System

Abstract

An object of the system according to the embodiment is to perform data analysis and prediction for establishing an effective strategy in negotiation.SOLUTION: A system according to an embodiment includes a data analysis part, a prediction part, an advice part, a feedback part, an information sharing part, a document creation part, a virtual presence part, and a security part. The data analysis unit analyzes data. The prediction unit predicts a future negotiation result based on the data analyzed by the data analysis unit. The advice unit provides advice to the negotiator based on the result predicted by the prediction unit. The feedback unit provides feedback to the negotiator based on the advice provided by the advice unit. The information sharing unit shares information necessary for negotiation. The document creation unit creates a document based on the information shared by the information sharing unit. The virtual presence portion supports interaction with the negotiating party through a virtual or voice-based interface. The security unit protects the security and privacy of the negotiation data.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 technologies lack sufficient data analysis and prediction to develop effective strategies in negotiations, and there is room for improvement.

[0005] The system according to the embodiment aims to perform data analysis and prediction to develop effective strategies in negotiations. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a prediction unit, an advice unit, a feedback unit, an information sharing unit, a document creation unit, a virtual presence unit, and a security unit. The data analysis unit analyzes data. The prediction unit predicts future negotiation outcomes based on data analyzed by the data analysis unit. The advice unit provides advice to a negotiator based on the outcome predicted by the prediction unit. The feedback unit provides feedback to a negotiator based on the advice provided by the advice unit. The information sharing unit shares information necessary for negotiation. The document creation unit creates documents based on the information shared by the information sharing unit. The virtual presence unit supports interaction with the negotiating counterpart through a virtual or voice-based interface. The security unit protects the security and privacy of negotiation data. [Effects of the Invention]

[0007] The system according to the embodiment can perform data analysis and prediction to develop effective strategies in negotiations. [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 AI ​​negotiation assistant according to the embodiment of the present invention is a system that analyzes data, makes predictions, provides advice, gives feedback, shares information, creates documents, provides virtual presence, and protects security, thereby enabling the AI ​​negotiation assistant to conduct negotiations efficiently and strategically.

[0029] An AI negotiation assistant according to an embodiment includes a data analysis unit, a prediction unit, an advice unit, a feedback unit, an information sharing unit, a document creation unit, a virtual presence unit, and a security unit. The data analysis unit analyzes data. For example, the data analysis unit collects and analyzes past behavioral data of a negotiating partner. The data analysis unit can also analyze market trends and economic indicators. The data analysis unit can also analyze data using a machine learning algorithm. The prediction unit predicts future negotiation results based on the data analyzed by the data analysis unit. For example, the prediction unit predicts what strategy will be effective in the next negotiation based on past negotiation data. The prediction unit can also predict future negotiation results using time series prediction. The prediction unit can also predict negotiation results using regression analysis. The advice unit provides advice to the negotiator based on the results predicted by the prediction unit. For example, the advice unit provides advice to the negotiator regarding the selection and timing of statements. The advice unit can also provide advice on improvements to the negotiation strategy. The advice unit can also provide advice via text message or voice assistant. The feedback unit provides feedback to the negotiator based on the advice provided by the advice unit. For example, the feedback unit provides feedback in real time based on the negotiator's statements and actions. The feedback unit can also provide feedback at a later date. The feedback unit can also provide real-time text message or voice feedback. The information sharing unit shares information necessary for the negotiation. For example, the information sharing unit shares negotiation goals and conditions. The information sharing unit can also share documents and database entries. The information sharing unit can also share information on the cloud to enable real-time collaborative editing. The document creation unit creates documents based on the information shared by the information sharing unit. For example, the document creation unit automatically creates documents that summarize the negotiation goals and conditions. The document creation unit can also create contracts and minutes. The document creation unit can also create presentations and videos.The virtual presence unit supports dialogue with the negotiating counterpart through a virtual or voice-based interface. For example, the virtual presence unit may analyze the negotiating counterpart's facial expressions and tone of voice to generate responses based on their emotions. The virtual presence unit may also infer the negotiating counterpart's intentions and, based on that, ask appropriate questions or make appropriate suggestions to the negotiator. The virtual presence unit may also support virtual presence using VR or AR. The security unit protects the security and privacy of negotiation data. For example, the security unit may encrypt data and perform access control. The security unit may also analyze the access history of negotiation data to detect signs of unauthorized access. The security unit may also automatically back up negotiation data to minimize the risk of data loss. This allows the AI ​​negotiation assistant according to the embodiment to proceed with negotiations efficiently and strategically. For example, the AI ​​negotiation assistant may provide advice and feedback to negotiators in real time to increase the success rate of negotiations. The AI ​​negotiation assistant may also enhance the security of negotiation data and minimize the risk of data loss. The AI ​​negotiation assistant may also quickly provide information necessary for negotiations to support negotiators' preparations.

