system

Abstract

The system according to this embodiment aims to improve the user's negotiation skills in real time. [Solution] The system according to this embodiment comprises an analysis unit, a provision unit, a simulation unit, an analysis unit, and a learning plan creation unit. The analysis unit analyzes the user's negotiation situation in real time. The provision unit provides advice based on the results analyzed by the analysis unit. The simulation unit realizes a virtual negotiation simulator. The analysis unit analyzes the user's negotiation style, strengths, and weaknesses. The learning plan creation unit creates individual learning plans.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: 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 as a 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 there is a lack of means for assisting in improving negotiation skills in real time and an effective practice environment is not provided.

[0005] The system according to the embodiment aims to improve the negotiation skills of a user in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, a simulation unit, an analysis unit, and a learning plan creation unit. The analysis unit analyzes the user's negotiation situation in real time. The provision unit provides advice based on the results analyzed by the analysis unit. The simulation unit implements a virtual negotiation simulator. The analysis unit analyzes the user's negotiation style, strengths, and weaknesses. The learning plan creation unit creates individual learning plans. [Effects of the Invention]

[0007] The system according to this embodiment can improve the user's negotiation skills in real time. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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] <0000​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The negotiation support system according to an embodiment of the present invention is an innovative platform that combines AI-powered real-time coaching with advanced negotiation simulation capabilities. This negotiation support system allows users to receive real-time advice from an AI assistant in actual negotiation scenarios and practice various negotiation scenarios in a virtual environment. For example, users can receive real-time advice from an AI assistant via a smartphone or earphones. The AI ​​analyzes the user's negotiation situation in real time and provides the optimal response. For example, if a user is negotiating a real estate transaction, the AI ​​provides real-time advice on the transaction price and conditions. Next, the user can practice various negotiation scenarios using a virtual negotiation simulator. The AI ​​generates virtual characters with various personalities and negotiation styles, allowing users to practice diverse negotiation scenarios in a safe environment. For example, a user can negotiate prices with a virtual character to improve their negotiation skills. Furthermore, the AI ​​analyzes the user's negotiation style, strengths, and weaknesses and creates an individualized learning plan. This allows users to receive effective training to overcome their weaknesses and develop their strengths. For example, if a user has difficulty controlling their emotions, the AI ​​performs emotion analysis and provides feedback on effective nonverbal communication. Furthermore, the AI ​​analyzes historical data and current market trends to predict negotiation outcomes. This allows users to simulate the results of various negotiation strategies and select the optimal one. For example, when a user negotiates a price, the AI ​​makes the best price offer based on historical data. This platform is intended for use by anyone aiming to improve their negotiation skills, including retail staff, business professionals, entrepreneurs, sales representatives, HR managers, real estate agents, and general consumers. Many people feel anxious about negotiating and unprepared, but this platform allows them to receive effective, practical training.This platform leverages cutting-edge generative AI technology to dramatically improve users' negotiation skills, significantly expanding their chances of success in business and personal life. Combining real-time support with practical simulations, it bridges the gap between theory and practice, becoming an innovative platform that provides users with both confidence and competence. As a result, the negotiation support system can dramatically improve users' negotiation skills, greatly expanding their chances of success in business and personal life.

[0029] The negotiation support system according to this embodiment comprises an analysis unit, a provision unit, a simulation unit, an analysis unit, and a learning plan creation unit. The analysis unit analyzes the user's negotiation status in real time. The analysis unit analyzes, for example, the user's statements, facial expressions, and voice tone to grasp the progress of the negotiation. The analysis unit can use AI to analyze the user's statements using natural language processing technology to identify the negotiation theme and progress. The analysis unit can also, for example, capture the user's facial expressions with a camera and estimate emotions using facial recognition technology. The analysis unit can also use voice analysis technology to analyze the user's voice tone and speed and estimate their state of tension or relaxation. The provision unit provides advice based on the results analyzed by the analysis unit. For example, if the user faces a difficult situation during negotiations, the provision unit provides appropriate countermeasures in real time. The provision unit can use AI to generate advice according to the user's statements and the progress of the negotiations. For example, if the user is negotiating a price, the provision unit can provide optimal price proposals and negotiation strategies. The service provider can adjust the tone and content of advice according to the user's emotional state. The simulation unit implements a virtual negotiation simulator. For example, the simulation unit generates virtual characters with various personalities and negotiation styles, providing an environment where users can practice diverse negotiation scenarios. The simulation unit can use AI to generate the virtual characters' statements and actions in real time. For example, the simulation unit allows users to negotiate prices with virtual characters and improve their negotiation skills. The simulation unit analyzes the user's negotiation style, strengths, and weaknesses. The analysis unit, for example, analyzes the user's past negotiation data to identify negotiation styles, strengths, and weaknesses. The analysis unit can use AI to analyze the user's statements and behavioral patterns and classify negotiation styles. For example, if a user has an aggressive negotiation style, the analysis unit can identify its strengths and weaknesses and suggest areas for improvement. The learning plan creation unit creates individual learning plans. For example, the learning plan creation unit provides effective training plans based on the user's negotiation style, strengths, and weaknesses.The learning plan creation unit can use AI to monitor the user's learning progress in real time and adjust the learning plan accordingly. For example, if a user has difficulty controlling their emotions, the learning plan creation unit can perform emotion analysis and provide effective nonverbal communication feedback. This allows the negotiation support system according to the embodiment to analyze the user's negotiation situation in real time, provide optimal advice, and enable practice in a virtual environment.

[0030] The analysis unit analyzes the user's negotiation status in real time. For example, the analysis unit analyzes the user's statements, facial expressions, and voice tone to understand the progress of the negotiation. Specifically, it analyzes the user's statements using natural language processing technology to identify the negotiation theme and progress. Natural language processing technology converts the user's statements into text data and performs keyword extraction and contextual analysis to understand the focus and progress of the negotiation. In addition, the user's facial expressions are captured by a camera, and emotions are estimated using facial recognition technology. Facial recognition technology detects facial feature points and analyzes subtle changes in facial expressions to understand the user's emotional state in real time. Furthermore, voice analysis technology is used to analyze the tone and speed of the user's voice to estimate their state of tension or relaxation. Voice analysis technology analyzes changes in the frequency components and volume of the voice to detect changes in emotion. As a result, the analysis unit can comprehensively analyze the user's statements, facial expressions, and voice tone to understand the progress of the negotiation and the user's emotional state in real time.

[0031] The service provider provides advice based on the results analyzed by the analysis department. For example, if a user faces a difficult situation during negotiations, the service provider will provide appropriate countermeasures in real time. Specifically, it uses AI to generate advice that is tailored to the user's statements and the progress of the negotiation. The AI ​​learns from past negotiation data and success stories to provide the best advice for the user's situation. For example, if a user is negotiating a price, it will provide the best price offer and negotiation strategy. The price offer is calculated based on market data and past negotiation history, presenting terms that are favorable to the user. It also adjusts the tone and content of the advice according to the user's emotional state. For example, if a user is nervous, it will provide advice to help them relax and encourage calm judgment. In this way, the service provider can provide users with appropriate advice in real time, increasing the success rate of negotiations.

[0032] The simulation unit implements a virtual negotiation simulator. For example, the simulation unit generates virtual characters with various personalities and negotiation styles, providing an environment where users can practice diverse negotiation scenarios. Specifically, it uses AI to generate the virtual characters' statements and actions in real time. The virtual characters respond appropriately to the user's statements and actions, providing a realistic negotiation experience. For example, users can negotiate prices with virtual characters to improve their negotiation skills. The simulation unit analyzes the user's negotiation style, strengths, and weaknesses, and provides a personalized training plan. This allows users to practice effectively in preparation for actual negotiations.

[0033] The analytics department analyzes users' past negotiation data to identify their negotiation style, strengths, and weaknesses. Specifically, it uses AI to analyze users' statements and behavioral patterns to classify their negotiation style. The AI ​​learns from past negotiation data and extracts user characteristics. For example, if a user has an aggressive negotiation style, the analytics department identifies its strengths and weaknesses and suggests areas for improvement. Strengths might include the ability to exert pressure on the other party, while weaknesses might include the risk of damaging relationships with the other party. This allows the analytics department to gain a detailed understanding of each user's negotiation style and provide personalized improvement measures.

[0034] The learning plan creation department creates individualized learning plans. Specifically, it provides effective training plans based on the user's negotiation style, strengths, and weaknesses. Using AI, it monitors the user's learning progress in real time and adjusts the learning plan accordingly. For example, if a user has difficulty controlling their emotions, it performs sentiment analysis and provides feedback on effective nonverbal communication. Sentiment analysis analyzes the user's facial expressions and tone of voice to detect changes in their emotions. This allows the user to understand their own emotional state and take appropriate measures. The learning plan creation department adjusts the training content according to the user's progress and provides an optimal learning environment. This enables the user to effectively improve their negotiation skills.

[0035] The prediction unit can analyze past data and current market trends to predict negotiation outcomes. For example, the prediction unit collects and analyzes past negotiation history and market data. The prediction unit can use AI to predict negotiation outcomes based on past data. The prediction unit can also collect and analyze current market trends in real time. The prediction unit can use AI to predict negotiation outcomes based on current market data. The prediction unit improves the accuracy of negotiation outcome predictions by combining past data and current market trends. For example, the prediction unit integrates past negotiation history and current market trends to propose the optimal negotiation strategy. The prediction unit can use AI to analyze past data and current market data to predict negotiation outcomes. This allows for improved accuracy in predicting negotiation outcomes by analyzing past data and market trends.

