System

Abstract

An object of a system according to an embodiment is to provide optimal information and strategy in a negotiation that requires expert knowledge.SOLUTION: A system according to an embodiment includes a data analysis unit, a legal knowledge providing unit, a technical term explanation unit, a contract condition optimization unit, a negotiation simulation unit, and a learning unit. The data analytics provides market insight based on the data analysis. The Legal Knowledge Provider provides knowledge about legal regulations and industry practices. The technical term explanation part explains a technical term. The contract condition optimization unit optimizes contract conditions. The negotiation simulation part simulates interpersonal negotiation. The learning unit learns from past data of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to obtain optimal information and strategies in negotiations that require specialized knowledge.

[0005] The system according to the embodiment aims to provide optimal information and strategies in negotiations that require specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a legal knowledge provision unit, a terminology explanation unit, a contract terms optimization unit, a negotiation simulation unit, and a learning unit. The data analysis unit provides market insights based on data analysis. The legal knowledge provision unit provides knowledge of legal regulations and industry practices. The terminology explanation unit explains terminology. The contract terms optimization unit optimizes contract terms. The negotiation simulation unit simulates interpersonal negotiations. The learning unit learns from the user's past data. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal information and strategies in negotiations that require specialized knowledge. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The negotiation support system according to an embodiment of the present invention uses AI to support negotiations that require specialized knowledge and proposes optimal information and negotiation strategies on behalf of the user. This allows the negotiation support system to efficiently and effectively handle negotiations that require specialized knowledge.

[0029] A negotiation support system according to an embodiment includes a data analysis unit, a legal knowledge provision unit, a terminology explanation unit, a contract terms optimization unit, a negotiation simulation unit, and a learning unit. The data analysis unit provides market insights based on data analysis. For example, the generation AI collects and analyzes market data required by the user for negotiations and provides the latest market trends and competitive situations. The generation AI receives inputs from prompts regarding market information the user desires to know, and the generation AI generates market insights based on the prompts. The legal knowledge provision unit provides knowledge regarding legal regulations and industry practices. For example, the generation AI analyzes legal requirements for a specific contract and standard contract terms in the industry and provides them to the user. The generation AI receives inputs from prompts regarding legal regulations and industry practices the user desires to know, and the generation AI provides knowledge based on the prompts. The terminology explanation unit explains terminology used in negotiations. For example, the generation AI analyzes terminology and industry-specific terminology contained in contracts and provides easy-to-understand explanations to the user. The generation AI receives inputs from prompts regarding terminology the user desires to know, and the generation AI provides explanations based on the prompts. The contract terms optimization unit makes suggestions to optimize the contract terms negotiated by the user. For example, the generation AI analyzes price negotiations, delivery date adjustments, quality assurance conditions, etc., and proposes optimal contract terms. The input to the generation AI is prompts regarding the contract terms the user wants to negotiate, and the generation AI makes optimal proposals based on those prompts. The negotiation simulation unit simulates interpersonal negotiations and proposes effective negotiation strategies to the user. For example, the generation AI predicts the other party's reaction and proposes optimal countermeasures. The input to the generation AI is prompts regarding the negotiation scenario the user wants to simulate, and the generation AI performs a simulation based on those prompts. The learning unit learns from the user's past negotiation data and uses that experience in the next negotiation. For example, the generation AI analyzes successful and unsuccessful strategies in past negotiations and proposes the optimal strategy for the next negotiation. The input to the generation AI is the user's past negotiation data, and the generation AI learns based on that data.As a result, the negotiation support system according to the embodiment allows users to efficiently and effectively handle negotiations that require specialized knowledge. For example, even in complex contract negotiations, optimal contract terms can be derived by utilizing market insights and legal knowledge provided by the generative AI. Furthermore, through simulations of interpersonal negotiations, the system can predict the other party's reactions and develop effective strategies. Furthermore, by learning from the user's past data, the system can improve negotiation skills. This streamlines the process and maximizes results.

[0030] The data analysis unit can integrate different data sources when analyzing market data to provide more comprehensive market insights. For example, when the generation AI analyzes market data, the data analysis unit collects different data sources, such as social media posts, news articles, and industry reports, and analyzes them in an integrated manner. This provides market insights from different perspectives. When the generation AI analyzes market data, it also integrates different data sources and analyzes data correlations. For example, it compares the content of social media posts and news articles to check for matching trends. When the generation AI analyzes market data, it also integrates different data sources and evaluates the reliability of the data. For example, it compares data from industry reports with data from social media posts and prioritizes matching information to reflect in the market insights. This allows the integration of different data sources to provide more comprehensive market insights.

[0031] When analyzing market data, the data analysis unit compares past market data with current market data and can provide insights that simultaneously consider both long-term trends and short-term fluctuations. For example, when the generation AI analyzes market data, the data analysis unit compares past market data with current data to identify long-term trends. For example, it analyzes the growth trend of a specific product category based on data from the past 10 years. When the generation AI analyzes market data, it also compares past market data with current data to identify short-term fluctuations. For example, it analyzes seasonal demand fluctuations based on data from the past year. When the generation AI analyzes market data, it also compares past market data with current data to provide market insights that simultaneously consider both long-term trends and short-term fluctuations. For example, it simultaneously analyzes long-term growth trends and short-term demand peaks. This enables more accurate market analysis by providing market insights that simultaneously consider both long-term trends and short-term fluctuations.

