system

Abstract

The system according to this embodiment aims to easily determine the optimal method for selling unwanted items. [Solution] The system according to the embodiment comprises a confirmation unit, an acquisition unit, and a proposal unit. The confirmation unit confirms the condition of the product. The acquisition unit acquires sales records based on the condition of the product confirmed by the confirmation unit. The proposal unit proposes the optimal sales method based on the sales records acquired by the acquisition unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to determine the optimal sales method for unwanted items, and there is a problem that it takes time and effort.

[0005] The system according to the embodiment aims to easily determine the optimal sales method for unwanted items.

Means for Solving the Problems

[0006] The system according to the embodiment includes a confirmation unit, an acquisition unit, and a proposal unit. The confirmation unit confirms the state of the product. The acquisition unit acquires the sales record based on the state of the product confirmed by the confirmation unit. The proposal unit proposes an optimal sales method based on the sales record acquired by the acquisition unit.

Effects of the Invention

[0007] The system according to this embodiment can easily determine the optimal method for selling unwanted items. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 unwanted goods sales agent according to an embodiment of the present invention is an agent service that allows users to sell unwanted goods simply by taking photos of the unwanted goods, determining the optimal sales method, and following simple instructions. The unwanted goods sales agent works by having the user take photos of the unwanted goods and read the JAN code, allowing a generating AI to check the condition of the goods and acquire product information. The generating AI acquires sales data from various sales sites and determines the optimal sales method. For example, if selling on an auction site is optimal, the generating AI provides this information to the user, and the user completes the listing simply by following simple instructions. The generating AI can also automatically create listing information in conjunction with sales sites. This allows users to sell unwanted goods at a high price without hassle. Furthermore, the generating AI creates draft product categories and features, and proposes appropriate pricing and sales strategies. This allows users to select the optimal sales method and sell unwanted goods efficiently. Thus, the unwanted goods sales agent allows users to sell unwanted goods at a high price without hassle.

[0029] The used goods sales agent according to this embodiment comprises a verification unit, an acquisition unit, and a proposal unit. The verification unit verifies the condition of the goods. For example, the verification unit analyzes a photograph of the goods to verify their condition. The verification unit can use a generating AI to evaluate the goods' appearance (scratches), functionality, frequency of use, etc. For example, the verification unit takes a high-resolution photograph of the goods, and the generating AI uses an image analysis algorithm to verify the goods' condition. The verification unit can also use the generating AI to perform video analysis to verify the goods' functionality. Furthermore, the verification unit can use the generating AI to analyze usage history data to evaluate the frequency of use of the goods. The acquisition unit acquires sales performance data based on the goods' condition verified by the verification unit. For example, the acquisition unit acquires sales performance data from each sales site. The acquisition unit can use a generating AI to collect data such as the number of sales, sales amount, and sales period from each sales site. For example, the acquisition unit can use the generating AI to acquire sales performance data from each sales site using its API. The acquisition unit can also use the generating AI to collect sales performance data using web scraping technology. Furthermore, the acquisition unit can also acquire sales performance data by having the generating AI analyze past sales data. The proposal unit proposes the optimal sales method based on the sales performance data acquired by the acquisition unit. The proposal unit proposes, for example, the optimal sales channel, pricing, and promotion methods. The proposal unit can use the generating AI to select sales channels, optimize prices, and formulate promotion strategies. For example, the proposal unit can have the generating AI analyze past sales data and propose the optimal sales channel. The proposal unit can also have the generating AI analyze market trends and propose the optimal pricing. Furthermore, the proposal unit can have the generating AI predict the effectiveness of promotions and propose the optimal promotion methods. As a result, the used goods sales agent according to the embodiment can efficiently perform everything from checking the condition of the products to acquiring sales performance data and proposing the optimal sales method.

[0030] The verification unit checks the condition of the product. For example, the verification unit analyzes photographs of the product to check its condition. Specifically, the verification unit takes photographs of the product using a high-resolution camera, and the generating AI uses an image analysis algorithm to detect scratches and stains on the product's appearance. The generating AI can analyze changes in pixels and color differences within the image to identify the location and size of scratches and stains. The generating AI can also perform video analysis to check the product's operation. For example, it can analyze a video of the product's operation and evaluate the normality of the operation. The generating AI can analyze the movement of each frame in the video to detect abnormal movements and sounds. Furthermore, the verification unit can use the generating AI to analyze usage history data to evaluate the frequency of product use. For example, it can acquire data recording the product's usage history and analyze that data to evaluate the frequency and duration of use. The generating AI can analyze patterns in the usage history data to understand the product's usage status in detail. As a result, the verification unit can comprehensively evaluate the product's appearance, operation, and frequency of use, and accurately check the product's condition.

[0031] The acquisition unit acquires sales performance data based on the product status confirmed by the verification unit. For example, the acquisition unit acquires sales performance data from each sales site. Specifically, the acquisition unit can use a generation AI to collect data such as the number of sales, sales amount, and sales period from each sales site. The generation AI acquires sales performance data using the API of each sales site. Through the API, data can be acquired from the sales site in real time and stored in a central database. The acquisition unit can also have the generation AI collect sales performance data using web scraping technology. By using web scraping technology, the HTML structure of the sales site can be analyzed and the necessary data can be extracted. Furthermore, the acquisition unit can also have the generation AI analyze past sales data to acquire sales performance data. For example, by analyzing sales trends for a specific product category or brand based on past sales data and comparing it with current sales performance, the market value of the product can be evaluated. As a result, the acquisition unit can acquire accurate and detailed sales performance data based on the product status confirmed by the verification unit.