[0036] The analysis unit can improve the accuracy of its analysis by referring to the user's past negotiation history when analyzing the negotiation situation. For example, the analysis unit can refer to successful patterns in past negotiations conducted by the user and propose the optimal countermeasures for similar situations. The analysis unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the analysis unit can extract effective strategies for specific negotiating partners from the user's past negotiation history and reflect them in the analysis. The analysis unit can propose the optimal strategy for specific negotiating partners based on the user's past negotiation data. For example, the analysis unit can analyze the causes of past negotiation failures and adjust the analysis method to prevent the same mistakes from being repeated. The analysis unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows the accuracy of the analysis to be improved by referring to past negotiation history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past negotiation data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0037] The analysis unit can perform analysis of negotiation situations while considering the attribute information of the negotiating party. For example, the analysis unit can consider the occupation and position of the negotiating party and perform analysis appropriate to their position. The analysis unit can use AI to analyze the attribute information of the negotiating party and reflect it in the progress of the negotiations. For example, the analysis unit can refer to the negotiating party's past negotiation style and patterns and reflect them in the analysis. The analysis unit can propose the optimal countermeasures based on the negotiating party's past negotiation data. For example, the analysis unit can consider the cultural background and values ​​of the negotiating party and perform appropriate analysis. The analysis unit can use AI to analyze the cultural background and values ​​of the negotiating party and reflect it in the progress of the negotiations. This makes it possible to perform more appropriate analysis by considering the attribute information of the negotiating party. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the attribute information of the negotiating party into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0038] The analysis unit can perform its analysis of negotiations while considering the geographical background of the negotiations. For example, the analysis unit can consider the economic conditions of the region where the negotiations are taking place and reflect this in its analysis. The analysis unit can use AI to analyze regional economic data and reflect this in the progress of the negotiations. For example, the analysis unit can consider the cultural background of the place where the negotiations are taking place and perform an appropriate analysis. Based on regional cultural data, the analysis unit can propose the most appropriate countermeasures. For example, the analysis unit can refer to the legal regulations of the region where the negotiations are taking place and reflect this in its analysis. Based on regional legal data, the analysis unit can reflect this in the progress of the negotiations. This makes it possible to perform a more appropriate analysis by considering the geographical background. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input regional economic data and cultural data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0039] The analysis unit can improve the accuracy of its analysis by referring to relevant legal documents when analyzing the negotiation situation. For example, the analysis unit can refer to contracts and agreements related to the negotiations and reflect them in its analysis. The analysis unit can use AI to analyze legal documents and reflect the progress of the negotiations. For example, the analysis unit can refer to legal regulations and guidelines related to the negotiations and perform appropriate analysis. Based on legal regulation data, the analysis unit can propose the optimal countermeasures. For example, the analysis unit can improve the accuracy of its analysis by referring to past precedents related to the negotiations. Based on past precedent data, the analysis unit can reflect the progress of the negotiations. In this way, the accuracy of the analysis can be improved by referring to legal documents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0040] The service provider can adjust the level of detail of the advice based on the importance of the negotiation when providing advice. For example, in the case of an important negotiation, the service provider will provide detailed advice. The service provider can use AI to analyze the importance of the negotiation and adjust the level of detail of the advice. For example, in the case of a less important negotiation, the service provider will provide concise advice. The service provider can adjust the specificity of the advice according to the importance of the negotiation. For example, the service provider can dynamically adjust the content of the advice based on the importance of the negotiation. This allows for the provision of appropriate advice by adjusting the level of detail of the advice according to the importance of the negotiation. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input negotiation importance data into a generating AI and have the generating AI perform analysis to adjust the level of detail of the advice.

[0041] The service provider can apply different advice algorithms depending on the negotiation category when providing advice. For example, in the case of real estate transaction negotiations, the service provider can apply an advice algorithm specialized in price negotiations. The service provider can use AI to analyze the negotiation category and apply the optimal advice algorithm. For example, in the case of business contract negotiations, the service provider can apply an advice algorithm specialized in contract terms. The service provider can dynamically adjust the advice algorithm depending on the negotiation category. For example, in the case of sales negotiations, the service provider can apply an advice algorithm specialized in customer psychology. This allows for the provision of more effective advice by applying an advice algorithm appropriate to the negotiation category. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input negotiation category data into a generating AI and have the generating AI perform analysis to apply the optimal advice algorithm.

[0042] The service provider can prioritize advice based on the progress of negotiations when providing advice. For example, in the early stages of negotiations, the service provider prioritizes advice on basic strategies. The service provider can use AI to analyze the progress of negotiations and determine the priority of advice. For example, in the middle stages of negotiations, the service provider prioritizes advice on specific proposals and counterarguments. The service provider can dynamically adjust the content of advice according to the progress of negotiations. For example, in the final stages of negotiations, the service provider prioritizes final advice toward reaching an agreement. This ensures that appropriate advice is provided by prioritizing advice according to the progress of negotiations. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input negotiation progress data into a generating AI and have the generating AI perform analysis to determine the priority of advice.

[0043] The advice provider can adjust the order of advice based on the relevance of the negotiation when providing advice. For example, the provider can provide advice on key points of the negotiation first. The provider can use AI to analyze the relevance of the negotiation and adjust the order of advice. For example, the provider can postpone advice on secondary points of the negotiation. The provider can dynamically adjust the order of advice as the negotiation progresses. This allows for the provision of appropriate advice by adjusting the order of advice based on the relevance of the negotiation. Some or all of the above processing in the provider can be performed using AI, for example, or without AI. For example, the provider can input negotiation relevance data into a generating AI and have the generating AI perform analysis to adjust the order of advice.

[0044] The simulation unit can improve the accuracy of scenarios by referring to the user's past negotiation history during simulation. For example, the simulation unit can refer to successful patterns in past negotiations conducted by the user and provide scenarios for similar situations. The simulation unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the simulation unit can generate effective scenarios for specific negotiating partners from the user's past negotiation history. The simulation unit can provide optimal scenarios for specific negotiating partners based on the user's past negotiation data. For example, the simulation unit can analyze the causes of past negotiation failures and adjust scenarios to prevent the same mistakes from being repeated. The simulation unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows for improved scenario accuracy by referring to past negotiation history. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past negotiation data into a generating AI and have the generating AI perform analysis to improve the accuracy of the scenarios.

[0045] The simulation unit can generate scenarios during simulation by considering the attribute information of the negotiating partner. For example, the simulation unit can consider the occupation and position of the negotiating partner and generate a scenario appropriate to their position. The simulation unit can use AI to analyze the attribute information of the negotiating partner and reflect it in the scenario. For example, the simulation unit can refer to the negotiating partner's past negotiation style and patterns and reflect them in the scenario. The simulation unit can generate the optimal scenario based on the negotiating partner's past negotiation data. For example, the simulation unit can consider the negotiating partner's cultural background and values ​​and generate an appropriate scenario. The simulation unit can use AI to analyze the negotiating partner's cultural background and values ​​and reflect them in the scenario. As a result, a more appropriate scenario is generated by considering the attribute information of the negotiating partner. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the attribute information of the negotiating partner into the generating AI and have the generating AI perform analysis to improve the accuracy of the scenario.

[0046] The simulation unit can generate scenarios during simulation, taking into account the geographical background of the negotiations. For example, the simulation unit considers the economic conditions of the region where the negotiations take place and reflects this in the scenario. The simulation unit can use AI to analyze regional economic data and reflect this in the scenario. For example, the simulation unit considers the cultural background of the location where the negotiations take place and generates an appropriate scenario. The simulation unit can generate an optimal scenario based on regional cultural data. For example, the simulation unit refers to legal regulations in the region where the negotiations take place and reflects this in the scenario. The simulation unit can reflect regional legal data in the scenario. This allows for the generation of more appropriate scenarios by considering the geographical background. Some or all of the above-described processes in the simulation unit may be performed using AI, or not. For example, the simulation unit can input regional economic and cultural data into a generating AI and have the generating AI perform analysis to improve the accuracy of the scenarios.

[0047] The simulation unit can improve the accuracy of scenarios by referring to relevant legal documents during simulation. For example, the simulation unit can refer to contracts and agreements related to negotiations and reflect them in the scenario. The simulation unit can use AI to analyze legal documents and reflect them in the scenario. For example, the simulation unit can refer to legal regulations and guidelines related to negotiations and generate appropriate scenarios. The simulation unit can generate optimal scenarios based on legal regulation data. For example, the simulation unit can refer to past precedents related to negotiations to improve the accuracy of scenarios. The simulation unit can reflect past precedent data in the scenario. In this way, the accuracy of scenarios can be improved by referring to legal documents. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of the scenarios.

[0048] The analysis unit can improve the accuracy of its analysis by referring to the user's past negotiation history when analyzing negotiation styles. For example, the analysis unit can refer to successful patterns in past negotiations conducted by the user and suggest the best course of action in similar situations. The analysis unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the analysis unit can extract effective strategies for specific negotiating partners from the user's past negotiation history and reflect them in the analysis. Based on the user's past negotiation data, the analysis unit can suggest the best strategy for specific negotiating partners. For example, the analysis unit can analyze the causes of past negotiation failures and adjust the analysis method to prevent the same mistakes from being repeated. The analysis unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows for improved accuracy of the analysis by referring to past negotiation history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past negotiation data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0049] The analysis department can perform analysis by considering the attribute information of the negotiating partner when analyzing negotiation styles. For example, the analysis department can consider the occupation and position of the negotiating partner and perform an analysis appropriate to their position. The analysis department can use AI to analyze the attribute information of the negotiating partner and reflect it in the analysis. For example, the analysis department can refer to the negotiating partner's past negotiation styles and patterns and reflect them in the analysis. Based on the negotiating partner's past negotiation data, the analysis department can propose the optimal countermeasures. For example, the analysis department can perform an appropriate analysis by considering the cultural background and values ​​of the negotiating partner. The analysis department can use AI to analyze the cultural background and values ​​of the negotiating partner and reflect them in the analysis. This makes it possible to perform a more appropriate analysis by considering the attribute information of the negotiating partner. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the attribute information of the negotiating partner into a generating AI and have the generating AI perform an analysis to improve the accuracy of the analysis.

