system

Abstract

The system according to this embodiment aims to efficiently collect tariff and regulatory information and negotiate terms in international trade. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a negotiation unit, and a setting unit. The collection unit collects customs duty and regulatory information. The analysis unit analyzes the information collected by the collection unit. The negotiation unit conducts negotiations based on the information obtained by the analysis unit. The setting unit sets up a meeting place for negotiations between the representatives after the negotiations by the negotiation unit have been completed.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time and effort to collect and negotiate terms of tariff and regulatory information in international trade, and it is difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently collect and negotiate terms of tariff and regulatory information in international trade.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a negotiation unit, and a setting unit. The collection unit collects customs and regulatory information. The analysis unit analyzes the information collected by the collection unit. The negotiation unit conducts negotiations based on the information obtained by the analysis unit. The setting unit sets up a meeting place for negotiations between the representatives after the negotiations by the negotiation unit have concluded. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect tariff and regulatory information and negotiate terms in international trade. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The international trade automated negotiation platform according to an embodiment of the present invention is a system in which an AI agent conducts tariff and regulatory research on international trade, and each AI agent conducts negotiations with potential trading partners on the international trade automated negotiation platform to complete initial negotiations, after which a meeting between the representatives is set up. The international trade automated negotiation platform collects and analyzes tariff and regulatory information of each country in real time. The generating AI obtains trade statistics data, tariff information of each country, regulatory trends, and financial market trends from the websites of relevant organizations, and analyzes regulatory changes and market fluctuations in each country in real time. This allows the AI ​​to determine the optimal trading conditions. Next, the AI ​​agents of each player (exporters, importers, customs brokers, shipping companies / logistics companies, financial institutions, government agencies, etc.) conduct negotiations on the automated negotiation platform based on the requests and conditions they have previously identified. A conversational AI listens to the requests and conditions each party desires, and each AI agent constantly negotiates to finalize deals that meet the conditions. After the initial negotiations are completed, a meeting between the representatives is set up. This facilitates negotiation of trade terms, quick responses to tariff adjustments and regulatory changes, eliminates information asymmetry, and enables efficient transactions. This mechanism reduces trade risk and improves transaction efficiency. For example, AI agents collect and analyze tariff and regulatory information from various countries in real time and propose transactions under optimal conditions, facilitating smoother negotiations of trade terms. Furthermore, the generation AI analyzes regulatory changes and market fluctuations in various countries in real time, reducing trade risk and improving transaction efficiency. As a result, the international trade automated negotiation platform facilitates negotiation of trade terms, quick responses to tariff adjustments and regulatory changes, eliminates information asymmetry, and enables efficient transactions.

[0029] The international trade automated negotiation platform according to this embodiment comprises a collection unit, an analysis unit, a negotiation unit, and a setting unit. The collection unit collects tariff and regulatory information. For example, the collection unit obtains trade statistics data, tariff information of various countries, regulatory trends, and financial market trends from the websites of relevant organizations. The collection unit may include AI processing. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes regulatory changes and market fluctuations in various countries in real time. The analysis unit may include generation AI processing. The negotiation unit conducts negotiations based on the information obtained by the analysis unit. For example, the negotiation unit conducts negotiations based on the requests and conditions that each player's AI agent has previously grasped on the automated negotiation platform. The negotiation unit may include AI processing. The setting unit sets up a negotiation venue between the representatives after the negotiations have been completed by the negotiation unit. The setting unit may also include AI processing. As a result, the international trade automated negotiation platform according to this embodiment can efficiently collect tariff and regulatory information, analyze it, negotiate terms, and set up a negotiation venue.

[0030] The data collection unit collects customs and regulatory information. Specifically, it automatically obtains trade statistics data, customs information from various countries, regulatory trends, and financial market trends from the websites and databases of relevant organizations. For example, it uses scraping technology to collect the latest customs rates and import regulation information from the websites of customs and trade-related organizations in various countries. Financial market trends are obtained from reports and databases provided by financial institutions and economic research institutions. The data collection unit can implement an automated data collection process using AI to regularly update and maintain up-to-date information. The AI ​​analyzes the structure of the websites and databases being collected and learns algorithms to efficiently extract the necessary information. This allows the data collection unit to collect data accurately and quickly from a wide range of sources and provide the latest information on international trade. Furthermore, the data collection unit centrally manages the collected data and builds a database that is easily accessible to the analysis and negotiation units. This allows the data collection unit to collect information efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, the analysis unit analyzes regulatory changes and market fluctuations in various countries in real time to assess trade risks and opportunities. For example, it calculates the costs and risks associated with the import and export of specific goods based on collected tariff and regulatory information. It also analyzes financial market trends to assess the impact of exchange rate fluctuations on trade. The analysis unit uses generative AI to analyze this data and extract complex patterns and trends. The generative AI processes large amounts of data and uses models learned from historical data to predict future regulatory changes and market fluctuations. For example, based on historical regulatory change data, the generative AI predicts what kind of regulatory changes a particular country is likely to make in the future. The generative AI also analyzes financial market data to predict future trends in exchange rates. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp trade risks and opportunities in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and strategy planning, improving the reliability and security of the entire system.

[0032] The Negotiation Department conducts negotiations based on information obtained by the Analysis Department. Specifically, the Negotiation Department negotiates based on the requests and conditions that each player's AI agent has previously identified on the automated negotiation platform. For example, the AI ​​agents for exporters and importers propose optimal transaction terms, taking into account tariffs, regulatory information, and exchange rate fluctuations. The Negotiation Department can automate the negotiation process using AI, enabling negotiations to proceed quickly and efficiently. The AI ​​agents generate responses to the other party's proposals and new proposals based on pre-set negotiation strategies and rules. For example, the AI ​​agent will present conditions such as tariff reductions or delivery date adjustments in response to the other party's proposal and proceed with the negotiation. The AI ​​agent also monitors the progress of the negotiations in real time and modifies the negotiation strategy as needed. This allows the Negotiation Department to conduct negotiations efficiently and effectively and determine the optimal transaction terms. Furthermore, the Negotiation Department can record the history and results of negotiations in a database and use them for future negotiations. This allows the Negotiation Department to develop more effective negotiation strategies based on past negotiation data and improve the overall negotiation capabilities of the system.