[0032] When analyzing legal regulations, the legal knowledge provision unit can compare legal regulations from different countries and regions and provide legal knowledge from an international perspective. For example, when the generation AI analyzes legal regulations, the legal knowledge provision unit compares legal regulations from different countries and regions and provides legal knowledge from an international perspective. For example, it can clarify the differences between data protection regulations in the United States and Europe. When the generation AI analyzes legal regulations, it can compare legal regulations from different countries and regions and provide legal knowledge from an international perspective. For example, it can analyze the differences between labor laws and regulations in Japan and China. When the generation AI analyzes legal regulations, it can compare legal regulations from different countries and regions and provide legal knowledge from an international perspective. For example, it can clarify the differences between environmental regulations in each country. This makes it possible to provide legal knowledge from an international perspective by comparing legal regulations from different countries and regions.

[0033] When analyzing legal regulations, the legal knowledge provision unit takes into account past legal precedents and the history of regulatory changes, making it possible to predict future legal risks. For example, when the generation AI analyzes legal regulations, the legal knowledge provision unit takes into account past legal precedents and the history of regulatory changes to predict future legal risks. For example, it analyzes future litigation risks based on past precedents. Also, when the generation AI analyzes legal regulations, it takes into account past legal precedents and the history of regulatory changes to predict future legal risks. For example, it analyzes the possibility of future regulatory tightening based on the history of regulatory changes. Also, when the generation AI analyzes legal regulations, it takes into account past legal precedents and the history of regulatory changes to predict future legal risks. For example, it compares past precedents with current regulations to predict future risks. In this way, future legal risks can be predicted by taking into account past legal precedents and the history of regulatory changes.

[0034] When explaining technical terms, the terminology explanation unit compares terminology from different industries and can clarify the differences in terminology between industries. For example, when the generation AI explains technical terms, the terminology explanation unit compares terminology from different industries and can clarify the differences in terminology between industries. For example, it explains the differences in terminology between the IT industry and the medical industry. Also, when the generation AI explains technical terms, it compares terminology from different industries and can clarify the differences in terminology between industries. For example, it explains the differences in terminology between the financial industry and the manufacturing industry. Also, when the generation AI explains technical terms, it compares terminology from different industries and can clarify the differences in terminology between industries. For example, it explains the differences in terminology between the energy industry and the construction industry. In this way, by comparing terminology from different industries, it can clarify the differences in terminology between industries.

[0035] When optimizing contract terms, the contract terms optimization unit can analyze past contract data, extract and propose patterns of successful contract terms. For example, when the generation AI optimizes contract terms, the contract terms optimization unit analyzes past contract data, extracts and propose patterns of successful contract terms. For example, it analyzes common points of successful contracts. Also, when the generation AI optimizes contract terms, it analyzes past contract data, extracts and propose patterns of successful contract terms. For example, it makes optimal proposals based on the conditions of successful contracts. Also, when the generation AI optimizes contract terms, it analyzes past contract data, extracts and propose patterns of successful contract terms. For example, it performs simulations based on the conditions of successful contracts. In this way, it is possible to propose optimal contract terms by analyzing past contract data and extracting patterns of successful contract terms.

[0036] When simulating an interpersonal negotiation, the negotiation simulation unit can analyze past negotiation data and extract and propose patterns of successful negotiation strategies. For example, when the generation AI simulates an interpersonal negotiation, the negotiation simulation unit analyzes past negotiation data and extracts and proposes patterns of successful negotiation strategies. For example, it analyzes common points of successful negotiations. Also, when the generation AI simulates an interpersonal negotiation, it analyzes past negotiation data and extracts and proposes patterns of successful negotiation strategies. For example, it makes an optimal proposal based on the successful negotiation strategies. Also, when the generation AI simulates an interpersonal negotiation, it analyzes past negotiation data and extracts and proposes patterns of successful negotiation strategies. For example, it performs a simulation based on successful negotiation strategies. In this way, it is possible to propose an optimal negotiation strategy by analyzing past negotiation data and extracting patterns of successful negotiation strategies.

[0037] When learning a user's past data, the learning unit can compare patterns of successful and unsuccessful negotiations and extract the optimal strategy. For example, when the generation AI learns a user's past data, the learning unit compares patterns of successful and unsuccessful negotiations and extracts the optimal strategy. For example, it analyzes commonalities between successful negotiations and proposes the optimal strategy. Also, when the generation AI learns a user's past data, it compares patterns of successful and unsuccessful negotiations and extracts the optimal strategy. For example, it analyzes the cause of unsuccessful negotiations and proposes workarounds. Also, when the generation AI learns a user's past data, it compares patterns of successful and unsuccessful negotiations and extracts the optimal strategy. For example, it performs a simulation based on the strategy of a successful negotiation. In this way, it is possible to extract and propose the optimal strategy by comparing patterns of successful and unsuccessful negotiations.

[0038] When learning the user's past data, the learning unit can compare data from different industries and propose the optimal strategy for each industry. For example, when the generation AI learns the user's past data, the learning unit compares data from different industries and proposes the optimal strategy for each industry. For example, it compares negotiation data from the IT industry and the manufacturing industry and proposes the optimal strategy. Also, when the generation AI learns the user's past data, it compares data from different industries and proposes the optimal strategy for each industry. For example, it compares negotiation data from the financial industry and the medical industry and proposes the optimal strategy. Also, when the generation AI learns the user's past data, it compares data from different industries and proposes the optimal strategy for each industry. For example, it compares negotiation data from the energy industry and the construction industry and proposes the optimal strategy. In this way, by comparing data from different industries, it is possible to propose the optimal strategy for each industry.

[0039] When learning the user's past data, the learning unit can compare data from different regions and propose the optimal strategy for each region. For example, when the generation AI learns the user's past data, the learning unit compares data from different regions and proposes the optimal strategy for each region. For example, it compares negotiation data from the United States and Europe and proposes the optimal strategy. Also, when the generation AI learns the user's past data, it compares data from different regions and proposes the optimal strategy for each region. For example, it compares negotiation data from Japan and China and proposes the optimal strategy. Also, when the generation AI learns the user's past data, it compares data from different regions and proposes the optimal strategy for each region. For example, it compares negotiation data from North America and South America and proposes the optimal strategy. In this way, by comparing data from different regions, it is possible to propose the optimal strategy for each region.