[0050] The analysis department can consider the geographical context of negotiations when analyzing negotiation styles. For example, the analysis department can consider the economic conditions of the region where negotiations take place and reflect them in the analysis. The analysis department can use AI to analyze regional economic data and reflect it in the analysis. For example, the analysis department can consider the cultural context of the place where negotiations take place and conduct an appropriate analysis. The analysis department can propose optimal countermeasures based on regional cultural data. For example, the analysis department can refer to legal regulations of the region where negotiations take place and reflect them in the analysis. The analysis department can reflect regional legal data in the analysis. This makes it possible to conduct a more appropriate analysis by considering the geographical context. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input regional economic and cultural data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0051] The analysis department can improve the accuracy of its analysis by referring to relevant legal documents when analyzing negotiation styles. For example, the analysis department can refer to contracts and agreements related to negotiations and incorporate them into the analysis. The analysis department can use AI to analyze legal documents and incorporate the results into the analysis. For example, the analysis department can refer to legal regulations and guidelines related to negotiations to conduct an appropriate analysis. Based on legal regulation data, the analysis department can propose the most appropriate countermeasures. For example, the analysis department can improve the accuracy of its analysis by referring to past court precedents related to negotiations. The analysis department can incorporate past court precedent data into the analysis. In this way, the accuracy of the analysis can be improved by referring to legal documents. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0052] The learning plan creation unit can improve the accuracy of the plan by referring to the user's past learning history when creating the plan. For example, the learning plan creation unit proposes an optimal learning plan based on what the user has learned in the past. The learning plan creation unit can use AI to analyze the user's past learning data and generate an optimal learning plan. For example, the learning plan creation unit can extract effective learning methods from the user's past learning history and reflect them in the plan. The learning plan creation unit can propose an optimal learning method based on the user's past learning data. For example, the learning plan creation unit can analyze what the user has struggled with in the past and provide a learning plan to overcome it. The learning plan creation unit can use AI to analyze the user's past learning data and identify areas of difficulty. This allows the accuracy of the plan to be improved by referring to the past learning history. Some or all of the above processes in the learning plan creation unit may be performed using AI, for example, or without AI. For example, the learning plan creation unit can input the user's past learning data into a generating AI and have the generating AI perform analysis to improve the accuracy of the plan.

[0053] The learning plan creation unit can customize the plan by taking into account the user's current lifestyle when creating the plan. For example, if the user is busy, the learning plan creation unit can provide a plan that allows for effective learning in a short amount of time. The learning plan creation unit can use AI to analyze the user's lifestyle data and generate an optimal learning plan. For example, if the user has ample time, the learning plan creation unit can provide a detailed and in-depth learning plan. The learning plan creation unit can adjust the schedule of the learning plan to match the user's lifestyle. For example, the learning plan creation unit can propose an optimal learning schedule based on the user's lifestyle data. This allows for the provision of a more appropriate learning plan by taking into account the current lifestyle. Some or all of the above processes in the learning plan creation unit may be performed using AI, for example, or without AI. For example, the learning plan creation unit can input the user's lifestyle data into a generating AI and have the generating AI perform the plan customization.

[0054] The learning plan creation unit can create a learning plan while considering the user's geographical background. For example, the learning plan creation unit can provide an appropriate learning plan by considering the characteristics of the area where the user lives. The learning plan creation unit can use AI to analyze regional characteristic data and generate an optimal learning plan. For example, if the user is traveling, the learning plan creation unit can provide a plan that allows them to learn even while on the move. The learning plan creation unit can dynamically adjust the learning plan according to the user's movement status. For example, if the user is in a different cultural area, the learning plan creation unit can provide a learning plan that is appropriate for that culture. The learning plan creation unit can generate an optimal learning plan based on regional cultural data. This allows for the provision of a more appropriate learning plan by considering geographical background. Some or all of the above-described processes in the learning plan creation unit may be performed using AI, for example, or without AI. For example, the learning plan creation unit can input regional characteristic data into a generating AI and have the generating AI perform analysis to improve the accuracy of the plan.

[0055] The learning plan creation unit can improve the accuracy of the plan by referring to relevant legal documents when creating the learning plan. For example, the learning plan creation unit can refer to legal regulations related to the learning content and provide an appropriate plan. The learning plan creation unit can use AI to analyze legal documents and reflect them in the plan. For example, the learning plan creation unit can refer to guidelines related to the learning content and reflect them in the plan. The learning plan creation unit can generate an optimal learning plan based on legal regulation data. For example, the learning plan creation unit can improve the accuracy of the plan by referring to past case precedents related to the learning content. The learning plan creation unit can reflect past case precedent data in the plan. In this way, the accuracy of the plan can be improved by referring to legal documents. Some or all of the above processes in the learning plan creation unit may be performed using AI, for example, or without using AI. For example, the learning plan creation unit can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of the plan.

[0056] The emotion analysis unit can improve the accuracy of its analysis by referring to the user's past emotional data during emotion analysis. For example, the emotion analysis unit can accurately estimate the user's current emotions based on emotional data the user has felt in the past. The emotion analysis unit can use AI to analyze the user's past emotional data and identify the user's current emotions. For example, the emotion analysis unit can extract emotional patterns in specific situations from the user's past emotional data and reflect them in the analysis. The emotion analysis unit can identify emotional patterns in specific situations based on the user's past emotional data. For example, the emotion analysis unit can analyze the emotional fluctuations the user has experienced in the past and predict the user's current emotions. The emotion analysis unit can use AI to analyze the user's past emotional data and identify emotional fluctuations. This allows for improved accuracy of the analysis by referring to past emotional data. Some or all of the above processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's past emotional data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0057] The sentiment analysis unit can perform sentiment analysis while considering the user's geographical background. For example, the sentiment analysis unit can perform appropriate sentiment analysis by considering the characteristics of the area where the user lives. The sentiment analysis unit can use AI to analyze regional characteristic data and reflect it in the sentiment analysis. For example, if the user is traveling, the sentiment analysis unit will perform sentiment analysis considering the characteristics of that area. The sentiment analysis unit can dynamically adjust the sentiment analysis according to the user's movement status. For example, if the user is in a different cultural area, the sentiment analysis unit will perform sentiment analysis appropriate to that culture. The sentiment analysis unit can perform optimal sentiment analysis based on regional cultural data. This makes it possible to perform more appropriate sentiment analysis by considering geographical background. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input regional characteristic data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0058] The prediction unit can improve the accuracy of its predictions by referring to the user's past negotiation data when predicting negotiation results. For example, the prediction unit can refer to successful patterns in past negotiations conducted by the user and make predictions in similar situations. The prediction unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the prediction unit can make effective predictions for specific negotiating partners based on the user's past negotiation data. The prediction unit can make optimal predictions for specific negotiating partners based on the user's past negotiation data. For example, the prediction unit can analyze the causes of past negotiation failures and adjust the prediction method to prevent the user from repeating the same mistakes. The prediction unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows for improved prediction accuracy by referring to past negotiation data. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's past negotiation data into a generating AI and have the generating AI perform analysis to improve prediction accuracy.

[0059] The prediction unit can make predictions of negotiation outcomes by considering the geographical background of the negotiations. For example, the prediction unit can consider the economic conditions of the region where the negotiations take place and reflect them in its predictions. The prediction unit can use AI to analyze regional economic data and reflect it in its predictions. For example, the prediction unit can make appropriate predictions by considering the cultural background of the place where the negotiations take place. The prediction unit can make optimal predictions based on regional cultural data. For example, the prediction unit can refer to legal regulations of the region where the negotiations take place and reflect them in its predictions. The prediction unit can reflect regional legal data in its predictions. This makes it possible to make more appropriate predictions by considering the geographical background. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input regional economic data and cultural data into a generating AI and have the generating AI perform analysis to improve the accuracy of the predictions.

[0060] The prediction unit can improve the accuracy of its predictions by referring to relevant legal documents when predicting negotiation results. For example, the prediction unit can refer to contracts and agreements related to the negotiations and reflect them in its predictions. The prediction unit can use AI to analyze legal documents and reflect them in its predictions. For example, the prediction unit can refer to legal regulations and guidelines related to the negotiations to make appropriate predictions. The prediction unit can make optimal predictions based on legal regulation data. For example, the prediction unit can refer to past precedents related to the negotiations to improve the accuracy of its predictions. The prediction unit can reflect past precedent data in its predictions. In this way, the accuracy of predictions can be improved by referring to legal documents. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of predictions.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The service provider can adjust the level of detail in the advice based on the importance of the negotiation. For example, in the case of an important negotiation, the service provider will provide detailed advice. The service provider can use AI to analyze the importance of the negotiation and adjust the level of detail in the advice. For example, in the case of a less important negotiation, the service provider will provide concise advice. The service provider can adjust the specificity of the advice according to the importance of the negotiation. For example, the service provider can dynamically adjust the content of the advice based on the importance of the negotiation. This allows for the provision of appropriate advice by adjusting the level of detail according to the importance of the negotiation.

[0063] The analysis unit can perform analysis of negotiation situations while considering the attribute information of the negotiating party. For example, the analysis unit can consider the occupation and position of the negotiating party and perform analysis appropriate to their position. The analysis unit can use AI to analyze the attribute information of the negotiating party and reflect it in the progress of the negotiation. For example, the analysis unit can refer to the negotiating party's past negotiation style and patterns and reflect them in the analysis. Based on the negotiating party's past negotiation data, the analysis unit can propose the optimal countermeasures. For example, the analysis unit can perform appropriate analysis while considering the cultural background and values ​​of the negotiating party. The analysis unit can use AI to analyze the cultural background and values ​​of the negotiating party and reflect it in the progress of the negotiation. This makes it possible to perform more appropriate analysis by considering the attribute information of the negotiating party.

[0064] The simulation unit can improve the accuracy of scenarios by referring to the user's past negotiation history during simulation. For example, the simulation unit can refer to successful patterns in past negotiations conducted by the user and provide scenarios for similar situations. The simulation unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the simulation unit can generate effective scenarios for specific negotiating partners from the user's past negotiation history. The simulation unit can provide optimal scenarios for specific negotiating partners based on the user's past negotiation data. For example, the simulation unit can analyze the causes of past negotiation failures and adjust scenarios to prevent the same mistakes from being repeated. The simulation unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows for improved scenario accuracy by referring to past negotiation history.