[0033] The Schedule Department sets up meetings between the parties involved after negotiations have concluded by the Negotiation Department. Specifically, based on the negotiation results, the Schedule Department adjusts the schedules for meetings and discussions among the parties and arranges the necessary resources. For example, after an agreement is reached, the Schedule Department sets up an online meeting between representatives from the exporter and importer, coordinating the date, time, and participants. The Schedule Department also prepares the meeting agenda and materials and distributes them to the parties in advance. The Schedule Department can automate these processes using AI, enabling efficient meeting scheduling. The AI ​​analyzes the schedules of the parties involved and proposes the optimal meeting date and time. The AI ​​also automatically generates and distributes the meeting agenda and materials. This allows the Schedule Department to quickly and efficiently schedule meetings and smoothly implement the negotiation results. Furthermore, the Schedule Department monitors the progress of the meetings and provides support as needed. For example, it responds in real time to problems and questions that arise during the meeting. The Schedule Department also records the meeting results and stores them in a database for future reference. This allows the Schedule Department to smoothly implement the negotiation results and improve the overall efficiency and reliability of the system.

[0034] The data collection unit can obtain trade statistics data, customs information from various countries, regulatory trends, and financial market trends from the websites of relevant organizations. For example, the data collection unit can collect trade statistics data from the websites of relevant organizations. The data collection unit can also collect customs information from various countries from the websites of relevant organizations. The data collection unit can also collect regulatory trends and financial market trends from the websites of relevant organizations. This allows for the efficient collection of necessary information from the websites of relevant organizations. Some or all of the above-described processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input data collected from the websites of relevant organizations into a generating AI, which can then analyze the data and extract the necessary information.

[0035] The analysis unit can analyze information collected by the data collection unit and analyze regulatory changes and market fluctuations in each country in real time. For example, the analysis unit can analyze tariff information collected by the data collection unit and analyze regulatory changes in each country in real time. The analysis unit can also analyze regulatory information collected by the data collection unit and analyze market fluctuations in real time. The analysis unit can also analyze financial market trends collected by the data collection unit and analyze regulatory changes and market fluctuations in real time. This allows for real-time analysis of regulatory changes and market fluctuations in each country. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data collected by the data collection unit into a generation AI, and the generation AI can analyze the data and analyze regulatory changes and market fluctuations in real time.

[0036] The Negotiation Department can conduct negotiations based on the information obtained by the Analysis Department, using each player's AI agent to negotiate on the automated negotiation platform based on their pre-determined requests and conditions. For example, the Negotiation Department can use the information obtained by the Analysis Department to have each player's AI agent negotiate on the automated negotiation platform based on their requests and conditions. The Negotiation Department can also conduct negotiations considering each player's requests and conditions based on the information obtained by the Analysis Department. The Negotiation Department can also use the information obtained by the Analysis Department to compile deals that match each player's conditions. This allows for efficient negotiations based on each player's requests and conditions. Some or all of the above processes in the Negotiation Department may be performed using AI or not. For example, the Negotiation Department can input the information obtained by the Analysis Department into a Generating AI, which can then support the progress of the negotiations.

[0037] The negotiation department can use conversational AI to gather each individual's requests and conditions, and each AI agent can continuously negotiate to finalize deals that meet those conditions. For example, the negotiation department can use conversational AI to gather each individual's requests and conditions, and an AI agent can conduct the negotiations. Alternatively, the negotiation department can use conversational AI to gather each individual's conditions, and an AI agent can finalize deals that meet those conditions. The negotiation department can use conversational AI to gather each individual's requests, and an AI agent can continuously negotiate. This allows for the efficient finalization of deals that meet requests and conditions by using conversational AI. Some or all of the above processes in the negotiation department may be performed using AI, or they may not. For example, the negotiation department can input the requests and conditions gathered by the conversational AI into a generation AI, and the generation AI can support the progress of the negotiations.

[0038] The configuration unit can set up a meeting between the representatives after the negotiations by the negotiation unit have concluded. For example, the configuration unit can set up an online meeting between the representatives after the negotiations by the negotiation unit have concluded. The configuration unit can also set up an in-person meeting between the representatives after the negotiations by the negotiation unit have concluded. The configuration unit can also set up a conference call between the representatives after the negotiations by the negotiation unit have concluded. This allows for the efficient setting up of a meeting between the representatives after the negotiations have concluded. Some or all of the above-described processes in the configuration unit may be performed using AI or not. For example, the configuration unit can use a generating AI to automatically set up a meeting between the representatives after the negotiations by the negotiation unit have concluded.

[0039] The data collection unit can select the optimal data collection method by referring to past data collection history when collecting customs and regulatory information from various countries. For example, the data collection unit can evaluate the reliability of previously collected information and prioritize collecting information from reliable sources. The data collection unit can also analyze past data collection history to identify frequently collected sources and collect information efficiently. Based on past data collection history, the data collection unit can predict what information should be collected at a specific time and collect it at the appropriate time. This allows the optimal data collection method to be selected by referring to past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data collection history into a generating AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter information while considering each country's economic indicators and political situation. For example, the data collection unit can prioritize collecting information from economically stable countries based on each country's economic indicators. The data collection unit can also prioritize collecting information from politically stable countries, taking into account each country's political situation. The data collection unit can also collect comprehensively reliable information by considering both economic indicators and political situations. This allows for the collection of highly reliable information by considering each country's economic indicators and political situation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input economic indicators and political situation data for each country into a generating AI, which can then filter the information.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a specific country, the data collection unit will prioritize the collection of customs and regulatory information for that country. If the user is in a specific region, the data collection unit can also prioritize the collection of economic indicators and political conditions for that region. The data collection unit can also quickly collect highly relevant information based on the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location data into a generating AI, which can then prioritize the collection of highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also analyze the user's social media activity and prioritize the collection of information of high interest. The data collection unit can also quickly collect relevant information based on the user's social media activity. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant information.

[0043] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis process. For example, the analysis unit can improve the accuracy of the analysis algorithm based on past analysis results. The analysis unit can also analyze past analysis results and adjust the parameters of the analysis algorithm. The analysis unit can also refer to past analysis results and identify areas for improvement in the analysis algorithm. This allows the analysis algorithm to be optimized by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input past analysis result data into a generating AI, and the generating AI can optimize the analysis algorithm.

