system

Abstract

The system according to this embodiment aims to evaluate the condition of the items being offered, propose the optimal exchange route, and conduct automated negotiations. [Solution] The system according to the embodiment comprises an evaluation unit, a proposal unit, and a negotiation unit. The evaluation unit evaluates the condition of the items to be offered. The proposal unit proposes an exchange route based on the information evaluated by the evaluation unit. The negotiation unit performs automatic negotiations based on the exchange route proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the exchange is limited to between two parties, there is a problem that the opportunity to obtain the target object is limited.

[0005] The system according to the embodiment aims to evaluate the state of the product, propose an optimal exchange route, and perform an automatic negotiation.

Means for Solving the Problems

[0006] The system according to the embodiment includes an evaluation unit, a proposal unit, and a negotiation unit. The evaluation unit evaluates the state of the product. The proposal unit proposes an exchange route based on the information evaluated by the evaluation unit. The negotiation unit performs an automatic negotiation based on the exchange route proposed by the proposal unit.

Effects of the Invention

[0007] The system according to this embodiment can evaluate the condition of the items being offered, propose the optimal exchange route, and conduct automated negotiations. [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 item exchange system according to an embodiment of the present invention is a system that uses AI to streamline the exchange of unwanted items, enabling users to acquire a wider variety of goods. In this unwanted item exchange system, a generating AI analyzes listing images and automatically evaluates the condition and quality of the items. Next, the unwanted item exchange system uses the AI ​​to calculate multiple exchange routes based on the listed items and wish lists, and proposes the optimal one. This prevents exchanges from being limited to just two parties, increasing the chances of obtaining desired items. Furthermore, the unwanted item exchange system uses a generating AI to automatically negotiate with multiple sellers based on the user's desired conditions. Problems that arise during the negotiation process are recorded and notified to the user, ensuring transparency. In addition, if there is a risk of the exchange route being interrupted, the unwanted item exchange system warns the user in advance and proposes alternatives. The AI ​​tracks all stages until the completion of the transaction, supporting quick responses. This reduces the number of exchanges that end incompletely and creates opportunities to acquire a variety of goods. The efficiency and reliability of negotiations are increased, improving the user experience. In addition, the visualization of the condition of listed items enables transparent and secure transactions. For example, if a user lists unwanted books and adds electronic devices to their wish list, the AI ​​analyzes other users' listings and suggests the best exchange route. The AI ​​evaluates the condition of the items and automatically negotiates based on the user's desired conditions. If a problem arises during the transaction, the user is notified and alternative solutions are proposed. The AI ​​tracks every step of the transaction until completion, supporting quick responses. In this way, the AI-powered system for exchanging unwanted items allows users to trade efficiently and with peace of mind. It also serves as a means of promoting sustainable consumption for environmentally conscious consumers and local communities. Thus, the unwanted item exchange system allows users to trade efficiently and with peace of mind.

[0029] The unwanted item exchange system according to the embodiment comprises an evaluation unit, a proposal unit, and a negotiation unit. The evaluation unit evaluates the condition of the items being offered for sale. The evaluation unit analyzes the listing images using, for example, a generating AI, and automatically evaluates the condition and quality of the items. The evaluation unit can analyze and evaluate the appearance, function, frequency of use, etc., of the items from the listing images. The evaluation unit can evaluate the condition of the items using, for example, an image analysis algorithm with a generating AI. The evaluation unit can also evaluate the quality of the items by, for example, having the generating AI set evaluation criteria based on training data. The proposal unit proposes an exchange route based on the information evaluated by the evaluation unit. The proposal unit can calculate multiple exchange routes based on the items being offered and wish lists using, for example, a generating AI, and propose the optimal one. The proposal unit can propose an exchange route considering, for example, intermediate points, exchange conditions, and route optimization methods. The proposal unit can calculate and propose an exchange route using, for example, an optimization algorithm with a generating AI. The proposal unit can also propose an optimal exchange route based on past exchange data using, for example, a generating AI. The negotiation unit automatically negotiates based on the exchange route proposed by the proposal unit. The negotiation unit automatically negotiates with multiple sellers based on the user's desired conditions, for example, using a generative AI. The negotiation unit can automatically negotiate by considering, for example, negotiation algorithms, negotiation conditions, and success criteria. The negotiation unit can negotiate based on the user's desired conditions, for example, using a generative AI that uses a negotiation algorithm. The negotiation unit can also select the optimal negotiation strategy based on past negotiation data, for example, using a generative AI, and then negotiate. As a result, the unwanted item exchange system according to this embodiment can efficiently perform condition evaluation of items for sale, propose exchange routes, and conduct automatic negotiations.

[0030] The evaluation unit assesses the condition of the listed items. For example, it uses generative AI to analyze listing images and automatically evaluate the condition and quality of the items. Specifically, the generative AI utilizes image analysis algorithms to extract detailed information such as the appearance, function, and frequency of use of the listed items. For example, it can detect the presence or absence of scratches, stains, color changes, and missing parts in the listing images with high accuracy and quantify the condition of the items. Furthermore, the generative AI can set evaluation criteria based on training data and comprehensively evaluate the quality of the listed items. For example, it can learn from data of previously evaluated listed items and quickly and accurately evaluate items with similar characteristics. The evaluation unit provides these evaluation results as feedback to the seller, offering them as detailed information about the listed items. This allows sellers to accurately understand the condition of their listed items and offer appropriate pricing and exchange terms. The evaluation unit can also save the evaluation results of listed items in a database and use them for future evaluations and analyses. For example, it can analyze evaluation trends of listed items in specific categories or brands to understand market trends. This allows the evaluation unit to efficiently and accurately evaluate the condition of listed items, improving the reliability of the entire system and user satisfaction.

[0031] The proposal department proposes exchange routes based on information evaluated by the evaluation department. For example, the proposal department uses a generative AI to calculate multiple exchange routes based on the listed items and wish lists, and proposes the optimal one. Specifically, the generative AI receives the evaluation results of the listed items and the user's desired conditions as input data, and calculates exchange routes using an optimization algorithm. For example, it simulates multiple exchange routes considering the category and value of the listed items and the user's desired exchange conditions (e.g., desired exchange date and location, evaluation of the exchange partner, etc.). Based on past exchange data, the generative AI can learn exchange routes and conditions with a high success rate and propose the optimal exchange route. The proposal department presents these calculation results to the user, allowing the user to make a selection. For example, by comparing multiple exchange routes and clearly indicating the advantages and disadvantages of each, it makes it easier for the user to select the optimal exchange route. The proposal department can also collect user feedback and use it to improve the accuracy of the proposal algorithm and the content of the proposals. In this way, the proposal department can propose flexible and optimal exchange routes that meet the user's needs, improving the overall usability of the system and user satisfaction.

[0032] The Negotiation Department automatically conducts negotiations based on the exchange routes proposed by the Proposal Department. For example, the Negotiation Department uses a generative AI to automatically negotiate with multiple sellers based on the user's desired conditions. Specifically, the generative AI receives the user's desired conditions (e.g., desired exchange date, exchange location, exchange conditions, etc.) as input data and uses a negotiation algorithm to select the optimal negotiation strategy. For example, it learns negotiation patterns and conditions with a high success rate based on past negotiation data and automatically selects the optimal negotiation strategy. Based on these negotiation strategies, the Negotiation Department negotiates with multiple sellers simultaneously to achieve an exchange under conditions closest to the user's desired conditions. The Negotiation Department notifies the user of the progress and results of the negotiations in real time and can adjust the user's desired conditions as needed. For example, if negotiations are difficult or the conditions are not suitable, it will present the user with an alternative and conduct renegotiations. The Negotiation Department can also save the negotiation results in a database and use them for future negotiations and analysis. This enables the Negotiation Department to achieve efficient and effective automated negotiations based on the user's desired conditions, improving the overall reliability of the system and user satisfaction.

