response reception unit provides an intuitive interface to allow customers to easily input their answers. In multiple-choice quizzes, multiple options are displayed, and customers can choose the option they believe is correct. In open-ended quizzes, a text box is provided where customers can freely enter their answers. After a customer enters an answer, the response reception unit immediately accepts it and either proceeds to the next question or displays the results. Furthermore, the response reception unit provides help functions and guidelines to support customers in their quiz-taking process. For example, it displays explanations of the quiz rules and answering methods to ensure customers can participate smoothly. The response reception unit can also set time limits for customers to answer the quiz. This allows customers to focus on the quiz and more effectively deepen their knowledge of the product.

[0074] The judgment unit determines the correct answer to a quiz based on the answer received by the answer reception unit. The judgment unit analyzes the customer's answer using, for example, a generative AI to determine whether it is correct or incorrect. Specifically, the generative AI compares the customer's answer with a pre-configured database of correct answers to determine whether it is correct or incorrect. In multiple-choice quizzes, it immediately determines whether the customer's chosen option is correct and displays the result. In written quizzes, the generative AI analyzes the customer's answer using natural language processing technology to determine whether it matches the correct answer. For example, the generative AI tokenizes the customer's answer, extracts important keywords and phrases, and compares them with the database of correct answers. This allows the judgment unit to determine the accuracy of the customer's answer with high precision. Furthermore, the judgment unit also has a function to provide feedback on the customer's answer. For example, if the answer is correct, it displays "That's correct!" and if it is incorrect, it displays "Unfortunately, that's incorrect." In the case of an incorrect answer, it also displays an explanation of the correct answer and related information to allow the customer to deepen their learning. This allows the evaluation unit to support customers in deepening their knowledge of the product through quizzes and increase their willingness to purchase.

[0075] The processing unit proceeds with the purchase process if the judgment unit determines it to be correct. The processing unit provides interfaces for, for example, credit card payments and shipping procedures. Specifically, the processing unit provides an intuitive and user-friendly interface so that customers can complete the purchase process smoothly. For credit card payments, it allows customers to enter their card information and complete the payment in a secure environment. For shipping procedures, it allows customers to enter their shipping address and select the shipping method and date. Furthermore, the processing unit displays the progress of the purchase process in real time, allowing customers to check the current status. For example, it displays "Payment Complete" when payment is complete and "Shipping in Progress" when shipping is in progress. In addition, the processing unit provides a help function and means of contacting customer support to assist customers if they need support while proceeding with the purchase process. In this way, the processing unit can support customers so that they can proceed with the purchase process with peace of mind, thereby improving customer satisfaction.

[0076] The question-generating unit can create quizzes about the product's features, usage, history, etc. For example, the question-generating unit can create quizzes about the product's main functions and features. For example, the question-generating unit can create quizzes about the product's usage and maintenance. For example, the question-generating unit can create quizzes about the product's history and background. This allows customers to deepen their knowledge of the product. Some or all of the above-described processes in the question-generating unit may be performed using a generative AI, or not. For example, the question-generating unit can input product descriptions and catalog information into a generative AI and have the generative AI generate the quizzes.

[0077] The question generation unit can adjust the difficulty level of quizzes based on the customer's answer history. For example, the question generation unit can generate new quizzes while avoiding questions the customer has answered correctly in the past. For example, the question generation unit can also generate quizzes on areas in which the customer excels, based on the customer's past answer history. For example, the question generation unit can analyze the customer's past answer history and generate quizzes on areas in which they struggle. This allows the system to provide quizzes of appropriate difficulty levels to the customer. Some or all of the above-described processes in the question generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the question generation unit can input customer answer history data into a generation AI and have the generation AI perform the quiz difficulty adjustment.

[0078] The judgment unit can avoid quiz content that has been answered correctly in the past. For example, the judgment unit can record the content of quizzes that the customer has answered correctly in the past and avoid the same content when presenting quizzes in the future. The judgment unit can also avoid quizzes from a specific period or quizzes from a specific category. This allows the customer to be provided with new knowledge. Some or all of the above processing in the judgment unit may be performed using a generative AI or not. For example, the judgment unit can input the customer's past quiz answer data into a generative AI and have the generative AI execute the process of avoiding content when presenting quizzes in the future.

