system

Abstract

The system according to this embodiment aims to quickly and comprehensively acquire product information and facilitate comparative analysis. [Solution] The system according to the embodiment comprises a scanning unit, a generation unit, an answering unit, and a payment unit. The scanning unit scans products using a camera. The generation unit analyzes the information of the products scanned by the scanning unit and generates a detailed overview, features, recommendation rating, and user reviews. The answering unit answers questions in real time based on the information generated by the generation unit. The payment unit performs electronic payment for purchasing products based on the information generated by the generation unit.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to quickly and detailedly obtain information on products and conduct comparative studies.

[0005] The system according to the embodiment aims to quickly and detailedly obtain information on products and facilitate comparative studies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a scanning unit, a generation unit, an answering unit, and a payment unit. The scanning unit scans products using a camera. The generation unit analyzes the information of the products scanned by the scanning unit and generates a detailed overview, features, recommendation rating, and user reviews. The answering unit answers questions in real time based on the information generated by the generation unit. The payment unit performs electronic payment for purchasing products based on the information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and comprehensively acquire product information and facilitate comparison and evaluation. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart 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 information provision system according to an embodiment of the present invention is an innovative system that combines camera search and generative AI. When a user scans multiple products in a store or online using a camera, the generative AI automatically generates and provides an overview, features, recommendation rating, and user reviews for each product. It can also answer related questions in real time. This allows for a smooth transition from product comparison to purchase. For example, a user scans multiple products in a store or online using a camera. Next, the generative AI automatically generates and provides an overview, features, recommendation rating, and user reviews for each product. Furthermore, it can answer related questions in real time. This allows for a smooth transition from product comparison to purchase. The target audience is general consumers and retailers. General consumers face challenges such as unreliability of sales staff explanations, the inability to find sales staff, and the inconvenience of repeatedly entering product names into search engines. Retailers face the challenge of preventing customers from leaving without making a decision. As a solution, the system allows users to easily scan multiple products using the camera search function, and the generative AI automatically presents detailed overviews, comparison information, and recommendation ratings. The AI ​​generates answers to product questions in real time. More customers will make decisions and purchases on the spot, and can even pay electronically. The AI ​​automatically generates detailed descriptions and user reviews of products scanned by the camera. It also provides personalized product recommendations and coupons to individual users and answers questions in real time in a natural conversational format. As a result, the product information system allows users to scan products with their cameras, the AI ​​generates detailed information, answers questions in real time, and makes electronic payments.

[0029] The product information provision system according to this embodiment comprises a scanning unit, a generation unit, an answering unit, and a payment unit. The scanning unit scans products using a camera. For example, the scanning unit scans products in a store with its camera and reads the product's barcode or QR code (registered trademark). The scanning unit can also scan online product images with its camera and identify products using image recognition technology. For example, the scanning unit scans products using a smartphone camera and acquires product information in real time. The generation unit uses generation AI to analyze the product information scanned by the scanning unit and generates a detailed overview, features, recommendation rating, and user reviews. For example, the generation unit generates a detailed overview of the product, such as its specifications, usage instructions, and advantages. The generation unit can also collect user reviews and provide reliable evaluations. For example, the generation unit uses generation AI to automatically generate product features and advantages, analyze user reviews, and calculate a recommendation rating. The answering unit answers questions in real time based on the information generated by the generation unit. For example, if a user asks a question about product details, the answering unit uses generation AI to provide an answer in a natural conversational format. Furthermore, the answering unit can also answer questions about product usage and benefits in real time. For example, the answering unit uses a generation AI to provide quick and accurate answers to user questions. The payment unit performs electronic payment for purchasing products based on the information generated by the generation unit. The payment unit provides methods such as credit card payment, electronic money, and QR code payment. The payment unit can also suggest the optimal payment method based on the user's purchase history. For example, the payment unit prioritizes suggesting payment methods the user has used in the past, enabling quick and smooth payment. As a result, the product information provision system according to this embodiment allows the user to scan products using a camera, the generation AI to provide detailed information, answer questions in real time, and perform electronic payment.

[0030] The scanning unit uses a camera to scan products. For example, the scanning unit can scan products in a store with its camera and read their barcodes or QR codes. Specifically, the scanning unit is equipped with a high-resolution camera that can quickly and accurately read barcodes and QR codes attached to products. This allows users to obtain product information simply by pointing the camera at the product, without having to pick it up. The scanning unit can also scan online product images with its camera and identify products using image recognition technology. For example, the scanning unit can scan products using a smartphone camera and obtain product information in real time. The image recognition technology employs a deep learning algorithm that analyzes features such as the shape, color, and logo of the product to identify it. This allows users to scan products they are interested in while online shopping and instantly obtain detailed information. Furthermore, the scanning unit offers multiple scanning modes, allowing users to select the optimal scanning method according to their needs. For example, using the continuous scanning mode allows users to scan multiple products at once and efficiently obtain information. In this way, the scanning unit can enable users to obtain product information quickly and accurately, improving the shopping experience.

[0031] The generation unit uses a generation AI to analyze product information scanned by the scanning unit and generate detailed summaries, features, recommendations, and user reviews. For example, the generation unit generates detailed summaries of product specifications, usage instructions, and benefits. The generation AI utilizes natural language processing technology to analyze scanned product information and provide it in a user-friendly format. Specifically, the generation AI automatically extracts manufacturer information, ingredients, usage instructions, and precautions to generate a detailed summary. The generation unit can also collect user reviews and provide reliable evaluations. For example, it uses the generation AI to automatically generate product features and benefits, and analyzes user reviews to calculate recommendations. The generation AI collects user reviews from online review sites and social media, and analyzes the content of the reviews using text mining technology. This allows the generation unit to objectively calculate and provide product evaluations and recommendations to users. Furthermore, the generation unit can provide individually customized information considering the user's past purchase history and preferences. For example, the generation AI prioritizes displaying information on related products based on the user's past purchases and search history. This allows the generation unit to provide users with optimal product information and increase their desire to purchase.

[0032] The answering unit responds to questions in real time based on information generated by the generation unit. For example, if a user asks about product details, the answering unit uses generative AI to provide an answer in a natural conversational format. The generative AI utilizes a pre-trained database to analyze the user's question and generate an appropriate answer. Specifically, the generative AI provides quick and accurate answers to questions about product specifications, usage, and benefits. The answering unit can also answer questions about product usage and benefits in real time. For example, the answering unit uses generative AI to provide quick and accurate answers to user questions. The generative AI utilizes natural language processing technology to understand the user's question and generate an appropriate answer. This allows users to obtain detailed product information in real time and make quick purchasing decisions. Furthermore, the answering unit can collect user feedback and continuously improve the accuracy and quality of its answers. For example, it updates the generative AI's training data based on user feedback to provide more accurate and useful answers. The answering unit also supports multiple languages ​​and can accommodate users who speak different languages. This allows the response unit to provide users with quick and accurate information, improving the purchasing experience.