[0033] The transparency unit can record problems that occur during each trading step and notify the user. For example, the transparency unit can use a generation AI to record problems that occur at each trading step in real time and immediately notify the user. For example, the transparency unit can record problems such as system errors, user dissatisfaction, and trading delays and notify the user. For example, the transparency unit uses a generation AI to record problems based on the type of problem and the recording method, and notify the user. In addition, for example, the transparency unit can use a generation AI to analyze the cause of the problem and notify the user. As a result, the transparency unit ensures the transparency of transactions, allowing users to trade with peace of mind.

[0034] The risk notification unit can provide users with advance warnings and alternative solutions if there is a risk of the exchange route being interrupted midway. For example, the risk notification unit can use a generation AI to monitor the risk of the exchange route being interrupted in real time and immediately notify users. For example, the risk notification unit can monitor risks such as logistics delays and transaction cancellations and provide users with warnings and alternative solutions. For example, the risk notification unit can use a generation AI to monitor risks based on the type of risk and notification method, and notify users. In addition, the risk notification unit can use a generation AI to analyze the cause of the risk and notify users. As a result, the risk notification unit can provide advance notification of risks in the exchange route, allowing users to take appropriate action.

[0035] The tracking unit can track all stages of a transaction until completion and support a rapid response. For example, the tracking unit can use generative AI to track all stages of a transaction in real time and immediately notify the user. For example, the tracking unit can track all stages from the start of a transaction to negotiation, delivery, and receipt, and notify the user. For example, the tracking unit uses generative AI to track the transaction based on tracking and notification methods and notify the user. In addition, for example, the tracking unit can use generative AI to analyze the progress of the transaction and notify the user. This allows the tracking unit to track all stages of a transaction and enable a rapid response.

[0036] The evaluation unit can analyze listing images using a generating AI and automatically evaluate the condition and quality of the items. For example, the evaluation unit can analyze the appearance, function, and frequency of use of an item from the listing image and perform an evaluation. For example, the generating AI uses an image analysis algorithm to evaluate the condition of the listed item. In addition, the evaluation unit can, for example, have the generating AI set evaluation criteria based on training data and evaluate the quality of the listed item. As a result, the evaluation unit improves the accuracy of its evaluations by automatically evaluating the condition and quality of the listed items.

[0037] The proposal unit can use generative AI to calculate multiple exchange routes based on listed items and wish lists, and propose the optimal one. For example, the proposal unit can propose an exchange route considering intermediate points, exchange conditions, and route optimization methods. For example, the proposal unit can use generative AI to calculate and propose an exchange route using an optimization algorithm. Alternatively, the proposal unit can use generative AI to propose the optimal exchange route based on past exchange data. As a result, the proposal unit can increase exchange opportunities by calculating multiple exchange routes and proposing the optimal one.

[0038] The negotiation department can use generative AI to automatically negotiate with multiple sellers based on the user's desired conditions. The negotiation department can automatically negotiate by considering, for example, negotiation algorithms, negotiation conditions, and success criteria. For example, the negotiation department uses generative AI to negotiate based on the user's desired conditions using negotiation algorithms. In addition, the negotiation department can use, for example, generative AI to select the optimal negotiation strategy based on past negotiation data and then negotiate. As a result, the negotiation department improves negotiation efficiency by automatically negotiating based on the user's desired conditions.

[0039] The evaluation unit can perform evaluations by considering the frequency and history of use of an item when analyzing listing images. For example, the evaluation unit can estimate the frequency of use of an item from the listing images and give a lower rating to items that have been used frequently. For example, the evaluation unit can analyze the usage history and give a higher rating to items that have been properly maintained. For example, the evaluation unit can accurately assess the condition of an item by comprehensively considering the frequency and history of use. As a result, the evaluation unit improves the accuracy of its evaluations by considering the frequency and history of use of an item.

[0040] The evaluation unit can consider the brand and year of manufacture of an item when analyzing the listing images. For example, the evaluation unit can consider the reliability of the brand and reflect it in the evaluation. For example, the evaluation unit can give a higher evaluation to items that are newly manufactured. For example, the evaluation unit can accurately assess the value of an item by comprehensively considering the brand and year of manufacture. As a result, the accuracy of the evaluation unit's evaluation is improved by considering the brand and year of manufacture of the item.

[0041] The evaluation unit can analyze listing images and evaluate items while considering their repair history and maintenance status. For example, the evaluation unit may give a lower rating to items with a repair history. For example, the evaluation unit may give a higher rating to items that have been regularly maintained. For example, the evaluation unit can accurately assess the condition of an item by comprehensively considering its repair history and maintenance status. As a result, the evaluation unit improves the accuracy of its evaluations by considering the repair history and maintenance status of items.

[0042] The evaluation unit can perform evaluations by considering the market value and demand of items when analyzing listing images. For example, the evaluation unit can give a higher evaluation to items with high market value. For example, the evaluation unit can give a higher evaluation to items with high demand. For example, the evaluation unit can accurately assess the value of an item by comprehensively considering market value and demand. As a result, the evaluation unit improves the accuracy of its evaluations by considering the market value and demand of items.

[0043] The proposal department can improve the reliability of its proposals by considering the past success rate of exchange routes. For example, the proposal department can prioritize proposing exchange routes that have been successful in the past. For example, the proposal department can adjust the content of its proposals based on exchange routes with high success rates. For example, the proposal department can make highly reliable proposals by considering past success rates. As a result, the proposal department can improve user satisfaction by improving the reliability of its proposals by considering the past success rate of exchange routes.

[0044] The proposal unit can suggest the optimal route by considering the distance and time of the exchange route. For example, the proposal unit can prioritize suggesting the shortest exchange route. For example, the proposal unit can prioritize suggesting the shortest exchange route. For example, the proposal unit can comprehensively consider distance and time to suggest the optimal exchange route. As a result, the proposal unit improves user satisfaction by suggesting the optimal route by considering the distance and time of the exchange route.

[0045] The proposal department can make sustainable proposals by considering the environmental impact of the exchange route when making a proposal. For example, the proposal department can prioritize proposing exchange routes with less environmental impact. For example, the proposal department can adjust the content of the proposal based on a sustainable exchange route. For example, the proposal department can make sustainable proposals by considering the environmental impact. As a result, the proposal department can improve user satisfaction by making sustainable proposals that consider the environmental impact of the exchange route.

[0046] The proposal department can make economical proposals by considering the cost of the exchange route. For example, the proposal department can prioritize proposing exchange routes with lower costs. For example, the proposal department can adjust the content of the proposal based on the economical exchange route. For example, the proposal department can make economical proposals by considering costs. As a result, the proposal department can improve user satisfaction by making economical proposals that take the cost of the exchange route into consideration.

[0047] The proposal department can make economical proposals by considering the cost of the exchange route. For example, the proposal department can prioritize proposing exchange routes with lower costs. For example, the proposal department can adjust the content of the proposal based on the economical exchange route. For example, the proposal department can make economical proposals by considering costs. As a result, the proposal department can improve user satisfaction by making economical proposals that take the cost of the exchange route into consideration.