[0079] The procedure execution unit can allow the user to try the quiz again or select a different product if they answer incorrectly. For example, the procedure execution unit can provide the customer with the option to try again if they answer the quiz incorrectly. The procedure execution unit can also set, for example, a limit on the number of retries and the timing of retries. The procedure execution unit can also provide the option to select a different product and present a range of selectable products. This allows the customer to try the quiz again or select a different product. Some or all of the above processing in the procedure execution unit may be performed using a generative AI or not. For example, the procedure execution unit can input the customer's quiz answer data into a generative AI and have the generative AI execute the options of trying again or selecting other products.

[0080] The procedure management unit can offer rewards and discounts based on the quiz's accuracy rate and response time. For example, the procedure management unit can offer points or coupons based on the customer's quiz accuracy rate. The procedure management unit can also offer discounts based on the response time. For example, the procedure management unit can set the content and conditions of the rewards and discounts and notify the customer. This can improve customer satisfaction and is expected to increase repeat customers. Some or all of the above processing in the procedure management unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the procedure management unit can input the customer's quiz response data into a generation AI and have the generation AI execute the provision of rewards and discounts.

[0081] The reception desk can estimate the customer's emotions and adjust the product selection interface based on the estimated emotions. For example, if the customer is stressed, the reception desk can provide a simple interface and minimize the steps involved in product selection. If the customer is relaxed, for example, the reception desk can provide detailed product information and suggest customizable options. If the customer is in a hurry, for example, the reception desk can prioritize voice input to allow for quick product selection. This allows for the provision of an interface that responds to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can analyze a customer's past purchase history and recommend the most suitable products. For example, the reception desk can automatically display products similar to those the customer has previously purchased as candidates. For example, the reception desk can also analyze the frequency of use of products the customer has previously purchased and recommend related products. For example, the reception desk can suggest products related to a specific season or event based on the customer's past purchase history. This allows the reception desk to recommend the most suitable products to the customer. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input customer purchase history data into a generative AI and have the generative AI recommend the most suitable products.

[0083] The reception desk can filter products based on the customer's current interests and preferences when they select a product. For example, the reception desk can prioritize displaying relevant products based on keywords the customer has recently searched for. The reception desk can also analyze what the customer has shared on social media and recommend products of interest. The reception desk can also filter and display relevant products based on the customer's browsing history of product pages. This allows the reception desk to provide products tailored to the customer's interests and preferences. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the customer's search history data into a generative AI and have the generative AI perform the filtering of relevant products.

[0084] The reception desk can estimate the customer's emotions and determine the priority of product selection based on the estimated emotions. For example, if the customer is excited, the reception desk may prioritize displaying popular or new products. If the customer is relaxed, the reception desk may also provide detailed product information and broaden the options. If the customer is stressed, the reception desk may also prioritize simple and intuitive product selections. This allows for the provision of product selection priorities that correspond to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk may input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reception desk can prioritize displaying highly relevant products when customers select products, taking into account their geographical location. For example, the reception desk can display products available at nearby stores based on the customer's current location. The reception desk can also recommend region-specific products and services based on the customer's geographical location. The reception desk can also prioritize displaying products that can be delivered, taking into account the customer's location. This allows for the provision of products based on the customer's geographical location. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input customer location data into a generative AI and have the generative AI display highly relevant products.

[0086] The reception desk can analyze a customer's social media activity when they select a product and recommend relevant products. For example, the reception desk can recommend relevant products based on products the customer has "liked" or shared on social media. The reception desk can also analyze posts from brands and influencers the customer follows and display relevant products. The reception desk can also recommend products of interest based on comments and reviews the customer has made on social media. This allows the reception desk to provide products based on the customer's social media activity. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the customer's social media data into a generative AI and have the generative AI perform the recommendation of relevant products.