[0030] The data analysis unit analyzes the negotiating partner's past behavioral data to understand their negotiation style and patterns. For example, the data analysis unit uses the generation AI to analyze the negotiating partner's past statements and behavioral data to identify emotional patterns. For example, it analyzes how the negotiating partner feels about specific topics. The data analysis unit also monitors the other party's emotional changes in real time during negotiations and dynamically adjusts the negotiation strategy based on that data. For example, if the other party shows anger, it suggests a calm response. The data analysis unit also allows the generation AI to learn the negotiating partner's emotional patterns and predict future emotional changes based on past data. For example, it predicts how the other party's emotions will change as the negotiation progresses and suggests an appropriate strategy. This allows the negotiating partner's style and patterns to be understood, making it possible to develop a more effective negotiation strategy.

[0031] The advice section can analyze the negotiator's statements and behavioral data and generate appropriate advice. For example, the generative AI in the advice section collects data on the cultural background of the negotiating partner and analyzes it in comparison with past negotiation data. For example, it understands the negotiation style and values ​​of a particular culture. In addition, the generative AI in the advice section proposes a strategy that takes into account the negotiating partner's values ​​and cultural background. For example, by incorporating culturally important elements into negotiations, the other party's trust can be gained. In addition, the generative AI in the advice section proposes appropriate communication methods and negotiation strategies based on the negotiating partner's cultural background. For example, it provides advice on avoiding cultural taboos. This allows negotiators to receive appropriate advice, thereby increasing the success rate of negotiations.

[0032] The feedback unit can provide real-time feedback based on the negotiator's statements and actions. For example, the generation AI analyzes the gestures and posture of the negotiating partner to identify non-verbal behavioral patterns. For example, it determines whether the partner is nervous or relaxed. The feedback unit also monitors the partner's non-verbal behavior in real time during negotiations and predicts the progress of the negotiation based on that data. For example, it determines whether the partner is showing interest. The feedback unit also learns the partner's non-verbal behavioral patterns based on past data and predicts future behavior. For example, it analyzes how specific gestures affect the success of the negotiation. This allows the negotiator's behavior to be immediately improved by providing feedback in real time.

[0033] The information sharing unit can collect information necessary for negotiations and provide it to negotiators. In the information sharing unit, for example, the generation AI collects negotiation data from different industries and analyzes the negotiation styles and patterns of each industry. For example, it analyzes the differences in negotiation styles between the technology industry and the financial industry. The information sharing unit also has the generation AI propose industry-specific negotiation strategies. For example, in the technology industry, it would propose a strategy that emphasizes technical details, while in the financial industry, it would propose a strategy that emphasizes risk management. The information sharing unit also analyzes success stories and failure stories for each industry based on negotiation data from different industries and proposes the optimal strategy. For example, it proposes a strategy that takes past success stories into consideration. This makes it possible to support negotiators' preparation by quickly providing the information necessary for negotiations.