[0044] The analysis unit can perform simulations to predict regulatory changes and market fluctuations in various countries during the analysis process. For example, the analysis unit can simulate regulatory changes in various countries and predict future impacts. The analysis unit can also simulate market fluctuations and predict optimal trading conditions. The analysis unit can also simulate both regulatory changes and market fluctuations to predict the overall impact. In this way, future impacts can be predicted by performing simulations to predict regulatory changes and market fluctuations in various countries. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input simulation data into a generative AI, which can then predict regulatory changes and market fluctuations.

[0045] The analysis unit can improve the accuracy of its analysis by considering the economic indicators and political situations of each country during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the economic indicators of each country. The analysis unit can also improve the accuracy of its analysis by considering the political situations of each country. The analysis unit can also improve the accuracy of its analysis comprehensively by considering both economic indicators and political situations. In this way, the accuracy of the analysis can be improved by considering the economic indicators and political situations of each country. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input economic indicator and political situation data for each country into a generative AI, which can then improve the accuracy of the analysis.

[0046] The analysis unit can improve the reliability of its analysis by referring to relevant literature and reports during the analysis. For example, the analysis unit can improve the reliability of its analysis by referring to relevant literature. The analysis unit can also improve the reliability of its analysis by referring to relevant reports. The analysis unit can also improve the reliability of its analysis by referring to both literature and reports. In this way, the reliability of the analysis can be improved by referring to relevant literature and reports. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input relevant literature and report data into a generating AI, and the generating AI can improve the reliability of the analysis.

[0047] The negotiation department can select the optimal negotiation strategy during negotiations by referring to past negotiation history. For example, the negotiation department can select the optimal negotiation strategy based on past negotiation history. The negotiation department can also analyze past negotiation history and identify areas for improvement in the negotiation strategy. The negotiation department can also adjust the parameters of the negotiation strategy by referring to past negotiation history. This allows the optimal negotiation strategy to be selected by referring to past negotiation history. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input past negotiation history data into a generating AI, which can then select the optimal negotiation strategy.

[0048] The negotiating department can conduct negotiations while considering the attribute information of each player. For example, the negotiating department can select the optimal negotiation strategy based on the attribute information of each player. The negotiating department can also adjust the negotiation process while considering the attribute information of each player. The negotiating department can also refer to the attribute information of each player to identify areas for improvement in the negotiation. This allows for more appropriate negotiations by considering the attribute information of each player. Some or all of the above processes in the negotiating department may be performed using AI or not. For example, the negotiating department can input the attribute information data of each player into a generating AI, and the generating AI can conduct the negotiations.

[0049] The negotiating department can conduct negotiations while considering the geographical location information of each player. For example, the negotiating department can select the optimal negotiation strategy based on the geographical location information of each player. The negotiating department can also adjust the negotiation process while considering the geographical location information of each player. The negotiating department can also refer to the geographical location information of each player to identify areas for improvement in the negotiation. This allows for more appropriate negotiations by considering the geographical location information of each player. Some or all of the above processes in the negotiating department may be performed using AI or not. For example, the negotiating department can input the geographical location data of each player into a generating AI, and the generating AI can conduct the negotiations.

[0050] The negotiating department can improve the accuracy of negotiations by referring to relevant market data during negotiations. For example, the negotiating department can improve the accuracy of negotiations based on relevant market data. The negotiating department can also refer to relevant market data and adjust the way negotiations are conducted. The negotiating department can also analyze market data and identify areas for improvement in negotiations. This allows for improved negotiation accuracy by referring to relevant market data. Some or all of the above processes in the negotiating department may be performed using AI or not. For example, the negotiating department can input relevant market data into a generating AI, which can then improve the accuracy of negotiations.

[0051] The configuration unit can select the optimal configuration method when setting up a negotiation venue by referring to past configuration history. For example, the configuration unit can select the optimal negotiation venue based on past configuration history. The configuration unit can also analyze past configuration history and identify areas for improvement in the configuration method. The configuration unit can also adjust the parameters of the configuration method by referring to past configuration history. In this way, the optimal negotiation venue can be set up by referring to past configuration history. Some or all of the above processes in the configuration unit may be performed using AI or not. For example, the configuration unit can input past configuration history data into a generating AI, and the generating AI can select the optimal configuration method.

[0052] The setting unit can select the optimal setting method when setting up a negotiation venue, taking into account the geographical location information of each player. For example, the setting unit can select the optimal negotiation venue based on the geographical location information of each player. The setting unit can also adjust the setting method of the negotiation venue, taking into account the geographical location information of each player. The setting unit can also refer to the geographical location information of each player and identify areas for improvement in the setting method. In this way, the optimal negotiation venue can be set by taking into account the geographical location information of each player. Some or all of the above processing in the setting unit may be performed using AI or not. For example, the setting unit can input the geographical location data of each player into a generating AI, and the generating AI can select the optimal setting method.

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

[0054] The data collection unit can select the optimal data collection method by referring to past data collection history when collecting customs and regulatory information from various countries. For example, it can evaluate the reliability of previously collected information and prioritize collecting information from reliable sources. It can also analyze past data collection history to identify frequently collected sources and collect information efficiently. Based on past data collection history, it can predict what information should be collected at a specific time and collect it at the appropriate time. In this way, the optimal data collection method can be selected by referring to past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data collection history into a generating AI, which can then select the optimal data collection method.

[0055] The data collection unit can filter information while considering each country's economic indicators and political situation. For example, it can prioritize collecting information from economically stable countries based on each country's economic indicators. It can also prioritize collecting information from politically stable countries, taking into account each country's political situation. It can also collect comprehensively reliable information by considering both economic indicators and political situations. This allows for the collection of highly reliable information by considering each country's economic indicators and political situation. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input economic indicators and political situation data for each country into a generating AI, which can then filter the information.

[0056] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis process. For example, it can improve the accuracy of the analysis algorithm based on past analysis results. It can also analyze past analysis results and adjust the parameters of the analysis algorithm. It can also refer to past analysis results to identify areas for improvement in the analysis algorithm. In this way, the analysis algorithm can be optimized by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input past analysis result data into a generating AI, and the generating AI can optimize the analysis algorithm.