[0065] The service provider can apply different advice algorithms depending on the negotiation category when providing advice. For example, in the case of real estate transaction negotiations, the service provider will apply an advice algorithm specialized in price negotiations. The service provider can use AI to analyze the negotiation category and apply the optimal advice algorithm. For example, in the case of business contract negotiations, the service provider will apply an advice algorithm specialized in contract terms. The service provider can dynamically adjust the advice algorithm depending on the negotiation category. For example, in sales negotiations, the service provider will apply an advice algorithm specialized in customer psychology. This allows for the provision of more effective advice by applying an advice algorithm tailored to the negotiation category.

[0066] The prediction unit can make predictions of negotiation outcomes by considering the geographical context of the negotiations. For example, the prediction unit can consider the economic conditions of the region where the negotiations are taking place and reflect this in its predictions. The prediction unit can use AI to analyze regional economic data and reflect this in its predictions. For example, the prediction unit can make appropriate predictions by considering the cultural context of the location where the negotiations are taking place. The prediction unit can make optimal predictions based on regional cultural data. For example, the prediction unit can refer to and reflect legal regulations of the region where the negotiations are taking place. The prediction unit can reflect this based on regional legal data. This makes it possible to make more appropriate predictions by considering the geographical context.

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

[0068] Step 1: The analysis unit analyzes the user's negotiation status in real time. The analysis unit analyzes the user's statements, facial expressions, voice tone, etc., to understand the progress of the negotiation. For example, it uses AI to analyze the content of statements with natural language processing technology, estimate emotions with facial recognition technology, and analyze voice tone and speed with voice analysis technology. Step 2: The service provider provides advice based on the results analyzed by the analysis team. The service provider provides appropriate countermeasures in real time if the user encounters a difficult situation during negotiations. For example, it uses AI to generate advice based on what is said and the progress of the negotiation, and provides optimal price negotiation proposals and negotiation strategies. Step 3: The simulation unit implements a virtual negotiation simulator. The simulation unit generates virtual characters with various personalities and negotiation styles, providing an environment where users can practice diverse negotiation scenarios. For example, AI can be used to generate the statements and actions of virtual characters in real time, allowing users to negotiate prices with these virtual characters. Step 4: The analysis department analyzes the user's negotiation style, strengths, and weaknesses. The analysis department analyzes the user's past negotiation data to identify their negotiation style, strengths, and weaknesses. For example, they use AI to analyze statements and behavioral patterns, classify negotiation styles, identify the strengths and weaknesses of aggressive negotiation styles, and suggest areas for improvement. Step 5: The learning plan creation team creates individual learning plans. The learning plan creation team provides effective training plans based on the user's negotiation style, strengths, and weaknesses. For example, they use AI to monitor the user's learning progress in real time and adjust the learning plan accordingly. If the user has difficulty controlling their emotions, they perform sentiment analysis and provide effective nonverbal communication feedback.

[0069] (Example of form 2) The negotiation support system according to an embodiment of the present invention is an innovative platform that combines AI-powered real-time coaching with advanced negotiation simulation capabilities. This negotiation support system allows users to receive real-time advice from an AI assistant in actual negotiation scenarios and practice various negotiation scenarios in a virtual environment. For example, users can receive real-time advice from an AI assistant via a smartphone or earphones. The AI ​​analyzes the user's negotiation situation in real time and provides the optimal response. For example, if a user is negotiating a real estate transaction, the AI ​​provides real-time advice on the transaction price and conditions. Next, the user can practice various negotiation scenarios using a virtual negotiation simulator. The AI ​​generates virtual characters with various personalities and negotiation styles, allowing users to practice diverse negotiation scenarios in a safe environment. For example, a user can negotiate prices with a virtual character to improve their negotiation skills. Furthermore, the AI ​​analyzes the user's negotiation style, strengths, and weaknesses and creates an individualized learning plan. This allows users to receive effective training to overcome their weaknesses and develop their strengths. For example, if a user has difficulty controlling their emotions, the AI ​​performs emotion analysis and provides feedback on effective nonverbal communication. Furthermore, the AI ​​analyzes historical data and current market trends to predict negotiation outcomes. This allows users to simulate the results of various negotiation strategies and select the optimal one. For example, when a user negotiates a price, the AI ​​makes the best price offer based on historical data. This platform is intended for use by anyone aiming to improve their negotiation skills, including retail staff, business professionals, entrepreneurs, sales representatives, HR managers, real estate agents, and general consumers. Many people feel anxious about negotiating and unprepared, but this platform allows them to receive effective, practical training.This platform leverages cutting-edge generative AI technology to dramatically improve users' negotiation skills, significantly expanding their chances of success in business and personal life. Combining real-time support with practical simulations, it bridges the gap between theory and practice, becoming an innovative platform that provides users with both confidence and competence. As a result, the negotiation support system can dramatically improve users' negotiation skills, greatly expanding their chances of success in business and personal life.

[0070] The negotiation support system according to this embodiment comprises an analysis unit, a provision unit, a simulation unit, an analysis unit, and a learning plan creation unit. The analysis unit analyzes the user's negotiation status in real time. The analysis unit analyzes, for example, the user's statements, facial expressions, and voice tone to grasp the progress of the negotiation. The analysis unit can use AI to analyze the user's statements using natural language processing technology to identify the negotiation theme and progress. The analysis unit can also, for example, capture the user's facial expressions with a camera and estimate emotions using facial recognition technology. The analysis unit can also use voice analysis technology to analyze the user's voice tone and speed and estimate their state of tension or relaxation. The provision unit provides advice based on the results analyzed by the analysis unit. For example, if the user faces a difficult situation during negotiations, the provision unit provides appropriate countermeasures in real time. The provision unit can use AI to generate advice according to the user's statements and the progress of the negotiations. For example, if the user is negotiating a price, the provision unit can provide optimal price proposals and negotiation strategies. The service provider can adjust the tone and content of advice according to the user's emotional state. The simulation unit implements a virtual negotiation simulator. For example, the simulation unit generates virtual characters with various personalities and negotiation styles, providing an environment where users can practice diverse negotiation scenarios. The simulation unit can use AI to generate the virtual characters' statements and actions in real time. For example, the simulation unit allows users to negotiate prices with virtual characters and improve their negotiation skills. The simulation unit analyzes the user's negotiation style, strengths, and weaknesses. The analysis unit, for example, analyzes the user's past negotiation data to identify negotiation styles, strengths, and weaknesses. The analysis unit can use AI to analyze the user's statements and behavioral patterns and classify negotiation styles. For example, if a user has an aggressive negotiation style, the analysis unit can identify its strengths and weaknesses and suggest areas for improvement. The learning plan creation unit creates individual learning plans. For example, the learning plan creation unit provides effective training plans based on the user's negotiation style, strengths, and weaknesses.The learning plan creation unit can use AI to monitor the user's learning progress in real time and adjust the learning plan accordingly. For example, if a user has difficulty controlling their emotions, the learning plan creation unit can perform emotion analysis and provide effective nonverbal communication feedback. This allows the negotiation support system according to the embodiment to analyze the user's negotiation situation in real time, provide optimal advice, and enable practice in a virtual environment.

[0071] The analysis unit analyzes the user's negotiation status in real time. For example, the analysis unit analyzes the user's statements, facial expressions, and voice tone to understand the progress of the negotiation. Specifically, it analyzes the user's statements using natural language processing technology to identify the negotiation theme and progress. Natural language processing technology converts the user's statements into text data and performs keyword extraction and contextual analysis to understand the focus and progress of the negotiation. In addition, the user's facial expressions are captured by a camera, and emotions are estimated using facial recognition technology. Facial recognition technology detects facial feature points and analyzes subtle changes in facial expressions to understand the user's emotional state in real time. Furthermore, voice analysis technology is used to analyze the tone and speed of the user's voice to estimate their state of tension or relaxation. Voice analysis technology analyzes changes in the frequency components and volume of the voice to detect changes in emotion. As a result, the analysis unit can comprehensively analyze the user's statements, facial expressions, and voice tone to understand the progress of the negotiation and the user's emotional state in real time.

[0072] The service provider provides advice based on the results analyzed by the analysis department. For example, if a user faces a difficult situation during negotiations, the service provider will provide appropriate countermeasures in real time. Specifically, it uses AI to generate advice that is tailored to the user's statements and the progress of the negotiation. The AI ​​learns from past negotiation data and success stories to provide the best advice for the user's situation. For example, if a user is negotiating a price, it will provide the best price offer and negotiation strategy. The price offer is calculated based on market data and past negotiation history, presenting terms that are favorable to the user. It also adjusts the tone and content of the advice according to the user's emotional state. For example, if a user is nervous, it will provide advice to help them relax and encourage calm judgment. In this way, the service provider can provide users with appropriate advice in real time, increasing the success rate of negotiations.

[0073] The simulation unit implements a virtual negotiation simulator. For example, the simulation unit generates virtual characters with various personalities and negotiation styles, providing an environment where users can practice diverse negotiation scenarios. Specifically, it uses AI to generate the virtual characters' statements and actions in real time. The virtual characters respond appropriately to the user's statements and actions, providing a realistic negotiation experience. For example, users can negotiate prices with virtual characters to improve their negotiation skills. The simulation unit analyzes the user's negotiation style, strengths, and weaknesses, and provides a personalized training plan. This allows users to practice effectively in preparation for actual negotiations.

[0074] The analytics department analyzes users' past negotiation data to identify their negotiation style, strengths, and weaknesses. Specifically, it uses AI to analyze users' statements and behavioral patterns to classify their negotiation style. The AI ​​learns from past negotiation data and extracts user characteristics. For example, if a user has an aggressive negotiation style, the analytics department identifies its strengths and weaknesses and suggests areas for improvement. Strengths might include the ability to exert pressure on the other party, while weaknesses might include the risk of damaging relationships with the other party. This allows the analytics department to gain a detailed understanding of each user's negotiation style and provide personalized improvement measures.