[0034] The document creation unit can automatically create documents that organize the goals and conditions of negotiations. In this document creation unit, for example, the generation AI analyzes the social media activity of the negotiating partner to identify their interests. For example, it analyzes what topics the partner is interested in. The document creation unit also proposes strategies that are advantageous to the negotiation based on information obtained from the negotiating partner's social media activity. For example, it makes proposals related to the partner's interests. In addition, the generation AI analyzes social media activity to identify the partner's personality and values. For example, it understands what values ​​the partner holds and proposes a strategy based on those. This makes it possible to support negotiators' preparations by automatically creating documents that organize the goals and conditions of negotiations.

[0035] The security unit can dynamically adjust the encryption level of the negotiation data to provide optimal security. For example, the generation AI analyzes the encryption level of the negotiation data in real time to provide optimal security. For example, the encryption level is adjusted according to the importance of the data. The security unit also provides optimal security based on the encryption level of the negotiation data, with the generation AI applying strong encryption for highly confidential data. The security unit also learns the encryption level of the negotiation data based on past data, with the generation AI dynamically adjusting the encryption level based on specific conditions. This makes it possible to optimize the security of the negotiation data by dynamically adjusting the encryption level.

[0036] The security department can assess the privacy risks of the negotiation data and propose countermeasures according to the risks. For example, the security department has the generation AI assess the privacy risks of the negotiation data in real time and propose countermeasures according to the risks. For example, it provides guidelines on data handling. The security department also has the generation AI propose appropriate countermeasures based on the privacy risks of the negotiation data. For example, it proposes data anonymization and access control. The security department also has the generation AI learn the privacy risks of the negotiation data based on past data and propose countermeasures according to the risks. For example, it provides effective countermeasures for specific risks. In this way, the privacy of the negotiation data can be protected by assessing privacy risks and proposing appropriate countermeasures.

[0037] The data analysis unit can propose a strategy that takes into account the cultural background and values ​​of the negotiating partner. For example, the generative AI collects data on the cultural background of the negotiating partner and analyzes it in comparison with past negotiation data. For example, it understands the negotiation style and values ​​of a particular culture. The generative AI then proposes a strategy that takes into account the values ​​and cultural background of the negotiating partner. For example, it can gain the trust of the other party by incorporating culturally important elements into negotiations. The data analysis unit also allows the generative AI to propose appropriate communication methods and negotiation strategies based on the cultural background of the negotiating partner. For example, it can provide advice on avoiding cultural taboos. This makes it possible to propose a more effective negotiation strategy by taking into account the cultural background and values ​​of the negotiating partner.

[0038] The data analysis unit analyzes the nonverbal behavior of the negotiating partner and can predict the progress of the negotiation based on that. For example, the data analysis unit allows the generation AI to analyze the gestures and posture of the negotiating partner and identify nonverbal behavior patterns. For example, it determines whether the partner is nervous or relaxed. The data analysis unit also allows the generation AI to monitor the partner's nonverbal behavior in real time during negotiations and predict the progress of the negotiation based on that data. For example, it determines whether the partner is showing interest. The data analysis unit also allows the generation AI to learn the negotiating partner's nonverbal behavior patterns based on past data and predict future behavior. For example, it analyzes how specific gestures affect the success of the negotiation. This makes it possible to predict the progress of the negotiation and take appropriate measures by analyzing nonverbal behavior.

[0039] The data analysis unit can analyze negotiation data from different industries and propose industry-specific negotiation strategies. For example, the generation AI collects negotiation data from different industries and analyzes the negotiation styles and patterns of each industry. For example, it analyzes the differences in negotiation styles between the technology industry and the financial industry. The data analysis unit then has the generation AI propose industry-specific negotiation strategies. For example, it proposes a strategy that emphasizes technical details in the technology industry, while emphasizing risk management in the financial industry. The data analysis unit also has the generation AI analyze success stories and failure stories for each industry based on negotiation data from different industries and propose an optimal strategy. For example, it proposes a strategy that takes past success stories into consideration. In this way, by analyzing negotiation data from different industries, it is possible to propose industry-specific negotiation strategies.