[0057] The analysis unit can perform simulations to predict regulatory changes and market fluctuations in various countries during the analysis process. For example, it can simulate regulatory changes in various countries to predict future impacts. It can also simulate market fluctuations to predict optimal trading conditions. It can even simulate both regulatory changes and market fluctuations to predict the overall impact. In this way, future impacts can be predicted by performing simulations to predict regulatory changes and market fluctuations in various countries. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input simulation data into a generative AI, which can then predict regulatory changes and market fluctuations.

[0058] The negotiation department can select the optimal negotiation strategy during negotiations by referring to past negotiation history. For example, it can select the optimal negotiation strategy based on past negotiation history. It can also analyze past negotiation history to identify areas for improvement in the negotiation strategy. It can also adjust the parameters of the negotiation strategy by referring to past negotiation history. In this way, the optimal negotiation strategy can be selected by referring to past negotiation history. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input past negotiation history data into a generating AI, and the generating AI can select the optimal negotiation strategy.

[0059] The configuration unit can select the optimal configuration method when setting up a negotiation venue by referring to past configuration history. For example, it can select the optimal negotiation venue based on past configuration history. It can also analyze past configuration history to identify areas for improvement in configuration methods. It can also adjust the parameters of the configuration method by referring to past configuration history. In this way, the optimal negotiation venue can be set up by referring to past configuration history. Some or all of the above processes in the configuration unit may be performed using AI or not. For example, the configuration unit can input past configuration history data into a generating AI, which can then select the optimal configuration method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects tariff and regulatory information. For example, it obtains trade statistics data, tariff information from various countries, regulatory trends, and financial market trends from the websites of relevant organizations. The data collection unit may include AI processing. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes regulatory changes and market fluctuations in various countries in real time. The analysis unit may include processing for generating AI. Step 3: The negotiation department conducts negotiations based on the information obtained by the analysis department. For example, each player's AI agent conducts negotiations based on the requests and conditions that were previously identified on the automated negotiation platform. The negotiation department may include AI processing. Step 4: The setup unit sets up a forum for negotiations between the representatives after the negotiations by the negotiation unit have concluded. The setup unit may also include AI processing.

[0062] (Example of form 2) The international trade automated negotiation platform according to an embodiment of the present invention is a system in which an AI agent conducts tariff and regulatory research on international trade, and each AI agent conducts negotiations with potential trading partners on the international trade automated negotiation platform to complete initial negotiations, after which a meeting between the representatives is set up. The international trade automated negotiation platform collects and analyzes tariff and regulatory information of each country in real time. The generating AI obtains trade statistics data, tariff information of each country, regulatory trends, and financial market trends from the websites of relevant organizations, and analyzes regulatory changes and market fluctuations in each country in real time. This allows the AI ​​to determine the optimal trading conditions. Next, the AI ​​agents of each player (exporters, importers, customs brokers, shipping companies / logistics companies, financial institutions, government agencies, etc.) conduct negotiations on the automated negotiation platform based on the requests and conditions they have previously identified. A conversational AI listens to the requests and conditions each party desires, and each AI agent constantly negotiates to finalize deals that meet the conditions. After the initial negotiations are completed, a meeting between the representatives is set up. This facilitates negotiation of trade terms, quick responses to tariff adjustments and regulatory changes, eliminates information asymmetry, and enables efficient transactions. This mechanism reduces trade risk and improves transaction efficiency. For example, AI agents collect and analyze tariff and regulatory information from various countries in real time and propose transactions under optimal conditions, facilitating smoother negotiations of trade terms. Furthermore, the generation AI analyzes regulatory changes and market fluctuations in various countries in real time, reducing trade risk and improving transaction efficiency. As a result, the international trade automated negotiation platform facilitates negotiation of trade terms, quick responses to tariff adjustments and regulatory changes, eliminates information asymmetry, and enables efficient transactions.

[0063] The international trade automated negotiation platform according to this embodiment comprises a collection unit, an analysis unit, a negotiation unit, and a setting unit. The collection unit collects tariff and regulatory information. For example, the collection unit obtains trade statistics data, tariff information of various countries, regulatory trends, and financial market trends from the websites of relevant organizations. The collection unit may include AI processing. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes regulatory changes and market fluctuations in various countries in real time. The analysis unit may include generation AI processing. The negotiation unit conducts negotiations based on the information obtained by the analysis unit. For example, the negotiation unit conducts negotiations based on the requests and conditions that each player's AI agent has previously grasped on the automated negotiation platform. The negotiation unit may include AI processing. The setting unit sets up a negotiation venue between the representatives after the negotiations have been completed by the negotiation unit. The setting unit may also include AI processing. As a result, the international trade automated negotiation platform according to this embodiment can efficiently collect tariff and regulatory information, analyze it, negotiate terms, and set up a negotiation venue.

[0064] The data collection unit collects customs and regulatory information. Specifically, it automatically obtains trade statistics data, customs information from various countries, regulatory trends, and financial market trends from the websites and databases of relevant organizations. For example, it uses scraping technology to collect the latest customs rates and import regulation information from the websites of customs and trade-related organizations in various countries. Financial market trends are obtained from reports and databases provided by financial institutions and economic research institutions. The data collection unit can implement an automated data collection process using AI to regularly update and maintain up-to-date information. The AI ​​analyzes the structure of the websites and databases being collected and learns algorithms to efficiently extract the necessary information. This allows the data collection unit to collect data accurately and quickly from a wide range of sources and provide the latest information on international trade. Furthermore, the data collection unit centrally manages the collected data and builds a database that is easily accessible to the analysis and negotiation units. This allows the data collection unit to collect information efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes the information collected by the data collection unit. Specifically, the analysis unit analyzes regulatory changes and market fluctuations in various countries in real time to assess trade risks and opportunities. For example, it calculates the costs and risks associated with the import and export of specific goods based on collected tariff and regulatory information. It also analyzes financial market trends to assess the impact of exchange rate fluctuations on trade. The analysis unit uses generative AI to analyze this data and extract complex patterns and trends. The generative AI processes large amounts of data and uses models learned from historical data to predict future regulatory changes and market fluctuations. For example, based on historical regulatory change data, the generative AI predicts what kind of regulatory changes a particular country is likely to make in the future. The generative AI also analyzes financial market data to predict future trends in exchange rates. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp trade risks and opportunities in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and strategy planning, improving the reliability and security of the entire system.