[0087] The question-setting unit can estimate the customer's emotions and adjust the quiz presentation method based on the estimated emotions. For example, if the customer is relaxed, the question-setting unit may present a quiz with detailed explanations. For example, if the customer is in a hurry, the question-setting unit may present a concise and to-the-point quiz. For example, if the customer is excited, the question-setting unit may present a visually stimulating quiz. This provides a quiz presentation method that is tailored to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 processing in the question-setting unit may be performed using or without a generative AI. For example, the question-setting unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The question-generating unit can select quiz content based on the product's key features when creating a quiz. For example, the question-generating unit can create quizzes about the product's main functions and features. For example, the question-generating unit can create quizzes about the product's usage and maintenance. For example, the question-generating unit can create quizzes about the product's history and background. This allows the system to provide quizzes based on the product's key features. Some or all of the above-described processes in the question-generating unit may be performed using a generative AI, or not. For example, the question-generating unit can input product descriptions and catalog information into a generative AI and have the generative AI select the quiz content.

[0089] The question-generating unit can apply different question-generating algorithms depending on the product category when generating quizzes. For example, in the case of electronic products, the question-generating unit may generate quizzes about technical features. For example, in the case of food products, the question-generating unit may generate quizzes about ingredients and nutritional value. For example, in the case of fashion items, the question-generating unit may generate quizzes about design and materials. This allows for the provision of quizzes tailored to the product category. Some or all of the above-described processing in the question-generating unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the question-generating unit can input product category data into a generative AI and have the generative AI execute the application of the question-generating algorithm.

[0090] The question-generating unit can estimate the customer's emotions and adjust the difficulty of the quiz based on the estimated emotions. For example, if the customer is relaxed, the question-generating unit may present a slightly more difficult quiz. For example, if the customer is stressed, the question-generating unit may present an easy quiz. For example, if the customer is excited, the question-generating unit may present a challenging quiz. This allows for providing quiz difficulty levels that correspond to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the question-generating unit may be performed using a generative AI or not. For example, the question-generating unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The question generation unit can generate new quizzes based on the customer's past answer history when presenting quizzes. For example, the question generation unit can generate new quizzes while avoiding questions the customer has answered correctly in the past. For example, the question generation unit can also present quizzes on areas in which the customer excels based on their past answer history. For example, the question generation unit can analyze the customer's past answer history and present quizzes on areas in which they struggle. This allows the system to provide new quizzes based on the customer's past answer history. Some or all of the above processing in the question generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the question generation unit can input customer answer history data into a generation AI and have the generation AI generate new quizzes.

[0092] The question-generating unit can enrich the quiz content by referring to product-related information when creating quiz questions. For example, the question-generating unit can obtain information from the product's official website or catalog to enrich the quiz content. For example, the question-generating unit can refer to product reviews and ratings to make the quiz content more comprehensive. For example, the question-generating unit can update the quiz content with the latest information based on product-related news and articles. This allows the system to provide quizzes based on product-related information. Some or all of the above processing in the question-generating unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the question-generating unit can input product-related information data into a generation AI and have the generation AI perform the task of enriching the quiz content.

[0093] The response reception unit can estimate the customer's emotions and adjust the response reception interface based on the estimated emotions. For example, if the customer is nervous, the response reception unit can provide an interface with calming colors to reduce visual stress. For example, if the customer is having fun, the response reception unit can provide an interface with bright colors to make the response process more enjoyable. For example, if the customer is tired, the response reception unit can provide a simple and highly visible interface to facilitate the response process. This allows for the provision of a response reception interface that is tailored to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, 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 processing in the response reception unit may be performed using a generative AI or not. For example, the response reception unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The response reception unit can, upon receiving a response, refer to the customer's past response history to suggest the most suitable response method. For example, the response reception unit can prioritize suggesting response methods (multiple choice, open-ended, etc.) that the customer has used in the past. For example, the response reception unit can also suggest response formats that the customer is good at based on their past response history. For example, the response reception unit can analyze the customer's past response history and avoid response formats that the customer is not good at. This allows the response reception unit to provide the most suitable response method based on the customer's past response history. Some or all of the above processing in the response reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response reception unit can input the customer's response history data into a generation AI and have the generation AI perform the task of suggesting the most suitable response method.