[0040] The data analysis unit can analyze the social media activity of the negotiating partner and extract information advantageous to the negotiation. In the data analysis unit, for example, the generation AI analyzes the social media activity of the negotiating partner to identify their interests. For example, it analyzes what topics the other party is interested in. The data analysis unit also proposes a strategy advantageous to the negotiation based on the information obtained from the social media activity of the negotiating partner. For example, it makes proposals related to the other party's interests. In addition, the data analysis unit analyzes the social media activity of the generation AI to identify the personality and values ​​of the negotiating partner. For example, it understands what values ​​the other party holds and proposes a strategy based on those values. In this way, by analyzing the social media activity of the negotiating partner, it is possible to extract information advantageous to the negotiation and formulate a strategy.

[0041] The advice unit can analyze the negotiator's past success stories and provide optimal advice based on that. In the advice unit, for example, the generation AI collects the negotiator's past success stories and analyzes the factors behind their success. For example, it identifies the factors that led to the success of a particular strategy or approach. In addition, the advice unit has the generation AI provide optimal advice to the negotiator based on past success stories. For example, it provides specific advice for recreating a successful strategy. In addition, the advice unit has the generation AI learn from past success stories and provide advice that is tailored to the negotiator's current situation. For example, it compares past success stories with the current situation and proposes the optimal strategy. In this way, by analyzing past success stories, it is possible to provide optimal advice to the negotiator.

[0042] The advisory unit can analyze the tone and speed of a negotiator's voice and suggest the optimal timing to speak. For example, the generation AI of the advisory unit analyzes the tone and speed of a negotiator's voice in real time to optimize the timing of speaking. For example, it adjusts the timing of speaking while watching the other party's reaction. The advisory unit also has the generation AI suggest the optimal timing to speak based on the tone and speed of the negotiator's voice. For example, it will speak when the other party has finished speaking. The advisory unit also has the generation AI learn the patterns of the negotiator's tone and speed of voice based on past data and predict the optimal timing to speak. For example, it will identify the timing when a particular tone and speed are effective. In this way, by analyzing the negotiator's tone and speed of voice, it can suggest the optimal timing to speak and increase the effectiveness of negotiations.

[0043] The advice unit supports negotiations in different languages ​​and can provide advice that transcends language barriers. For example, the generative AI in the advice unit supports negotiations in different languages ​​and performs real-time translation. For example, it translates what a negotiator says in English into Japanese. The advice unit also allows the generative AI to provide appropriate advice in negotiations in different languages. For example, it suggests statements that take cultural nuances into consideration. The advice unit also allows the generative AI to learn data in different languages ​​and provide advice that transcends language barriers. For example, it suggests strategies that are effective in negotiations in a specific language. In this way, by supporting negotiations in different languages, it is possible to provide advice that transcends language barriers and increase the effectiveness of negotiations.

[0044] The advice unit can monitor the negotiator's physical condition and provide advice based on that. For example, the generation AI monitors the negotiator's heart rate and stress level in real time and provides advice based on that data. For example, if stress is high, it will suggest ways to relax. The advice unit also provides appropriate advice based on the negotiator's physical condition. For example, if the heart rate is elevated, it will encourage deep breathing. The advice unit also learns the negotiator's physical condition based on past data and provides advice based on that. For example, it provides advice that is effective for specific conditions. In this way, by monitoring the negotiator's physical condition, appropriate advice can be provided and the effectiveness of negotiations can be improved.

[0045] The information sharing unit can record the progress of negotiations in real time and automatically generate a detailed report after the negotiations are completed. In the information sharing unit, for example, the generation AI records the progress of negotiations in real time and automatically generates a detailed report after the negotiations are completed. For example, it records statements and decisions made at each stage of the negotiations. In addition, the information sharing unit has the generation AI collect data in real time during negotiations and generate a report based on that data after the negotiations are completed. For example, it creates a report summarizing the progress and results of the negotiations. In addition, the information sharing unit builds a system in which the generation AI records the progress of negotiations and automatically generates a detailed report after the negotiations are completed. For example, it records statements and decisions made at each stage of the negotiations and creates a report based on that. In this way, the transparency and efficiency of negotiations can be improved by recording the progress of negotiations in real time and automatically generating detailed reports.