[0075] The learning plan creation department creates individualized learning plans. Specifically, it provides effective training plans based on the user's negotiation style, strengths, and weaknesses. Using AI, it monitors the user's learning progress in real time and adjusts the learning plan accordingly. For example, if a user has difficulty controlling their emotions, it performs sentiment analysis and provides feedback on effective nonverbal communication. Sentiment analysis analyzes the user's facial expressions and tone of voice to detect changes in their emotions. This allows the user to understand their own emotional state and take appropriate measures. The learning plan creation department adjusts the training content according to the user's progress and provides an optimal learning environment. This enables the user to effectively improve their negotiation skills.

[0076] The emotion analysis unit can provide emotion analysis and feedback on nonverbal communication. For example, the emotion analysis unit can capture the user's facial expressions with a camera and estimate emotions using facial recognition technology. The emotion analysis unit can use AI to analyze the user's facial expression data and identify their emotional state. The emotion analysis unit can also record the user's voice and estimate emotions using voice analysis technology. The emotion analysis unit can analyze the tone and speed of the user's voice and estimate their state of tension or relaxation. The emotion analysis unit provides feedback on nonverbal communication. For example, the emotion analysis unit can analyze the user's gestures and posture and suggest areas for improvement. The emotion analysis unit can use AI to analyze the user's nonverbal communication data and provide effective feedback. For example, the emotion analysis unit can analyze changes in the user's gaze and facial expressions and provide appropriate feedback. This allows for the improvement of negotiation skills by analyzing the user's emotions and providing feedback on nonverbal communication.

[0077] The prediction unit can analyze past data and current market trends to predict negotiation outcomes. For example, the prediction unit collects and analyzes past negotiation history and market data. The prediction unit can use AI to predict negotiation outcomes based on past data. The prediction unit can also collect and analyze current market trends in real time. The prediction unit can use AI to predict negotiation outcomes based on current market data. The prediction unit improves the accuracy of negotiation outcome predictions by combining past data and current market trends. For example, the prediction unit integrates past negotiation history and current market trends to propose the optimal negotiation strategy. The prediction unit can use AI to analyze past data and current market data to predict negotiation outcomes. This allows for improved accuracy in predicting negotiation outcomes by analyzing past data and market trends.

[0078] The analysis unit can estimate the user's emotions and adjust the negotiation situation analysis method based on the estimated user emotions. For example, if the user is nervous, the analysis unit will analyze the negotiation situation in more detail and provide advice to help the user feel at ease. The analysis unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the analysis unit will analyze the negotiation situation concisely and help the user proceed with the negotiation with confidence. The analysis unit can adjust the analysis method according to the user's emotional state. For example, if the user is anxious, the analysis unit will quickly analyze the negotiation situation and provide immediate countermeasures. This allows for more appropriate advice to be provided by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0079] The analysis unit can improve the accuracy of its analysis by referring to the user's past negotiation history when analyzing the negotiation situation. For example, the analysis unit can refer to successful patterns in past negotiations conducted by the user and propose the optimal countermeasures for similar situations. The analysis unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the analysis unit can extract effective strategies for specific negotiating partners from the user's past negotiation history and reflect them in the analysis. The analysis unit can propose the optimal strategy for specific negotiating partners based on the user's past negotiation data. For example, the analysis unit can analyze the causes of past negotiation failures and adjust the analysis method to prevent the same mistakes from being repeated. The analysis unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows the accuracy of the analysis to be improved by referring to past negotiation history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past negotiation data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0080] The analysis unit can perform analysis of negotiation situations while considering the attribute information of the negotiating party. For example, the analysis unit can consider the occupation and position of the negotiating party and perform analysis appropriate to their position. The analysis unit can use AI to analyze the attribute information of the negotiating party and reflect it in the progress of the negotiations. For example, the analysis unit can refer to the negotiating party's past negotiation style and patterns and reflect them in the analysis. The analysis unit can propose the optimal countermeasures based on the negotiating party's past negotiation data. For example, the analysis unit can consider the cultural background and values ​​of the negotiating party and perform appropriate analysis. The analysis unit can use AI to analyze the cultural background and values ​​of the negotiating party and reflect it in the progress of the negotiations. This makes it possible to perform more appropriate analysis by considering the attribute information of the negotiating party. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the attribute information of the negotiating party into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. The analysis unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can adjust the display method according to the user's emotional state. For example, if the user is in a hurry, the analysis unit provides a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can perform its analysis of negotiations while considering the geographical background of the negotiations. For example, the analysis unit can consider the economic conditions of the region where the negotiations are taking place and reflect this in its analysis. The analysis unit can use AI to analyze regional economic data and reflect this in the progress of the negotiations. For example, the analysis unit can consider the cultural background of the place where the negotiations are taking place and perform an appropriate analysis. Based on regional cultural data, the analysis unit can propose the most appropriate countermeasures. For example, the analysis unit can refer to the legal regulations of the region where the negotiations are taking place and reflect this in its analysis. Based on regional legal data, the analysis unit can reflect this in the progress of the negotiations. This makes it possible to perform a more appropriate analysis by considering the geographical background. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input regional economic data and cultural data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant legal documents when analyzing the negotiation situation. For example, the analysis unit can refer to contracts and agreements related to the negotiations and reflect them in its analysis. The analysis unit can use AI to analyze legal documents and reflect the progress of the negotiations. For example, the analysis unit can refer to legal regulations and guidelines related to the negotiations and perform appropriate analysis. Based on legal regulation data, the analysis unit can propose the optimal countermeasures. For example, the analysis unit can improve the accuracy of its analysis by referring to past precedents related to the negotiations. Based on past precedent data, the analysis unit can reflect the progress of the negotiations. In this way, the accuracy of the analysis can be improved by referring to legal documents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0084] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is nervous, the service provider will provide advice in a calm tone. The service provider can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the service provider will provide advice in a friendly tone. The service provider can adjust the way advice is expressed according to the user's emotional state. For example, if the user is anxious, the service provider will provide quick and concise advice. By adjusting the way advice is expressed according to the user's emotions, more effective advice can be provided. 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The service provider can adjust the level of detail of the advice based on the importance of the negotiation when providing advice. For example, in the case of an important negotiation, the service provider will provide detailed advice. The service provider can use AI to analyze the importance of the negotiation and adjust the level of detail of the advice. For example, in the case of a less important negotiation, the service provider will provide concise advice. The service provider can adjust the specificity of the advice according to the importance of the negotiation. For example, the service provider can dynamically adjust the content of the advice based on the importance of the negotiation. This allows for the provision of appropriate advice by adjusting the level of detail of the advice according to the importance of the negotiation. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input negotiation importance data into a generating AI and have the generating AI perform analysis to adjust the level of detail of the advice.

[0086] The service provider can apply different advice algorithms depending on the negotiation category when providing advice. For example, in the case of real estate transaction negotiations, the service provider can apply an advice algorithm specialized in price negotiations. The service provider can use AI to analyze the negotiation category and apply the optimal advice algorithm. For example, in the case of business contract negotiations, the service provider can apply an advice algorithm specialized in contract terms. The service provider can dynamically adjust the advice algorithm depending on the negotiation category. For example, in the case of sales negotiations, the service provider can apply an advice algorithm specialized in customer psychology. This allows for the provision of more effective advice by applying an advice algorithm appropriate to the negotiation category. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input negotiation category data into a generating AI and have the generating AI perform analysis to apply the optimal advice algorithm.

[0087] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is nervous, the service provider will provide short, concise advice. The service provider can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the service provider will provide longer advice that includes detailed explanations. The service provider can adjust the length of the advice according to the user's emotional state. For example, if the user is anxious, the service provider will provide quick and concise advice. By adjusting the length of the advice according to the user's emotions, more effective advice can be provided. 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The service provider can prioritize advice based on the progress of negotiations when providing advice. For example, in the early stages of negotiations, the service provider prioritizes advice on basic strategies. The service provider can use AI to analyze the progress of negotiations and determine the priority of advice. For example, in the middle stages of negotiations, the service provider prioritizes advice on specific proposals and counterarguments. The service provider can dynamically adjust the content of advice according to the progress of negotiations. For example, in the final stages of negotiations, the service provider prioritizes final advice toward reaching an agreement. This ensures that appropriate advice is provided by prioritizing advice according to the progress of negotiations. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input negotiation progress data into a generating AI and have the generating AI perform analysis to determine the priority of advice.

[0089] The advice provider can adjust the order of advice based on the relevance of the negotiation when providing advice. For example, the provider can provide advice on key points of the negotiation first. The provider can use AI to analyze the relevance of the negotiation and adjust the order of advice. For example, the provider can postpone advice on secondary points of the negotiation. The provider can dynamically adjust the order of advice as the negotiation progresses. This allows for the provision of appropriate advice by adjusting the order of advice based on the relevance of the negotiation. Some or all of the above processing in the provider can be performed using AI, for example, or without AI. For example, the provider can input negotiation relevance data into a generating AI and have the generating AI perform analysis to adjust the order of advice.

[0090] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated emotions. For example, if the user is nervous, the simulation unit provides a simple and easy-to-understand scenario. The simulation unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the simulation unit provides a complex and challenging scenario. The simulation unit can dynamically adjust the scenario according to the user's emotional state. For example, if the user is anxious, the simulation unit provides a fast-paced scenario. This allows for more effective practice by adjusting the simulation scenario according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, 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 simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0091] The simulation unit can improve the accuracy of scenarios by referring to the user's past negotiation history during simulation. For example, the simulation unit can refer to successful patterns in past negotiations conducted by the user and provide scenarios for similar situations. The simulation unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the simulation unit can generate effective scenarios for specific negotiating partners from the user's past negotiation history. The simulation unit can provide optimal scenarios for specific negotiating partners based on the user's past negotiation data. For example, the simulation unit can analyze the causes of past negotiation failures and adjust scenarios to prevent the same mistakes from being repeated. The simulation unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows for improved scenario accuracy by referring to past negotiation history. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past negotiation data into a generating AI and have the generating AI perform analysis to improve the accuracy of the scenarios.