[0095] The response reception unit can provide hints for the answer based on the customer's current situation when a response is received. For example, if the customer is having trouble answering, the response reception unit can provide relevant information and hints. For example, if the customer is in a hurry, the response reception unit can provide concise hints. For example, if the customer is relaxed, the response reception unit can provide detailed hints. This allows the response reception unit to provide hints for the answer that are appropriate to the customer's current situation. Some or all of the above processing in the response reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the response reception unit can input customer situation data into a generative AI and have the generative AI perform the task of providing hints for the answer.

[0096] The response reception unit can estimate the customer's emotions and determine the priority of response reception based on the estimated emotions. For example, if the customer is excited, the response reception unit may prioritize receiving answers to important quizzes. For example, if the customer is relaxed, the response reception unit may also prioritize receiving detailed answers. For example, if the customer is stressed, the response reception unit may also prioritize receiving answers to simple quizzes. This provides a priority for response reception according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the response reception unit may be performed using a generative AI or not using a generative AI. For example, the response reception unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The response reception unit can, upon receiving a response, present relevant response methods while considering the customer's geographical location. For example, the response reception unit can present region-specific response methods based on the customer's current location. The response reception unit can also, for example, suggest region-related response methods based on the customer's geographical location. The response reception unit can also, for example, present the optimal response method while considering the customer's location. This allows for the provision of response methods based on the customer's geographical location. Some or all of the above processing in the response reception unit may be performed using a generative AI, or not. For example, the response reception unit can input customer location data into a generative AI and have the generative AI perform the task of presenting relevant response methods.

[0098] The response reception unit can analyze the customer's social media activity and suggest relevant response methods when receiving responses. For example, the response reception unit can suggest relevant response methods based on what the customer has shared on social media. For example, the response reception unit can also analyze posts from brands and influencers that the customer follows and suggest relevant response methods. For example, the response reception unit can suggest the most suitable response method based on the customer's comments and reviews on social media. This allows the system to provide response methods based on the customer's social media activity. Some or all of the above processing in the response reception unit may be performed using or without generative AI. For example, the response reception unit can input the customer's social media data into a generative AI and have the generative AI perform the task of suggesting relevant response methods.

[0099] The judgment unit can estimate the customer's emotions and adjust the judgment criteria based on the estimated emotions. For example, the judgment unit may apply slightly stricter criteria if the customer is relaxed. For example, the judgment unit may apply more lenient criteria if the customer is stressed. For example, the judgment unit may apply challenging criteria if the customer is excited. This allows for the provision of judgment criteria that correspond to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the judgment unit may be performed using a generative AI or not using a generative AI. For example, the judgment unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The judgment unit can improve the accuracy of its judgment by referring to the customer's past answer history during the judgment process. For example, the judgment unit can analyze patterns of quizzes that the customer has answered correctly in the past to improve the accuracy of its judgment. For example, the judgment unit can also reflect the customer's strengths and weaknesses in its judgment based on the customer's past answer history. For example, the judgment unit can evaluate the consistency of answers based on the customer's past answer history to improve the accuracy of its judgment. This allows the judgment unit to provide accuracy based on the customer's past answer history. Some or all of the above processing in the judgment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the judgment unit can input the customer's answer history data into a generative AI and have the generative AI perform the improvement of the judgment accuracy.

[0101] The judgment unit can apply different judgment algorithms depending on the product category during the judgment process. For example, in the case of electronic products, the judgment unit may apply a judgment algorithm that emphasizes technical knowledge. For example, in the case of food products, the judgment unit may also apply a judgment algorithm that emphasizes knowledge of ingredients and nutritional value. For example, in the case of fashion items, the judgment unit may also apply a judgment algorithm that emphasizes knowledge of design and materials. This allows the judgment unit to provide judgment algorithms appropriate to the product category. Some or all of the above processing in the judgment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the judgment unit can input product category data into a generation AI and have the generation AI execute the application of the judgment algorithm.