[0033] The payment unit performs electronic payments for purchasing goods based on information generated by the generation unit. The payment unit offers various payment methods, such as credit card payments, e-money, and QR code payments. Specifically, the payment unit provides an interface for users to input necessary information and complete the payment, depending on the payment method selected. For example, with credit card payments, users complete the payment by entering card information and verifying the security code. For e-money and QR code payments, users can make payments using the corresponding app. Furthermore, the payment unit can suggest the most suitable payment method based on the user's purchase history. For example, it prioritizes suggesting payment methods previously used by the user, enabling quick and smooth payments. This allows users to easily purchase goods without cumbersome procedures. Additionally, the payment unit incorporates enhanced security measures and features to protect users' personal and payment information. For example, payment information is encrypted and transmitted using secure communication protocols. The payment unit also implements a monitoring system to detect fraudulent transactions and immediately issues warnings if unusual transactions are detected. This allows the payment unit to provide users with a safe and fast payment service, improving the purchasing experience.

[0034] The generation unit can automatically generate detailed product descriptions and user reviews using a generation AI. For example, the generation unit uses the generation AI to generate detailed summaries of product specifications, usage instructions, and benefits. The generation unit can also collect user reviews and provide reliable evaluations. For example, the generation unit uses the generation AI to automatically generate product features and benefits, and analyzes user reviews to calculate a recommendation rating. This allows users to easily obtain product information by automatically generating detailed product descriptions and user reviews using the generation AI. Some or all of the above processes in the generation unit are performed using the generation AI. For example, the generation unit inputs detailed product descriptions and user reviews into the generation AI, and the generation AI automatically generates the information.

[0035] The generation unit can provide personalized product recommendations and coupons to individual users using a generation AI. For example, the generation unit can use the generation AI to suggest products based on the user's past purchase history and interests. The generation unit can also provide personalized coupons to users using the generation AI. For example, the generation unit can use the generation AI to analyze the user's purchase history and suggest the most suitable products. By providing personalized product recommendations and coupons using the generation AI, it is possible to increase the user's willingness to purchase. Some or all of the above processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's purchase history and interests into the generation AI, and the generation AI automatically generates recommended products and coupons.

[0036] The answering unit can answer questions in real time using a generative AI in a natural conversational format. For example, if a user asks about product details, the answering unit will use the generative AI to provide an answer in a natural conversational format. The answering unit can also answer questions about how to use a product or its benefits in real time. For example, the answering unit will use the generative AI to provide a quick and accurate answer to the user's question. This allows users to obtain information smoothly by having the generative AI answer questions in real time in a natural conversational format. Some or all of the above processing in the answering unit is performed using the generative AI. For example, the answering unit inputs the user's question into the generative AI, and the generative AI automatically generates an answer.

[0037] The payment unit can perform electronic payments for purchasing goods using a generating AI. The payment unit offers various payment methods, such as credit card payments, e-money, and QR code payments. Furthermore, the payment unit can suggest the optimal payment method based on the user's purchase history. For example, it might prioritize suggesting payment methods previously used by the user, ensuring quick and smooth transactions. This allows users to purchase goods smoothly through electronic payments using the generating AI. Some or all of the above-described processes in the payment unit are performed using the generating AI. For example, the payment unit inputs the user's purchase history into the generating AI, which then automatically suggests the optimal payment method.

[0038] The scanning unit can analyze the user's past scanning history and select the optimal scanning method during scanning. For example, the scanning unit can suggest the optimal scanning method based on the types of products the user has scanned in the past. The scanning unit can also select the optimal scanning method for a specific time period based on the user's past scanning history. For example, the scanning unit can analyze the user's past scanning history and select a method to improve scanning accuracy. In this way, the scanning unit can provide the optimal scanning method by analyzing the user's past scanning history. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's past scanning history into a generating AI, and the generating AI can automatically select the optimal scanning method.

[0039] The scanning unit can filter the results based on the user's current purchasing intent and areas of interest during the scan. For example, if the user shows high purchasing intent, the scanning unit will scan only relevant products. The scanning unit can also prioritize scanning products in specific categories based on the user's areas of interest. For example, if the user's purchasing intent is low, the scanning unit will narrow down the scan target and provide more relevant information. This allows the scanning unit to provide highly relevant information by filtering based on the user's purchasing intent and areas of interest. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit inputs the user's purchasing intent and areas of interest into a generating AI, which then automatically performs the filtering.

[0040] The scanning unit can prioritize scanning for highly relevant products by considering the user's geographical location during the scanning process. For example, if the user is in a specific region, the scanning unit will prioritize scanning for popular products in that region. The scanning unit can also prioritize scanning for products available at nearby stores based on the user's current location. For example, based on the user's geographical location, the scanning unit will prioritize scanning for products with region-specific benefits. This allows the system to provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the scanning unit may be performed using AI, or it may be performed without AI. For example, the scanning unit can input the user's geographical location information into a generating AI, which will then automatically select highly relevant products.

[0041] The scanning unit can analyze the user's social media activity during scanning and scan for related products. For example, the scanning unit prioritizes scanning products that the user has shown interest in on social media. The scanning unit can also scan products purchased by the user's social media followers. For example, the scanning unit analyzes the content of the user's social media posts and scans for related products. This allows the system to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit inputs the user's social media activity into a generating AI, which then automatically selects related products.

[0042] The generation unit can adjust the level of detail of the information it generates based on the importance of the product. For example, for highly important products, the generation unit can generate detailed descriptions and many user reviews. Conversely, for less important products, the generation unit can generate concise descriptions and a small number of user reviews. For example, the generation unit can adjust the level of detail of product features and recommendations according to importance. This allows for the provision of more appropriate information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the importance of the product into the generation AI, and the generation AI automatically adjusts the level of detail of the information.

[0043] The generation unit can apply different generation algorithms depending on the product category during generation. For example, in the case of electronic products, the generation unit can apply a generation algorithm that emphasizes technical details. Similarly, in the case of fashion products, it can apply a generation algorithm that emphasizes visual appeal. For example, in the case of food products, it can apply a generation algorithm that emphasizes ingredients and nutritional information. This allows for the provision of more appropriate information by applying a generation algorithm according to the product category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs the product category into the generation AI, which then automatically applies an appropriate generation algorithm.

[0044] The generation unit can determine the priority of information to generate based on the product submission date during the generation process. For example, the generation unit can prioritize generating detailed information for new products. It can also prioritize generating information for products during sales periods. For example, the generation unit prioritizes generating seasonal information for seasonal products. By prioritizing information based on the product submission date, it is possible to provide more appropriate information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the product submission date into the generation AI, and the generation AI automatically determines the information priority.