[0066] The Negotiation Department conducts negotiations based on information obtained by the Analysis Department. Specifically, the Negotiation Department negotiates based on the requests and conditions that each player's AI agent has previously identified on the automated negotiation platform. For example, the AI ​​agents for exporters and importers propose optimal transaction terms, taking into account tariffs, regulatory information, and exchange rate fluctuations. The Negotiation Department can automate the negotiation process using AI, enabling negotiations to proceed quickly and efficiently. The AI ​​agents generate responses to the other party's proposals and new proposals based on pre-set negotiation strategies and rules. For example, the AI ​​agent will present conditions such as tariff reductions or delivery date adjustments in response to the other party's proposal and proceed with the negotiation. The AI ​​agent also monitors the progress of the negotiations in real time and modifies the negotiation strategy as needed. This allows the Negotiation Department to conduct negotiations efficiently and effectively and determine the optimal transaction terms. Furthermore, the Negotiation Department can record the history and results of negotiations in a database and use them for future negotiations. This allows the Negotiation Department to develop more effective negotiation strategies based on past negotiation data and improve the overall negotiation capabilities of the system.

[0067] The Schedule Department sets up meetings between the parties involved after negotiations have concluded by the Negotiation Department. Specifically, based on the negotiation results, the Schedule Department adjusts the schedules for meetings and discussions among the parties and arranges the necessary resources. For example, after an agreement is reached, the Schedule Department sets up an online meeting between representatives from the exporter and importer, coordinating the date, time, and participants. The Schedule Department also prepares the meeting agenda and materials and distributes them to the parties in advance. The Schedule Department can automate these processes using AI, enabling efficient meeting scheduling. The AI ​​analyzes the schedules of the parties involved and proposes the optimal meeting date and time. The AI ​​also automatically generates and distributes the meeting agenda and materials. This allows the Schedule Department to quickly and efficiently schedule meetings and smoothly implement the negotiation results. Furthermore, the Schedule Department monitors the progress of the meetings and provides support as needed. For example, it responds in real time to problems and questions that arise during the meeting. The Schedule Department also records the meeting results and stores them in a database for future reference. This allows the Schedule Department to smoothly implement the negotiation results and improve the overall efficiency and reliability of the system.

[0068] The data collection unit can obtain trade statistics data, customs information from various countries, regulatory trends, and financial market trends from the websites of relevant organizations. For example, the data collection unit can collect trade statistics data from the websites of relevant organizations. The data collection unit can also collect customs information from various countries from the websites of relevant organizations. The data collection unit can also collect regulatory trends and financial market trends from the websites of relevant organizations. This allows for the efficient collection of necessary information from the websites of relevant organizations. Some or all of the above-described processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input data collected from the websites of relevant organizations into a generating AI, which can then analyze the data and extract the necessary information.

[0069] The analysis unit can analyze information collected by the data collection unit and analyze regulatory changes and market fluctuations in each country in real time. For example, the analysis unit can analyze tariff information collected by the data collection unit and analyze regulatory changes in each country in real time. The analysis unit can also analyze regulatory information collected by the data collection unit and analyze market fluctuations in real time. The analysis unit can also analyze financial market trends collected by the data collection unit and analyze regulatory changes and market fluctuations in real time. This allows for real-time analysis of regulatory changes and market fluctuations in each country. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data collected by the data collection unit into a generation AI, and the generation AI can analyze the data and analyze regulatory changes and market fluctuations in real time.

[0070] The Negotiation Department can conduct negotiations based on the information obtained by the Analysis Department, using each player's AI agent to negotiate on the automated negotiation platform based on their pre-determined requests and conditions. For example, the Negotiation Department can use the information obtained by the Analysis Department to have each player's AI agent negotiate on the automated negotiation platform based on their requests and conditions. The Negotiation Department can also conduct negotiations considering each player's requests and conditions based on the information obtained by the Analysis Department. The Negotiation Department can also use the information obtained by the Analysis Department to compile deals that match each player's conditions. This allows for efficient negotiations based on each player's requests and conditions. Some or all of the above processes in the Negotiation Department may be performed using AI or not. For example, the Negotiation Department can input the information obtained by the Analysis Department into a Generating AI, which can then support the progress of the negotiations.

[0071] The negotiation department can use conversational AI to gather each individual's requests and conditions, and each AI agent can continuously negotiate to finalize deals that meet those conditions. For example, the negotiation department can use conversational AI to gather each individual's requests and conditions, and an AI agent can conduct the negotiations. Alternatively, the negotiation department can use conversational AI to gather each individual's conditions, and an AI agent can finalize deals that meet those conditions. The negotiation department can use conversational AI to gather each individual's requests, and an AI agent can continuously negotiate. This allows for the efficient finalization of deals that meet requests and conditions by using conversational AI. Some or all of the above processes in the negotiation department may be performed using AI, or they may not. For example, the negotiation department can input the requests and conditions gathered by the conversational AI into a generation AI, and the generation AI can support the progress of the negotiations.

[0072] The configuration unit can set up a meeting between the representatives after the negotiations by the negotiation unit have concluded. For example, the configuration unit can set up an online meeting between the representatives after the negotiations by the negotiation unit have concluded. The configuration unit can also set up an in-person meeting between the representatives after the negotiations by the negotiation unit have concluded. The configuration unit can also set up a conference call between the representatives after the negotiations by the negotiation unit have concluded. This allows for the efficient setting up of a meeting between the representatives after the negotiations have concluded. Some or all of the above-described processes in the configuration unit may be performed using AI or not. For example, the configuration unit can use a generating AI to automatically set up a meeting between the representatives after the negotiations by the negotiation unit have concluded.

[0073] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting particularly important information, such as customs and regulatory information. If the user is relaxed, the data collection unit can collect a wide range of information and perform detailed analysis. If the user is in a hurry, the data collection unit can quickly collect the necessary information and provide it in the shortest possible time. This allows for the collection of more relevant information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of the information.