[0032] The Proposal Department proposes optimal sales methods based on sales performance data acquired by the Acquisition Department. For example, the Proposal Department proposes optimal sales channels, pricing, and promotional methods. Specifically, the Proposal Department can use Generative AI to select sales channels, optimize pricing, and develop promotional strategies. The Generative AI analyzes past sales data and proposes the most suitable sales channels for specific product categories or brands. For example, if a particular sales site has high sales, it will propose prioritizing the use of that site. The Generative AI can also analyze market trends and propose optimal pricing. By analyzing market trends in real time, it can set optimal prices in response to competitor pricing and demand fluctuations. Furthermore, the Proposal Department can use the Generative AI to predict promotional effectiveness and propose optimal promotional methods. For example, by analyzing past promotional data, if a particular promotional method proved effective, it will propose using that method again. This allows the Proposal Department to propose optimal sales methods based on sales performance data acquired by the Acquisition Department, maximizing product sales efficiency.

[0033] The system includes a creation unit for generating listing information. This unit creates listing information such as product photos, product names, descriptions, and prices. Using a generation AI, the creation unit can analyze product photos and automatically generate product names and descriptions. For example, the generation AI analyzes product photos, extracts product features, and generates product names and descriptions. The creation unit can also use the generation AI to read product JAN codes, obtain related product information, and create listing information. Furthermore, the generation AI can analyze market prices and set appropriate prices. This allows the creation unit to automatically generate listing information, reducing the user's workload.

[0034] The verification unit can analyze product photos to check the product's condition. For example, the verification unit can take high-resolution photos of the product, and the generating AI uses an image analysis algorithm to check the product's condition. The verification unit can use the generating AI to evaluate the product's appearance, functionality, and frequency of use. For example, the verification unit can use the generating AI to analyze product photos, extract product features, and check its condition. The verification unit can also use the generating AI to analyze product photos from multiple angles to grasp the overall appearance of the product. Furthermore, the verification unit can use the generating AI to analyze product photos and check for scratches and stains in detail. As a result, the verification unit can accurately check the product's condition by analyzing product photos.

[0035] The data acquisition unit can acquire sales performance data from each sales site. For example, the data acquisition unit collects data such as the number of sales, sales amount, and sales period from each sales site. The data acquisition unit can use a generating AI to acquire sales performance data using the API of each sales site. For example, the generating AI can acquire sales performance data using the API of each sales site. The data acquisition unit can also use the generating AI to collect sales performance data using web scraping technology. Furthermore, the data acquisition unit can also use the generating AI to analyze past sales data to acquire sales performance data. As a result, the data acquisition unit can collect data to determine the optimal sales method by acquiring sales performance data from each sales site.

[0036] The proposal department can propose the optimal sales method. For example, the proposal department can propose the optimal sales channel, pricing, and promotion methods. The proposal department can use generative AI to select sales channels, optimize pricing, and plan promotion strategies. For example, the proposal department's generative AI can analyze past sales data and propose the optimal sales channel. The proposal department can also use generative AI to analyze market trends and propose the optimal pricing. Furthermore, the proposal department can use generative AI to predict the effectiveness of promotions and propose the optimal promotion methods. As a result, the proposal department can propose the optimal sales method, enabling users to sell their unwanted items efficiently.

[0037] The verification unit can more accurately assess the condition of a product by integrating photos taken from multiple angles when analyzing product images. For example, the generation AI in the verification unit analyzes photos taken from different angles to grasp the overall appearance of the product. The verification unit can also use the generation AI to integrate multiple photos to check for scratches and stains in detail. Furthermore, the verification unit can use the generation AI to analyze each part of the product from different angles to comprehensively evaluate its condition. As a result, the verification unit can more accurately assess the condition of a product by integrating photos taken from multiple angles.

[0038] The verification unit can evaluate the condition of a product by considering its usage history and storage conditions when checking its condition. For example, the verification unit's generating AI can analyze the product's usage history and reflect it in the condition evaluation. The verification unit can also evaluate the condition by considering the storage conditions of the product. Furthermore, the verification unit can comprehensively evaluate the condition based on the frequency of use and storage environment of the product. As a result, the verification unit can evaluate the condition of a product more accurately by considering its usage history and storage conditions.

[0039] The verification unit can apply region-specific condition evaluation criteria when analyzing product photos, taking into account the user's geographical location information. For example, the generation AI in the verification unit can apply region-specific condition evaluation criteria based on the user's geographical location information. Furthermore, the generation AI in the verification unit can evaluate the condition of the product while considering the local climate and environmental conditions. In addition, the generation AI in the verification unit can apply condition evaluation criteria that reflect local market trends. As a result, the verification unit can evaluate the condition of the product more accurately by applying region-specific condition evaluation criteria.

[0040] The verification unit can analyze the user's social media activity and supplementarily utilize relevant information when checking the condition of a product. For example, the verification unit's generating AI analyzes the user's social media activity to understand the product's usage status. The verification unit can also use the generating AI to evaluate the product's condition based on the content of social media posts. Furthermore, the verification unit can use the information obtained from the user's social media activity supplementarily to check the product's condition. As a result, the verification unit can more accurately check the product's condition by analyzing the user's social media activity.

[0041] The data acquisition unit can acquire sales performance data by considering not only past sales data but also current market trends. For example, the data acquisition unit can use a generating AI to integrate past sales data and current market trends to acquire sales performance data. Furthermore, the data acquisition unit can use a generating AI to acquire sales performance data that reflects current market trends. In addition, the data acquisition unit can use a generating AI to compare past sales data with market trends to acquire optimal sales performance data. As a result, the data acquisition unit can acquire more accurate sales performance data by considering past sales data and current market trends.

[0042] The acquisition unit can apply different acquisition algorithms depending on the product category and characteristics when acquiring sales performance data. For example, the acquisition unit's generating AI can apply the optimal acquisition algorithm according to the product category. Furthermore, the acquisition unit's generating AI can apply different acquisition algorithms based on the product characteristics. In addition, the acquisition unit's generating AI can select the optimal acquisition algorithm considering the product category and characteristics. As a result, the acquisition unit can acquire more accurate sales performance data by applying different acquisition algorithms depending on the product category and characteristics.