[0040] When the learning unit learns the user's past data, it can visualize the data and make suggestions in a visually easy-to-understand format. For example, when the generation AI learns the user's past data, the learning unit visualizes the data and makes suggestions in a visually easy-to-understand format. For example, the flow of negotiations can be shown in a flowchart. Also, when the generation AI learns the user's past data, it visualizes the data and makes suggestions in a visually easy-to-understand format. For example, each step of the negotiation can be shown in a diagram. Also, when the generation AI learns the user's past data, it visualizes the data and makes suggestions in a visually easy-to-understand format. For example, the negotiation scenario can be shown in a mind map. In this way, by visualizing the data, it is possible to make suggestions in a visually easy-to-understand format.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The negotiation support system can further include a cultural background analysis unit. The cultural background analysis unit can analyze the cultural background of the negotiating partner and propose an optimal negotiation strategy based on the culture. For example, when the generating AI analyzes the cultural background of the negotiating partner, it takes into account the cultural characteristics of the negotiating partner's country or region and proposes a negotiation strategy based on the culture. For example, it may prioritize a strategy that emphasizes courtesy for an Asian negotiating partner. When analyzing the cultural background of the negotiating partner, the generating AI may take into account the negotiating partner's religious background and propose a negotiation strategy based on religion. For example, it may avoid certain time periods for a Muslim negotiating partner. When analyzing the cultural background of the negotiating partner, the generating AI may take into account the negotiating partner's business practices and propose a negotiation strategy based on those practices. For example, it may emphasize direct communication for an American negotiating partner. This improves the success rate of negotiations by proposing negotiation strategies that take cultural background into account.

[0043] The negotiation support system can further include a health status monitoring unit. The health status monitoring unit can monitor the health status of the negotiating partner and propose an optimal negotiation strategy based on the health status. For example, when the generating AI monitors the health status of the negotiating partner, it measures the stress level of the negotiating partner and suggests postponing the negotiation if the stress level is high. When the generating AI monitors the health status of the negotiating partner, it measures the fatigue level of the negotiating partner and suggests shortening the negotiation if the fatigue level is high. When the generating AI monitors the health status of the negotiating partner, it measures the heart rate of the negotiating partner and suggests relaxing if the heart rate is high. This improves the success rate of negotiations by proposing a negotiation strategy that takes health status into consideration.

[0044] The negotiation support system can further include an environmental factor analysis unit. The environmental factor analysis unit can analyze the environmental factors in which the negotiation takes place and propose an optimal negotiation strategy based on the environment. For example, the generation AI can analyze the noise level in the place where the negotiation takes place, and if the noise level is high, suggest negotiating in a quiet place. The generation AI can also analyze the lighting conditions in the place where the negotiation takes place, and if the lighting is dim, suggest negotiating in a bright place. The generation AI can also analyze the temperature in the place where the negotiation takes place, and if the temperature is high, suggest negotiating in a cool place. This improves the success rate of negotiations by proposing a negotiation strategy that takes environmental factors into account.

[0045] The negotiation support system can further include a time management unit. The time management unit can manage negotiation time and propose optimal timing for negotiation. For example, when the generation AI manages negotiation time, it takes into account the schedule of the other party and proposes the optimal time slot. When the generation AI manages negotiation time, it also monitors the progress of the negotiation and proposes breaks at appropriate times. When the generation AI manages negotiation time, it also predicts the end time of the negotiation and proposes efficient time allocation. This improves the success rate of negotiations by proposing negotiation strategies that take time management into consideration.

[0046] The negotiation support system can further include a risk assessment unit. The risk assessment unit can assess the risks in negotiations and propose an optimal negotiation strategy based on the risks. For example, when the generation AI evaluates the risks of negotiations, it evaluates the credit risk of the negotiating party and suggests careful negotiations if the credit risk is high. When the generation AI evaluates the risks of negotiations, it also evaluates the legal risks of negotiations and suggests legal advice if the legal risk is high. When the generation AI evaluates the risks of negotiations, it also evaluates the market risks of negotiations and suggests closely monitoring market trends if the market risk is high. This improves the success rate of negotiations by proposing a negotiation strategy that takes risk assessment into account.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The data analysis unit provides market insights based on data analysis. For example, the generation AI collects and analyzes market data required by the user for negotiations, and provides the latest market trends and competitive situations. The input to the generation AI is a prompt regarding the market information the user wants to know, and the generation AI generates market insights based on the prompt. Step 2: The legal knowledge provider provides knowledge about legal regulations and industry practices. For example, the generation AI analyzes the legal requirements for a specific contract and standard industry contract terms and conditions, and provides them to the user. The input to the generation AI is prompts about the legal regulations and industry practices that the user wants to know, and the generation AI provides knowledge based on those prompts. Step 3: The terminology explanation section explains the technical terms used in negotiations. For example, the generation AI analyzes technical terms and industry-specific terms contained in contracts and explains them in an easy-to-understand manner to the user. The input to the generation AI is a prompt regarding the technical terms the user wants to know, and the generation AI provides an explanation based on that prompt. Step 4: The contract terms optimization unit makes proposals to optimize the contract terms negotiated by the user. For example, the generation AI analyzes price negotiations, delivery date adjustments, quality assurance conditions, etc., and proposes optimal contract terms. The input to the generation AI is a prompt regarding the contract terms the user wants to negotiate, and the generation AI makes optimal proposals based on that prompt. Step 5: The negotiation simulation unit simulates interpersonal negotiations and proposes effective negotiation strategies to the user. For example, the generation AI predicts the other party's reactions and proposes optimal countermeasures. The input to the generation AI is a prompt regarding the negotiation scenario the user wants to simulate, and the generation AI performs the simulation based on that prompt. Step 6: The learning unit learns from the user's past negotiation data and uses that experience in the next negotiation. For example, the generation AI analyzes successful and unsuccessful strategies in past negotiations and proposes the optimal strategy for the next negotiation. The input to the generation AI is the user's past negotiation data, and the generation AI learns based on that data.