[0074] The data collection unit can select the optimal data collection method by referring to past data collection history when collecting customs and regulatory information from various countries. For example, the data collection unit can evaluate the reliability of previously collected information and prioritize collecting information from reliable sources. The data collection unit can also analyze past data collection history to identify frequently collected sources and collect information efficiently. Based on past data collection history, the data collection unit can predict what information should be collected at a specific time and collect it at the appropriate time. This allows the optimal data collection method to be selected by referring to past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data collection history into a generating AI, which can then select the optimal data collection method.

[0075] The data collection unit can filter information while considering each country's economic indicators and political situation. For example, the data collection unit can prioritize collecting information from economically stable countries based on each country's economic indicators. The data collection unit can also prioritize collecting information from politically stable countries, taking into account each country's political situation. The data collection unit can also collect comprehensively reliable information by considering both economic indicators and political situations. This allows for the collection of highly reliable information by considering each country's economic indicators and political situation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input economic indicators and political situation data for each country into a generating AI, which can then filter the information.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can quickly collect and provide information to the user. If the user is relaxed, the data collection unit can also collect detailed information and analyze it over time. If the user is in a hurry, the data collection unit can quickly collect the necessary information and provide it in the shortest possible time. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection based on 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. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of information collection.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a specific country, the data collection unit will prioritize the collection of customs and regulatory information for that country. If the user is in a specific region, the data collection unit can also prioritize the collection of economic indicators and political conditions for that region. The data collection unit can also quickly collect highly relevant information based on the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location data into a generating AI, which can then prioritize the collection of highly relevant information.

[0078] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also analyze the user's social media activity and prioritize the collection of information of high interest. The data collection unit can also quickly collect relevant information based on the user's social media activity. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant information.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide the analysis results in a simple and easy-to-understand format. If the user is relaxed, the analysis unit can also provide detailed analysis results to facilitate a deeper understanding. If the user is in a hurry, the analysis unit can also quickly provide concise analysis results. By adjusting the presentation of the analysis results based on the user's emotions, the analysis results can be provided in a more appropriate format. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the presentation of the analysis results.

[0080] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis process. For example, the analysis unit can improve the accuracy of the analysis algorithm based on past analysis results. The analysis unit can also analyze past analysis results and adjust the parameters of the analysis algorithm. The analysis unit can also refer to past analysis results and identify areas for improvement in the analysis algorithm. This allows the analysis algorithm to be optimized by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input past analysis result data into a generating AI, and the generating AI can optimize the analysis algorithm.

[0081] The analysis unit can perform simulations to predict regulatory changes and market fluctuations in various countries during the analysis process. For example, the analysis unit can simulate regulatory changes in various countries and predict future impacts. The analysis unit can also simulate market fluctuations and predict optimal trading conditions. The analysis unit can also simulate both regulatory changes and market fluctuations to predict the overall impact. In this way, future impacts can be predicted by performing simulations to predict regulatory changes and market fluctuations in various countries. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input simulation data into a generative AI, which can then predict regulatory changes and market fluctuations.

[0082] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide detailed analysis results to reassure them. If the user is relaxed, the analysis unit can also provide concise analysis results to facilitate understanding. If the user is in a hurry, the analysis unit can quickly provide concise analysis results. By adjusting the level of detail in the analysis results based on the user's emotions, the analysis results can be provided with a more appropriate level of detail. 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. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the level of detail in the analysis results.

[0083] The analysis unit can improve the accuracy of its analysis by considering the economic indicators and political situations of each country during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the economic indicators of each country. The analysis unit can also improve the accuracy of its analysis by considering the political situations of each country. The analysis unit can also improve the accuracy of its analysis comprehensively by considering both economic indicators and political situations. In this way, the accuracy of the analysis can be improved by considering the economic indicators and political situations of each country. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input economic indicator and political situation data for each country into a generative AI, which can then improve the accuracy of the analysis.

[0084] The analysis unit can improve the reliability of its analysis by referring to relevant literature and reports during the analysis. For example, the analysis unit can improve the reliability of its analysis by referring to relevant literature. The analysis unit can also improve the reliability of its analysis by referring to relevant reports. The analysis unit can also improve the reliability of its analysis by referring to both literature and reports. In this way, the reliability of the analysis can be improved by referring to relevant literature and reports. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input relevant literature and report data into a generating AI, and the generating AI can improve the reliability of the analysis.

[0085] The negotiation unit can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is feeling anxious, the negotiation unit will proceed cautiously. If the user is relaxed, the negotiation unit can proceed more aggressively. If the user is in a hurry, the negotiation unit can proceed quickly. This allows for more appropriate negotiations by adjusting the negotiation process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI, which can then adjust the negotiation process.

[0086] The negotiation department can select the optimal negotiation strategy during negotiations by referring to past negotiation history. For example, the negotiation department can select the optimal negotiation strategy based on past negotiation history. The negotiation department can also analyze past negotiation history and identify areas for improvement in the negotiation strategy. The negotiation department can also adjust the parameters of the negotiation strategy by referring to past negotiation history. This allows the optimal negotiation strategy to be selected by referring to past negotiation history. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input past negotiation history data into a generating AI, which can then select the optimal negotiation strategy.

[0087] The negotiating department can conduct negotiations while considering the attribute information of each player. For example, the negotiating department can select the optimal negotiation strategy based on the attribute information of each player. The negotiating department can also adjust the negotiation process while considering the attribute information of each player. The negotiating department can also refer to the attribute information of each player to identify areas for improvement in the negotiation. This allows for more appropriate negotiations by considering the attribute information of each player. Some or all of the above processes in the negotiating department may be performed using AI or not. For example, the negotiating department can input the attribute information data of each player into a generating AI, and the generating AI can conduct the negotiations.

[0088] The negotiation unit can estimate the user's emotions and determine negotiation priorities based on those estimated emotions. For example, if the user is feeling anxious, the negotiation unit will prioritize important negotiations. If the user is relaxed, the negotiation unit can also flexibly adjust negotiation priorities. If the user is in a hurry, the negotiation unit can also prioritize negotiations that need to be handled quickly. This allows important negotiations to be prioritized by determining negotiation priorities based on 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. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI, which can then determine negotiation priorities.