[0048] The negotiating department can select the optimal negotiation strategy by referring to past negotiation history. For example, the negotiating department can proceed with negotiations based on past successful negotiation strategies. For example, the negotiating department can analyze past negotiation history and select the optimal negotiation strategy. For example, the negotiating department can adjust the negotiation process by referring to past negotiation history. As a result, the success rate of negotiations improves as the negotiating department selects the optimal negotiation strategy by referring to past negotiation history.

[0049] The negotiating department can adjust its approach to negotiations by considering the other party's reputation and trustworthiness. For example, if the other party is highly regarded, the negotiating department may proceed quickly. If the other party is not very trustworthy, the negotiating department may proceed cautiously. For example, the negotiating department can adjust its approach by comprehensively considering the other party's reputation and trustworthiness. As a result, the negotiating department can improve its success rate by adjusting its approach to negotiations by considering the other party's reputation and trustworthiness.

[0050] The negotiating department can adjust its approach to negotiations by considering the other party's culture and customs. For example, the negotiating department can select an appropriate negotiation method, taking into account the other party's culture. For example, the negotiating department can adjust its approach to negotiations by considering the other party's customs. For example, the negotiating department can adjust its approach to negotiations by comprehensively considering culture and customs. As a result, the success rate of negotiations improves when the negotiating department adjusts its approach to negotiations by considering the other party's culture and customs.

[0051] The negotiating department can adjust its approach to negotiations by referring to the other party's past transaction history. For example, the negotiating department can adjust its approach based on the other party's past transaction history. The negotiating department can, for example, analyze past transaction history and select the optimal negotiation strategy. For example, the negotiating department can adjust its approach to negotiations by referring to the other party's past transaction history. As a result, the negotiating department can improve its success rate in negotiations by adjusting its approach by referring to the other party's past transaction history.

[0052] The transparency unit can record problems occurring during each trading step in real time and immediately notify the user. For example, the transparency unit records problems occurring at each trading step in real time and immediately notifies the user. For example, if a problem occurs, the transparency unit can immediately notify the user to encourage a quick response. The transparency unit ensures transparency by, for example, recording problems in real time and immediately notifying the user. Thus, transparency is ensured by the transparency unit recording problems occurring during each trading step in real time and immediately notifying the user.

[0053] The transparency unit can record in detail any problems that occur during each trading step, making them available for later reference. For example, the transparency unit can record in detail any problems that occur at each trading step, making them available for later reference. For example, if a problem occurs, the transparency unit can record its details, making them available for later reference. For example, the transparency unit ensures transparency by recording in detail any problems at each trading step. Thus, transparency is ensured by the transparency unit recording in detail any problems that occur during each trading step, making them available for later reference.

[0054] The risk notification unit can monitor the risk of interruption in the exchange route in real time and immediately notify the user. For example, the risk notification unit can monitor the risk of interruption in the exchange route in real time and immediately notify the user. For example, if a risk occurs, the risk notification unit can immediately notify the user and encourage a quick response. For example, the risk notification unit ensures transparency by monitoring risks in real time and immediately notifying the user. Thus, transparency is ensured by the risk notification unit monitoring the risk of interruption in the exchange route in real time and immediately notifying the user.

[0055] The risk notification unit can analyze in detail the risk of interruption in the exchange route and identify the cause of the risk. For example, the risk notification unit can analyze in detail the risk of interruption in the exchange route and identify the cause of the risk. For example, if a risk occurs, the risk notification unit can analyze in detail and identify its cause. For example, the risk notification unit can analyze in detail the risks of each exchange route and ensure transparency. In this way, transparency is ensured by the risk notification unit analyzing in detail the risk of interruption in the exchange route and identifying the cause of the risk.

[0056] The tracking unit can track all stages of a transaction in real time and notify the user immediately. For example, the tracking unit can track all stages of a transaction in real time and notify the user immediately. For example, the tracking unit can track each stage of a transaction in real time and notify the user immediately. The tracking unit ensures transparency by tracking transactions in real time and notifying the user immediately. Thus, transparency is ensured by the tracking unit tracking all stages of a transaction in real time and notifying the user immediately.

[0057] The tracking unit can record in detail all stages until the completion of a transaction, making them available for later reference. For example, the tracking unit can record in detail all stages until the completion of a transaction, making them available for later reference. For example, the tracking unit can record in detail each stage of a transaction, making them available for later reference. For example, the tracking unit ensures transparency by recording in detail each transaction step. This ensures transparency by allowing the tracking unit to record in detail all stages until the completion of a transaction, making them available for later reference.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The evaluation unit can not only assess the condition of the listed item but also the reliability of the seller. For example, the evaluation unit can assess reliability based on the seller's past transaction history and ratings. For example, the evaluation unit can assess reliability by considering the seller's transaction success rate and user feedback. For example, by assessing the reliability of the seller, the evaluation unit can help users conduct transactions with peace of mind. In this way, the reliability of transactions is improved by the evaluation unit comprehensively evaluating the condition of the listed item and the reliability of the seller.

[0060] The risk notification unit not only notifies users of the risk of interruptions in the exchange route, but can also analyze the causes of the risks and provide this information to the user. For example, the risk notification unit can analyze the causes of risks such as logistics delays and transaction cancellations in detail and notify the user. For example, the risk notification unit can identify the causes of risks and propose countermeasures to the user. For example, by analyzing the causes of risks, the risk notification unit can enable users to take appropriate action. In this way, the reliability of transactions is improved by the risk notification unit analyzing the causes of risks and providing this information to the user.

[0061] The evaluation unit can not only assess the condition of the listed item, but also consider its market value and demand. For example, the evaluation unit can give a higher evaluation to items with high market value. For example, the evaluation unit can give a higher evaluation to items with high demand. For example, the evaluation unit can accurately assess the value of an item by comprehensively considering market value and demand. As a result, the evaluation unit improves the accuracy of its evaluations by considering the market value and demand of the item.

[0062] The negotiation department not only conducts negotiations based on the user's desired conditions, but can also notify the user of the negotiation progress in real time. For example, the negotiation department can track each step of the negotiation in real time and notify the user. For example, the negotiation department can record the progress of the negotiation in detail and provide it to the user. For example, by notifying the user of the progress of the negotiation in real time, the negotiation department can proceed with the negotiation with confidence. In this way, the transparency of the negotiation is improved by the negotiation department notifying the user of the progress in real time.

[0063] The proposal department can not only propose exchange routes, but also make sustainable proposals that consider the environmental impact of those routes. For example, the proposal department can prioritize proposing exchange routes with less environmental impact. For example, the proposal department can adjust the content of its proposals based on sustainable exchange routes. For example, the proposal department can make sustainable proposals that consider the environmental impact. As a result, the proposal department can improve user satisfaction by making sustainable proposals that consider the environmental impact of exchange routes.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The evaluation unit assesses the condition of the listed item. The evaluation unit uses a generating AI to analyze the listing image and automatically evaluate the condition and quality of the item. For example, it analyzes the appearance, function, and frequency of use of the item from the listing image and performs an evaluation. The generating AI can also use an image analysis algorithm to evaluate the condition of the listed item and set evaluation criteria based on the training data to evaluate the quality. Step 2: The proposal unit proposes an exchange route based on the information evaluated by the evaluation unit. The proposal unit uses a generation AI to calculate multiple exchange routes based on the listed items and wish lists, and proposes the optimal one. It proposes an exchange route considering intermediate points, exchange conditions, and route optimization methods. The generation AI can also calculate the exchange route using an optimization algorithm and propose the optimal exchange route based on past exchange data. Step 3: The Negotiation Department automatically negotiates based on the exchange route proposed by the Proposal Department. The Negotiation Department uses a generative AI to automatically negotiate with multiple sellers based on the user's desired conditions. The automated negotiation takes into account the negotiation algorithm, negotiation conditions, and success criteria. The generative AI can also use a negotiation algorithm to negotiate based on the user's desired conditions and select the optimal negotiation strategy based on past negotiation data.