[0049] (Example 2) The negotiation support system according to an embodiment of the present invention uses AI to support negotiations that require specialized knowledge and proposes optimal information and negotiation strategies on behalf of the user. This allows the negotiation support system to efficiently and effectively handle negotiations that require specialized knowledge.

[0050] A negotiation support system according to an embodiment includes a data analysis unit, a legal knowledge provision unit, a terminology explanation unit, a contract terms optimization unit, a negotiation simulation unit, and a learning unit. The data analysis unit provides market insights based on data analysis. For example, the generation AI collects and analyzes market data required by the user for negotiations and provides the latest market trends and competitive situations. The generation AI receives inputs from prompts regarding market information the user desires to know, and the generation AI generates market insights based on the prompts. The legal knowledge provision unit provides knowledge regarding legal regulations and industry practices. For example, the generation AI analyzes legal requirements for a specific contract and standard contract terms in the industry and provides them to the user. The generation AI receives inputs from prompts regarding legal regulations and industry practices the user desires to know, and the generation AI provides knowledge based on the prompts. The terminology explanation unit explains terminology used in negotiations. For example, the generation AI analyzes terminology and industry-specific terminology contained in contracts and provides easy-to-understand explanations to the user. The generation AI receives inputs from prompts regarding terminology the user desires to know, and the generation AI provides explanations based on the prompts. The contract terms optimization unit makes suggestions to optimize the contract terms negotiated by the user. For example, the generation AI analyzes price negotiations, delivery date adjustments, quality assurance conditions, etc., and proposes optimal contract terms. The input to the generation AI is prompts regarding the contract terms the user wants to negotiate, and the generation AI makes optimal proposals based on those prompts. The negotiation simulation unit simulates interpersonal negotiations and proposes effective negotiation strategies to the user. For example, the generation AI predicts the other party's reaction and proposes optimal countermeasures. The input to the generation AI is prompts regarding the negotiation scenario the user wants to simulate, and the generation AI performs a simulation based on those prompts. The learning unit learns from the user's past negotiation data and uses that experience in the next negotiation. For example, the generation AI analyzes successful and unsuccessful strategies in past negotiations and proposes the optimal strategy for the next negotiation. The input to the generation AI is the user's past negotiation data, and the generation AI learns based on that data.As a result, the negotiation support system according to the embodiment allows users to efficiently and effectively handle negotiations that require specialized knowledge. For example, even in complex contract negotiations, optimal contract terms can be derived by utilizing market insights and legal knowledge provided by the generative AI. Furthermore, through simulations of interpersonal negotiations, the system can predict the other party's reactions and develop effective strategies. Furthermore, by learning from the user's past data, the system can improve negotiation skills. This streamlines the process and maximizes results.

[0051] When analyzing market data, the data analysis unit uses the emotion estimation function to analyze the emotions of market participants and provide market insights that take into account emotional fluctuations. For example, when the generation AI analyzes market data, the data analysis unit collects social media posts and comments on news articles and performs emotion analysis. This allows the data analysis unit to grasp the emotional fluctuations of market participants and provide market insights that take into account the impact of emotional fluctuations on the market. Furthermore, when the generation AI analyzes market data, the emotion estimation function is used to monitor the emotions of market participants in real time and provide market insights that correspond to the emotional fluctuations. For example, it analyzes the tendency for the market to become active during periods of increased positive emotion. Furthermore, when the generation AI analyzes market data, the emotion estimation function is used to analyze the emotions of market participants and provide market insights that take into account the impact of emotional fluctuations on specific market segments. For example, it analyzes emotional fluctuations toward a specific product category. This allows the data analysis unit to provide market insights that take into account the emotional fluctuations of market participants, enabling more accurate market analysis.

[0052] The data analysis unit can integrate different data sources when analyzing market data to provide more comprehensive market insights. For example, when the generation AI analyzes market data, the data analysis unit collects different data sources, such as social media posts, news articles, and industry reports, and analyzes them in an integrated manner. This provides market insights from different perspectives. When the generation AI analyzes market data, it also integrates different data sources and analyzes data correlations. For example, it compares the content of social media posts and news articles to check for matching trends. When the generation AI analyzes market data, it also integrates different data sources and evaluates the reliability of the data. For example, it compares data from industry reports with data from social media posts and prioritizes matching information to reflect in the market insights. This allows the integration of different data sources to provide more comprehensive market insights.

[0053] When analyzing market data, the data analysis unit compares past market data with current market data and can provide insights that simultaneously consider both long-term trends and short-term fluctuations. For example, when the generation AI analyzes market data, the data analysis unit compares past market data with current data to identify long-term trends. For example, it analyzes the growth trend of a specific product category based on data from the past 10 years. When the generation AI analyzes market data, it also compares past market data with current data to identify short-term fluctuations. For example, it analyzes seasonal demand fluctuations based on data from the past year. When the generation AI analyzes market data, it also compares past market data with current data to provide market insights that simultaneously consider both long-term trends and short-term fluctuations. For example, it simultaneously analyzes long-term growth trends and short-term demand peaks. This enables more accurate market analysis by providing market insights that simultaneously consider both long-term trends and short-term fluctuations.