[0045] The generation unit can adjust the order of information generated based on the relevance of the products during generation. For example, the generation unit can prioritize generating information on products that the user has shown interest in. The generation unit can also prioritize generating information on highly relevant products. For example, the generation unit can prioritize generating information on highly relevant products based on the user's past purchase history. By adjusting the order of information based on the relevance of the products, more appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the relevance of the products into the generation AI, and the generation AI automatically adjusts the order of the information.

[0046] The answering unit can adjust the level of detail in its answers based on the importance of the question. For example, it can provide detailed answers to high-importance questions, and concise answers to low-importance questions. For instance, it can adjust the level of detail in its answers according to the importance of the question. This allows for the provision of more appropriate answers by adjusting the level of detail based on the importance of the question. Some or all of the above processing in the answering unit may be performed using AI, or not. For example, the answering unit inputs the importance of the question into a generating AI, and the generating AI automatically adjusts the level of detail in its answers.

[0047] The answering unit can apply different answering algorithms depending on the category of the question when providing an answer. For example, for technical questions, the answering unit can apply an answering algorithm that emphasizes technical details. Alternatively, for general questions, the answering unit can apply a concise and easy-to-understand answering algorithm. For example, for product-related questions, the answering unit can apply an answering algorithm that emphasizes product features and recommendations. This allows for more appropriate answers to be provided by applying answering algorithms according to the question category. Some or all of the above processing in the answering unit may be performed using AI, or not. For example, the answering unit can input the question category into a generating AI, which can then automatically apply an appropriate answering algorithm.

[0048] The answering unit can prioritize answers based on when the question was submitted. For example, it will prioritize answers to recently submitted questions. It can also prioritize answers to questions submitted during sales periods. For example, it will prioritize seasonal answers to questions about seasonal products. This allows for more appropriate answers to be provided by prioritizing answers based on when the question was submitted. Some or all of the above processing in the answering unit may be performed using AI or not. For example, the answering unit inputs the question submission date into a generating AI, and the generating AI automatically determines the priority of answers.

[0049] The answering unit can adjust the order of answers based on the relevance of the questions when providing responses. For example, the answering unit will prioritize answering questions that the user has shown interest in. The answering unit can also prioritize answering highly relevant questions. For example, the answering unit will prioritize answering highly relevant questions based on the user's past question history. This allows for the provision of more appropriate answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the answering unit may be performed using AI or not. For example, the answering unit inputs the relevance of the questions into a generating AI, and the generating AI automatically adjusts the order of the answers.

[0050] The payment unit can analyze the user's past purchase history to select the optimal payment method at the time of payment. For example, the payment unit may prioritize suggesting payment methods the user has used in the past. The payment unit can also select the optimal payment method for a specific time period based on the user's past purchase history. For example, the payment unit may analyze the user's past purchase history and select the most efficient payment method. In this way, the payment unit can provide the optimal payment method by analyzing the user's past purchase history. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit may input the user's past purchase history into a generating AI, and the generating AI may automatically select the optimal payment method.

[0051] The payment unit can customize the payment method at the time of payment based on the user's current purchase intent. For example, if the user shows high purchase intent, the payment unit can provide a fast payment method. Alternatively, if the user's purchase intent is low, the payment unit can provide detailed payment options. For example, the payment unit can offer benefits or coupons according to the user's purchase intent. This allows for the provision of a more appropriate payment method by customizing the payment method according to the user's purchase intent. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit inputs the user's purchase intent into a generating AI, and the generating AI automatically customizes the payment method.

[0052] The payment unit can select the optimal payment method at the time of payment, taking into account the user's geographical location. For example, if the user is in a specific region, the payment unit can provide payment methods available in that region. The payment unit can also suggest payment methods available at nearby stores based on the user's current location. For example, the payment unit can provide payment methods with region-specific benefits based on the user's geographical location. In this way, the optimal payment method can be provided by taking the user's geographical location into consideration. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit inputs the user's geographical location information into a generating AI, and the generating AI automatically selects the optimal payment method.

[0053] The payment unit can analyze the user's social media activity and suggest payment methods at the time of payment. For example, the payment unit can offer a payment method with benefits for products shared by the user on social media. The payment unit can also suggest payment methods used by the user's social media followers. For example, the payment unit can analyze the content of the user's social media posts and suggest relevant payment methods. In this way, the optimal payment method can be provided by analyzing the user's social media activity. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit can input the user's social media activity into a generating AI, and the generating AI can automatically suggest the optimal payment method.

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

[0055] The scanning unit can analyze the user's past scanning history and select the optimal scanning method during scanning. For example, the scanning unit can suggest the optimal scanning method based on the types of products the user has scanned in the past. The scanning unit can also select the optimal scanning method for a specific time period based on the user's past scanning history. For example, the scanning unit can analyze the user's past scanning history and select a method to improve scanning accuracy. In this way, the scanning unit can provide the optimal scanning method by analyzing the user's past scanning history. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's past scanning history into a generating AI, and the generating AI can automatically select the optimal scanning method.

[0056] The scanning unit can filter the results based on the user's current purchasing intent and areas of interest during the scan. For example, if the user shows high purchasing intent, the scanning unit will scan only relevant products. The scanning unit can also prioritize scanning products in specific categories based on the user's areas of interest. For example, if the user's purchasing intent is low, the scanning unit will narrow down the scan target and provide more relevant information. This allows the scanning unit to provide highly relevant information by filtering based on the user's purchasing intent and areas of interest. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit inputs the user's purchasing intent and areas of interest into a generating AI, which then automatically performs the filtering.

[0057] The scanning unit can prioritize scanning for highly relevant products by considering the user's geographical location during the scanning process. For example, if the user is in a specific region, the scanning unit will prioritize scanning for popular products in that region. The scanning unit can also prioritize scanning for products available at nearby stores based on the user's current location. For example, based on the user's geographical location, the scanning unit will prioritize scanning for products with region-specific benefits. This allows the system to provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the scanning unit may be performed using AI, or it may be performed without AI. For example, the scanning unit can input the user's geographical location information into a generating AI, which will then automatically select highly relevant products.

[0058] The generation unit can adjust the level of detail of the information it generates based on the importance of the product. For example, for highly important products, the generation unit can generate detailed descriptions and many user reviews. Conversely, for less important products, the generation unit can generate concise descriptions and a small number of user reviews. For example, the generation unit can adjust the level of detail of product features and recommendations according to importance. This allows for the provision of more appropriate information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the importance of the product into the generation AI, and the generation AI automatically adjusts the level of detail of the information.