[0043] The data acquisition unit can prioritize acquiring region-specific sales data by considering the user's geographical location when acquiring sales performance data. For example, the generation AI in the data acquisition unit prioritizes acquiring region-specific sales data based on the user's geographical location information. Furthermore, the generation AI in the data acquisition unit can acquire sales data that reflects regional market trends. In addition, the generation AI in the data acquisition unit can acquire optimal sales data by considering the user's geographical location information. As a result, the data acquisition unit can acquire more accurate sales performance data by prioritizing the acquisition of region-specific sales data.

[0044] The data acquisition unit can analyze users' social media activity and supplement it with relevant sales data when acquiring sales performance data. For example, the data acquisition unit uses a generation AI to analyze users' social media activity and acquire relevant sales data. The data acquisition unit can also use the generation AI to supplement sales data based on the content of social media posts. Furthermore, the data acquisition unit can acquire sales data by supplementing it with information obtained from users' social media activity by the generation AI. As a result, the data acquisition unit can acquire more accurate sales performance data by analyzing users' social media activity.

[0045] The suggestion function can adjust the level of detail in suggestions based on the importance of the products. For example, the suggestion function's generating AI can include detailed information in suggestions for important products. It can also simplify suggestions for less important products. Furthermore, the suggestion function can adjust the level of detail in suggestions according to the importance of the products. This allows the suggestion function to provide optimal suggestions for the user by adjusting the level of detail based on the importance of the products.

[0046] The suggestion function can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion function's generating AI can apply the optimal suggestion algorithm based on the product category. Furthermore, the suggestion function's generating AI can apply different suggestion algorithms based on the product's characteristics. Additionally, the suggestion function's generating AI can select the optimal suggestion algorithm considering both the product category and its characteristics. This allows the suggestion function to provide the best possible suggestions to the user by applying different suggestion algorithms depending on the product category.

[0047] The proposal department can prioritize proposals based on the product submission deadline. For example, the proposal department can prioritize proposals for products whose submission deadlines are approaching, while the proposal department can postpone proposals for products with ample time before submission. Furthermore, the proposal department can adjust the priority of proposals based on the submission deadlines generated by the AI. This allows the proposal department to provide optimal proposals to users by prioritizing proposals based on product submission deadlines.

[0048] The suggestion function can adjust the order of suggestions based on the relevance of the products. For example, the suggestion function can prioritize suggesting highly relevant products based on the generation AI. It can also postpone suggesting less relevant products based on the generation AI. Furthermore, the suggestion function can adjust the order of suggestions based on the relevance of the products, enabling it to provide the most optimal suggestions for the user.

[0049] The creation unit can automatically complete and generate product details when creating listing information. For example, the generation AI can extract detailed information from product photos and automatically reflect it in the listing information. The creation unit can also have the generation AI read the product's JAN code and automatically complete the relevant details. Furthermore, the creation unit can have the generation AI analyze the product's characteristics and automatically add detailed information to the listing information. As a result, the creation unit can automatically complete product details and create listing information that is optimal for the user.

[0050] The creation unit can apply different creation algorithms depending on the product category and characteristics when creating listing information. For example, the generation AI can apply the optimal creation algorithm according to the product category. Furthermore, the generation AI can apply different creation algorithms based on the product characteristics. Additionally, the generation AI can select the optimal creation algorithm considering both the product category and characteristics. As a result, the creation unit can create optimal listing information for users by applying different creation algorithms depending on the product category and characteristics.

[0051] The creation unit can supplementally utilize region-specific information by considering the user's geographical location when creating listing information. For example, the generation AI in the creation unit can reflect region-specific information in the listing information based on the user's geographical location. Furthermore, the generation AI in the creation unit can create listing information while considering regional market trends. In addition, the generation AI in the creation unit can create optimal listing information by considering the user's geographical location. As a result, the creation unit can create optimal listing information for the user by supplementally utilizing region-specific information.

[0052] The creation unit can analyze the user's social media activity and use relevant information supplementarily when creating listing information. For example, the generation AI in the creation unit can analyze the user's social media activity and reflect relevant information in the listing information. The creation unit can also use the generation AI to supplement the listing information based on the content of social media posts. Furthermore, the creation unit can use the generation AI to supplementarily create listing information by utilizing information obtained from the user's social media activity. As a result, the creation unit can create optimal listing information for the user by analyzing the user's social media activity.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The verification unit can evaluate the condition of a product by considering the user's past purchase history. For example, the generation AI analyzes the user's past purchase history and applies evaluation criteria for similar products. The verification unit can also evaluate the current condition of a product by referring to the condition of products the user has purchased in the past. Furthermore, the verification unit can evaluate the condition of a product more accurately based on the information obtained from the user's purchase history. In this way, the verification unit can evaluate the condition of a product more accurately by considering the user's past purchase history.

[0055] The data acquisition unit can prioritize the acquisition of relevant sales data by considering the user's purchase history when acquiring sales performance data. For example, the generation AI analyzes the user's purchase history and prioritizes the acquisition of sales performance data for related products. The data acquisition unit can also acquire sales performance data for current products by referring to the sales performance of products the user has purchased in the past. Furthermore, the data acquisition unit can prioritize the acquisition of relevant sales data based on information obtained from the user's purchase history. As a result, the data acquisition unit can acquire more accurate sales performance data by considering the user's purchase history.

[0056] The proposal function can consider the user's past sales history when suggesting the optimal sales method. For example, a generation AI can analyze the user's past sales history and suggest the optimal sales method for similar products. Furthermore, the proposal function can suggest sales methods for current products by referencing sales methods used for products the user has sold in the past. In addition, the proposal function can suggest the optimal sales method based on information obtained from the user's sales history. As a result, the proposal function can suggest more appropriate sales methods by considering the user's past sales history.