[0054] When analyzing legal regulations, the legal knowledge provision unit can use the emotion estimation function to analyze the emotional impact of the legal document and provide it to the user in a format that is emotionally easy to understand. For example, when the generation AI analyzes legal regulations, the legal knowledge provision unit uses the emotion estimation function to analyze the emotional impact of the legal document and provide it to the user in a format that is emotionally easy to understand. For example, it can provide a concise explanation of difficult parts of the legal document. Also, when the generation AI analyzes legal regulations, it can use the emotion estimation function to analyze the emotional impact of the legal document and provide it to the user in a format that is emotionally easy to understand. For example, it can emphasize the positive aspects of the legal document. Also, when the generation AI analyzes legal regulations, it can use the emotion estimation function to analyze the emotional impact of the legal document and provide it to the user in a format that is emotionally easy to understand. For example, it can alleviate the negative aspects of the legal document. In this way, by analyzing the emotional impact of the legal document and providing it to the user in a format that is emotionally easy to understand, a deeper understanding of legal knowledge is achieved.

[0055] When analyzing legal regulations, the legal knowledge provision unit can compare legal regulations from different countries and regions and provide legal knowledge from an international perspective. For example, when the generation AI analyzes legal regulations, the legal knowledge provision unit compares legal regulations from different countries and regions and provides legal knowledge from an international perspective. For example, it can clarify the differences between data protection regulations in the United States and Europe. When the generation AI analyzes legal regulations, it can compare legal regulations from different countries and regions and provide legal knowledge from an international perspective. For example, it can analyze the differences between labor laws and regulations in Japan and China. When the generation AI analyzes legal regulations, it can compare legal regulations from different countries and regions and provide legal knowledge from an international perspective. For example, it can clarify the differences between environmental regulations in each country. This makes it possible to provide legal knowledge from an international perspective by comparing legal regulations from different countries and regions.

[0056] When analyzing legal regulations, the legal knowledge provision unit takes into account past legal precedents and the history of regulatory changes, making it possible to predict future legal risks. For example, when the generation AI analyzes legal regulations, the legal knowledge provision unit takes into account past legal precedents and the history of regulatory changes to predict future legal risks. For example, it analyzes future litigation risks based on past precedents. Also, when the generation AI analyzes legal regulations, it takes into account past legal precedents and the history of regulatory changes to predict future legal risks. For example, it analyzes the possibility of future regulatory tightening based on the history of regulatory changes. Also, when the generation AI analyzes legal regulations, it takes into account past legal precedents and the history of regulatory changes to predict future legal risks. For example, it compares past precedents with current regulations to predict future risks. In this way, future legal risks can be predicted by taking into account past legal precedents and the history of regulatory changes.

[0057] When explaining technical terms, the technical terminology explanation unit can use the emotion estimation function to evaluate the user's level of understanding in real time and provide an explanation based on the level of understanding. For example, when the generation AI explains technical terms, the technical terminology explanation unit uses the emotion estimation function to evaluate the user's level of understanding in real time and provide an explanation based on the level of understanding. For example, if the level of understanding is low, a more detailed explanation is provided. Also, when the generation AI explains technical terms, the emotion estimation function can be used to evaluate the user's level of understanding in real time and provide an explanation based on the level of understanding. For example, if the level of understanding is high, a concise explanation is provided. Also, when the generation AI explains technical terms, the emotion estimation function can be used to evaluate the user's level of understanding in real time and provide an explanation based on the level of understanding. For example, if the level of understanding is low, an explanation is provided using diagrams and examples. In this way, explanations based on the user's level of understanding are provided, thereby deepening their understanding of the technical terms.

[0058] When explaining technical terms, the terminology explanation unit compares terminology from different industries and can clarify the differences in terminology between industries. For example, when the generation AI explains technical terms, the terminology explanation unit compares terminology from different industries and can clarify the differences in terminology between industries. For example, it explains the differences in terminology between the IT industry and the medical industry. Also, when the generation AI explains technical terms, it compares terminology from different industries and can clarify the differences in terminology between industries. For example, it explains the differences in terminology between the financial industry and the manufacturing industry. Also, when the generation AI explains technical terms, it compares terminology from different industries and can clarify the differences in terminology between industries. For example, it explains the differences in terminology between the energy industry and the construction industry. In this way, by comparing terminology from different industries, it can clarify the differences in terminology between industries.

[0059] When optimizing contract terms, the contract terms optimization unit can use the emotion estimation function to analyze the emotions of the negotiating party and propose optimal contract terms based on their emotions. For example, when the generation AI optimizes contract terms, the contract terms optimization unit uses the emotion estimation function to analyze the emotions of the negotiating party and propose optimal contract terms based on their emotions. For example, it prioritizes terms that the negotiating party has positive emotions about. Also, when the generation AI optimizes contract terms, it uses the emotion estimation function to analyze the emotions of the negotiating party and propose optimal contract terms based on their emotions. For example, it avoids terms that the negotiating party has negative emotions about. Also, when the generation AI optimizes contract terms, it uses the emotion estimation function to analyze the emotions of the negotiating party and propose optimal contract terms based on their emotions. For example, it monitors the emotional fluctuations of the negotiating party in real time. This improves the success rate of negotiations by analyzing the emotions of the negotiating party and proposing optimal contract terms based on their emotions.

[0060] When optimizing contract terms, the contract terms optimization unit can analyze past contract data, extract and propose patterns of successful contract terms. For example, when the generation AI optimizes contract terms, the contract terms optimization unit analyzes past contract data, extracts and propose patterns of successful contract terms. For example, it analyzes common points of successful contracts. Also, when the generation AI optimizes contract terms, it analyzes past contract data, extracts and propose patterns of successful contract terms. For example, it makes optimal proposals based on the conditions of successful contracts. Also, when the generation AI optimizes contract terms, it analyzes past contract data, extracts and propose patterns of successful contract terms. For example, it performs simulations based on the conditions of successful contracts. In this way, it is possible to propose optimal contract terms by analyzing past contract data and extracting patterns of successful contract terms.