[0089] The negotiating department can conduct negotiations while considering the geographical location information of each player. For example, the negotiating department can select the optimal negotiation strategy based on the geographical location information of each player. The negotiating department can also adjust the negotiation process while considering the geographical location information of each player. The negotiating department can also refer to the geographical location information of each player to identify areas for improvement in the negotiation. This allows for more appropriate negotiations by considering the geographical location information of each player. Some or all of the above processes in the negotiating department may be performed using AI or not. For example, the negotiating department can input the geographical location data of each player into a generating AI, and the generating AI can conduct the negotiations.

[0090] The negotiating department can improve the accuracy of negotiations by referring to relevant market data during negotiations. For example, the negotiating department can improve the accuracy of negotiations based on relevant market data. The negotiating department can also refer to relevant market data and adjust the way negotiations are conducted. The negotiating department can also analyze market data and identify areas for improvement in negotiations. This allows for improved negotiation accuracy by referring to relevant market data. Some or all of the above processes in the negotiating department may be performed using AI or not. For example, the negotiating department can input relevant market data into a generating AI, which can then improve the accuracy of negotiations.

[0091] The settings unit can estimate the user's emotions and adjust the negotiation environment based on the estimated emotions. For example, if the user is feeling anxious, the settings unit can set the environment to be calm for negotiations. If the user is relaxed, the settings unit can also set the environment to be casual for negotiations. If the user is in a hurry, the settings unit can also set the environment to allow for quick negotiations. This allows for a more appropriate negotiation environment to be provided by adjusting the negotiation environment based on 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. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input user emotion data into a generative AI, which can then adjust the negotiation environment.

[0092] The configuration unit can select the optimal configuration method when setting up a negotiation venue by referring to past configuration history. For example, the configuration unit can select the optimal negotiation venue based on past configuration history. The configuration unit can also analyze past configuration history and identify areas for improvement in the configuration method. The configuration unit can also adjust the parameters of the configuration method by referring to past configuration history. In this way, the optimal negotiation venue can be set up by referring to past configuration history. Some or all of the above processes in the configuration unit may be performed using AI or not. For example, the configuration unit can input past configuration history data into a generating AI, and the generating AI can select the optimal configuration method.

[0093] The settings unit can estimate the user's emotions and determine the priority of negotiation sessions based on the estimated emotions. For example, if the user is feeling anxious, the settings unit will prioritize important negotiation sessions. If the user is relaxed, the settings unit can also flexibly adjust the priority of negotiation sessions. If the user is in a hurry, the settings unit can also prioritize negotiation sessions that should proceed quickly. In this way, by determining the priority of negotiation sessions based on the user's emotions, important negotiation sessions can be prioritized. 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. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input user emotion data into a generative AI, which can then determine the priority of negotiation sessions.

[0094] The setting unit can select the optimal setting method when setting up a negotiation venue, taking into account the geographical location information of each player. For example, the setting unit can select the optimal negotiation venue based on the geographical location information of each player. The setting unit can also adjust the setting method of the negotiation venue, taking into account the geographical location information of each player. The setting unit can also refer to the geographical location information of each player and identify areas for improvement in the setting method. In this way, the optimal negotiation venue can be set by taking into account the geographical location information of each player. Some or all of the above processing in the setting unit may be performed using AI or not. For example, the setting unit can input the geographical location data of each player into a generating AI, and the generating AI can select the optimal setting method.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is feeling anxious, it can prioritize collecting particularly important information, such as customs and regulatory information. If the user is relaxed, it can collect a wide range of information and perform detailed analysis. If the user is in a hurry, it can quickly collect the necessary information and provide it in the shortest possible time. This allows for the collection of more relevant information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of the information.

[0097] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis results can be provided in a simple and easy-to-understand format. If the user is relaxed, detailed analysis results can be provided to facilitate a deeper understanding. If the user is in a hurry, concise analysis results can be provided quickly. By adjusting the presentation of the analysis results based on the user's emotions, the results can be provided in a more appropriate format. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the presentation of the analysis results.

[0098] The negotiation unit can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is feeling anxious, the negotiation can proceed cautiously. If the user is relaxed, the negotiation can proceed more aggressively. If the user is in a hurry, the negotiation can proceed quickly. This allows for more appropriate negotiations by adjusting the negotiation process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI, which can then adjust the negotiation process.

[0099] The settings unit can estimate the user's emotions and adjust the negotiation environment based on those emotions. For example, if the user is feeling anxious, the settings can be adjusted to conduct negotiations in a calm environment. If the user is relaxed, the settings can be adjusted to conduct negotiations in a casual environment. If the user is in a hurry, the settings can be adjusted to conduct negotiations quickly. By adjusting the negotiation environment based on the user's emotions, a more appropriate negotiation environment can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input user emotion data into a generative AI, which can then adjust the negotiation environment.

[0100] The data collection unit can select the optimal data collection method by referring to past data collection history when collecting customs and regulatory information from various countries. For example, it can evaluate the reliability of previously collected information and prioritize collecting information from reliable sources. It can also analyze past data collection history to identify frequently collected sources and collect information efficiently. Based on past data collection history, it can predict what information should be collected at a specific time and collect it at the appropriate time. In this way, the optimal data collection method can be selected by referring to past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data collection history into a generating AI, which can then select the optimal data collection method.

[0101] The data collection unit can filter information while considering each country's economic indicators and political situation. For example, it can prioritize collecting information from economically stable countries based on each country's economic indicators. It can also prioritize collecting information from politically stable countries, taking into account each country's political situation. It can also collect comprehensively reliable information by considering both economic indicators and political situations. This allows for the collection of highly reliable information by considering each country's economic indicators and political situation. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input economic indicators and political situation data for each country into a generating AI, which can then filter the information.

[0102] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis process. For example, it can improve the accuracy of the analysis algorithm based on past analysis results. It can also analyze past analysis results and adjust the parameters of the analysis algorithm. It can also refer to past analysis results to identify areas for improvement in the analysis algorithm. In this way, the analysis algorithm can be optimized by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input past analysis result data into a generating AI, and the generating AI can optimize the analysis algorithm.

[0103] The analysis unit can perform simulations to predict regulatory changes and market fluctuations in various countries during the analysis process. For example, it can simulate regulatory changes in various countries to predict future impacts. It can also simulate market fluctuations to predict optimal trading conditions. It can even simulate both regulatory changes and market fluctuations to predict the overall impact. In this way, future impacts can be predicted by performing simulations to predict regulatory changes and market fluctuations in various countries. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input simulation data into a generative AI, which can then predict regulatory changes and market fluctuations.