[0102] The judgment unit can estimate the customer's emotions and adjust the display method of the judgment result based on the estimated customer emotions. For example, if the customer is nervous, the judgment unit can provide a simple and highly visible display method. For example, if the customer is relaxed, the judgment unit can also provide a display method that includes detailed information. For example, if the customer is in a hurry, the judgment unit can also provide a display method that gets straight to the point. This allows for the display method of the judgment result to be tailored to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the judgment unit may be performed using a generative AI or not using a generative AI. For example, the judgment unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The determination unit can improve the accuracy of its determination by considering the customer's geographical location information during the determination process. For example, the determination unit can reflect region-specific knowledge in its determination based on the customer's current location. For example, the determination unit can also perform determinations that emphasize region-related knowledge based on the customer's geographical location information. For example, the determination unit can improve the accuracy of its determination by considering the customer's location information. This allows for the provision of determination accuracy based on the customer's geographical location information. Some or all of the above-described processes in the determination unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the determination unit can input customer location data into a generative AI and have the generative AI perform the task of improving the accuracy of the determination.

[0104] The judgment unit can analyze the customer's social media activity during the judgment process to improve the accuracy of the judgment. For example, the judgment unit can reflect relevant knowledge in the judgment based on what the customer has shared on social media. For example, the judgment unit can also analyze posts from brands and influencers that the customer follows and reflect relevant knowledge in the judgment. For example, the judgment unit can improve the accuracy of the judgment based on the customer's comments and reviews on social media. This allows for the provision of judgment accuracy based on the customer's social media activity. Some or all of the above processing in the judgment unit may be performed using generative AI, or not. For example, the judgment unit can input the customer's social media data into a generative AI and have the generative AI perform the task of improving the accuracy of the judgment.

[0105] The procedure execution unit can estimate the customer's emotions and adjust the procedure execution method based on the estimated customer emotions. For example, if the customer is relaxed, the procedure execution unit can provide a procedure execution method that includes detailed explanations. For example, if the customer is in a hurry, the procedure execution unit can also provide a concise and quick procedure execution method. For example, if the customer is stressed, the procedure execution unit can also provide a simple and intuitive procedure execution method. This allows for the provision of a procedure execution method that is appropriate to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the procedure execution unit may be performed using a generative AI or not using a generative AI. For example, the procedure execution unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The procedure execution unit can select the optimal procedure execution method by referring to the customer's past purchase history during the execution of a procedure. For example, the procedure execution unit may prioritize suggesting procedure execution methods that the customer has used in the past. For example, the procedure execution unit may also suggest procedure execution methods that it is good at based on the customer's past purchase history. For example, the procedure execution unit may select the optimal procedure execution method based on the customer's past purchase history. This allows the system to provide the optimal procedure execution method based on the customer's past purchase history. Some or all of the above processing in the procedure execution unit may be performed using a generation AI, or not. For example, the procedure execution unit can input customer purchase history data into a generation AI and have the generation AI select the optimal procedure execution method.

[0107] The procedure progress unit can customize the means of proceeding with the procedure based on the customer's current situation during the procedure. For example, if the customer is in a hurry, the procedure progress unit can provide a fast procedure progress method. For example, if the customer is relaxed, the procedure progress unit can provide a procedure progress method that includes detailed explanations. For example, if the customer is stressed, the procedure progress unit can provide a simple and intuitive procedure progress method. This allows the procedure progress unit to provide means of proceeding with the procedure that are appropriate to the customer's current situation. Some or all of the above processing in the procedure progress unit may be performed using a generative AI, or not. For example, the procedure progress unit can input customer situation data into a generative AI and have the generative AI perform the customization of the means of proceeding with the procedure.

[0108] The procedure execution unit can estimate the customer's emotions and determine the priority of the procedure execution based on the estimated emotions. For example, if the customer is excited, the procedure execution unit may prioritize important procedures. For example, if the customer is relaxed, the procedure execution unit may also prioritize detailed procedures. For example, if the customer is stressed, the procedure execution unit may also prioritize simple procedures. This provides a priority for procedures execution according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the procedure execution unit may be performed using a generative AI or not using a generative AI. For example, the procedure execution unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The procedure progress unit can select the optimal procedure progress method while considering the customer's geographical location information. For example, the procedure progress unit can provide a region-specific procedure progress method based on the customer's current location. For example, the procedure progress unit can also propose a region-related procedure progress method based on the customer's geographical location information. For example, the procedure progress unit can select the optimal procedure progress method while considering the customer's location information. This allows the system to provide the optimal procedure progress method based on the customer's geographical location information. Some or all of the above processing in the procedure progress unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the procedure progress unit can input customer location data into a generation AI and have the generation AI select the optimal procedure progress method.