[0061] When simulating an interpersonal negotiation, the negotiation simulation unit can use the emotion estimation function to predict the emotions of the negotiating partner and propose an optimal negotiation strategy based on the emotions. For example, when the generation AI simulates an interpersonal negotiation, the negotiation simulation unit uses the emotion estimation function to predict the emotions of the negotiating partner and propose an optimal negotiation strategy based on the emotions. For example, prioritizing a strategy in which the negotiating partner has positive emotions. Also, when the generation AI simulates an interpersonal negotiation, it uses the emotion estimation function to predict the emotions of the negotiating partner and propose an optimal negotiation strategy based on the emotions. For example, avoiding a strategy in which the negotiating partner has negative emotions. Also, when the generation AI simulates an interpersonal negotiation, it uses the emotion estimation function to predict the emotions of the negotiating partner and propose an optimal negotiation strategy based on the emotions. For example, monitoring the emotional fluctuations of the negotiating partner in real time. As a result, by predicting the emotions of the negotiating partner and proposing an optimal negotiation strategy based on the emotions, the success rate of the negotiation is improved.

[0062] When simulating an interpersonal negotiation, the negotiation simulation unit can analyze past negotiation data and extract and propose patterns of successful negotiation strategies. For example, when the generation AI simulates an interpersonal negotiation, the negotiation simulation unit analyzes past negotiation data and extracts and proposes patterns of successful negotiation strategies. For example, it analyzes common points of successful negotiations. Also, when the generation AI simulates an interpersonal negotiation, it analyzes past negotiation data and extracts and proposes patterns of successful negotiation strategies. For example, it makes an optimal proposal based on the successful negotiation strategies. Also, when the generation AI simulates an interpersonal negotiation, it analyzes past negotiation data and extracts and proposes patterns of successful negotiation strategies. For example, it performs a simulation based on successful negotiation strategies. In this way, it is possible to propose an optimal negotiation strategy by analyzing past negotiation data and extracting patterns of successful negotiation strategies.

[0063] When learning the user's past data, the learning unit can use the emotion estimation function to analyze emotional fluctuations in past negotiations and propose an optimal strategy based on emotions. For example, when the generation AI learns the user's past data, the learning unit uses the emotion estimation function to analyze emotional fluctuations in past negotiations and propose an optimal strategy based on emotions. For example, prioritizing a strategy in which positive emotions were strong in past negotiations. Also, when the generation AI learns the user's past data, the emotion estimation function can be used to analyze emotional fluctuations in past negotiations and propose an optimal strategy based on emotions. For example, avoiding a strategy in which negative emotions were strong in past negotiations. Also, when the generation AI learns the user's past data, the emotion estimation function can be used to analyze emotional fluctuations in past negotiations and propose an optimal strategy based on emotions. For example, analyzing strategies that had large emotional fluctuations in past negotiations. In this way, by analyzing emotional fluctuations in past negotiations and proposing an optimal strategy based on emotions, the success rate of negotiations is improved.

[0064] When learning a user's past data, the learning unit can compare patterns of successful and unsuccessful negotiations and extract the optimal strategy. For example, when the generation AI learns a user's past data, the learning unit compares patterns of successful and unsuccessful negotiations and extracts the optimal strategy. For example, it analyzes commonalities between successful negotiations and proposes the optimal strategy. Also, when the generation AI learns a user's past data, it compares patterns of successful and unsuccessful negotiations and extracts the optimal strategy. For example, it analyzes the cause of unsuccessful negotiations and proposes workarounds. Also, when the generation AI learns a user's past data, it compares patterns of successful and unsuccessful negotiations and extracts the optimal strategy. For example, it performs a simulation based on the strategy of a successful negotiation. In this way, it is possible to extract and propose the optimal strategy by comparing patterns of successful and unsuccessful negotiations.

[0065] When learning the user's past data, the learning unit can compare data from different industries and propose the optimal strategy for each industry. For example, when the generation AI learns the user's past data, the learning unit compares data from different industries and proposes the optimal strategy for each industry. For example, it compares negotiation data from the IT industry and the manufacturing industry and proposes the optimal strategy. Also, when the generation AI learns the user's past data, it compares data from different industries and proposes the optimal strategy for each industry. For example, it compares negotiation data from the financial industry and the medical industry and proposes the optimal strategy. Also, when the generation AI learns the user's past data, it compares data from different industries and proposes the optimal strategy for each industry. For example, it compares negotiation data from the energy industry and the construction industry and proposes the optimal strategy. In this way, by comparing data from different industries, it is possible to propose the optimal strategy for each industry.

[0066] When learning the user's past data, the learning unit can compare data from different regions and propose the optimal strategy for each region. For example, when the generation AI learns the user's past data, the learning unit compares data from different regions and proposes the optimal strategy for each region. For example, it compares negotiation data from the United States and Europe and proposes the optimal strategy. Also, when the generation AI learns the user's past data, it compares data from different regions and proposes the optimal strategy for each region. For example, it compares negotiation data from Japan and China and proposes the optimal strategy. Also, when the generation AI learns the user's past data, it compares data from different regions and proposes the optimal strategy for each region. For example, it compares negotiation data from North America and South America and proposes the optimal strategy. In this way, by comparing data from different regions, it is possible to propose the optimal strategy for each region.