[0092] The simulation unit can generate scenarios during simulation by considering the attribute information of the negotiating partner. For example, the simulation unit can consider the occupation and position of the negotiating partner and generate a scenario appropriate to their position. The simulation unit can use AI to analyze the attribute information of the negotiating partner and reflect it in the scenario. For example, the simulation unit can refer to the negotiating partner's past negotiation style and patterns and reflect them in the scenario. The simulation unit can generate the optimal scenario based on the negotiating partner's past negotiation data. For example, the simulation unit can consider the negotiating partner's cultural background and values ​​and generate an appropriate scenario. The simulation unit can use AI to analyze the negotiating partner's cultural background and values ​​and reflect them in the scenario. As a result, a more appropriate scenario is generated by considering the attribute information of the negotiating partner. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the attribute information of the negotiating partner into the generating AI and have the generating AI perform analysis to improve the accuracy of the scenario.

[0093] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, if the user is nervous, the simulation unit provides a simple and highly visible display method. The simulation unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the simulation unit provides a display method that includes detailed information. The simulation unit can dynamically adjust the display method according to the user's emotional state. For example, if the user is in a hurry, the simulation unit provides a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. 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-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The simulation unit can generate scenarios during simulation, taking into account the geographical background of the negotiations. For example, the simulation unit considers the economic conditions of the region where the negotiations take place and reflects this in the scenario. The simulation unit can use AI to analyze regional economic data and reflect this in the scenario. For example, the simulation unit considers the cultural background of the location where the negotiations take place and generates an appropriate scenario. The simulation unit can generate an optimal scenario based on regional cultural data. For example, the simulation unit refers to legal regulations in the region where the negotiations take place and reflects this in the scenario. The simulation unit can reflect regional legal data in the scenario. This allows for the generation of more appropriate scenarios by considering the geographical background. Some or all of the above-described processes in the simulation unit may be performed using AI, or not. For example, the simulation unit can input regional economic and cultural data into a generating AI and have the generating AI perform analysis to improve the accuracy of the scenarios.

[0095] The simulation unit can improve the accuracy of scenarios by referring to relevant legal documents during simulation. For example, the simulation unit can refer to contracts and agreements related to negotiations and reflect them in the scenario. The simulation unit can use AI to analyze legal documents and reflect them in the scenario. For example, the simulation unit can refer to legal regulations and guidelines related to negotiations and generate appropriate scenarios. The simulation unit can generate optimal scenarios based on legal regulation data. For example, the simulation unit can refer to past precedents related to negotiations to improve the accuracy of scenarios. The simulation unit can reflect past precedent data in the scenario. In this way, the accuracy of scenarios can be improved by referring to legal documents. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of the scenarios.

[0096] The analysis unit can estimate the user's emotions and adjust the negotiation style analysis method based on the estimated user emotions. For example, if the user is nervous, the analysis unit will analyze the negotiation style in more detail and provide reassuring feedback to the user. The analysis unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the analysis unit will provide a concise analysis of their negotiation style to help them proceed with the negotiation with confidence. The analysis unit can dynamically adjust the analysis method according to the user's emotional state. For example, if the user is anxious, the analysis unit will quickly analyze their negotiation style and provide immediate countermeasures. This allows for more appropriate feedback to be provided by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0097] The analysis unit can improve the accuracy of its analysis by referring to the user's past negotiation history when analyzing negotiation styles. For example, the analysis unit can refer to successful patterns in past negotiations conducted by the user and suggest the best course of action in similar situations. The analysis unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the analysis unit can extract effective strategies for specific negotiating partners from the user's past negotiation history and reflect them in the analysis. Based on the user's past negotiation data, the analysis unit can suggest the best strategy for specific negotiating partners. For example, the analysis unit can analyze the causes of past negotiation failures and adjust the analysis method to prevent the same mistakes from being repeated. The analysis unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows for improved accuracy of the analysis by referring to past negotiation history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past negotiation data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0098] The analysis department can perform analysis by considering the attribute information of the negotiating partner when analyzing negotiation styles. For example, the analysis department can consider the occupation and position of the negotiating partner and perform an analysis appropriate to their position. The analysis department can use AI to analyze the attribute information of the negotiating partner and reflect it in the analysis. For example, the analysis department can refer to the negotiating partner's past negotiation styles and patterns and reflect them in the analysis. Based on the negotiating partner's past negotiation data, the analysis department can propose the optimal countermeasures. For example, the analysis department can perform an appropriate analysis by considering the cultural background and values ​​of the negotiating partner. The analysis department can use AI to analyze the cultural background and values ​​of the negotiating partner and reflect them in the analysis. This makes it possible to perform a more appropriate analysis by considering the attribute information of the negotiating partner. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the attribute information of the negotiating partner into a generating AI and have the generating AI perform an analysis to improve the accuracy of the analysis.

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. The analysis unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can dynamically adjust the display method according to the user's emotional state. For example, if the user is in a hurry, the analysis unit provides a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. 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 AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0100] The analysis department can consider the geographical context of negotiations when analyzing negotiation styles. For example, the analysis department can consider the economic conditions of the region where negotiations take place and reflect them in the analysis. The analysis department can use AI to analyze regional economic data and reflect it in the analysis. For example, the analysis department can consider the cultural context of the place where negotiations take place and conduct an appropriate analysis. The analysis department can propose optimal countermeasures based on regional cultural data. For example, the analysis department can refer to legal regulations of the region where negotiations take place and reflect them in the analysis. The analysis department can reflect regional legal data in the analysis. This makes it possible to conduct a more appropriate analysis by considering the geographical context. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input regional economic and cultural data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0101] The analysis department can improve the accuracy of its analysis by referring to relevant legal documents when analyzing negotiation styles. For example, the analysis department can refer to contracts and agreements related to negotiations and incorporate them into the analysis. The analysis department can use AI to analyze legal documents and incorporate the results into the analysis. For example, the analysis department can refer to legal regulations and guidelines related to negotiations to conduct an appropriate analysis. Based on legal regulation data, the analysis department can propose the most appropriate countermeasures. For example, the analysis department can improve the accuracy of its analysis by referring to past court precedents related to negotiations. The analysis department can incorporate past court precedent data into the analysis. In this way, the accuracy of the analysis can be improved by referring to legal documents. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0102] The learning plan creation unit can estimate the user's emotions and adjust the content of the learning plan based on the estimated emotions. For example, if the user is nervous, the learning plan creation unit can provide a learning plan with relaxing content. The learning plan creation unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the learning plan creation unit can provide a learning plan with challenging content. The learning plan creation unit can dynamically adjust the content of the learning plan according to the user's emotional state. For example, if the user is anxious, the learning plan creation unit can provide a learning plan with content that allows for rapid learning. By adjusting the content of the learning plan according to the user's emotions, more effective learning becomes possible. 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-described processes in the learning plan creation unit may be performed using AI, for example, or without using AI. For example, the learning plan creation unit can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0103] The learning plan creation unit can improve the accuracy of the plan by referring to the user's past learning history when creating the plan. For example, the learning plan creation unit proposes an optimal learning plan based on what the user has learned in the past. The learning plan creation unit can use AI to analyze the user's past learning data and generate an optimal learning plan. For example, the learning plan creation unit can extract effective learning methods from the user's past learning history and reflect them in the plan. The learning plan creation unit can propose an optimal learning method based on the user's past learning data. For example, the learning plan creation unit can analyze what the user has struggled with in the past and provide a learning plan to overcome it. The learning plan creation unit can use AI to analyze the user's past learning data and identify areas of difficulty. This allows the accuracy of the plan to be improved by referring to the past learning history. Some or all of the above processes in the learning plan creation unit may be performed using AI, for example, or without AI. For example, the learning plan creation unit can input the user's past learning data into a generating AI and have the generating AI perform analysis to improve the accuracy of the plan.

[0104] The learning plan creation unit can customize the plan by taking into account the user's current lifestyle when creating the plan. For example, if the user is busy, the learning plan creation unit can provide a plan that allows for effective learning in a short amount of time. The learning plan creation unit can use AI to analyze the user's lifestyle data and generate an optimal learning plan. For example, if the user has ample time, the learning plan creation unit can provide a detailed and in-depth learning plan. The learning plan creation unit can adjust the schedule of the learning plan to match the user's lifestyle. For example, the learning plan creation unit can propose an optimal learning schedule based on the user's lifestyle data. This allows for the provision of a more appropriate learning plan by taking into account the current lifestyle. Some or all of the above processes in the learning plan creation unit may be performed using AI, for example, or without AI. For example, the learning plan creation unit can input the user's lifestyle data into a generating AI and have the generating AI perform the plan customization.

[0105] The learning plan creation unit can estimate the user's emotions and determine the priority of the learning plan based on the estimated emotions. For example, if the user is nervous, the learning plan creation unit will prioritize content that promotes relaxation. The learning plan creation unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the learning plan creation unit will prioritize challenging content. The learning plan creation unit can dynamically adjust the priority of the learning plan according to the user's emotional state. For example, if the user is anxious, the learning plan creation unit will prioritize content that allows for quick learning. This enables more effective learning by determining the priority of the learning plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the learning plan creation unit may be performed using AI, for example, or without AI. For example, the learning plan creation unit can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0106] The learning plan creation unit can create a learning plan while considering the user's geographical background. For example, the learning plan creation unit can provide an appropriate learning plan by considering the characteristics of the area where the user lives. The learning plan creation unit can use AI to analyze regional characteristic data and generate an optimal learning plan. For example, if the user is traveling, the learning plan creation unit can provide a plan that allows them to learn even while on the move. The learning plan creation unit can dynamically adjust the learning plan according to the user's movement status. For example, if the user is in a different cultural area, the learning plan creation unit can provide a learning plan that is appropriate for that culture. The learning plan creation unit can generate an optimal learning plan based on regional cultural data. This allows for the provision of a more appropriate learning plan by considering geographical background. Some or all of the above-described processes in the learning plan creation unit may be performed using AI, for example, or without AI. For example, the learning plan creation unit can input regional characteristic data into a generating AI and have the generating AI perform analysis to improve the accuracy of the plan.