[0110] The procedure progress unit can analyze the customer's social media activity and propose methods for proceeding with the procedure during the process. For example, the procedure progress unit can propose relevant methods of proceeding based on the content the customer has shared on social media. For example, the procedure progress unit can also analyze posts from brands and influencers the customer follows and propose relevant methods of proceeding. For example, the procedure progress unit can propose the optimal method of proceeding based on the customer's comments and reviews on social media. This allows the system to provide methods of proceeding with the procedure based on the customer's social media activity. Some or all of the above processing in the procedure progress unit may be performed using or without a generative AI. For example, the procedure progress unit can input the customer's social media data into a generative AI and have the generative AI propose methods of proceeding with the procedure.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The product purchase support system can further analyze customer purchase history and customize quiz content based on past purchase patterns. For example, it can attract customer interest by presenting quizzes related to products the customer has purchased in the past. It can also deepen customer knowledge by presenting quizzes on the usage and maintenance of products the customer has purchased in the past. Furthermore, it can increase customer interest by generating quizzes based on reviews and ratings of products the customer has purchased in the past. In this way, it is possible to provide quizzes based on the customer's purchase history and improve customer satisfaction.

[0113] The product purchase support system can further estimate the customer's emotions and adjust the timing of quiz presentations based on those emotions. For example, if the customer is relaxed, the timing of the quiz presentation can be delayed to allow the customer to fully understand the product information. If the customer is in a hurry, the quiz can be presented quickly to facilitate the purchase process. Furthermore, if the customer is stressed, the quiz presentation can be temporarily suspended, and content that helps the customer relax can be provided. This allows for quiz presentation timing tailored to the customer's emotions, thereby improving customer satisfaction.

[0114] The product purchase support system can further customize quiz content by taking into account the customer's geographical location. For example, if a customer is in a specific region, the system can present quizzes related to products in that region to increase their interest. Similarly, if a customer is traveling, the system can present quizzes related to products in their travel destination to enhance their engagement. Furthermore, if a customer is participating in a specific event, the system can present quizzes related to products in that event to further increase their interest. This allows the system to provide quizzes based on the customer's geographical location, thereby improving customer satisfaction.

[0115] The product purchase support system can further analyze customers' social media activity and generate relevant quizzes. For example, it can generate relevant quizzes based on products that customers have "liked" or shared on social media. It can also analyze posts from brands and influencers that customers follow and generate relevant quizzes. Furthermore, it can generate quizzes on products that customers are interested in based on their comments and reviews on social media. This allows the system to provide quizzes based on customers' social media activity, thereby improving customer satisfaction.

[0116] The product purchase support system can further estimate the customer's emotions and adjust the difficulty of the quiz based on those emotions. For example, if the customer is relaxed, it can present a slightly more difficult quiz. If the customer is stressed, it can present an easy quiz. Furthermore, if the customer is excited, it can present a challenging quiz. This allows the system to provide quiz difficulty levels that match the customer's emotions, thereby improving customer satisfaction.

[0117] The product purchase support system can further analyze customer purchase history and adjust the frequency of quizzes based on past purchase patterns. For example, regularly presenting quizzes about products that customers frequently purchase can increase their interest. Similarly, presenting seasonal quizzes about products that customers purchase during specific seasons can enhance customer engagement. Furthermore, if a customer purchases products related to a particular event, presenting quizzes related to that event can further increase their interest. This allows the system to provide quiz frequencies tailored to customer purchase history, thereby improving customer satisfaction.

[0118] The product purchase support system can further estimate the customer's emotions and adjust the quiz format based on those emotions. For example, if the customer is relaxed, it can present a quiz with detailed explanations. If the customer is in a hurry, it can present a concise and to-the-point quiz. Furthermore, if the customer is excited, it can present a visually stimulating quiz. This allows the system to provide a quiz format that matches the customer's emotions, thereby improving customer satisfaction.