[0059] The generation unit can apply different generation algorithms depending on the product category during generation. For example, in the case of electronic products, the generation unit can apply a generation algorithm that emphasizes technical details. Similarly, in the case of fashion products, it can apply a generation algorithm that emphasizes visual appeal. For example, in the case of food products, it can apply a generation algorithm that emphasizes ingredients and nutritional information. This allows for the provision of more appropriate information by applying a generation algorithm according to the product category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs the product category into the generation AI, which then automatically applies an appropriate generation algorithm.

[0060] The generation unit can determine the priority of information to generate based on the product submission date during the generation process. For example, the generation unit can prioritize generating detailed information for new products. It can also prioritize generating information for products during sales periods. For example, the generation unit prioritizes generating seasonal information for seasonal products. By prioritizing information based on the product submission date, it is possible to provide more appropriate information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the product submission date into the generation AI, and the generation AI automatically determines the information priority.

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

[0062] Step 1: The scanning unit uses a camera to scan products. For example, it can scan products in a store with its camera and read their barcodes or QR codes. It can also scan online product images with its camera and identify products using image recognition technology. It can also scan products using a smartphone camera and obtain product information in real time. Step 2: The generation unit analyzes the product information scanned by the scanning unit and generates a detailed overview, features, recommendation rating, and user reviews. Using generation AI, it generates a detailed overview of the product, including specifications, usage instructions, and benefits, and collects user reviews to provide a reliable evaluation. It automatically generates product features and benefits, and analyzes user reviews to calculate the recommendation rating. Step 3: The answering unit answers questions in real time based on the information generated by the generation unit. When a user asks about product details, the generation AI provides answers in a natural conversational format. It can also answer questions about how to use the product and its benefits in real time. The generation AI provides quick and accurate answers to user questions. Step 4: The payment unit performs electronic payment for purchasing goods based on the information generated by the generation unit. It offers methods such as credit card payment, e-money, and QR code payment. It can also suggest the most suitable payment method based on the user's purchase history. It prioritizes suggesting payment methods the user has used in the past to ensure quick and smooth payment.

[0063] (Example of form 2) The product information provision system according to an embodiment of the present invention is an innovative system that combines camera search and generative AI. When a user scans multiple products in a store or online using a camera, the generative AI automatically generates and provides an overview, features, recommendation rating, and user reviews for each product. It can also answer related questions in real time. This allows for a smooth transition from product comparison to purchase. For example, a user scans multiple products in a store or online using a camera. Next, the generative AI automatically generates and provides an overview, features, recommendation rating, and user reviews for each product. Furthermore, it can answer related questions in real time. This allows for a smooth transition from product comparison to purchase. The target audience is general consumers and retailers. General consumers face challenges such as unreliability of sales staff explanations, the inability to find sales staff, and the inconvenience of repeatedly entering product names into search engines. Retailers face the challenge of preventing customers from leaving without making a decision. As a solution, the system allows users to easily scan multiple products using the camera search function, and the generative AI automatically presents detailed overviews, comparison information, and recommendation ratings. The AI ​​generates answers to product questions in real time. More customers will make decisions and purchases on the spot, and can even pay electronically. The AI ​​automatically generates detailed descriptions and user reviews of products scanned by the camera. It also provides personalized product recommendations and coupons to individual users and answers questions in real time in a natural conversational format. As a result, the product information system allows users to scan products with their cameras, the AI ​​generates detailed information, answers questions in real time, and makes electronic payments.

[0064] The product information provision system according to this embodiment comprises a scanning unit, a generation unit, an answering unit, and a payment unit. The scanning unit scans products using a camera. For example, the scanning unit scans products in a store with its camera and reads the product's barcode or QR code. The scanning unit can also scan online product images with its camera and identify products using image recognition technology. For example, the scanning unit scans products using a smartphone camera and acquires product information in real time. The generation unit uses generation AI to analyze the product information scanned by the scanning unit and generates a detailed overview, features, recommendation rating, and user reviews. For example, the generation unit generates a detailed overview of the product, such as its specifications, usage instructions, and advantages. The generation unit can also collect user reviews and provide reliable evaluations. For example, the generation unit uses generation AI to automatically generate product features and advantages, analyze user reviews, and calculate a recommendation rating. The answering unit answers questions in real time based on the information generated by the generation unit. For example, if a user asks a question about product details, the answering unit uses generation AI to provide an answer in a natural conversational format. Furthermore, the answering unit can also answer questions about product usage and benefits in real time. For example, the answering unit uses a generation AI to provide quick and accurate answers to user questions. The payment unit performs electronic payment for purchasing products based on the information generated by the generation unit. The payment unit provides methods such as credit card payment, electronic money, and QR code payment. The payment unit can also suggest the optimal payment method based on the user's purchase history. For example, the payment unit prioritizes suggesting payment methods the user has used in the past, enabling quick and smooth payment. As a result, the product information provision system according to this embodiment allows the user to scan products using a camera, the generation AI to provide detailed information, answer questions in real time, and perform electronic payment.

[0065] The scanning unit uses a camera to scan products. For example, the scanning unit can scan products in a store with its camera and read their barcodes or QR codes. Specifically, the scanning unit is equipped with a high-resolution camera that can quickly and accurately read barcodes and QR codes attached to products. This allows users to obtain product information simply by pointing the camera at the product, without having to pick it up. The scanning unit can also scan online product images with its camera and identify products using image recognition technology. For example, the scanning unit can scan products using a smartphone camera and obtain product information in real time. The image recognition technology employs a deep learning algorithm that analyzes features such as the shape, color, and logo of the product to identify it. This allows users to scan products they are interested in while online shopping and instantly obtain detailed information. Furthermore, the scanning unit offers multiple scanning modes, allowing users to select the optimal scanning method according to their needs. For example, using the continuous scanning mode allows users to scan multiple products at once and efficiently obtain information. In this way, the scanning unit can enable users to obtain product information quickly and accurately, improving the shopping experience.

[0066] The generation unit uses a generation AI to analyze product information scanned by the scanning unit and generate detailed summaries, features, recommendations, and user reviews. For example, the generation unit generates detailed summaries of product specifications, usage instructions, and benefits. The generation AI utilizes natural language processing technology to analyze scanned product information and provide it in a user-friendly format. Specifically, the generation AI automatically extracts manufacturer information, ingredients, usage instructions, and precautions to generate a detailed summary. The generation unit can also collect user reviews and provide reliable evaluations. For example, it uses the generation AI to automatically generate product features and benefits, and analyzes user reviews to calculate recommendations. The generation AI collects user reviews from online review sites and social media, and analyzes the content of the reviews using text mining technology. This allows the generation unit to objectively calculate and provide product evaluations and recommendations to users. Furthermore, the generation unit can provide individually customized information considering the user's past purchase history and preferences. For example, the generation AI prioritizes displaying information on related products based on the user's past purchases and search history. This allows the generation unit to provide users with optimal product information and increase their desire to purchase.