[0107] The learning plan creation unit can improve the accuracy of the plan by referring to relevant legal documents when creating the learning plan. For example, the learning plan creation unit can refer to legal regulations related to the learning content and provide an appropriate plan. The learning plan creation unit can use AI to analyze legal documents and reflect them in the plan. For example, the learning plan creation unit can refer to guidelines related to the learning content and reflect them in the plan. The learning plan creation unit can generate an optimal learning plan based on legal regulation data. For example, the learning plan creation unit can improve the accuracy of the plan by referring to past case precedents related to the learning content. The learning plan creation unit can reflect past case precedent data in the plan. In this way, the accuracy of the plan can be improved by referring to legal documents. Some or all of the above processes in the learning plan creation unit may be performed using AI, for example, or without using AI. For example, the learning plan creation unit can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of the plan.

[0108] The emotion analysis unit can estimate the user's emotions and adjust nonverbal communication feedback based on the estimated emotions. For example, if the user is tense, the emotion analysis unit provides relaxing nonverbal communication feedback. The emotion analysis unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the emotion analysis unit provides challenging nonverbal communication feedback. The emotion analysis unit can dynamically adjust nonverbal communication feedback according to the user's emotional state. For example, if the user is anxious, the emotion analysis unit provides nonverbal communication feedback that allows for a quick response. This allows for more effective feedback by adjusting nonverbal communication feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0109] The emotion analysis unit can improve the accuracy of its analysis by referring to the user's past emotional data during emotion analysis. For example, the emotion analysis unit can accurately estimate the user's current emotions based on emotional data the user has felt in the past. The emotion analysis unit can use AI to analyze the user's past emotional data and identify the user's current emotions. For example, the emotion analysis unit can extract emotional patterns in specific situations from the user's past emotional data and reflect them in the analysis. The emotion analysis unit can identify emotional patterns in specific situations based on the user's past emotional data. For example, the emotion analysis unit can analyze the emotional fluctuations the user has experienced in the past and predict the user's current emotions. The emotion analysis unit can use AI to analyze the user's past emotional data and identify emotional fluctuations. This allows for improved accuracy of the analysis by referring to past emotional data. Some or all of the above processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's past emotional data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0110] The emotion analysis unit can estimate the user's emotions and adjust the way feedback is displayed based on the estimated emotions. For example, if the user is nervous, the emotion analysis unit provides a simple and highly visible display method. The emotion analysis unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the emotion analysis unit provides a display method that includes detailed information. The emotion analysis unit can dynamically adjust the display method according to the user's emotional state. For example, if the user is in a hurry, the emotion analysis unit provides a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. 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 emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0111] The sentiment analysis unit can perform sentiment analysis while considering the user's geographical background. For example, the sentiment analysis unit can perform appropriate sentiment analysis by considering the characteristics of the area where the user lives. The sentiment analysis unit can use AI to analyze regional characteristic data and reflect it in the sentiment analysis. For example, if the user is traveling, the sentiment analysis unit will perform sentiment analysis considering the characteristics of that area. The sentiment analysis unit can dynamically adjust the sentiment analysis according to the user's movement status. For example, if the user is in a different cultural area, the sentiment analysis unit will perform sentiment analysis appropriate to that culture. The sentiment analysis unit can perform optimal sentiment analysis based on regional cultural data. This makes it possible to perform more appropriate sentiment analysis by considering geographical background. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input regional characteristic data into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0112] The prediction unit can estimate the user's emotions and adjust the prediction method for negotiation results based on the estimated user emotions. For example, if the user is nervous, the prediction unit applies a cautious prediction method. The prediction unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the prediction unit applies an assertive prediction method. The prediction unit can dynamically adjust the prediction method according to the user's emotional state. For example, if the user is anxious, the prediction unit applies a rapid prediction method. By adjusting the prediction method according to the user's emotions, more appropriate predictions become possible. 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0113] The prediction unit can improve the accuracy of its predictions by referring to the user's past negotiation data when predicting negotiation results. For example, the prediction unit can refer to successful patterns in past negotiations conducted by the user and make predictions in similar situations. The prediction unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the prediction unit can make effective predictions for specific negotiating partners based on the user's past negotiation data. The prediction unit can make optimal predictions for specific negotiating partners based on the user's past negotiation data. For example, the prediction unit can analyze the causes of past negotiation failures and adjust the prediction method to prevent the user from repeating the same mistakes. The prediction unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows for improved prediction accuracy by referring to past negotiation data. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's past negotiation data into a generating AI and have the generating AI perform analysis to improve prediction accuracy.

[0114] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. The prediction unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the prediction unit provides a display method that includes detailed information. The prediction unit can dynamically adjust the display method according to the user's emotional state. For example, if the user is in a hurry, the prediction unit provides a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. 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-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0115] The prediction unit can make predictions of negotiation outcomes by considering the geographical background of the negotiations. For example, the prediction unit can consider the economic conditions of the region where the negotiations take place and reflect them in its predictions. The prediction unit can use AI to analyze regional economic data and reflect it in its predictions. For example, the prediction unit can make appropriate predictions by considering the cultural background of the place where the negotiations take place. The prediction unit can make optimal predictions based on regional cultural data. For example, the prediction unit can refer to legal regulations of the region where the negotiations take place and reflect them in its predictions. The prediction unit can reflect regional legal data in its predictions. This makes it possible to make more appropriate predictions by considering the geographical background. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input regional economic data and cultural data into a generating AI and have the generating AI perform analysis to improve the accuracy of the predictions.

[0116] The prediction unit can improve the accuracy of its predictions by referring to relevant legal documents when predicting negotiation results. For example, the prediction unit can refer to contracts and agreements related to the negotiations and reflect them in its predictions. The prediction unit can use AI to analyze legal documents and reflect them in its predictions. For example, the prediction unit can refer to legal regulations and guidelines related to the negotiations to make appropriate predictions. The prediction unit can make optimal predictions based on legal regulation data. For example, the prediction unit can refer to past precedents related to the negotiations to improve the accuracy of its predictions. The prediction unit can reflect past precedent data in its predictions. In this way, the accuracy of predictions can be improved by referring to legal documents. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input legal document data into a generating AI and have the generating AI perform analysis to improve the accuracy of predictions.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The analysis unit can estimate the user's emotions and adjust the negotiation situation analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit will analyze the negotiation situation in more detail and provide advice to help the user feel at ease. The analysis unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the analysis unit will provide a concise analysis of the negotiation situation to help the user proceed with the negotiation with confidence. The analysis unit can adjust the analysis method according to the user's emotional state. For example, if the user is anxious, the analysis unit will quickly analyze the negotiation situation and provide immediate countermeasures. In this way, by adjusting the analysis method according to the user's emotions, more appropriate advice can be provided.

[0119] The service provider can adjust the level of detail in the advice based on the importance of the negotiation. For example, in the case of an important negotiation, the service provider will provide detailed advice. The service provider can use AI to analyze the importance of the negotiation and adjust the level of detail in the advice. For example, in the case of a less important negotiation, the service provider will provide concise advice. The service provider can adjust the specificity of the advice according to the importance of the negotiation. For example, the service provider can dynamically adjust the content of the advice based on the importance of the negotiation. This allows for the provision of appropriate advice by adjusting the level of detail according to the importance of the negotiation.

[0120] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on those emotions. For example, if the user is nervous, the simulation unit will provide a simple and easy-to-understand scenario. The simulation unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the simulation unit will provide a complex and challenging scenario. The simulation unit can dynamically adjust the scenario according to the user's emotional state. For example, if the user is anxious, the simulation unit will provide a fast-paced scenario. This allows for more effective practice by adjusting the simulation scenario according to the user's emotions.

[0121] The analysis unit can perform analysis of negotiation situations while considering the attribute information of the negotiating party. For example, the analysis unit can consider the occupation and position of the negotiating party and perform analysis appropriate to their position. The analysis unit can use AI to analyze the attribute information of the negotiating party and reflect it in the progress of the negotiation. For example, the analysis unit can refer to the negotiating party's past negotiation style and patterns and reflect them in the analysis. Based on the negotiating party's past negotiation data, the analysis unit can propose the optimal countermeasures. For example, the analysis unit can perform appropriate analysis while considering the cultural background and values ​​of the negotiating party. The analysis unit can use AI to analyze the cultural background and values ​​of the negotiating party and reflect it in the progress of the negotiation. This makes it possible to perform more appropriate analysis by considering the attribute information of the negotiating party.

[0122] The service provider can estimate the user's emotions and adjust the way advice is delivered based on those emotions. For example, if the user is nervous, the service provider will deliver advice in a calm tone. The service provider can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the service provider will deliver advice in a friendly tone. The service provider can adjust the way advice is delivered according to the user's emotional state. For example, if the user is anxious, the service provider will deliver quick and concise advice. By adjusting the way advice is delivered according to the user's emotions, more effective advice can be provided.

[0123] The simulation unit can improve the accuracy of scenarios by referring to the user's past negotiation history during simulation. For example, the simulation unit can refer to successful patterns in past negotiations conducted by the user and provide scenarios for similar situations. The simulation unit can use AI to analyze the user's past negotiation data and extract successful patterns. For example, the simulation unit can generate effective scenarios for specific negotiating partners from the user's past negotiation history. The simulation unit can provide optimal scenarios for specific negotiating partners based on the user's past negotiation data. For example, the simulation unit can analyze the causes of past negotiation failures and adjust scenarios to prevent the same mistakes from being repeated. The simulation unit can use AI to analyze the user's past negotiation data and identify the causes of failures. This allows for improved scenario accuracy by referring to past negotiation history.