[0119] The product purchase support system can further adjust the timing of quiz questions based on the customer's geographical location. For example, if a customer is in a specific region, the timing of quizzes related to products in that region can be adjusted. Similarly, if a customer is traveling, the timing of quizzes related to products in their travel destination can be adjusted. Furthermore, if a customer is participating in a specific event, the timing of quizzes related to products in that event can be adjusted. This allows for quiz timing based on the customer's geographical location, thereby improving customer satisfaction.

[0120] The product purchase support system can further estimate the customer's emotions and adjust the quiz content based on those emotions. For example, if the customer is relaxed, it can present a quiz with detailed explanations. If the customer is in a hurry, it can present a concise and to-the-point quiz. Furthermore, if the customer is excited, it can present a visually stimulating quiz. This allows the system to provide quiz content tailored to the customer's emotions, thereby improving customer satisfaction.

[0121] The product purchase support system can further analyze customers' social media activity and adjust the timing of relevant quizzes. For example, it can adjust the timing of relevant quizzes based on products that customers have "liked" or shared on social media. It can also analyze posts from brands and influencers that customers follow and adjust the timing of relevant quizzes. Furthermore, it can adjust the timing of quizzes on products of interest based on comments and reviews on customers' social media. This allows the system to provide quizzes tailored to customers' social media activity, thereby improving customer satisfaction.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk allows the customer to select products and begin the purchase process. The reception desk provides an interface that allows customers to select products and begin the purchase process, for example, through an online shopping site or mobile application. Step 2: The question-generating unit creates quizzes about the products selected by the reception unit. The question-generating unit uses a generation AI to create quizzes about, for example, the product's features, usage, and history. The generation AI generates quizzes based on the product's description and catalog information. Step 3: The response reception unit receives customer responses to the quiz questions posed by the question generation unit. The response reception unit provides an interface for customers to answer the quiz, for example, by offering multiple-choice or written response formats. Step 4: The judgment unit determines the correct answer to the quiz based on the answer received by the answer reception unit. The judgment unit analyzes the customer's answer using, for example, a generation AI to determine whether it is correct or incorrect. Step 5: The procedure progress unit proceeds with the purchase process if the judgment unit determines it to be correct. The procedure progress unit provides interfaces for, for example, credit card payment and delivery procedures.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, question unit, answer reception unit, judgment unit, and procedure progress unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the customer to select a product and start the purchase procedure. The question unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a quiz about the product's features and usage using a generating AI. The answer reception unit is implemented, for example, by the control unit 46A of the smart device 14 and provides an interface for the customer to answer the quiz. The judgment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the customer's answer using a generating AI to determine whether it is correct or incorrect. The procedure progress unit is implemented, for example, by the control unit 46A of the smart device 14 and provides an interface for credit card payment and delivery procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, question unit, answer reception unit, judgment unit, and procedure progress unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the customer to select a product and start the purchase procedure. The question unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a quiz about the product's features and usage using a generating AI. The answer reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the customer to answer the quiz. The judgment unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the customer's answer using a generating AI to determine whether it is correct or incorrect. The procedure progress unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for credit card payment and delivery procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, question unit, answer reception unit, judgment unit, and procedure progress unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the customer to select a product and start the purchase procedure. The question unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a quiz about the product's features and usage using a generating AI. The answer reception unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides an interface for the customer to answer the quiz. The judgment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the customer's answer using a generating AI to determine whether it is correct or incorrect. The procedure progress unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides an interface for credit card payment and delivery procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, question unit, answer reception unit, judgment unit, and procedure progress unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the customer to select a product and start the purchase procedure. The question unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a quiz about the features and usage of the product using a generating AI. The answer reception unit is implemented by, for example, the control unit 46A of the robot 414 and provides an interface for the customer to answer the quiz. The judgment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the customer's answer using a generating AI and determines whether it is correct or incorrect. The procedure progress unit is implemented by, for example, the control unit 46A of the robot 414 and provides an interface for credit card payment and delivery procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) The reception area where customers select products and begin the purchase process, A question-setting unit that issues quizzes about the products selected by the reception unit, A response receiving unit that receives customer responses to quizzes issued by the aforementioned question issuing unit, Based on the answers received by the aforementioned answer receiving unit, a determination unit determines the correct answer to the quiz, The procedure progress unit includes a unit that proceeds with the purchase procedure if the determination unit determines that the answer is correct. A system characterized by the following features. (Note 2) The aforementioned question section is, The quiz will cover product features, usage instructions, history, and other related topics. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned question section is, The difficulty level of the quiz is adjusted based on the customer's answer history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, Avoid questions you have answered correctly in the past. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned procedural department, If you answer the quiz incorrectly, you will be able to either try the quiz again or choose a different product. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned procedural department, Rewards and discounts are offered based on the quiz's accuracy rate and response time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates customer emotions and adjusts the product selection interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the customer's past purchase history and recommend the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When selecting products, filtering is performed based on the customer's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates customer emotions and prioritizes product selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When selecting products, the system prioritizes displaying highly relevant products based on the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When customers select products, the system analyzes their social media activity and recommends relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned question section is, The system estimates customer emotions and adjusts the quiz questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question section is, When creating quiz questions, the content of the quiz is selected based on the product's key features. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned question section is, When creating quiz questions, different question algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question section is, The system estimates customer emotions and adjusts the difficulty of the quiz based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question section is, When a quiz is presented, a new quiz is generated based on the customer's past answer history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question section is, When creating quiz questions, enrich the quiz content by referring to related product information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned response reception department, It estimates customer emotions and adjusts the response submission interface based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned response reception unit, When receiving a response, the system will refer to the customer's past response history to suggest the most suitable response method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned response reception department, When receiving a response, provide hints for the response based on the customer's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned response reception unit, The system estimates customer emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned response reception unit, When receiving responses, the system will consider the customer's geographical location and present relevant response methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned response reception department, When receiving responses, we analyze the customer's social media activity and suggest relevant response methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The determination unit, We estimate customer emotions and adjust the judgment criteria based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The determination unit, During the decision-making process, we refer to the customer's past response history to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 27) The determination unit, When making a determination, different determination algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The determination unit, The system estimates the customer's emotions and adjusts how the judgment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The determination unit, When making a decision, we take into account the customer's geographical location to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 30) The determination unit, During the assessment process, we analyze the customer's social media activity to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned procedural department, We estimate the customer's emotions and adjust the procedure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned procedural department, During the process, the system will refer to the customer's past purchase history to select the most appropriate procedure. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned procedural department, During the process, the means of proceeding will be customized based on the customer's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned procedural department, The system estimates the customer's emotions and determines the priority of the procedure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned procedural department, During the process, the optimal procedure will be selected considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned procedural department, During the process, we analyze the client's social media activity and propose methods for proceeding with the process. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where customers select products and begin the purchase process, A question-setting unit that issues quizzes about the products selected by the reception unit, A response receiving unit that receives customer responses to quizzes issued by the aforementioned question issuing unit, Based on the answers received by the aforementioned answer receiving unit, a determination unit determines the correct answer to the quiz, The procedure progress unit includes a unit that proceeds with the purchase procedure if the determination unit determines that the answer is correct. A system characterized by the following features.

2. The aforementioned question section is, The quiz will cover product features, usage instructions, history, and other related topics. The system according to feature 1.

3. The aforementioned question section is, The difficulty level of the quiz is adjusted based on the customer's answer history. The system according to feature 1.

4. The determination unit, Avoid questions you have answered correctly in the past. The system according to feature 1.

5. The aforementioned procedural department, If you answer the quiz incorrectly, you will be able to either try the quiz again or choose a different product. The system according to feature 1.

6. The aforementioned procedural department, Rewards and discounts are offered based on the quiz's accuracy rate and response time. The system according to feature 1.

7. The aforementioned reception unit is It estimates customer emotions and adjusts the product selection interface based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the customer's past purchase history and recommend the most suitable products. The system according to feature 1.

Images