[0066] (Example of form 2) The unwanted item exchange system according to an embodiment of the present invention is a system that uses AI to streamline the exchange of unwanted items, enabling users to acquire a wider variety of goods. In this unwanted item exchange system, a generating AI analyzes listing images and automatically evaluates the condition and quality of the items. Next, the unwanted item exchange system uses the AI ​​to calculate multiple exchange routes based on the listed items and wish lists, and proposes the optimal one. This prevents exchanges from being limited to just two parties, increasing the chances of obtaining desired items. Furthermore, the unwanted item exchange system uses a generating AI to automatically negotiate with multiple sellers based on the user's desired conditions. Problems that arise during the negotiation process are recorded and notified to the user, ensuring transparency. In addition, if there is a risk of the exchange route being interrupted, the unwanted item exchange system warns the user in advance and proposes alternatives. The AI ​​tracks all stages until the completion of the transaction, supporting quick responses. This reduces the number of exchanges that end incompletely and creates opportunities to acquire a variety of goods. The efficiency and reliability of negotiations are increased, improving the user experience. In addition, the visualization of the condition of listed items enables transparent and secure transactions. For example, if a user lists unwanted books and adds electronic devices to their wish list, the AI ​​analyzes other users' listings and suggests the best exchange route. The AI ​​evaluates the condition of the items and automatically negotiates based on the user's desired conditions. If a problem arises during the transaction, the user is notified and alternative solutions are proposed. The AI ​​tracks every step of the transaction until completion, supporting quick responses. In this way, the AI-powered system for exchanging unwanted items allows users to trade efficiently and with peace of mind. It also serves as a means of promoting sustainable consumption for environmentally conscious consumers and local communities. Thus, the unwanted item exchange system allows users to trade efficiently and with peace of mind.

[0067] The unwanted item exchange system according to the embodiment comprises an evaluation unit, a proposal unit, and a negotiation unit. The evaluation unit evaluates the condition of the items being offered for sale. The evaluation unit analyzes the listing images using, for example, a generating AI, and automatically evaluates the condition and quality of the items. The evaluation unit can analyze and evaluate the appearance, function, frequency of use, etc., of the items from the listing images. The evaluation unit can evaluate the condition of the items using, for example, an image analysis algorithm with a generating AI. The evaluation unit can also evaluate the quality of the items by, for example, having the generating AI set evaluation criteria based on training data. The proposal unit proposes an exchange route based on the information evaluated by the evaluation unit. The proposal unit can calculate multiple exchange routes based on the items being offered and wish lists using, for example, a generating AI, and propose the optimal one. The proposal unit can propose an exchange route considering, for example, intermediate points, exchange conditions, and route optimization methods. The proposal unit can calculate and propose an exchange route using, for example, an optimization algorithm with a generating AI. The proposal unit can also propose an optimal exchange route based on past exchange data using, for example, a generating AI. The negotiation unit automatically negotiates based on the exchange route proposed by the proposal unit. The negotiation unit automatically negotiates with multiple sellers based on the user's desired conditions, for example, using a generative AI. The negotiation unit can automatically negotiate by considering, for example, negotiation algorithms, negotiation conditions, and success criteria. The negotiation unit can negotiate based on the user's desired conditions, for example, using a generative AI that uses a negotiation algorithm. The negotiation unit can also select the optimal negotiation strategy based on past negotiation data, for example, using a generative AI, and then negotiate. As a result, the unwanted item exchange system according to this embodiment can efficiently perform condition evaluation of items for sale, propose exchange routes, and conduct automatic negotiations.

[0068] The evaluation unit assesses the condition of the listed items. For example, it uses generative AI to analyze listing images and automatically evaluate the condition and quality of the items. Specifically, the generative AI utilizes image analysis algorithms to extract detailed information such as the appearance, function, and frequency of use of the listed items. For example, it can detect the presence or absence of scratches, stains, color changes, and missing parts in the listing images with high accuracy and quantify the condition of the items. Furthermore, the generative AI can set evaluation criteria based on training data and comprehensively evaluate the quality of the listed items. For example, it can learn from data of previously evaluated listed items and quickly and accurately evaluate items with similar characteristics. The evaluation unit provides these evaluation results as feedback to the seller, offering them as detailed information about the listed items. This allows sellers to accurately understand the condition of their listed items and offer appropriate pricing and exchange terms. The evaluation unit can also save the evaluation results of listed items in a database and use them for future evaluations and analyses. For example, it can analyze evaluation trends of listed items in specific categories or brands to understand market trends. This allows the evaluation unit to efficiently and accurately evaluate the condition of listed items, improving the reliability of the entire system and user satisfaction.

[0069] The proposal department proposes exchange routes based on information evaluated by the evaluation department. For example, the proposal department uses a generative AI to calculate multiple exchange routes based on the listed items and wish lists, and proposes the optimal one. Specifically, the generative AI receives the evaluation results of the listed items and the user's desired conditions as input data, and calculates exchange routes using an optimization algorithm. For example, it simulates multiple exchange routes considering the category and value of the listed items and the user's desired exchange conditions (e.g., desired exchange date and location, evaluation of the exchange partner, etc.). Based on past exchange data, the generative AI can learn exchange routes and conditions with a high success rate and propose the optimal exchange route. The proposal department presents these calculation results to the user, allowing the user to make a selection. For example, by comparing multiple exchange routes and clearly indicating the advantages and disadvantages of each, it makes it easier for the user to select the optimal exchange route. The proposal department can also collect user feedback and use it to improve the accuracy of the proposal algorithm and the content of the proposals. In this way, the proposal department can propose flexible and optimal exchange routes that meet the user's needs, improving the overall usability of the system and user satisfaction.

[0070] The Negotiation Department automatically conducts negotiations based on the exchange routes proposed by the Proposal Department. For example, the Negotiation Department uses a generative AI to automatically negotiate with multiple sellers based on the user's desired conditions. Specifically, the generative AI receives the user's desired conditions (e.g., desired exchange date, exchange location, exchange conditions, etc.) as input data and uses a negotiation algorithm to select the optimal negotiation strategy. For example, it learns negotiation patterns and conditions with a high success rate based on past negotiation data and automatically selects the optimal negotiation strategy. Based on these negotiation strategies, the Negotiation Department negotiates with multiple sellers simultaneously to achieve an exchange under conditions closest to the user's desired conditions. The Negotiation Department notifies the user of the progress and results of the negotiations in real time and can adjust the user's desired conditions as needed. For example, if negotiations are difficult or the conditions are not suitable, it will present the user with an alternative and conduct renegotiations. The Negotiation Department can also save the negotiation results in a database and use them for future negotiations and analysis. This enables the Negotiation Department to achieve efficient and effective automated negotiations based on the user's desired conditions, improving the overall reliability of the system and user satisfaction.