[0104] The negotiation department can select the optimal negotiation strategy during negotiations by referring to past negotiation history. For example, it can select the optimal negotiation strategy based on past negotiation history. It can also analyze past negotiation history to identify areas for improvement in the negotiation strategy. It can also adjust the parameters of the negotiation strategy by referring to past negotiation history. In this way, the optimal negotiation strategy can be selected by referring to past negotiation history. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input past negotiation history data into a generating AI, and the generating AI can select the optimal negotiation strategy.

[0105] The configuration unit can select the optimal configuration method when setting up a negotiation venue by referring to past configuration history. For example, it can select the optimal negotiation venue based on past configuration history. It can also analyze past configuration history to identify areas for improvement in configuration methods. It can also adjust the parameters of the configuration method by referring to past configuration history. In this way, the optimal negotiation venue can be set up by referring to past configuration history. Some or all of the above processes in the configuration unit may be performed using AI or not. For example, the configuration unit can input past configuration history data into a generating AI, which can then select the optimal configuration method.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The data collection unit collects tariff and regulatory information. For example, it obtains trade statistics data, tariff information from various countries, regulatory trends, and financial market trends from the websites of relevant organizations. The data collection unit may include AI processing. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes regulatory changes and market fluctuations in various countries in real time. The analysis unit may include processing for generating AI. Step 3: The negotiation department conducts negotiations based on the information obtained by the analysis department. For example, each player's AI agent conducts negotiations based on the requests and conditions that were previously identified on the automated negotiation platform. The negotiation department may include AI processing. Step 4: The setup unit sets up a forum for negotiations between the representatives after the negotiations by the negotiation unit have concluded. The setup unit may also include AI processing.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the collection unit, analysis unit, negotiation unit, and setting unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects tariff and regulatory information in real time. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The negotiation unit is implemented by the control unit 46A of the smart device 14 and conducts negotiations on terms and conditions. The setting unit is implemented by the specific processing unit 290 of the data processing device 12 and sets up a forum for negotiations between the parties involved. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, negotiation unit, and setting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects tariff and regulatory information in real time. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The negotiation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and conducts negotiations on terms and conditions. The setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and sets up a forum for negotiations between the parties involved. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

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

[0131] The 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.

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

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

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

[0135] Figure 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.

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

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

[0138] In the 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.

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0142] The data processing system 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, negotiation unit, and setting unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects tariff and regulatory information in real time. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The negotiation unit is implemented by the control unit 46A of the headset terminal 314 and conducts negotiations on terms and conditions. The setting unit is implemented by the specific processing unit 290 of the data processing device 12 and sets up a forum for negotiations between the parties involved. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

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

[0147] The 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.

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

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, negotiation unit, and setting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects customs and regulatory information in real time. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The negotiation unit is implemented by the control unit 46A of the robot 414 and conducts negotiations on terms and conditions. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets up a forum for negotiations between the parties involved. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A collection department that collects customs and regulatory information, An analysis unit analyzes the information collected by the aforementioned collection unit, A negotiation unit conducts negotiations based on the information obtained by the analysis unit, The system includes a setting unit that sets up a forum for negotiations between the representatives after the negotiations have been concluded by the aforementioned negotiation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Trade statistics data, customs information from various countries, regulatory trends, and financial market trends can be obtained from the websites of relevant organizations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The information collected by the aforementioned data collection unit is analyzed to analyze regulatory changes and market fluctuations in each country in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned negotiating body said, Based on the information obtained by the aforementioned analysis unit, each player's AI agent conducts negotiations on the automated negotiation platform based on the requests and conditions that were previously identified. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned negotiating body said, The conversational AI listens to each individual's requests and conditions, and their AI agent constantly negotiates to find suitable deals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The setting unit is, After the negotiations by the aforementioned negotiating department have concluded, a meeting will be arranged for the representatives to negotiate with each other. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting customs and regulatory information from various countries, we select the most suitable collection method by referring to past collection records. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, the information is filtered while also considering the economic indicators and political situation of each country. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, simulations are performed to predict regulatory changes and market fluctuations in each country. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, we improve the accuracy of the analysis by taking into account the economic indicators and political situation of each country. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, we refer to relevant literature and reports to improve the reliability of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) 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 21) The aforementioned negotiating body said, When negotiating, take into account the attribute information of each player. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned negotiating body said, During negotiations, the geographical location of each player will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned negotiating body said, During negotiations, refer to relevant market data to improve the accuracy of the negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The setting unit is, It estimates the user's emotions and adjusts the negotiation setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The setting unit is, When setting up a negotiation meeting, refer to past setting history to select the optimal setting method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The setting unit is, It estimates the user's emotions and determines the priority of the negotiation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The setting unit is, When setting up a negotiation venue, the optimal setting method is selected by considering the geographical location information of each player. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects customs and regulatory information, An analysis unit analyzes the information collected by the aforementioned collection unit, A negotiation unit conducts negotiations based on the information obtained by the analysis unit, The system includes a setting unit that sets up a forum for negotiations between the representatives after the negotiations have been concluded by the aforementioned negotiation unit. A system characterized by the following features.

2. The aforementioned collection unit is Trade statistics data, customs information from various countries, regulatory trends, and financial market trends can be obtained from the websites of relevant organizations. The system according to feature 1.

3. The aforementioned analysis unit, The information collected by the aforementioned data collection unit is analyzed to analyze regulatory changes and market fluctuations in each country in real time. The system according to feature 1.

4. The aforementioned negotiating body said, Based on the information obtained by the aforementioned analysis unit, each player's AI agent conducts negotiations on the automated negotiation platform based on the requests and conditions that were previously identified. The system according to feature 1.

5. The aforementioned negotiating body said, The conversational AI listens to each individual's requests and conditions, and their AI agent constantly negotiates to find suitable deals. The system according to feature 1.

6. The aforementioned setting unit is, After the negotiations by the aforementioned negotiating department have concluded, a meeting will be arranged for the representatives to negotiate with each other. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting customs and regulatory information from various countries, we select the most suitable collection method by referring to past collection records. The system according to feature 1.

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