[0124] The service provider can apply different advice algorithms depending on the negotiation category when providing advice. For example, in the case of real estate transaction negotiations, the service provider will apply an advice algorithm specialized in price negotiations. The service provider can use AI to analyze the negotiation category and apply the optimal advice algorithm. For example, in the case of business contract negotiations, the service provider will apply an advice algorithm specialized in contract terms. The service provider can dynamically adjust the advice algorithm depending on the negotiation category. For example, in sales negotiations, the service provider will apply an advice algorithm specialized in customer psychology. This allows for the provision of more effective advice by applying an advice algorithm tailored to the negotiation category.

[0125] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, if the user is nervous, the simulation unit provides a simple and highly visible display method. The simulation unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the simulation unit provides a display method that includes detailed information. The simulation unit can dynamically adjust the display method according to the user's emotional state. For example, if the user is in a hurry, the simulation unit provides a display method that gets straight to the point. This improves visibility by adjusting the display method according to the user's emotions.

[0126] The learning plan creation unit can estimate the user's emotions and adjust the content of the learning plan based on those emotions. For example, if the user is feeling nervous, the learning plan creation unit can provide a learning plan with relaxing content. The learning plan creation unit can use AI to analyze the user's facial expressions and voice data to identify their emotional state. For example, if the user is relaxed, the learning plan creation unit can provide a learning plan with challenging content. The learning plan creation unit can dynamically adjust the content of the learning plan according to the user's emotional state. For example, if the user is feeling anxious, the learning plan creation unit can provide a learning plan that allows for rapid learning. By adjusting the content of the learning plan according to the user's emotions, more effective learning becomes possible.

[0127] The prediction unit can make predictions of negotiation outcomes by considering the geographical context of the negotiations. For example, the prediction unit can consider the economic conditions of the region where the negotiations are taking place and reflect this in its predictions. The prediction unit can use AI to analyze regional economic data and reflect this in its predictions. For example, the prediction unit can make appropriate predictions by considering the cultural context of the location where the negotiations are taking place. The prediction unit can make optimal predictions based on regional cultural data. For example, the prediction unit can refer to and reflect legal regulations of the region where the negotiations are taking place. The prediction unit can reflect this based on regional legal data. This makes it possible to make more appropriate predictions by considering the geographical context.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The analysis unit analyzes the user's negotiation status in real time. The analysis unit analyzes the user's statements, facial expressions, voice tone, etc., to understand the progress of the negotiation. For example, it uses AI to analyze the content of statements with natural language processing technology, estimate emotions with facial recognition technology, and analyze voice tone and speed with voice analysis technology. Step 2: The service provider provides advice based on the results analyzed by the analysis team. The service provider provides appropriate countermeasures in real time if the user encounters a difficult situation during negotiations. For example, it uses AI to generate advice based on what is said and the progress of the negotiation, and provides optimal price negotiation proposals and negotiation strategies. Step 3: The simulation unit implements a virtual negotiation simulator. The simulation unit generates virtual characters with various personalities and negotiation styles, providing an environment where users can practice diverse negotiation scenarios. For example, AI can be used to generate the statements and actions of virtual characters in real time, allowing users to negotiate prices with these virtual characters. Step 4: The analysis department analyzes the user's negotiation style, strengths, and weaknesses. The analysis department analyzes the user's past negotiation data to identify their negotiation style, strengths, and weaknesses. For example, they use AI to analyze statements and behavioral patterns, classify negotiation styles, identify the strengths and weaknesses of aggressive negotiation styles, and suggest areas for improvement. Step 5: The learning plan creation team creates individual learning plans. The learning plan creation team provides effective training plans based on the user's negotiation style, strengths, and weaknesses. For example, they use AI to monitor the user's learning progress in real time and adjust the learning plan accordingly. If the user has difficulty controlling their emotions, they perform sentiment analysis and provide effective nonverbal communication feedback.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the analysis unit, provision unit, simulation unit, analysis unit, and learning plan creation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit detects the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and these are analyzed by the control unit 46A. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, and generates advice in real time based on the analysis results. The simulation unit provides negotiation scenarios with a virtual character using the display 40A and speaker 40B of the smart device 14. The analysis unit analyzes the user's negotiation data using the specific processing unit 290 of the data processing unit 12, and the learning plan creation unit creates individual learning plans using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the analysis unit, provision unit, simulation unit, analysis unit, and learning plan creation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit detects the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and these are analyzed by the control unit 46A. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, and generates advice in real time based on the analysis results. The simulation unit provides negotiation scenarios with a virtual character using the display and speaker 240 of the smart glasses 214. The analysis unit analyzes the user's negotiation data using the specific processing unit 290 of the data processing unit 12, and the learning plan creation unit creates individual learning plans using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the analysis unit, provision unit, simulation unit, analysis unit, and learning plan creation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit detects the user's facial expressions and voice using the camera 42 and microphone 238 of the headset terminal 314, and these are analyzed by the control unit 46A. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, and generates advice in real time based on the analysis results. The simulation unit provides negotiation scenarios with a virtual character using the display 343 and speaker 240 of the headset terminal 314. The analysis unit analyzes the user's negotiation data using the specific processing unit 290 of the data processing unit 12, and the learning plan creation unit creates individual learning plans using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the analysis unit, provision unit, simulation unit, analysis unit, and learning plan creation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit detects the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and these are analyzed by the control unit 46A. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, and generates advice in real time based on the analysis results. The simulation unit provides negotiation scenarios with a virtual character using the display and speaker 240 of the robot 414. The analysis unit analyzes the user's negotiation data using the specific processing unit 290 of the data processing unit 12, and the learning plan creation unit creates individual learning plans using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) An analysis unit that analyzes the user's negotiation status in real time, A provision unit that provides advice based on the results of the analysis performed by the aforementioned analysis unit, The simulation department realizes a virtual negotiation simulator, The analysis department analyzes users' negotiation styles, strengths, and weaknesses, It comprises a learning plan creation unit that creates individual learning plans. A system characterized by the following features. (Note 2) It has an emotion analysis department that provides emotion analysis and feedback on nonverbal communication. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a forecasting unit that analyzes past data and current market trends to predict negotiation results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We estimate the user's emotions and adjust the negotiation situation analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, When analyzing negotiation status, the system improves the accuracy of the analysis by referencing the user's past negotiation history. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, When analyzing the negotiation situation, the analysis takes into account the attribute information of the negotiating partner. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing the negotiation situation, the geographical context of the negotiations should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing negotiation situations, we improve the accuracy of the analysis by referring to relevant legal documents. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, When providing advice, we adjust the level of detail based on the importance of the negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the negotiation category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing advice, we prioritize the advice based on the progress of the negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on its relevance to the negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation scenario based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned simulation unit, During simulations, the system improves scenario accuracy by referencing the user's past negotiation history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned simulation unit, During the simulation, the scenario is generated while taking into account the attribute information of the negotiating partner. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During the simulation, the geographical context of the negotiations is taken into consideration when generating scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During simulations, we refer to relevant legal documents to improve the accuracy of the scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is We estimate the user's emotions and adjust the negotiation style analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When analyzing negotiation styles, referencing the user's past negotiation history improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is When analyzing negotiation styles, the analysis should take into account the attributes of the negotiating partner. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is When analyzing negotiation styles, the geographical context of the negotiations should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is When analyzing negotiation styles, referencing relevant legal documents improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning plan creation unit, It estimates the user's emotions and adjusts the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning plan creation unit, When creating a learning plan, we improve the accuracy of the plan by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning plan creation unit, When creating a learning plan, customize the plan to take into account the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning plan creation unit, It estimates the user's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning plan creation unit, When creating a learning plan, take the user's geographical background into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning plan creation unit, When creating a study plan, refer to relevant legal documents to improve the accuracy of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts nonverbal communication feedback based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned emotion analysis unit, During sentiment analysis, we improve the accuracy of the analysis by referencing the user's past sentiment data. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned emotion analysis unit, When performing sentiment analysis, the analysis takes into account the user's geographical background. The system described in Appendix 2, characterized by the features described herein. (Note 38) The prediction unit, We estimate the user's emotions and adjust the negotiation outcome prediction method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The prediction unit, When predicting negotiation outcomes, we improve prediction accuracy by referencing the user's past negotiation data. The system described in Appendix 3, characterized by the features described herein. (Note 40) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The prediction unit, When predicting the outcome of negotiations, the geographical context of the negotiations should be taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 42) The prediction unit, When predicting negotiation outcomes, referencing relevant legal documents improves the accuracy of the predictions. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0202] 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. An analysis unit that analyzes the user's negotiation status in real time, A provision unit that provides advice based on the results of the analysis performed by the aforementioned analysis unit, The simulation department realizes a virtual negotiation simulator, The analysis department analyzes users' negotiation styles, strengths, and weaknesses, It comprises a learning plan creation unit that creates individual learning plans. A system characterized by the following features.

2. It has an emotion analysis department that provides emotion analysis and feedback on nonverbal communication. The system according to feature 1.

3. It includes a forecasting unit that analyzes past data and current market trends to predict negotiation results. The system according to feature 1.

4. The aforementioned analysis unit, We estimate the user's emotions and adjust the negotiation situation analysis method based on the estimated user emotions. The system according to feature 1.

5. The aforementioned analysis unit, When analyzing negotiation status, the system improves the accuracy of the analysis by referencing the user's past negotiation history. The system according to feature 1.

6. The aforementioned analysis unit, When analyzing the negotiation situation, the analysis takes into account the attribute information of the negotiating partner. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing the negotiation situation, the geographical context of the negotiations should be taken into consideration. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing negotiation situations, we improve the accuracy of the analysis by referring to relevant legal documents. The system according to feature 1.

10. The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system according to feature 1.

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