[0057] The creation unit can supplement information by considering the user's past listing history when creating listing information. For example, the generation AI can analyze the user's past listing history and automatically supplement listing information for similar products. The creation unit can also create current listing information by referring to information on products the user has previously listed. Furthermore, the creation unit can supplement listing information based on information obtained from the user's listing history. As a result, the creation unit can create more accurate listing information by considering the user's past listing history.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The verification unit checks the condition of the product. For example, the verification unit takes high-resolution photos of the product, and the generating AI uses an image analysis algorithm to evaluate the product's appearance (damage), functionality, and frequency of use. The generating AI can also perform video analysis to check the product's functionality and analyze usage history data to evaluate the frequency of use. Step 2: The acquisition unit acquires sales records based on the product status confirmed by the verification unit. The acquisition unit collects sales records from each sales site, for example, using a generation AI. The generation AI can acquire sales record data using the APIs of each sales site, collect data using web scraping technology, or acquire sales records by analyzing past sales data. Step 3: The proposal department proposes the optimal sales method based on the sales performance acquired by the acquisition department. The proposal department uses a generation AI to propose, for example, the optimal sales channel, pricing, and promotion methods. The generation AI can analyze past sales data to propose the optimal sales channel, analyze market trends to propose the optimal pricing, and predict the effectiveness of promotions to propose the optimal promotion methods.

[0060] (Example of form 2) The unwanted goods sales agent according to an embodiment of the present invention is an agent service that allows users to sell unwanted goods simply by taking photos of the unwanted goods, determining the optimal sales method, and following simple instructions. The unwanted goods sales agent works by having the user take photos of the unwanted goods and read the JAN code, allowing a generating AI to check the condition of the goods and acquire product information. The generating AI acquires sales data from various sales sites and determines the optimal sales method. For example, if selling on an auction site is optimal, the generating AI provides this information to the user, and the user completes the listing simply by following simple instructions. The generating AI can also automatically create listing information in conjunction with sales sites. This allows users to sell unwanted goods at a high price without hassle. Furthermore, the generating AI creates draft product categories and features, and proposes appropriate pricing and sales strategies. This allows users to select the optimal sales method and sell unwanted goods efficiently. Thus, the unwanted goods sales agent allows users to sell unwanted goods at a high price without hassle.

[0061] The used goods sales agent according to this embodiment comprises a verification unit, an acquisition unit, and a proposal unit. The verification unit verifies the condition of the goods. For example, the verification unit analyzes a photograph of the goods to verify their condition. The verification unit can use a generating AI to evaluate the goods' appearance (scratches), functionality, frequency of use, etc. For example, the verification unit takes a high-resolution photograph of the goods, and the generating AI uses an image analysis algorithm to verify the goods' condition. The verification unit can also use the generating AI to perform video analysis to verify the goods' functionality. Furthermore, the verification unit can use the generating AI to analyze usage history data to evaluate the frequency of use of the goods. The acquisition unit acquires sales performance data based on the goods' condition verified by the verification unit. For example, the acquisition unit acquires sales performance data from each sales site. The acquisition unit can use a generating AI to collect data such as the number of sales, sales amount, and sales period from each sales site. For example, the acquisition unit can use the generating AI to acquire sales performance data from each sales site using its API. The acquisition unit can also use the generating AI to collect sales performance data using web scraping technology. Furthermore, the acquisition unit can also acquire sales performance data by having the generating AI analyze past sales data. The proposal unit proposes the optimal sales method based on the sales performance data acquired by the acquisition unit. The proposal unit proposes, for example, the optimal sales channel, pricing, and promotion methods. The proposal unit can use the generating AI to select sales channels, optimize prices, and formulate promotion strategies. For example, the proposal unit can have the generating AI analyze past sales data and propose the optimal sales channel. The proposal unit can also have the generating AI analyze market trends and propose the optimal pricing. Furthermore, the proposal unit can have the generating AI predict the effectiveness of promotions and propose the optimal promotion methods. As a result, the used goods sales agent according to the embodiment can efficiently perform everything from checking the condition of the products to acquiring sales performance data and proposing the optimal sales method.

[0062] The verification unit checks the condition of the product. For example, the verification unit analyzes photographs of the product to check its condition. Specifically, the verification unit takes photographs of the product using a high-resolution camera, and the generating AI uses an image analysis algorithm to detect scratches and stains on the product's appearance. The generating AI can analyze changes in pixels and color differences within the image to identify the location and size of scratches and stains. The generating AI can also perform video analysis to check the product's operation. For example, it can analyze a video of the product's operation and evaluate the normality of the operation. The generating AI can analyze the movement of each frame in the video to detect abnormal movements and sounds. Furthermore, the verification unit can use the generating AI to analyze usage history data to evaluate the frequency of product use. For example, it can acquire data recording the product's usage history and analyze that data to evaluate the frequency and duration of use. The generating AI can analyze patterns in the usage history data to understand the product's usage status in detail. As a result, the verification unit can comprehensively evaluate the product's appearance, operation, and frequency of use, and accurately check the product's condition.

[0063] The acquisition unit acquires sales performance data based on the product status confirmed by the verification unit. For example, the acquisition unit acquires sales performance data from each sales site. Specifically, the acquisition unit can use a generation AI to collect data such as the number of sales, sales amount, and sales period from each sales site. The generation AI acquires sales performance data using the API of each sales site. Through the API, data can be acquired from the sales site in real time and stored in a central database. The acquisition unit can also have the generation AI collect sales performance data using web scraping technology. By using web scraping technology, the HTML structure of the sales site can be analyzed and the necessary data can be extracted. Furthermore, the acquisition unit can also have the generation AI analyze past sales data to acquire sales performance data. For example, by analyzing sales trends for a specific product category or brand based on past sales data and comparing it with current sales performance, the market value of the product can be evaluated. As a result, the acquisition unit can acquire accurate and detailed sales performance data based on the product status confirmed by the verification unit.