[0071] The transparency unit can record problems that occur during each trading step and notify the user. For example, the transparency unit can use a generation AI to record problems that occur at each trading step in real time and immediately notify the user. For example, the transparency unit can record problems such as system errors, user dissatisfaction, and trading delays and notify the user. For example, the transparency unit uses a generation AI to record problems based on the type of problem and the recording method, and notify the user. In addition, for example, the transparency unit can use a generation AI to analyze the cause of the problem and notify the user. As a result, the transparency unit ensures the transparency of transactions, allowing users to trade with peace of mind.

[0072] The risk notification unit can provide users with advance warnings and alternative solutions if there is a risk of the exchange route being interrupted midway. For example, the risk notification unit can use a generation AI to monitor the risk of the exchange route being interrupted in real time and immediately notify users. For example, the risk notification unit can monitor risks such as logistics delays and transaction cancellations and provide users with warnings and alternative solutions. For example, the risk notification unit can use a generation AI to monitor risks based on the type of risk and notification method, and notify users. In addition, the risk notification unit can use a generation AI to analyze the cause of the risk and notify users. As a result, the risk notification unit can provide advance notification of risks in the exchange route, allowing users to take appropriate action.

[0073] The tracking unit can track all stages of a transaction until completion and support a rapid response. For example, the tracking unit can use generative AI to track all stages of a transaction in real time and immediately notify the user. For example, the tracking unit can track all stages from the start of a transaction to negotiation, delivery, and receipt, and notify the user. For example, the tracking unit uses generative AI to track the transaction based on tracking and notification methods and notify the user. In addition, for example, the tracking unit can use generative AI to analyze the progress of the transaction and notify the user. This allows the tracking unit to track all stages of a transaction and enable a rapid response.

[0074] The evaluation unit can analyze listing images using a generating AI and automatically evaluate the condition and quality of the items. For example, the evaluation unit can analyze the appearance, function, and frequency of use of an item from the listing image and perform an evaluation. For example, the generating AI uses an image analysis algorithm to evaluate the condition of the listed item. In addition, the evaluation unit can, for example, have the generating AI set evaluation criteria based on training data and evaluate the quality of the listed item. As a result, the evaluation unit improves the accuracy of its evaluations by automatically evaluating the condition and quality of the listed items.

[0075] The proposal unit can use generative AI to calculate multiple exchange routes based on listed items and wish lists, and propose the optimal one. For example, the proposal unit can propose an exchange route considering intermediate points, exchange conditions, and route optimization methods. For example, the proposal unit can use generative AI to calculate and propose an exchange route using an optimization algorithm. Alternatively, the proposal unit can use generative AI to propose the optimal exchange route based on past exchange data. As a result, the proposal unit can increase exchange opportunities by calculating multiple exchange routes and proposing the optimal one.

[0076] The negotiation department can use generative AI to automatically negotiate with multiple sellers based on the user's desired conditions. The negotiation department can automatically negotiate by considering, for example, negotiation algorithms, negotiation conditions, and success criteria. For example, the negotiation department uses generative AI to negotiate based on the user's desired conditions using negotiation algorithms. In addition, the negotiation department can use, for example, generative AI to select the optimal negotiation strategy based on past negotiation data and then negotiate. As a result, the negotiation department improves negotiation efficiency by automatically negotiating based on the user's desired conditions.

[0077] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can provide a detailed explanation of the evaluation results and a display method that provides reassurance. For example, if the user is excited, the evaluation unit can display the evaluation results concisely to encourage quick decision-making. For example, if the user is relaxed, the evaluation unit can display the evaluation results in a visually appealing format. In this way, the evaluation unit improves user satisfaction by adjusting the display method of the evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The evaluation unit can perform evaluations by considering the frequency and history of use of an item when analyzing listing images. For example, the evaluation unit can estimate the frequency of use of an item from the listing images and give a lower rating to items that have been used frequently. For example, the evaluation unit can analyze the usage history and give a higher rating to items that have been properly maintained. For example, the evaluation unit can accurately assess the condition of an item by comprehensively considering the frequency and history of use. As a result, the evaluation unit improves the accuracy of its evaluations by considering the frequency and history of use of an item.

[0079] The evaluation unit can consider the brand and year of manufacture of an item when analyzing the listing images. For example, the evaluation unit can consider the reliability of the brand and reflect it in the evaluation. For example, the evaluation unit can give a higher evaluation to items that are newly manufactured. For example, the evaluation unit can accurately assess the value of an item by comprehensively considering the brand and year of manufacture. As a result, the accuracy of the evaluation unit's evaluation is improved by considering the brand and year of manufacture of the item.

[0080] The evaluation unit can estimate the user's emotions and determine the priority of evaluation results based on the estimated emotions. For example, if the user is in a hurry, the evaluation unit will prioritize displaying important evaluation results. For example, if the user is relaxed, the evaluation unit can sequentially display detailed evaluation results. For example, if the user is feeling anxious, the evaluation unit can prioritize displaying evaluation results that provide reassurance. In this way, the evaluation unit improves user satisfaction by determining the priority of evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The evaluation unit can analyze listing images and evaluate items while considering their repair history and maintenance status. For example, the evaluation unit may give a lower rating to items with a repair history. For example, the evaluation unit may give a higher rating to items that have been regularly maintained. For example, the evaluation unit can accurately assess the condition of an item by comprehensively considering its repair history and maintenance status. As a result, the evaluation unit improves the accuracy of its evaluations by considering the repair history and maintenance status of items.

[0082] The evaluation unit can perform evaluations by considering the market value and demand of items when analyzing listing images. For example, the evaluation unit can give a higher evaluation to items with high market value. For example, the evaluation unit can give a higher evaluation to items with high demand. For example, the evaluation unit can accurately assess the value of an item by comprehensively considering market value and demand. As a result, the evaluation unit improves the accuracy of its evaluations by considering the market value and demand of items.

[0083] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion unit can explain the suggestions in detail to provide reassurance. If the user is excited, the suggestion unit can display the suggestions concisely to encourage quick decision-making. If the user is relaxed, the suggestion unit can display the suggestions in a visually appealing format. In this way, the suggestion unit improves user satisfaction by adjusting the way it presents its 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The proposal department can improve the reliability of its proposals by considering the past success rate of exchange routes. For example, the proposal department can prioritize proposing exchange routes that have been successful in the past. For example, the proposal department can adjust the content of its proposals based on exchange routes with high success rates. For example, the proposal department can make highly reliable proposals by considering past success rates. As a result, the proposal department can improve user satisfaction by improving the reliability of its proposals by considering the past success rate of exchange routes.

[0085] The proposal unit can suggest the optimal route by considering the distance and time of the exchange route. For example, the proposal unit can prioritize suggesting the shortest exchange route. For example, the proposal unit can prioritize suggesting the shortest exchange route. For example, the proposal unit can comprehensively consider distance and time to suggest the optimal exchange route. As a result, the proposal unit improves user satisfaction by suggesting the optimal route by considering the distance and time of the exchange route.

[0086] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will prioritize displaying important suggestions. If the user is relaxed, the suggestion unit can sequentially display detailed suggestions. If the user is feeling anxious, the suggestion unit can prioritize displaying reassuring suggestions. In this way, the suggestion unit improves user satisfaction by prioritizing 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The proposal department can make sustainable proposals by considering the environmental impact of the exchange route when making a proposal. For example, the proposal department can prioritize proposing exchange routes with less environmental impact. For example, the proposal department can adjust the content of the proposal based on a sustainable exchange route. For example, the proposal department can make sustainable proposals by considering the environmental impact. As a result, the proposal department can improve user satisfaction by making sustainable proposals that consider the environmental impact of the exchange route.