[0067] When the learning unit learns the user's past data, it can visualize the data and make suggestions in a visually easy-to-understand format. For example, when the generation AI learns the user's past data, the learning unit visualizes the data and makes suggestions in a visually easy-to-understand format. For example, the flow of negotiations can be shown in a flowchart. Also, when the generation AI learns the user's past data, it visualizes the data and makes suggestions in a visually easy-to-understand format. For example, each step of the negotiation can be shown in a diagram. Also, when the generation AI learns the user's past data, it visualizes the data and makes suggestions in a visually easy-to-understand format. For example, the negotiation scenario can be shown in a mind map. In this way, by visualizing the data, it is possible to make suggestions in a visually easy-to-understand format.

[0068] When learning the user's past data, the learning unit can use the emotion estimation function to monitor emotional fluctuations in past negotiations in real time and propose an optimal strategy based on emotions. For example, when the generation AI learns the user's past data, the learning unit uses the emotion estimation function to monitor emotional fluctuations in past negotiations in real time and propose an optimal strategy based on emotions. For example, it prioritizes strategies that were characterized by strong positive emotions in past negotiations. Also, when the generation AI learns the user's past data, it uses the emotion estimation function to monitor emotional fluctuations in past negotiations in real time and propose an optimal strategy based on emotions. For example, it avoids strategies that were characterized by strong negative emotions in past negotiations. Also, when the generation AI learns the user's past data, it uses the emotion estimation function to monitor emotional fluctuations in past negotiations in real time and propose an optimal strategy based on emotions. For example, it analyzes strategies that were characterized by large emotional fluctuations in past negotiations. In this way, by monitoring emotional fluctuations in past negotiations in real time and proposing an optimal strategy based on emotions, the success rate of negotiations is improved.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The negotiation support system can further include a cultural background analysis unit. The cultural background analysis unit can analyze the cultural background of the negotiating partner and propose an optimal negotiation strategy based on the culture. For example, when the generating AI analyzes the cultural background of the negotiating partner, it takes into account the cultural characteristics of the negotiating partner's country or region and proposes a negotiation strategy based on the culture. For example, it may prioritize a strategy that emphasizes courtesy for an Asian negotiating partner. When analyzing the cultural background of the negotiating partner, the generating AI may take into account the negotiating partner's religious background and propose a negotiation strategy based on religion. For example, it may avoid certain time periods for a Muslim negotiating partner. When analyzing the cultural background of the negotiating partner, the generating AI may take into account the negotiating partner's business practices and propose a negotiation strategy based on those practices. For example, it may emphasize direct communication for an American negotiating partner. This improves the success rate of negotiations by proposing negotiation strategies that take cultural background into account.

[0071] The negotiation support system can further include a health status monitoring unit. The health status monitoring unit can monitor the health status of the negotiating partner and propose an optimal negotiation strategy based on the health status. For example, when the generating AI monitors the health status of the negotiating partner, it measures the stress level of the negotiating partner and suggests postponing the negotiation if the stress level is high. When the generating AI monitors the health status of the negotiating partner, it measures the fatigue level of the negotiating partner and suggests shortening the negotiation if the fatigue level is high. When the generating AI monitors the health status of the negotiating partner, it measures the heart rate of the negotiating partner and suggests relaxing if the heart rate is high. This improves the success rate of negotiations by proposing a negotiation strategy that takes health status into consideration.

[0072] The negotiation support system can further include an environmental factor analysis unit. The environmental factor analysis unit can analyze the environmental factors in which the negotiation takes place and propose an optimal negotiation strategy based on the environment. For example, the generation AI can analyze the noise level in the place where the negotiation takes place, and if the noise level is high, suggest negotiating in a quiet place. The generation AI can also analyze the lighting conditions in the place where the negotiation takes place, and if the lighting is dim, suggest negotiating in a bright place. The generation AI can also analyze the temperature in the place where the negotiation takes place, and if the temperature is high, suggest negotiating in a cool place. This improves the success rate of negotiations by proposing a negotiation strategy that takes environmental factors into account.

[0073] The negotiation support system can further include a time management unit. The time management unit can manage negotiation time and propose optimal timing for negotiation. For example, when the generation AI manages negotiation time, it takes into account the schedule of the other party and proposes the optimal time slot. When the generation AI manages negotiation time, it also monitors the progress of the negotiation and proposes breaks at appropriate times. When the generation AI manages negotiation time, it also predicts the end time of the negotiation and proposes efficient time allocation. This improves the success rate of negotiations by proposing negotiation strategies that take time management into consideration.

[0074] The negotiation support system can further include a risk assessment unit. The risk assessment unit can assess the risks in negotiations and propose an optimal negotiation strategy based on the risks. For example, when the generation AI evaluates the risks of negotiations, it evaluates the credit risk of the negotiating party and suggests careful negotiations if the credit risk is high. When the generation AI evaluates the risks of negotiations, it also evaluates the legal risks of negotiations and suggests legal advice if the legal risk is high. When the generation AI evaluates the risks of negotiations, it also evaluates the market risks of negotiations and suggests closely monitoring market trends if the market risk is high. This improves the success rate of negotiations by proposing a negotiation strategy that takes risk assessment into account.

[0075] The data analysis unit can further use the emotion estimation function to monitor the emotional fluctuations of the negotiating partner in real time and provide market insights based on those emotional fluctuations. For example, the generation AI monitors the negotiating partner's emotions in real time and analyzes the tendency for the market to become active during periods of rising positive emotions. The generation AI also monitors the negotiating partner's emotions in real time and analyzes the tendency for the market to become sluggish during periods of rising negative emotions. The generation AI also monitors the negotiating partner's emotions in real time and provides market insights that take into account the impact of emotional fluctuations on specific market segments. This allows for more accurate market analysis by providing market insights that take into account emotional fluctuations.