[0064] The Proposal Department proposes optimal sales methods based on sales performance data acquired by the Acquisition Department. For example, the Proposal Department proposes optimal sales channels, pricing, and promotional methods. Specifically, the Proposal Department can use Generative AI to select sales channels, optimize pricing, and develop promotional strategies. The Generative AI analyzes past sales data and proposes the most suitable sales channels for specific product categories or brands. For example, if a particular sales site has high sales, it will propose prioritizing the use of that site. The Generative AI can also analyze market trends and propose optimal pricing. By analyzing market trends in real time, it can set optimal prices in response to competitor pricing and demand fluctuations. Furthermore, the Proposal Department can use the Generative AI to predict promotional effectiveness and propose optimal promotional methods. For example, by analyzing past promotional data, if a particular promotional method proved effective, it will propose using that method again. This allows the Proposal Department to propose optimal sales methods based on sales performance data acquired by the Acquisition Department, maximizing product sales efficiency.

[0065] The system includes a creation unit for generating listing information. This unit creates listing information such as product photos, product names, descriptions, and prices. Using a generation AI, the creation unit can analyze product photos and automatically generate product names and descriptions. For example, the generation AI analyzes product photos, extracts product features, and generates product names and descriptions. The creation unit can also use the generation AI to read product JAN codes, obtain related product information, and create listing information. Furthermore, the generation AI can analyze market prices and set appropriate prices. This allows the creation unit to automatically generate listing information, reducing the user's workload.

[0066] The verification unit can analyze product photos to check the product's condition. For example, the verification unit can take high-resolution photos of the product, and the generating AI uses an image analysis algorithm to check the product's condition. The verification unit can use the generating AI to evaluate the product's appearance, functionality, and frequency of use. For example, the verification unit can use the generating AI to analyze product photos, extract product features, and check its condition. The verification unit can also use the generating AI to analyze product photos from multiple angles to grasp the overall appearance of the product. Furthermore, the verification unit can use the generating AI to analyze product photos and check for scratches and stains in detail. As a result, the verification unit can accurately check the product's condition by analyzing product photos.

[0067] The data acquisition unit can acquire sales performance data from each sales site. For example, the data acquisition unit collects data such as the number of sales, sales amount, and sales period from each sales site. The data acquisition unit can use a generating AI to acquire sales performance data using the API of each sales site. For example, the generating AI can acquire sales performance data using the API of each sales site. The data acquisition unit can also use the generating AI to collect sales performance data using web scraping technology. Furthermore, the data acquisition unit can also use the generating AI to analyze past sales data to acquire sales performance data. As a result, the data acquisition unit can collect data to determine the optimal sales method by acquiring sales performance data from each sales site.

[0068] The proposal department can propose the optimal sales method. For example, the proposal department can propose the optimal sales channel, pricing, and promotion methods. The proposal department can use generative AI to select sales channels, optimize pricing, and plan promotion strategies. For example, the proposal department's generative AI can analyze past sales data and propose the optimal sales channel. The proposal department can also use generative AI to analyze market trends and propose the optimal pricing. Furthermore, the proposal department can use generative AI to predict the effectiveness of promotions and propose the optimal promotion methods. As a result, the proposal department can propose the optimal sales method, enabling users to sell their unwanted items efficiently.

[0069] The verification unit can estimate the user's emotions and adjust the timing of product status checks based on the estimated emotions. For example, if the user is stressed, the generating AI will quickly check the product status to reduce the user's burden. If the user is relaxed, the generating AI will perform a detailed status check to provide more accurate information. Furthermore, if the user is in a hurry, the generating AI will perform a simplified status check, allowing the user to quickly move on to the next step. In this way, the verification unit can reduce the user's burden by adjusting the timing of product status checks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The verification unit can more accurately assess the condition of a product by integrating photos taken from multiple angles when analyzing product images. For example, the generation AI in the verification unit analyzes photos taken from different angles to grasp the overall appearance of the product. The verification unit can also use the generation AI to integrate multiple photos to check for scratches and stains in detail. Furthermore, the verification unit can use the generation AI to analyze each part of the product from different angles to comprehensively evaluate its condition. As a result, the verification unit can more accurately assess the condition of a product by integrating photos taken from multiple angles.

[0071] The verification unit can evaluate the condition of a product by considering its usage history and storage conditions when checking its condition. For example, the verification unit's generating AI can analyze the product's usage history and reflect it in the condition evaluation. The verification unit can also evaluate the condition by considering the storage conditions of the product. Furthermore, the verification unit can comprehensively evaluate the condition based on the frequency of use and storage environment of the product. As a result, the verification unit can evaluate the condition of a product more accurately by considering its usage history and storage conditions.

[0072] The verification unit can estimate the user's emotions and determine the priority of checking the product's condition based on the estimated emotions. For example, if the user is stressed, the generation AI will prioritize checking the condition of important products. If the user is relaxed, the generation AI can perform a detailed condition check. Furthermore, if the user is in a hurry, the generation AI can prioritize a simplified condition check. In this way, the verification unit can reduce the user's burden by determining the priority of checking the product's condition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0073] The verification unit can apply region-specific condition evaluation criteria when analyzing product photos, taking into account the user's geographical location information. For example, the generation AI in the verification unit can apply region-specific condition evaluation criteria based on the user's geographical location information. Furthermore, the generation AI in the verification unit can evaluate the condition of the product while considering the local climate and environmental conditions. In addition, the generation AI in the verification unit can apply condition evaluation criteria that reflect local market trends. As a result, the verification unit can evaluate the condition of the product more accurately by applying region-specific condition evaluation criteria.