[0088] The proposal department can make economical proposals by considering the cost of the exchange route. For example, the proposal department can prioritize proposing exchange routes with lower costs. For example, the proposal department can adjust the content of the proposal based on the economical exchange route. For example, the proposal department can make economical proposals by considering costs. As a result, the proposal department can improve user satisfaction by making economical proposals that take the cost of the exchange route into consideration.

[0089] The proposal department can make economical proposals by considering the cost of the exchange route. For example, the proposal department can prioritize proposing exchange routes with lower costs. For example, the proposal department can adjust the content of the proposal based on the economical exchange route. For example, the proposal department can make economical proposals by considering costs. As a result, the proposal department can improve user satisfaction by making economical proposals that take the cost of the exchange route into consideration.

[0090] The negotiation system can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is feeling anxious, the system can conduct the negotiation carefully and provide reassurance. If the user is excited, the system can conduct the negotiation quickly to encourage decision-making. If the user is relaxed, the system can conduct the negotiation flexibly and maintain a relaxed atmosphere. In this way, the system improves user satisfaction by adjusting the negotiation process according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The negotiating department can select the optimal negotiation strategy by referring to past negotiation history. For example, the negotiating department can proceed with negotiations based on past successful negotiation strategies. For example, the negotiating department can analyze past negotiation history and select the optimal negotiation strategy. For example, the negotiating department can adjust the negotiation process by referring to past negotiation history. As a result, the success rate of negotiations improves as the negotiating department selects the optimal negotiation strategy by referring to past negotiation history.

[0092] The negotiating department can adjust its approach to negotiations by considering the other party's reputation and trustworthiness. For example, if the other party is highly regarded, the negotiating department may proceed quickly. If the other party is not very trustworthy, the negotiating department may proceed cautiously. For example, the negotiating department can adjust its approach by comprehensively considering the other party's reputation and trustworthiness. As a result, the negotiating department can improve its success rate by adjusting its approach to negotiations by considering the other party's reputation and trustworthiness.

[0093] The negotiation system can estimate the user's emotions and determine negotiation priorities based on those estimated emotions. For example, if the user is in a hurry, the system will prioritize important negotiations. If the user is relaxed, the system can proceed with detailed negotiations sequentially. If the user is feeling anxious, the system can prioritize negotiations that provide reassurance. In this way, the system improves user satisfaction by determining negotiation priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The negotiating department can adjust its approach to negotiations by considering the other party's culture and customs. For example, the negotiating department can select an appropriate negotiation method, taking into account the other party's culture. For example, the negotiating department can adjust its approach to negotiations by considering the other party's customs. For example, the negotiating department can adjust its approach to negotiations by comprehensively considering culture and customs. As a result, the success rate of negotiations improves when the negotiating department adjusts its approach to negotiations by considering the other party's culture and customs.

[0095] The negotiating department can adjust its approach to negotiations by referring to the other party's past transaction history. For example, the negotiating department can adjust its approach based on the other party's past transaction history. The negotiating department can, for example, analyze past transaction history and select the optimal negotiation strategy. For example, the negotiating department can adjust its approach to negotiations by referring to the other party's past transaction history. As a result, the negotiating department can improve its success rate in negotiations by adjusting its approach by referring to the other party's past transaction history.

[0096] The transparency unit can estimate the user's emotions and adjust the notification method for problems based on the estimated user emotions. For example, if the user is feeling anxious, the transparency unit can provide a notification method that explains the problem in detail and provides reassurance. For example, if the user is excited, the transparency unit can provide a concise notification of the problem and encourage a quick response. For example, if the user is relaxed, the transparency unit can provide a visually appealing notification of the problem. In this way, the transparency unit improves user satisfaction by adjusting the notification method for problems according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The transparency unit can record problems occurring during each trading step in real time and immediately notify the user. For example, the transparency unit records problems occurring at each trading step in real time and immediately notifies the user. For example, if a problem occurs, the transparency unit can immediately notify the user to encourage a quick response. The transparency unit ensures transparency by, for example, recording problems in real time and immediately notifying the user. Thus, transparency is ensured by the transparency unit recording problems occurring during each trading step in real time and immediately notifying the user.

[0098] The transparency unit can estimate the user's emotions and prioritize issues based on those emotions. For example, if the user is in a hurry, the transparency unit will prioritize notifying the user of important issues. For example, if the user is relaxed, the transparency unit can sequentially notify the user of detailed issues. For example, if the user is feeling anxious, the transparency unit can prioritize notifying the user of issues that provide reassurance. In this way, the transparency unit improves user satisfaction by prioritizing issues according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The transparency unit can record in detail any problems that occur during each trading step, making them available for later reference. For example, the transparency unit can record in detail any problems that occur at each trading step, making them available for later reference. For example, if a problem occurs, the transparency unit can record its details, making them available for later reference. For example, the transparency unit ensures transparency by recording in detail any problems at each trading step. Thus, transparency is ensured by the transparency unit recording in detail any problems that occur during each trading step, making them available for later reference.

[0100] The risk notification unit can estimate the user's emotions and adjust the risk notification method based on the estimated emotions. For example, if the user is feeling anxious, the risk notification unit can provide a notification method that explains the risk in detail and provides reassurance. For example, if the user is excited, the risk notification unit can provide a concise notification of the risk and encourage a quick response. For example, if the user is relaxed, the risk notification unit can provide a visually appealing notification of the risk. In this way, the risk notification unit improves user satisfaction by adjusting the risk notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The risk notification unit can monitor the risk of interruption in the exchange route in real time and immediately notify the user. For example, the risk notification unit can monitor the risk of interruption in the exchange route in real time and immediately notify the user. For example, if a risk occurs, the risk notification unit can immediately notify the user and encourage a quick response. For example, the risk notification unit ensures transparency by monitoring risks in real time and immediately notifying the user. Thus, transparency is ensured by the risk notification unit monitoring the risk of interruption in the exchange route in real time and immediately notifying the user.

[0102] The risk notification unit can estimate the user's emotions and determine the priority of risk notifications based on the estimated emotions. For example, if the user is in a hurry, the risk notification unit will prioritize notifying the user of important risks. For example, if the user is relaxed, the risk notification unit can sequentially notify the user of detailed risks. For example, if the user is feeling anxious, the risk notification unit can prioritize notifying the user of risks that provide reassurance. In this way, the risk notification unit improves user satisfaction by determining the priority of risk notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The risk notification unit can analyze in detail the risk of interruption in the exchange route and identify the cause of the risk. For example, the risk notification unit can analyze in detail the risk of interruption in the exchange route and identify the cause of the risk. For example, if a risk occurs, the risk notification unit can analyze in detail and identify its cause. For example, the risk notification unit can analyze in detail the risks of each exchange route and ensure transparency. In this way, transparency is ensured by the risk notification unit analyzing in detail the risk of interruption in the exchange route and identifying the cause of the risk.

[0104] The tracking unit can estimate the user's emotions and adjust how tracking information is displayed based on the estimated emotions. For example, if the user is feeling anxious, the tracking unit can provide a detailed explanation of the tracking information and a reassuring display method. For example, if the user is excited, the tracking unit can display the tracking information concisely to encourage quick decision-making. For example, if the user is relaxed, the tracking unit can display the tracking information in a visually appealing format. In this way, the tracking unit improves user satisfaction by adjusting how tracking 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 generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The tracking unit can track all stages of a transaction in real time and notify the user immediately. For example, the tracking unit can track all stages of a transaction in real time and notify the user immediately. For example, the tracking unit can track each stage of a transaction in real time and notify the user immediately. The tracking unit ensures transparency by tracking transactions in real time and notifying the user immediately. Thus, transparency is ensured by the tracking unit tracking all stages of a transaction in real time and notifying the user immediately.