[0046] The information sharing unit automatically generates legal documents related to negotiations, minimizing legal risks. For example, the information sharing unit allows the generation AI to automatically generate legal documents related to negotiations, minimizing legal risks. For example, it automatically generates contracts and agreements. The information sharing unit also allows the generation AI to analyze legal risks during negotiations and generate appropriate legal documents based on that analysis. For example, it creates contracts based on specific conditions. The information sharing unit also allows the generation AI to learn from past legal documents and automatically generate legal documents related to negotiations. For example, it creates new contracts based on past contracts. In this way, legal risks can be minimized by automatically generating legal documents related to negotiations.

[0047] The information sharing unit can support the creation of documents in different formats. For example, the information sharing unit supports the generation AI in creating documents in different formats and automatically generates presentations and videos. For example, creating a presentation summarizing the progress of negotiations. The information sharing unit also allows the generation AI to collect data during negotiations and create documents in different formats based on that data. For example, creating a video summarizing the results of the negotiations. The information sharing unit also supports the generation AI in creating documents in different formats based on past data. For example, creating a new presentation based on past presentations. In this way, by supporting the creation of documents in different formats, the transparency and efficiency of negotiations can be improved.

[0048] The information sharing unit can share data related to negotiations on the cloud, enabling real-time collaborative editing. The information sharing unit, for example, allows the generation AI to share data related to negotiations on the cloud, enabling real-time collaborative editing. For example, the progress of the negotiations can be shared on the cloud, allowing multiple users to edit simultaneously. The information sharing unit also allows the generation AI to share data on the cloud during negotiations, supporting real-time collaborative editing. For example, the results of the negotiations can be shared on the cloud, allowing multiple users to edit simultaneously. The information sharing unit also supports collaborative editing on the cloud by the generation AI based on past data. For example, past negotiation data can be shared on the cloud, allowing multiple users to edit simultaneously. This allows data related to negotiations to be shared on the cloud, enabling real-time collaborative editing, thereby improving the transparency and efficiency of negotiations.

[0049] The virtual presence unit can infer the intentions of the negotiating partner and, based on that, ask appropriate questions and make appropriate proposals to the negotiator. For example, the generation AI of the virtual presence unit analyzes the negotiating partner's remarks and actions to infer their intentions. For example, it understands what the other party wants and asks appropriate questions and makes appropriate proposals based on that. The virtual presence unit also analyzes the other party's intentions in real time during negotiations and, based on that, asks appropriate questions and makes appropriate proposals to the negotiator. For example, if the other party wants specific conditions, it asks questions related to those conditions. The virtual presence unit also learns the intentions of the negotiating partner based on past data and, based on that, asks appropriate questions and makes appropriate proposals. For example, it asks questions and makes effective proposals for specific intentions. This makes it possible to infer the intentions of the negotiating partner and, based on that, improve the effectiveness of negotiations.

[0050] The virtual presence unit can provide a culturally appropriate virtual presence by taking into account the cultural background of the negotiating partner. For example, the generation AI collects data on the cultural background of the negotiating partner and provides a virtual presence based on that data. For example, it creates an avatar that reflects the etiquette and manners of a particular culture. The virtual presence unit also analyzes the cultural background of the negotiating partner in real time during negotiations and adjusts the virtual presence based on that analysis. For example, it uses culturally appropriate expressions and gestures. The virtual presence unit also learns the cultural background of the negotiating partner based on past data and provides a virtual presence based on that analysis. For example, it creates an avatar that reflects the communication style of a particular culture. This allows the generation AI to take into account the cultural background of the negotiating partner and provide a culturally appropriate virtual presence, thereby improving the effectiveness of negotiations.