[0074] The verification unit can analyze the user's social media activity and supplementarily utilize relevant information when checking the condition of a product. For example, the verification unit's generating AI analyzes the user's social media activity to understand the product's usage status. The verification unit can also use the generating AI to evaluate the product's condition based on the content of social media posts. Furthermore, the verification unit can use the information obtained from the user's social media activity supplementarily to check the product's condition. As a result, the verification unit can more accurately check the product's condition by analyzing the user's social media activity.

[0075] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring sales data based on the estimated emotions. For example, if the user is stressed, the generating AI can quickly acquire sales data. If the user is relaxed, the generating AI can acquire detailed sales data. Furthermore, if the user is in a hurry, the generating AI can acquire simplified sales data. In this way, the acquisition unit can reduce the burden on the user by adjusting the timing of acquiring sales data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] The data acquisition unit can acquire sales performance data by considering not only past sales data but also current market trends. For example, the data acquisition unit can use a generating AI to integrate past sales data and current market trends to acquire sales performance data. Furthermore, the data acquisition unit can use a generating AI to acquire sales performance data that reflects current market trends. In addition, the data acquisition unit can use a generating AI to compare past sales data with market trends to acquire optimal sales performance data. As a result, the data acquisition unit can acquire more accurate sales performance data by considering past sales data and current market trends.

[0077] The acquisition unit can apply different acquisition algorithms depending on the product category and characteristics when acquiring sales performance data. For example, the acquisition unit's generating AI can apply the optimal acquisition algorithm according to the product category. Furthermore, the acquisition unit's generating AI can apply different acquisition algorithms based on the product characteristics. In addition, the acquisition unit's generating AI can select the optimal acquisition algorithm considering the product category and characteristics. As a result, the acquisition unit can acquire more accurate sales performance data by applying different acquisition algorithms depending on the product category and characteristics.

[0078] The acquisition unit can estimate the user's emotions and determine the priority of acquiring sales data based on the estimated emotions. For example, if the user is stressed, the generation AI will prioritize acquiring important sales data. If the user is relaxed, the generation AI can acquire detailed sales data. Furthermore, if the user is in a hurry, the generation AI can prioritize acquiring simplified sales data. In this way, the acquisition unit can reduce the user's burden by determining the priority of acquiring sales data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0079] The data acquisition unit can prioritize acquiring region-specific sales data by considering the user's geographical location when acquiring sales performance data. For example, the generation AI in the data acquisition unit prioritizes acquiring region-specific sales data based on the user's geographical location information. Furthermore, the generation AI in the data acquisition unit can acquire sales data that reflects regional market trends. In addition, the generation AI in the data acquisition unit can acquire optimal sales data by considering the user's geographical location information. As a result, the data acquisition unit can acquire more accurate sales performance data by prioritizing the acquisition of region-specific sales data.

[0080] The data acquisition unit can analyze users' social media activity and supplement it with relevant sales data when acquiring sales performance data. For example, the data acquisition unit uses a generation AI to analyze users' social media activity and acquire relevant sales data. The data acquisition unit can also use the generation AI to supplement sales data based on the content of social media posts. Furthermore, the data acquisition unit can acquire sales data by supplementing it with information obtained from users' social media activity by the generation AI. As a result, the data acquisition unit can acquire more accurate sales performance data by analyzing users' social media activity.

[0081] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the generative AI will provide simple and easy-to-understand suggestions. If the user is relaxed, the generative AI can provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, the generative AI can provide concise and quick suggestions. In this way, the suggestion unit can provide suggestions that are easy for the user to understand by adjusting the way suggestions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The suggestion function can adjust the level of detail in suggestions based on the importance of the products. For example, the suggestion function's generating AI can include detailed information in suggestions for important products. It can also simplify suggestions for less important products. Furthermore, the suggestion function can adjust the level of detail in suggestions according to the importance of the products. This allows the suggestion function to provide optimal suggestions for the user by adjusting the level of detail based on the importance of the products.

[0083] The suggestion function can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion function's generating AI can apply the optimal suggestion algorithm based on the product category. Furthermore, the suggestion function's generating AI can apply different suggestion algorithms based on the product's characteristics. Additionally, the suggestion function's generating AI can select the optimal suggestion algorithm considering both the product category and its characteristics. This allows the suggestion function to provide the best possible suggestions to the user by applying different suggestion algorithms depending on the product category.

[0084] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is stressed, the generative AI will provide short, concise suggestions. If the user is relaxed, the generative AI can provide longer suggestions with more detailed explanations. Furthermore, if the user is in a hurry, the generative AI can provide quick and concise suggestions. This allows the suggestion unit to provide suggestions that are easy for the user to understand by adjusting the length of the suggestions 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.

[0085] The proposal department can prioritize proposals based on the product submission deadline. For example, the proposal department can prioritize proposals for products whose submission deadlines are approaching, while the proposal department can postpone proposals for products with ample time before submission. Furthermore, the proposal department can adjust the priority of proposals based on the submission deadlines generated by the AI. This allows the proposal department to provide optimal proposals to users by prioritizing proposals based on product submission deadlines.

[0086] The suggestion function can adjust the order of suggestions based on the relevance of the products. For example, the suggestion function can prioritize suggesting highly relevant products based on the generation AI. It can also postpone suggesting less relevant products based on the generation AI. Furthermore, the suggestion function can adjust the order of suggestions based on the relevance of the products, enabling it to provide the most optimal suggestions for the user.

[0087] The creation unit can estimate the user's emotions and adjust how the listing information is created based on the estimated emotions. For example, if the user is stressed, the generation AI can create simple and quick listing information. If the user is relaxed, the generation AI can create listing information that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can create simplified listing information. In this way, the creation unit can create the most suitable listing information for the user by adjusting how the listing information is created according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] The creation unit can automatically complete and generate product details when creating listing information. For example, the generation AI can extract detailed information from product photos and automatically reflect it in the listing information. The creation unit can also have the generation AI read the product's JAN code and automatically complete the relevant details. Furthermore, the creation unit can have the generation AI analyze the product's characteristics and automatically add detailed information to the listing information. As a result, the creation unit can automatically complete product details and create listing information that is optimal for the user.