[0067] The answering unit responds to questions in real time based on information generated by the generation unit. For example, if a user asks about product details, the answering unit uses generative AI to provide an answer in a natural conversational format. The generative AI utilizes a pre-trained database to analyze the user's question and generate an appropriate answer. Specifically, the generative AI provides quick and accurate answers to questions about product specifications, usage, and benefits. The answering unit can also answer questions about product usage and benefits in real time. For example, the answering unit uses generative AI to provide quick and accurate answers to user questions. The generative AI utilizes natural language processing technology to understand the user's question and generate an appropriate answer. This allows users to obtain detailed product information in real time and make quick purchasing decisions. Furthermore, the answering unit can collect user feedback and continuously improve the accuracy and quality of its answers. For example, it updates the generative AI's training data based on user feedback to provide more accurate and useful answers. The answering unit also supports multiple languages ​​and can accommodate users who speak different languages. This allows the response unit to provide users with quick and accurate information, improving the purchasing experience.

[0068] The payment unit performs electronic payments for purchasing goods based on information generated by the generation unit. The payment unit offers various payment methods, such as credit card payments, e-money, and QR code payments. Specifically, the payment unit provides an interface for users to input necessary information and complete the payment, depending on the payment method selected. For example, with credit card payments, users complete the payment by entering card information and verifying the security code. For e-money and QR code payments, users can make payments using the corresponding app. Furthermore, the payment unit can suggest the most suitable payment method based on the user's purchase history. For example, it prioritizes suggesting payment methods previously used by the user, enabling quick and smooth payments. This allows users to easily purchase goods without cumbersome procedures. Additionally, the payment unit incorporates enhanced security measures and features to protect users' personal and payment information. For example, payment information is encrypted and transmitted using secure communication protocols. The payment unit also implements a monitoring system to detect fraudulent transactions and immediately issues warnings if unusual transactions are detected. This allows the payment unit to provide users with a safe and fast payment service, improving the purchasing experience.

[0069] The generation unit can automatically generate detailed product descriptions and user reviews using a generation AI. For example, the generation unit uses the generation AI to generate detailed summaries of product specifications, usage instructions, and benefits. The generation unit can also collect user reviews and provide reliable evaluations. For example, the generation unit uses the generation AI to automatically generate product features and benefits, and analyzes user reviews to calculate a recommendation rating. This allows users to easily obtain product information by automatically generating detailed product descriptions and user reviews using the generation AI. Some or all of the above processes in the generation unit are performed using the generation AI. For example, the generation unit inputs detailed product descriptions and user reviews into the generation AI, and the generation AI automatically generates the information.

[0070] The generation unit can provide personalized product recommendations and coupons to individual users using a generation AI. For example, the generation unit can use the generation AI to suggest products based on the user's past purchase history and interests. The generation unit can also provide personalized coupons to users using the generation AI. For example, the generation unit can use the generation AI to analyze the user's purchase history and suggest the most suitable products. By providing personalized product recommendations and coupons using the generation AI, it is possible to increase the user's willingness to purchase. Some or all of the above processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's purchase history and interests into the generation AI, and the generation AI automatically generates recommended products and coupons.

[0071] The answering unit can answer questions in real time using a generative AI in a natural conversational format. For example, if a user asks about product details, the answering unit will use the generative AI to provide an answer in a natural conversational format. The answering unit can also answer questions about how to use a product or its benefits in real time. For example, the answering unit will use the generative AI to provide a quick and accurate answer to the user's question. This allows users to obtain information smoothly by having the generative AI answer questions in real time in a natural conversational format. Some or all of the above processing in the answering unit is performed using the generative AI. For example, the answering unit inputs the user's question into the generative AI, and the generative AI automatically generates an answer.

[0072] The payment unit can perform electronic payments for purchasing goods using a generating AI. The payment unit offers various payment methods, such as credit card payments, e-money, and QR code payments. Furthermore, the payment unit can suggest the optimal payment method based on the user's purchase history. For example, it might prioritize suggesting payment methods previously used by the user, ensuring quick and smooth transactions. This allows users to purchase goods smoothly through electronic payments using the generating AI. Some or all of the above-described processes in the payment unit are performed using the generating AI. For example, the payment unit inputs the user's purchase history into the generating AI, which then automatically suggests the optimal payment method.

[0073] The scanning unit can estimate the user's emotions and adjust the scanning timing based on the estimated emotions. For example, if the user is excited, the scanning unit can speed up the scanning timing to provide information quickly. Conversely, if the user is relaxed, the scanning unit can delay the scanning timing to provide more detailed information. For example, if the user is stressed, the scanning unit can adjust the scanning timing and wait until the user calms down. This allows for the provision of information at a more appropriate time by adjusting the scanning timing according to the user'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 scanning unit may be performed using AI or not. For example, the scanning unit inputs the user's emotion data into the generative AI, which then automatically adjusts the scanning timing.

[0074] The scanning unit can analyze the user's past scanning history and select the optimal scanning method during scanning. For example, the scanning unit can suggest the optimal scanning method based on the types of products the user has scanned in the past. The scanning unit can also select the optimal scanning method for a specific time period based on the user's past scanning history. For example, the scanning unit can analyze the user's past scanning history and select a method to improve scanning accuracy. In this way, the scanning unit can provide the optimal scanning method by analyzing the user's past scanning history. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's past scanning history into a generating AI, and the generating AI can automatically select the optimal scanning method.

[0075] The scanning unit can filter the results based on the user's current purchasing intent and areas of interest during the scan. For example, if the user shows high purchasing intent, the scanning unit will scan only relevant products. The scanning unit can also prioritize scanning products in specific categories based on the user's areas of interest. For example, if the user's purchasing intent is low, the scanning unit will narrow down the scan target and provide more relevant information. This allows the scanning unit to provide highly relevant information by filtering based on the user's purchasing intent and areas of interest. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit inputs the user's purchasing intent and areas of interest into a generating AI, which then automatically performs the filtering.

[0076] The scanning unit can estimate the user's emotions and determine the priority of products to scan based on the estimated emotions. For example, if the user is excited, the scanning unit will prioritize scanning popular products. Conversely, if the user is relaxed, the scanning unit can prioritize scanning products that require more detailed information. For example, if the user is stressed, the scanning unit will prioritize scanning products that are easy to understand. This allows for the provision of more appropriate information by prioritizing products according to the user'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 scanning unit may be performed using AI or not. For example, the scanning unit inputs user emotion data into the generative AI, which then automatically determines the product priority.