[0076] The legal knowledge provision unit can further use the emotion estimation function to monitor the emotional impact of legal documents in real time and provide legal knowledge according to emotional fluctuations. For example, the generation AI can monitor the emotional impact of legal documents in real time and emphasize parts that increase positive emotions. The generation AI can also monitor the emotional impact of legal documents in real time and mitigate parts that increase negative emotions. The generation AI can also monitor the emotional impact of legal documents in real time and provide legal knowledge according to emotional fluctuations. In this way, by monitoring the emotional impact of legal documents in real time, legal knowledge can be provided to users in a form that is emotionally easy to understand.

[0077] The terminology explanation unit can further use the emotion estimation function to monitor the user's emotional fluctuations in real time and provide explanations according to the emotional fluctuations. For example, the generation AI monitors the user's emotions in real time and provides a concise explanation when positive emotions increase. The generation AI also monitors the user's emotions in real time and provides a detailed explanation when negative emotions increase. The generation AI also monitors the user's emotions in real time and provides explanations according to the emotional fluctuations. In this way, by monitoring the user's emotional fluctuations in real time, it is possible to provide explanations according to the emotions.

[0078] The contract terms optimization unit can further use the emotion estimation function to monitor the emotional fluctuations of the negotiating partner in real time and propose optimal contract terms in accordance with the emotional fluctuations. For example, the generation AI monitors the negotiating partner's emotions in real time and prioritizes conditions that increase positive emotions. The generation AI also monitors the negotiating partner's emotions in real time and avoids conditions that increase negative emotions. The generation AI also monitors the negotiating partner's emotions in real time and proposes optimal contract terms in accordance with emotional fluctuations. In this way, by monitoring the negotiating partner's emotional fluctuations in real time, it is possible to propose optimal contract terms based on emotions.

[0079] The negotiation simulation unit can further use the emotion estimation function to monitor the emotional fluctuations of the negotiating partner in real time and propose the optimal negotiation strategy in accordance with the emotional fluctuations. For example, the generation AI monitors the negotiating partner's emotions in real time and prioritizes strategies that increase positive emotions. The generation AI also monitors the negotiating partner's emotions in real time and avoids strategies that increase negative emotions. The generation AI also monitors the negotiating partner's emotions in real time and proposes the optimal negotiation strategy in accordance with the emotional fluctuations. In this way, by monitoring the negotiating partner's emotional fluctuations in real time, it is possible to propose the optimal negotiation strategy based on emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The data analysis unit provides market insights based on data analysis. For example, the generation AI collects and analyzes market data required by the user for negotiations, and provides the latest market trends and competitive situations. The input to the generation AI is a prompt regarding the market information the user wants to know, and the generation AI generates market insights based on the prompt. Step 2: The legal knowledge provider provides knowledge about legal regulations and industry practices. For example, the generation AI analyzes the legal requirements for a specific contract and standard industry contract terms and conditions, and provides them to the user. The input to the generation AI is prompts about the legal regulations and industry practices that the user wants to know, and the generation AI provides knowledge based on those prompts. Step 3: The terminology explanation section explains the technical terms used in negotiations. For example, the generation AI analyzes technical terms and industry-specific terms contained in contracts and explains them in an easy-to-understand manner to the user. The input to the generation AI is a prompt regarding the technical terms the user wants to know, and the generation AI provides an explanation based on that prompt. Step 4: The contract terms optimization unit makes proposals to optimize the contract terms negotiated by the user. For example, the generation AI analyzes price negotiations, delivery date adjustments, quality assurance conditions, etc., and proposes optimal contract terms. The input to the generation AI is a prompt regarding the contract terms the user wants to negotiate, and the generation AI makes optimal proposals based on that prompt. Step 5: The negotiation simulation unit simulates interpersonal negotiations and proposes effective negotiation strategies to the user. For example, the generation AI predicts the other party's reactions and proposes optimal countermeasures. The input to the generation AI is a prompt regarding the negotiation scenario the user wants to simulate, and the generation AI performs the simulation based on that prompt. Step 6: The learning unit learns from the user's past negotiation data and uses that experience in the next negotiation. For example, the generation AI analyzes successful and unsuccessful strategies in past negotiations and proposes the optimal strategy for the next negotiation. The input to the generation AI is the user's past negotiation data, and the generation AI learns based on that data.

[0082] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data analysis department that provides market insights based on data analysis; The Legal Knowledge Department provides knowledge on legal regulations and industry practices. A technical term explanation section that explains technical terms; a contract terms optimization unit that optimizes contract terms; a negotiation simulation unit that performs a simulation of interpersonal negotiations; A learning unit that learns from past data of the user. A system characterized by:

2. The data analysis unit When analyzing market data, analyze the sentiment of market participants and provide market insights that take into account fluctuations in said sentiment.

2. The system of claim 1.

3. The data analysis unit Integrate different data sources when analyzing market data to provide more comprehensive market insights 2. The system of claim 1.

4. The data analysis unit When analyzing market data, compare past market data with current market data to provide insights that simultaneously consider long-term trends and short-term fluctuations.

2. The system of claim 1.

5. The legal knowledge providing unit When analyzing the legal regulations, the emotional impact of legal documents is analyzed and presented to the user in an emotionally understandable format.

2. The system of claim 1.

6. The legal knowledge providing unit When analyzing the legal regulations, compare the legal regulations of different countries and regions and provide legal knowledge from an international perspective.

2. The system of claim 1.

7. The legal knowledge providing unit When analyzing the legal regulations, we take into account past legal precedents and regulatory change history to predict future legal risks.

2. The system of claim 1.

8. The technical term explanation section When explaining the technical term, the level of understanding of the user is evaluated in real time, and the explanation is provided according to the level of understanding.

2. The system of claim 1.

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