[0089] The creation unit can apply different creation algorithms depending on the product category and characteristics when creating listing information. For example, the generation AI can apply the optimal creation algorithm according to the product category. Furthermore, the generation AI can apply different creation algorithms based on the product characteristics. Additionally, the generation AI can select the optimal creation algorithm considering both the product category and characteristics. As a result, the creation unit can create optimal listing information for users by applying different creation algorithms depending on the product category and characteristics.

[0090] The creation unit can estimate the user's emotions and adjust how the listing information is displayed based on the estimated emotions. For example, if the user is stressed, the generation AI can provide a simple and highly visible display. If the user is relaxed, the generation AI can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can provide a simplified display. In this way, the creation unit can provide the optimal display method for the user by adjusting how the listing information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The creation unit can supplementally utilize region-specific information by considering the user's geographical location when creating listing information. For example, the generation AI in the creation unit can reflect region-specific information in the listing information based on the user's geographical location. Furthermore, the generation AI in the creation unit can create listing information while considering regional market trends. In addition, the generation AI in the creation unit can create optimal listing information by considering the user's geographical location. As a result, the creation unit can create optimal listing information for the user by supplementally utilizing region-specific information.

[0092] The creation unit can analyze the user's social media activity and use relevant information supplementarily when creating listing information. For example, the generation AI in the creation unit can analyze the user's social media activity and reflect relevant information in the listing information. The creation unit can also use the generation AI to supplement the listing information based on the content of social media posts. Furthermore, the creation unit can use the generation AI to supplementarily create listing information by utilizing information obtained from the user's social media activity. As a result, the creation unit can create optimal listing information for the user by analyzing the user's social media activity.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The verification unit can evaluate the condition of a product by considering the user's past purchase history. For example, the generation AI analyzes the user's past purchase history and applies evaluation criteria for similar products. The verification unit can also evaluate the current condition of a product by referring to the condition of products the user has purchased in the past. Furthermore, the verification unit can evaluate the condition of a product more accurately based on the information obtained from the user's purchase history. In this way, the verification unit can evaluate the condition of a product more accurately by considering the user's past purchase history.

[0095] The data acquisition unit can prioritize the acquisition of relevant sales data by considering the user's purchase history when acquiring sales performance data. For example, the generation AI analyzes the user's purchase history and prioritizes the acquisition of sales performance data for related products. The data acquisition unit can also acquire sales performance data for current products by referring to the sales performance of products the user has purchased in the past. Furthermore, the data acquisition unit can prioritize the acquisition of relevant sales data based on information obtained from the user's purchase history. As a result, the data acquisition unit can acquire more accurate sales performance data by considering the user's purchase history.

[0096] The proposal function can consider the user's past sales history when suggesting the optimal sales method. For example, a generation AI can analyze the user's past sales history and suggest the optimal sales method for similar products. Furthermore, the proposal function can suggest sales methods for current products by referencing sales methods used for products the user has sold in the past. In addition, the proposal function can suggest the optimal sales method based on information obtained from the user's sales history. As a result, the proposal function can suggest more appropriate sales methods by considering the user's past sales history.

[0097] The creation unit can supplement information by considering the user's past listing history when creating listing information. For example, the generation AI can analyze the user's past listing history and automatically supplement listing information for similar products. The creation unit can also create current listing information by referring to information on products the user has previously listed. Furthermore, the creation unit can supplement listing information based on information obtained from the user's listing history. As a result, the creation unit can create more accurate listing information by considering the user's past listing history.

[0098] The verification unit can estimate the user's emotions when checking the condition of a product and adjust the evaluation criteria based on those emotions. For example, if the user is stressed, the generating AI will relax the evaluation criteria to reduce the user's burden. If the user is relaxed, the generating AI can perform a more detailed evaluation and provide more accurate information. Furthermore, if the user is in a hurry, the generating AI can perform a simplified evaluation, allowing the user to quickly move on to the next step. In this way, the verification unit can reduce the user's burden by adjusting the evaluation criteria according to the user's emotions.

[0099] The data acquisition unit can estimate the user's emotions when acquiring sales data and adjust the acquisition method based on the estimated emotions. For example, if the user is stressed, the generating AI will quickly acquire sales data. If the user is relaxed, the generating AI can acquire detailed sales data. Furthermore, if the user is in a hurry, the generating AI can acquire simplified sales data. In this way, the data acquisition unit can reduce the burden on the user by adjusting the acquisition method according to the user's emotions.

[0100] The proposal department can estimate the user's emotions when suggesting the optimal sales method and adjust the proposal content based on those emotions. For example, if the user is stressed, the generating AI will make a simple and easy-to-understand proposal. If the user is relaxed, the generating AI can make a proposal that includes detailed information. Furthermore, if the user is in a hurry, the generating AI can make a concise and quick proposal. In this way, the proposal department can adjust the proposal content according to the user's emotions, making it possible to make proposals that are easy for the user to understand.

[0101] The creation unit can estimate the user's emotions when creating listing information and adjust the creation method based on the estimated emotions. For example, if the user is stressed, the generation AI will create simple and quick listing information. If the user is relaxed, the generation AI can create listing information that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can create simplified listing information. In this way, the creation unit can create the most suitable listing information for the user by adjusting the creation method according to the user's emotions.

[0102] The verification unit can estimate the user's emotions when checking the condition of a product and determine the priority of the check based on the estimated emotions. For example, if the user is stressed, the generating AI will prioritize checking the condition of important products. If the user is relaxed, the generating AI can perform a detailed check. Furthermore, if the user is in a hurry, the generating AI can prioritize a simplified check. In this way, the verification unit can reduce the burden on the user by determining the priority of checks according to the user's emotions.