[0077] The scanning unit can prioritize scanning for highly relevant products by considering the user's geographical location during the scanning process. For example, if the user is in a specific region, the scanning unit will prioritize scanning for popular products in that region. The scanning unit can also prioritize scanning for products available at nearby stores based on the user's current location. For example, based on the user's geographical location, the scanning unit will prioritize scanning for products with region-specific benefits. This allows the system to provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the scanning unit may be performed using AI, or it may be performed without AI. For example, the scanning unit can input the user's geographical location information into a generating AI, which will then automatically select highly relevant products.

[0078] The scanning unit can analyze the user's social media activity during scanning and scan for related products. For example, the scanning unit prioritizes scanning products that the user has shown interest in on social media. The scanning unit can also scan products purchased by the user's social media followers. For example, the scanning unit analyzes the content of the user's social media posts and scans for related products. This allows the system to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit inputs the user's social media activity into a generating AI, which then automatically selects related products.

[0079] The generation unit can estimate the user's emotions and adjust the way the generated information is presented based on the estimated emotions. For example, if the user is relaxed, the generation AI may select a presentation method that includes detailed information. If the user is in a hurry, the generation AI may select a concise and to-the-point presentation method. For example, if the user is excited, the generation AI may select a visually appealing presentation method. By adjusting the presentation method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI automatically adjusts the presentation method of the information.

[0080] The generation unit can adjust the level of detail of the information it generates based on the importance of the product. For example, for highly important products, the generation unit can generate detailed descriptions and many user reviews. Conversely, for less important products, the generation unit can generate concise descriptions and a small number of user reviews. For example, the generation unit can adjust the level of detail of product features and recommendations according to importance. This allows for the provision of more appropriate information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the importance of the product into the generation AI, and the generation AI automatically adjusts the level of detail of the information.

[0081] The generation unit can apply different generation algorithms depending on the product category during generation. For example, in the case of electronic products, the generation unit can apply a generation algorithm that emphasizes technical details. Similarly, in the case of fashion products, it can apply a generation algorithm that emphasizes visual appeal. For example, in the case of food products, it can apply a generation algorithm that emphasizes ingredients and nutritional information. This allows for the provision of more appropriate information by applying a generation algorithm according to the product category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs the product category into the generation AI, which then automatically applies an appropriate generation algorithm.

[0082] The generation unit can estimate the user's emotions and adjust the length of the information it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit's AI can generate short, concise information. Conversely, if the user is relaxed, the generation unit's AI can generate longer information including detailed explanations. For example, if the user is excited, the generation unit's AI can generate information with visually stimulating effects. This allows for the provision of more appropriate information by adjusting the length of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, which then automatically adjusts the length of the information.

[0083] The generation unit can determine the priority of information to generate based on the product submission date during the generation process. For example, the generation unit can prioritize generating detailed information for new products. It can also prioritize generating information for products during sales periods. For example, the generation unit prioritizes generating seasonal information for seasonal products. By prioritizing information based on the product submission date, it is possible to provide more appropriate information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the product submission date into the generation AI, and the generation AI automatically determines the information priority.

[0084] The generation unit can adjust the order of information generated based on the relevance of the products during generation. For example, the generation unit can prioritize generating information on products that the user has shown interest in. The generation unit can also prioritize generating information on highly relevant products. For example, the generation unit can prioritize generating information on highly relevant products based on the user's past purchase history. By adjusting the order of information based on the relevance of the products, more appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the relevance of the products into the generation AI, and the generation AI automatically adjusts the order of the information.

[0085] The response unit can estimate the user's emotions and adjust the way it expresses its response based on those emotions. For example, if the user is relaxed, the response unit can provide a detailed response. If the user is in a hurry, it can provide a concise and to-the-point response. If the user is excited, for example, the response unit can provide a visually appealing response. By adjusting the way it expresses its response according to the user's emotions, it can provide a more appropriate response. 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 response unit may be performed using AI or not. For example, the response unit inputs the user's emotion data into the generative AI, and the generative AI automatically adjusts the way it expresses its response.

[0086] The answering unit can adjust the level of detail in its answers based on the importance of the question. For example, it can provide detailed answers to high-importance questions, and concise answers to low-importance questions. For instance, it can adjust the level of detail in its answers according to the importance of the question. This allows for the provision of more appropriate answers by adjusting the level of detail based on the importance of the question. Some or all of the above processing in the answering unit may be performed using AI, or not. For example, the answering unit inputs the importance of the question into a generating AI, and the generating AI automatically adjusts the level of detail in its answers.

[0087] The answering unit can apply different answering algorithms depending on the category of the question when providing an answer. For example, for technical questions, the answering unit can apply an answering algorithm that emphasizes technical details. Alternatively, for general questions, the answering unit can apply a concise and easy-to-understand answering algorithm. For example, for product-related questions, the answering unit can apply an answering algorithm that emphasizes product features and recommendations. This allows for more appropriate answers to be provided by applying answering algorithms according to the question category. Some or all of the above processing in the answering unit may be performed using AI, or not. For example, the answering unit can input the question category into a generating AI, which can then automatically apply an appropriate answering algorithm.

[0088] The response unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the response unit will provide a short, to-the-point response. Conversely, if the user is relaxed, the response unit can provide a longer response with more detailed explanations. For example, if the user is excited, the response unit will provide a response with visually stimulating effects. This allows for the provision of more appropriate responses by adjusting the length of the response according to the user'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 response unit may be performed using AI or not. For example, the response unit inputs the user's emotion data into the generative AI, which then automatically adjusts the length of the response.

[0089] The answering unit can prioritize answers based on when the question was submitted. For example, it will prioritize answers to recently submitted questions. It can also prioritize answers to questions submitted during sales periods. For example, it will prioritize seasonal answers to questions about seasonal products. This allows for more appropriate answers to be provided by prioritizing answers based on when the question was submitted. Some or all of the above processing in the answering unit may be performed using AI or not. For example, the answering unit inputs the question submission date into a generating AI, and the generating AI automatically determines the priority of answers.

[0090] The answering unit can adjust the order of answers based on the relevance of the questions when providing responses. For example, the answering unit will prioritize answering questions that the user has shown interest in. The answering unit can also prioritize answering highly relevant questions. For example, the answering unit will prioritize answering highly relevant questions based on the user's past question history. This allows for the provision of more appropriate answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the answering unit may be performed using AI or not. For example, the answering unit inputs the relevance of the questions into a generating AI, and the generating AI automatically adjusts the order of the answers.

[0091] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated emotions. For example, if the user is relaxed, the payment unit can provide detailed payment options. Alternatively, if the user is in a hurry, it can provide a concise and quick payment method. For example, if the user is excited, the payment unit can provide a visually appealing payment interface. This allows for a more appropriate payment method to be provided by adjusting the payment method according to the user'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 payment unit may be performed using AI or not. For example, the payment unit inputs user emotion data into a generative AI, which then automatically adjusts the payment method.