[0106] The tracking unit can estimate the user's emotions and prioritize tracking information based on those emotions. For example, if the user is in a hurry, the tracking unit will prioritize displaying important tracking information. If the user is relaxed, the tracking unit can sequentially display detailed tracking information. If the user is feeling anxious, the tracking unit can prioritize displaying reassuring tracking information. By prioritizing tracking information according to the user's emotions, the tracking unit improves user satisfaction. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The tracking unit can record in detail all stages until the completion of a transaction, making them available for later reference. For example, the tracking unit can record in detail all stages until the completion of a transaction, making them available for later reference. For example, the tracking unit can record in detail each stage of a transaction, making them available for later reference. For example, the tracking unit ensures transparency by recording in detail each transaction step. This ensures transparency by allowing the tracking unit to record in detail all stages until the completion of a transaction, making them available for later reference.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The evaluation unit can not only assess the condition of the listed item but also the reliability of the seller. For example, the evaluation unit can assess reliability based on the seller's past transaction history and ratings. For example, the evaluation unit can assess reliability by considering the seller's transaction success rate and user feedback. For example, by assessing the reliability of the seller, the evaluation unit can help users conduct transactions with peace of mind. In this way, the reliability of transactions is improved by the evaluation unit comprehensively evaluating the condition of the listed item and the reliability of the seller.

[0110] The transparency unit can estimate the user's emotions and adjust notification content based on those emotions to ensure transparency in transactions. For example, if the user is feeling anxious, the transparency unit can provide a notification method that carefully explains the details of the problem and provides reassurance. For example, if the user is excited, the transparency unit can notify them of the problem concisely and encourage a quick response. For example, if the user is relaxed, the transparency unit can notify them of the problem in a visually appealing format. In this way, the transparency unit improves user satisfaction by adjusting notification content according to the user's emotions.

[0111] The risk notification unit not only notifies users of the risk of interruptions in the exchange route, but can also analyze the causes of the risks and provide this information to the user. For example, the risk notification unit can analyze the causes of risks such as logistics delays and transaction cancellations in detail and notify the user. For example, the risk notification unit can identify the causes of risks and propose countermeasures to the user. For example, by analyzing the causes of risks, the risk notification unit can enable users to take appropriate action. In this way, the reliability of transactions is improved by the risk notification unit analyzing the causes of risks and providing this information to the user.

[0112] The tracking unit not only tracks every stage until the transaction is completed, but can also estimate the user's emotions and adjust how tracking information is displayed based on those emotions. For example, if the user is feeling anxious, the tracking unit can explain the tracking information in detail and provide a reassuring display method. For example, if the user is excited, the tracking unit can display the tracking information concisely to encourage quick decision-making. For example, if the user is relaxed, the tracking unit can display the tracking information in a visually appealing format. In this way, the tracking unit improves user satisfaction by adjusting how tracking information is displayed according to the user's emotions.

[0113] The evaluation unit can not only assess the condition of the listed item, but also consider its market value and demand. For example, the evaluation unit can give a higher evaluation to items with high market value. For example, the evaluation unit can give a higher evaluation to items with high demand. For example, the evaluation unit can accurately assess the value of an item by comprehensively considering market value and demand. As a result, the evaluation unit improves the accuracy of its evaluations by considering the market value and demand of the item.

[0114] The suggestion function not only proposes exchange routes, but can also estimate the user's emotions and adjust the way the suggestions are presented based on those emotions. For example, if the user is feeling anxious, the suggestion function can explain the suggestions in detail to provide reassurance. If the user is excited, for example, the suggestion function can display the suggestions concisely to encourage quick decision-making. If the user is relaxed, for example, the suggestion function can display the suggestions in a visually appealing format. In this way, the suggestion function improves user satisfaction by adjusting the way suggestions are presented according to the user's emotions.

[0115] The negotiation department not only conducts negotiations based on the user's desired conditions, but can also notify the user of the negotiation progress in real time. For example, the negotiation department can track each step of the negotiation in real time and notify the user. For example, the negotiation department can record the progress of the negotiation in detail and provide it to the user. For example, by notifying the user of the progress of the negotiation in real time, the negotiation department can proceed with the negotiation with confidence. In this way, the transparency of the negotiation is improved by the negotiation department notifying the user of the progress in real time.

[0116] The evaluation unit not only assesses the condition of the listed item, but can also estimate the user's emotions and adjust the display method of the evaluation results based on those emotions. For example, if the user is feeling anxious, the evaluation unit can provide a detailed explanation of the evaluation results and a display method that provides reassurance. For example, if the user is excited, the evaluation unit can display the evaluation results concisely to encourage quick decision-making. For example, if the user is relaxed, the evaluation unit can display the evaluation results in a visually appealing format. In this way, the evaluation unit improves user satisfaction by adjusting the display method of evaluation results according to the user's emotions.

[0117] The proposal department can not only propose exchange routes, but also make sustainable proposals that consider the environmental impact of those routes. For example, the proposal department can prioritize proposing exchange routes with less environmental impact. For example, the proposal department can adjust the content of its proposals based on sustainable exchange routes. For example, the proposal department can make sustainable proposals that consider the environmental impact. As a result, the proposal department can improve user satisfaction by making sustainable proposals that consider the environmental impact of exchange routes.