[0103] The data acquisition unit can estimate the user's emotions when acquiring sales data and determine the acquisition priority based on the estimated emotions. For example, if the user is stressed, the generating AI will prioritize acquiring important sales data. If the user is relaxed, the generating AI can acquire detailed sales data. Furthermore, if the user is in a hurry, the generating AI can prioritize acquiring simplified sales data. In this way, the data acquisition unit can reduce the burden on the user by determining the acquisition priority according to the user's emotions.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The verification unit checks the condition of the product. For example, the verification unit takes high-resolution photos of the product, and the generating AI uses an image analysis algorithm to evaluate the product's appearance (damage), functionality, and frequency of use. The generating AI can also perform video analysis to check the product's functionality and analyze usage history data to evaluate the frequency of use. Step 2: The acquisition unit acquires sales records based on the product status confirmed by the verification unit. The acquisition unit collects sales records from each sales site, for example, using a generation AI. The generation AI can acquire sales record data using the APIs of each sales site, collect data using web scraping technology, or acquire sales records by analyzing past sales data. Step 3: The proposal department proposes the optimal sales method based on the sales performance acquired by the acquisition department. The proposal department uses a generation AI to propose, for example, the optimal sales channel, pricing, and promotion methods. The generation AI can analyze past sales data to propose the optimal sales channel, analyze market trends to propose the optimal pricing, and predict the effectiveness of promotions to propose the optimal promotion methods.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the verification unit, acquisition unit, proposal unit, and creation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the verification unit is implemented by taking a photograph of the product using the camera 42 of the smart device 14, and the generating AI performing image analysis using the specific processing unit 290 of the data processing unit 12. The acquisition unit acquires sales performance data for each sales site using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal sales method using the specific processing unit 290 of the data processing unit 12. The creation unit can create listing information using, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the verification unit, acquisition unit, proposal unit, and creation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the verification unit is implemented by taking a photograph of the product using the camera 42 of the smart glasses 214, and the generating AI performing image analysis using the specific processing unit 290 of the data processing unit 12. The acquisition unit acquires sales performance data for each sales site using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal sales method using the specific processing unit 290 of the data processing unit 12. The creation unit can create listing information using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the verification unit, acquisition unit, proposal unit, and creation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the verification unit is implemented by taking a photograph of the product using the camera 42 of the headset terminal 314, and the generating AI performing image analysis using the specific processing unit 290 of the data processing unit 12. The acquisition unit acquires sales performance data for each sales site using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal sales method using the specific processing unit 290 of the data processing unit 12. The creation unit can create listing information using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the verification unit, acquisition unit, proposal unit, and creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the verification unit is implemented by taking a photograph of the product using the camera 42 of the robot 414, and the generating AI performing image analysis using the specific processing unit 290 of the data processing unit 12. The acquisition unit acquires sales performance data for each sales site using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal sales method using the specific processing unit 290 of the data processing unit 12. The creation unit can create listing information using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) A confirmation unit to check the condition of the product, An acquisition unit that acquires sales records based on the condition of the product confirmed by the aforementioned confirmation unit, A proposal unit proposes the optimal sales method based on the sales performance acquired by the acquisition unit, Equipped with A system characterized by the following features. (Note 2) It includes a section for creating listing information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned verification unit is Analyze product photos to check the product's condition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, Obtain sales data from each sales site. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose the optimal sales method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is The system estimates the user's emotions and adjusts the timing of product status checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is When analyzing product photos, photos taken from multiple angles are integrated to more accurately determine the product's condition. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned verification unit is When checking the condition of a product, we evaluate its condition by taking into account its usage history and storage conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of checking the product's condition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned verification unit is When analyzing product photos, we apply region-specific condition evaluation criteria, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned verification unit is When checking the condition of a product, the system analyzes the user's social media activity and uses relevant information to complement the product's condition. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, The system estimates user sentiment and adjusts the timing of sales data acquisition based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, When acquiring sales data, we will consider not only past sales data but also current market trends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, When acquiring sales data, different acquisition algorithms are applied depending on the product category and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, The system estimates user sentiment and prioritizes the acquisition of sales data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, When acquiring sales data, the system prioritizes acquiring region-specific sales data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, When acquiring sales performance data, analyze users' social media activity and use relevant sales data as a complement. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, We estimate user sentiment and adjust how listing information is created based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned creation unit, When creating a listing, the product details will be automatically filled in. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned creation unit, When creating listing information, different creation algorithms are applied depending on the product category and characteristics. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned creation unit, The system estimates user sentiment and adjusts how listing information is displayed based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned creation unit, When creating listing information, the system takes the user's geographical location into consideration and uses region-specific information as supplementary data. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned creation unit, When creating listing information, analyze the user's social media activity and use relevant information to complement the listing. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0178] 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 confirmation unit to check the condition of the product, An acquisition unit that acquires sales records based on the condition of the product confirmed by the aforementioned confirmation unit, A proposal unit proposes the optimal sales method based on the sales performance acquired by the acquisition unit, Equipped with A system characterized by the following features.

2. It includes a section for creating listing information. The system according to feature 1.

3. The aforementioned verification unit is Analyze product photos to check the product's condition. The system according to feature 1.

4. The acquisition unit is, Obtain sales data from each sales site. The system according to feature 1.

5. The aforementioned proposal section is, We propose the optimal sales method. The system according to feature 1.

6. The aforementioned verification unit is The system estimates the user's emotions and adjusts the timing of product status checks based on those estimated emotions. The system according to feature 1.

7. The aforementioned verification unit is When analyzing product photos, photos taken from multiple angles are integrated to more accurately determine the product's condition. The system according to feature 1.

8. The aforementioned verification unit is When checking the condition of a product, we evaluate its condition by taking into account its usage history and storage conditions. The system according to feature 1.

9. The aforementioned verification unit is The system estimates the user's emotions and determines the priority of checking the product's condition based on those estimated emotions. The system according to feature 1.

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