[0092] The payment unit can analyze the user's past purchase history to select the optimal payment method at the time of payment. For example, the payment unit may prioritize suggesting payment methods the user has used in the past. The payment unit can also select the optimal payment method for a specific time period based on the user's past purchase history. For example, the payment unit may analyze the user's past purchase history and select the most efficient payment method. In this way, the payment unit can provide the optimal payment method by analyzing the user's past purchase history. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit may input the user's past purchase history into a generating AI, and the generating AI may automatically select the optimal payment method.

[0093] The payment unit can customize the payment method at the time of payment based on the user's current purchase intent. For example, if the user shows high purchase intent, the payment unit can provide a fast payment method. Alternatively, if the user's purchase intent is low, the payment unit can provide detailed payment options. For example, the payment unit can offer benefits or coupons according to the user's purchase intent. This allows for the provision of a more appropriate payment method by customizing the payment method according to the user's purchase intent. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit inputs the user's purchase intent into a generating AI, and the generating AI automatically customizes the payment method.

[0094] The payment unit can estimate the user's emotions and determine payment priorities based on those emotions. For example, if the user is in a hurry, the payment unit will prioritize quick payment. It can also provide detailed payment options if the user is relaxed. For example, if the user is excited, the payment unit will provide a visually appealing payment interface. This allows for a more appropriate payment method by prioritizing payments according to the user'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 payment unit may be performed using AI or not. For example, the payment unit inputs user emotion data into a generative AI, which then automatically determines payment priorities.

[0095] The payment unit can select the optimal payment method at the time of payment, taking into account the user's geographical location. For example, if the user is in a specific region, the payment unit can provide payment methods available in that region. The payment unit can also suggest payment methods available at nearby stores based on the user's current location. For example, the payment unit can provide payment methods with region-specific benefits based on the user's geographical location. In this way, the optimal payment method can be provided by taking the user's geographical location into consideration. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit inputs the user's geographical location information into a generating AI, and the generating AI automatically selects the optimal payment method.

[0096] The payment unit can analyze the user's social media activity and suggest payment methods at the time of payment. For example, the payment unit can offer a payment method with benefits for products shared by the user on social media. The payment unit can also suggest payment methods used by the user's social media followers. For example, the payment unit can analyze the content of the user's social media posts and suggest relevant payment methods. In this way, the optimal payment method can be provided by analyzing the user's social media activity. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit can input the user's social media activity into a generating AI, and the generating AI can automatically suggest the optimal payment method.

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

[0098] The scanning unit can estimate the user's emotions and adjust the scanning timing based on the estimated emotions. For example, if the user is excited, the scanning unit can speed up the scanning timing to provide information quickly. Conversely, if the user is relaxed, the scanning unit can delay the scanning timing to provide more detailed information. For example, if the user is stressed, the scanning unit can adjust the scanning timing and wait until the user calms down. This allows for the provision of information at a more appropriate time by adjusting the scanning timing according to the user'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 scanning unit may be performed using AI or not. For example, the scanning unit inputs the user's emotion data into the generative AI, which then automatically adjusts the scanning timing.

[0099] The scanning unit can analyze the user's past scanning history and select the optimal scanning method during scanning. For example, the scanning unit can suggest the optimal scanning method based on the types of products the user has scanned in the past. The scanning unit can also select the optimal scanning method for a specific time period based on the user's past scanning history. For example, the scanning unit can analyze the user's past scanning history and select a method to improve scanning accuracy. In this way, the scanning unit can provide the optimal scanning method by analyzing the user's past scanning history. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's past scanning history into a generating AI, and the generating AI can automatically select the optimal scanning method.

[0100] The scanning unit can filter the results based on the user's current purchasing intent and areas of interest during the scan. For example, if the user shows high purchasing intent, the scanning unit will scan only relevant products. The scanning unit can also prioritize scanning products in specific categories based on the user's areas of interest. For example, if the user's purchasing intent is low, the scanning unit will narrow down the scan target and provide more relevant information. This allows the scanning unit to provide highly relevant information by filtering based on the user's purchasing intent and areas of interest. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit inputs the user's purchasing intent and areas of interest into a generating AI, which then automatically performs the filtering.

[0101] The scanning unit can estimate the user's emotions and determine the priority of products to scan based on the estimated emotions. For example, if the user is excited, the scanning unit will prioritize scanning popular products. Conversely, if the user is relaxed, the scanning unit can prioritize scanning products that require more detailed information. For example, if the user is stressed, the scanning unit will prioritize scanning products that are easy to understand. This allows for the provision of more appropriate information by prioritizing products according to the user'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 scanning unit may be performed using AI or not. For example, the scanning unit inputs user emotion data into the generative AI, which then automatically determines the product priority.

[0102] The scanning unit can prioritize scanning for highly relevant products by considering the user's geographical location during the scanning process. For example, if the user is in a specific region, the scanning unit will prioritize scanning for popular products in that region. The scanning unit can also prioritize scanning for products available at nearby stores based on the user's current location. For example, based on the user's geographical location, the scanning unit will prioritize scanning for products with region-specific benefits. This allows the system to provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the scanning unit may be performed using AI, or it may be performed without AI. For example, the scanning unit can input the user's geographical location information into a generating AI, which will then automatically select highly relevant products.

[0103] The generation unit can estimate the user's emotions and adjust the way the generated information is presented based on the estimated emotions. For example, if the user is relaxed, the generation AI may select a presentation method that includes detailed information. If the user is in a hurry, the generation AI may select a concise and to-the-point presentation method. For example, if the user is excited, the generation AI may select a visually appealing presentation method. By adjusting the presentation method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI automatically adjusts the presentation method of the information.

[0104] The generation unit can adjust the level of detail of the information it generates based on the importance of the product. For example, for highly important products, the generation unit can generate detailed descriptions and many user reviews. Conversely, for less important products, the generation unit can generate concise descriptions and a small number of user reviews. For example, the generation unit can adjust the level of detail of product features and recommendations according to importance. This allows for the provision of more appropriate information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the importance of the product into the generation AI, and the generation AI automatically adjusts the level of detail of the information.

[0105] The generation unit can apply different generation algorithms depending on the product category during generation. For example, in the case of electronic products, the generation unit can apply a generation algorithm that emphasizes technical details. Similarly, in the case of fashion products, it can apply a generation algorithm that emphasizes visual appeal. For example, in the case of food products, it can apply a generation algorithm that emphasizes ingredients and nutritional information. This allows for the provision of more appropriate information by applying a generation algorithm according to the product category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs the product category into the generation AI, which then automatically applies an appropriate generation algorithm.