[0118] The negotiation team not only conducts negotiations based on the user's desired conditions, but can also estimate the user's emotions and adjust the negotiation process based on those estimates. For example, if the user is feeling anxious, the negotiation team can proceed carefully and provide reassurance. For example, if the user is excited, the negotiation team can proceed quickly to encourage decision-making. For example, if the user is relaxed, the negotiation team can proceed flexibly and maintain a relaxed atmosphere. In this way, the negotiation team can improve user satisfaction by adjusting the negotiation process according to the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The evaluation unit assesses the condition of the listed item. The evaluation unit uses a generating AI to analyze the listing image and automatically evaluate the condition and quality of the item. For example, it analyzes the appearance, function, and frequency of use of the item from the listing image and performs an evaluation. The generating AI can also use an image analysis algorithm to evaluate the condition of the listed item and set evaluation criteria based on the training data to evaluate the quality. Step 2: The proposal unit proposes an exchange route based on the information evaluated by the evaluation unit. The proposal unit uses a generation AI to calculate multiple exchange routes based on the listed items and wish lists, and proposes the optimal one. It proposes an exchange route considering intermediate points, exchange conditions, and route optimization methods. The generation AI can also calculate the exchange route using an optimization algorithm and propose the optimal exchange route based on past exchange data. Step 3: The Negotiation Department automatically negotiates based on the exchange route proposed by the Proposal Department. The Negotiation Department uses a generative AI to automatically negotiate with multiple sellers based on the user's desired conditions. The automated negotiation takes into account the negotiation algorithm, negotiation conditions, and success criteria. The generative AI can also use a negotiation algorithm to negotiate based on the user's desired conditions and select the optimal negotiation strategy based on past negotiation data.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the evaluation unit, proposal unit, negotiation unit, transparency unit, risk notification unit, and tracking unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the evaluation unit uses the camera 42 of the smart device 14 to acquire images of the items for sale, and the specific processing unit 290 of the data processing unit 12 performs image analysis to evaluate the condition and quality of the items. The proposal unit calculates the exchange route using the specific processing unit 290 of the data processing unit 12 and proposes the optimal one. The negotiation unit automatically negotiates based on the user's desired conditions using the specific processing unit 290 of the data processing unit 12. The transparency unit records problems that occur at each transaction step using the specific processing unit 290 of the data processing unit 12 and notifies the user. The risk notification unit monitors the risks of the exchange route using the specific processing unit 290 of the data processing unit 12 and proposes warnings and alternatives to the user. The tracking unit tracks all stages until the completion of the transaction using the specific processing unit 290 of the data processing unit 12 and notifies the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the evaluation unit, proposal unit, negotiation unit, transparency unit, risk notification unit, and tracking unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the evaluation unit uses the camera 42 of the smart glasses 214 to acquire images of the items for sale, and the specific processing unit 290 of the data processing unit 12 performs image analysis to evaluate the condition and quality of the items. The proposal unit calculates the exchange route using the specific processing unit 290 of the data processing unit 12 and proposes the optimal one. The negotiation unit automatically negotiates based on the user's desired conditions using the specific processing unit 290 of the data processing unit 12. The transparency unit records problems that occur at each transaction step using the specific processing unit 290 of the data processing unit 12 and notifies the user. The risk notification unit monitors the risks of the exchange route using the specific processing unit 290 of the data processing unit 12 and proposes warnings and alternatives to the user. The tracking unit tracks all stages until the completion of the transaction using the specific processing unit 290 of the data processing unit 12 and notifies the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the evaluation unit, proposal unit, negotiation unit, transparency unit, risk notification unit, and tracking unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the evaluation unit uses the camera 42 of the headset terminal 314 to acquire images of the items for sale, and the specific processing unit 290 of the data processing unit 12 performs image analysis to evaluate the condition and quality of the items. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to calculate exchange routes and propose the best one. The negotiation unit uses the specific processing unit 290 of the data processing unit 12 to automatically negotiate based on the user's desired conditions. The transparency unit uses the specific processing unit 290 of the data processing unit 12 to record problems that occur at each transaction step and notify the user. The risk notification unit uses the specific processing unit 290 of the data processing unit 12 to monitor the risks of the exchange routes and propose warnings and alternatives to the user. The tracking unit uses the specific processing unit 290 of the data processing unit 12 to track all stages until the completion of the transaction and notify the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the evaluation unit, proposal unit, negotiation unit, transparency unit, risk notification unit, and tracking unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the evaluation unit uses the camera 42 of the robot 414 to acquire images of the items for sale, and the specific processing unit 290 of the data processing unit 12 performs image analysis to evaluate the condition and quality of the items. The proposal unit calculates the exchange route using the specific processing unit 290 of the data processing unit 12 and proposes the optimal one. The negotiation unit automatically negotiates based on the user's desired conditions using the specific processing unit 290 of the data processing unit 12. The transparency unit records problems that occur at each transaction step using the specific processing unit 290 of the data processing unit 12 and notifies the user. The risk notification unit monitors the risks of the exchange route using the specific processing unit 290 of the data processing unit 12 and proposes warnings and alternatives to the user. The tracking unit tracks all stages until the completion of the transaction using the specific processing unit 290 of the data processing unit 12 and notifies the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The evaluation section evaluates the condition of the listed items, A proposal unit proposes an exchange route based on the information evaluated by the evaluation unit, A negotiation unit that conducts automated negotiations based on the exchange route proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features. (Note 2) It includes a transparency unit that records problems that occur during each transaction step and notifies the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system includes a risk notification unit that provides advance warnings and proposes alternative solutions to users if there is a risk of the exchange route being interrupted midway. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a tracking unit that tracks every stage until the transaction is completed and supports a quick response. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Using a generation AI, the system analyzes listing images and automatically evaluates the condition and quality of the items. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Using a generation AI, it calculates multiple exchange routes based on listed items and wish lists, and proposes the optimal one. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned negotiating body said, Using a generation AI, the system automatically negotiates with multiple sellers based on the user's desired conditions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The evaluation unit, When analyzing listing images, we evaluate the item considering its frequency of use and usage history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit, When analyzing listing images, the brand and year of manufacture of the item are taken into consideration during the evaluation process. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit, The system estimates the user's emotions and prioritizes evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, When analyzing listing images, we evaluate the item considering its repair history and maintenance status. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, When analyzing listing images, the market value and demand for the item are taken into consideration during the evaluation process. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, consider the past success rate of the exchange route to improve the reliability of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we will suggest the optimal route considering the distance and time of the exchange route. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, consider the environmental impact of the exchange route and make a sustainable proposal. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we will consider the cost of the exchange route and make an economical proposal. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we will consider the cost of the exchange route and make an economical proposal. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned negotiating body said, During negotiations, refer to past negotiation history to select the optimal negotiation strategy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned negotiating body said, During negotiations, adjust your approach based on the other party's reputation and trustworthiness. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned negotiating body said, The system estimates the user's emotions and determines negotiation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned negotiating body said, When negotiating, adjust your approach to take into account the other party's culture and customs. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned negotiating body said, During negotiations, we adjust our approach by referring to the other party's past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The transparency-enhancing section is, It estimates the user's emotions and adjusts how problems are notified based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The transparency-enhancing section is, The system records problems that occur during each transaction step in real time and immediately notifies the user. The system described in Appendix 2, characterized by the features described herein. (Note 29) The transparency-enhancing section is, It estimates user emotions and determines the priority of issues based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The transparency-enhancing section is, Record in detail any problems that occur during each trading step so that you can refer to them later. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned risk notification unit, The system estimates the user's emotions and adjusts the risk notification method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned risk notification unit, The system monitors the risk of interruptions in the exchange route in real time and immediately notifies the user. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned risk notification unit, The system estimates user sentiment and prioritizes risk notifications based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned risk notification unit, We will conduct a detailed analysis of the risk of interruptions in the exchange route and identify the root cause of the risk. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned tracking unit is It estimates the user's emotions and adjusts how tracking information is displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned tracking unit is Track every step of the transaction in real time and notify the user immediately. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned tracking unit is It estimates the user's emotions and prioritizes tracking information based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned tracking unit is Record every step of the transaction in detail so you can refer to it later. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The evaluation section evaluates the condition of the listed items, A proposal unit proposes an exchange route based on the information evaluated by the evaluation unit, A negotiation unit that conducts automated negotiations based on the exchange route proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features.

2. It includes a transparency unit that records problems that occur during each transaction step and notifies the user. The system according to feature 1.

3. The system includes a risk notification unit that provides advance warnings and proposes alternative solutions to users if there is a risk of the exchange route being interrupted midway. The system according to feature 1.

4. It includes a tracking unit that tracks every stage until the transaction is completed and supports a quick response. The system according to feature 1.

5. The evaluation unit, Using AI generation, the system analyzes listing images and automatically evaluates the condition and quality of the items. The system according to feature 1.

6. The aforementioned proposal section is, Using a generation AI, it calculates multiple exchange routes based on the items being offered and wish lists, and proposes the optimal one. The system according to feature 1.

7. The aforementioned negotiating body said, Using a generation AI, the system automatically negotiates with multiple sellers based on the user's desired conditions. The system according to feature 1.

8. The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system according to feature 1.

Images