[0106] The generation unit can estimate the user's emotions and adjust the length of the information it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit's AI can generate short, concise information. Conversely, if the user is relaxed, the generation unit's AI can generate longer information including detailed explanations. For example, if the user is excited, the generation unit's AI can generate information with visually stimulating effects. This allows for the provision of more appropriate information by adjusting the length of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, which then automatically adjusts the length of the information.

[0107] The generation unit can determine the priority of information to generate based on the product submission date during the generation process. For example, the generation unit can prioritize generating detailed information for new products. It can also prioritize generating information for products during sales periods. For example, the generation unit prioritizes generating seasonal information for seasonal products. By prioritizing information based on the product submission date, it is possible to provide more appropriate information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the product submission date into the generation AI, and the generation AI automatically determines the information priority.

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

[0109] Step 1: The scanning unit uses a camera to scan products. For example, it can scan products in a store with its camera and read their barcodes or QR codes. It can also scan online product images with its camera and identify products using image recognition technology. It can also scan products using a smartphone camera and obtain product information in real time. Step 2: The generation unit analyzes the product information scanned by the scanning unit and generates a detailed overview, features, recommendation rating, and user reviews. Using generation AI, it generates a detailed overview of the product, including specifications, usage instructions, and benefits, and collects user reviews to provide a reliable evaluation. It automatically generates product features and benefits, and analyzes user reviews to calculate the recommendation rating. Step 3: The answering unit answers questions in real time based on the information generated by the generation unit. When a user asks about product details, the generation AI provides answers in a natural conversational format. It can also answer questions about how to use the product and its benefits in real time. The generation AI provides quick and accurate answers to user questions. Step 4: The payment unit performs electronic payment for purchasing goods based on the information generated by the generation unit. It offers methods such as credit card payment, e-money, and QR code payment. It can also suggest the most suitable payment method based on the user's purchase history. It prioritizes suggesting payment methods the user has used in the past to ensure quick and smooth payment.

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

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

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

[0113] Each of the multiple elements described above, including the scanning unit, generation unit, answering unit, and payment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the scanning unit scans products using the camera 42 of the smart device 14 and reads barcodes or QR codes. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generation AI to generate detailed product summaries, features, recommendations, and user reviews. The answering unit is implemented in the control unit 46A of the smart device 14 and uses generation AI to provide real-time answers to user questions. The payment unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides payment methods such as credit card payment, electronic money, and QR code payment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0129] Each of the multiple elements described above, including the scanning unit, generation unit, answering unit, and payment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the scanning unit scans products using the camera 42 of the smart glasses 214 and reads barcodes or QR codes. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generation AI to generate detailed product summaries, features, recommendations, and user reviews. The answering unit is implemented in the control unit 46A of the smart glasses 214 and uses generation AI to provide real-time answers to user questions. The payment unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides payment methods such as credit card payment, electronic money, and QR code payment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0145] Each of the multiple elements described above, including the scanning unit, generation unit, answering unit, and payment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the scanning unit scans products using the camera 42 of the headset terminal 314 and reads barcodes or QR codes. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generation AI to generate detailed product summaries, features, recommendations, and user reviews. The answering unit is implemented in the control unit 46A of the headset terminal 314 and uses generation AI to provide real-time answers to user questions. The payment unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides payment methods such as credit card payment, electronic money, and QR code payment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0162] Each of the multiple elements described above, including the scanning unit, generation unit, answering unit, and payment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the scanning unit scans products using the camera 42 of the robot 414 and reads barcodes or QR codes. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generation AI to generate a detailed overview, features, recommendation rating, and user reviews of products. The answering unit is implemented in the control unit 46A of the robot 414 and uses generation AI to provide real-time answers to user questions. The payment unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides payment methods such as credit card payment, electronic money, and QR code payment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0181] (Note 1) A scanning unit that uses a camera to scan products, A generation unit analyzes the information of the products scanned by the aforementioned scanning unit and generates a detailed overview, features, recommendation rating, and user reviews. A response unit that answers questions in real time based on the information generated by the generation unit, The system includes a settlement unit that performs electronic payment for purchasing goods based on the information generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​automatically generates detailed product descriptions and user reviews. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates personalized product recommendations and coupons for each user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned response section is, The generated AI answers questions in real time using a natural conversational format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned settlement unit, Using AI to generate data for electronic payments to purchase products. The system described in Appendix 1, characterized by the features described herein. (Note 6) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scanning unit is During scanning, the system analyzes the user's past scan history and selects the optimal scanning method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The scanning unit is During scanning, filtering is performed based on the user's current purchasing intent and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The scanning unit is It estimates the user's emotions and determines the priority of products to scan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The scanning unit is During scanning, the system prioritizes scanning for highly relevant products, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The scanning unit is During scanning, the system analyzes the user's social media activity and scans for related products. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts how the information generated is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, adjust the level of detail of the information generated based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the information generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the priority of the information to be generated is determined based on the product submission date. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, adjust the order of the information generated based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned response section is, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned response section is, When responding, adjust the level of detail in your answer based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned response section is, When answering, different answer algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned response section is, It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned response section is, When responding, prioritize your answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned response section is, When answering, adjust the order of your answers based on their relevance to the questions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned settlement unit, It estimates the user's emotions and adjusts the payment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned settlement unit, At the time of payment, the system analyzes the user's past purchase history to select the most suitable payment method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned settlement unit, At checkout, the payment method is customized based on the user's current purchasing intent. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned settlement unit, It estimates the user's emotions and determines payment priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned settlement unit, During payment, the system selects the most suitable payment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned settlement unit, At the time of payment, the system analyzes the user's social media activity and suggests payment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A scanning unit that uses a camera to scan products, A generation unit analyzes the information of the products scanned by the aforementioned scanning unit and generates a detailed overview, features, recommendation rating, and user reviews. A response unit that answers questions in real time based on the information generated by the generation unit, The system includes a settlement unit that performs electronic payment for purchasing goods based on the information generated by the generation unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates detailed product descriptions and user reviews automatically. The system according to feature 1.

3. The generating unit is The AI ​​generates personalized product recommendations and coupons for each user. The system according to feature 1.

4. The aforementioned response section is, The generated AI provides real-time answers to questions in a natural conversational format. The system according to feature 1.

5. The aforementioned settlement unit, Using AI to generate data, electronic payments are made for purchasing products. The system according to feature 1.

6. The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system according to feature 1.

7. The scanning unit is During scanning, the system analyzes the user's past scan history and selects the optimal scanning method. The system according to feature 1.

8. The scanning unit is During scanning, filtering is performed based on the user's current purchasing intent and areas of interest. The system according to feature 1.

Images