[0051] The virtual presence unit can support virtual presence on different platforms. For example, the generation AI in the virtual presence unit supports virtual presence on different platforms, enabling negotiations using VR or AR. For example, it creates an avatar that supports negotiations in a VR space. The virtual presence unit also supports virtual presence on different platforms in real time during negotiations using the generation AI. For example, in negotiations using AR, it provides an avatar that reflects the other party's facial expressions and gestures. The virtual presence unit also supports virtual presence on different platforms using past data from the generation AI. For example, in negotiations using VR or AR, it creates an effective avatar. This allows the effectiveness of negotiations to be improved by supporting virtual presence on different platforms.

[0052] The virtual presence unit can analyze the negotiating partner's past virtual presence data and propose an optimal communication strategy. In the virtual presence unit, for example, the generation AI collects and analyzes the negotiating partner's past virtual presence data. For example, it analyzes the avatar's reactions and actions in past negotiations. In addition, the virtual presence unit proposes an optimal communication strategy during negotiations based on the past virtual presence data. For example, it proposes an effective strategy with reference to past data. In addition, the virtual presence unit learns the negotiating partner's virtual presence patterns based on past data and proposes an optimal communication strategy based on that. For example, it proposes an effective strategy for a specific pattern. In this way, by analyzing the negotiating partner's past virtual presence data, it is possible to propose an optimal communication strategy and improve the effectiveness of negotiations.

[0053] The security department can analyze the access history of the negotiation data and detect signs of unauthorized access. For example, the generation AI analyzes the access history of the negotiation data in real time to detect signs of unauthorized access. For example, it detects abnormal access patterns and issues an alert. The security department also has the generation AI identify signs of unauthorized access based on the access history of the negotiation data. For example, it detects access from unusual times or locations. The security department also has the generation AI learn the access history of the negotiation data based on past data and predict signs of unauthorized access. For example, it detects unauthorized access based on specific patterns. This makes it possible to strengthen the security of the negotiation data by detecting signs of unauthorized access.

[0054] The security unit can dynamically adjust the encryption level of the negotiation data to provide optimal security. For example, the generation AI analyzes the encryption level of the negotiation data in real time to provide optimal security. For example, the encryption level is adjusted according to the importance of the data. The security unit also provides optimal security based on the encryption level of the negotiation data, with the generation AI applying strong encryption for highly confidential data. The security unit also learns the encryption level of the negotiation data based on past data, with the generation AI dynamically adjusting the encryption level based on specific conditions. This makes it possible to optimize the security of the negotiation data by dynamically adjusting the encryption level.

[0055] The security department can assess the privacy risks of the negotiation data and propose countermeasures according to the risks. For example, the security department has the generation AI assess the privacy risks of the negotiation data in real time and propose countermeasures according to the risks. For example, it provides guidelines on data handling. The security department also has the generation AI propose appropriate countermeasures based on the privacy risks of the negotiation data. For example, it proposes data anonymization and access control. The security department also has the generation AI learn the privacy risks of the negotiation data based on past data and propose countermeasures according to the risks. For example, it provides effective countermeasures for specific risks. In this way, the privacy of the negotiation data can be protected by assessing privacy risks and proposing appropriate countermeasures.

[0056] The security unit can integrate different security protocols and provide multiple layers of security. For example, the generation AI can integrate different security protocols and provide multiple layers of security. For example, it can combine data encryption, access control, and a monitoring system. In addition, the generation AI can integrate multiple security protocols to enhance the security of the negotiation data. For example, it can simultaneously apply data encryption and access control. In addition, the generation AI can learn different security protocols based on past data and provide multiple layers of security. For example, it can dynamically adjust security protocols based on specific conditions. This allows the security of the negotiation data to be enhanced by integrating different security protocols and providing multiple layers of security.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The AI ​​negotiation assistant can also be equipped with a cultural analysis unit that proposes strategies that take into account the cultural background of the negotiating partner. The cultural analysis unit, for example, collects cultural characteristics of the negotiating partner's country or region and adjusts the negotiation strategy based on that information. For example, it makes proposals that take into account the etiquette and manners of a particular culture. The cultural analysis unit can also suggest appropriate communication methods based on the negotiating partner's cultural background. For example, using culturally appropriate expressions and gestures can increase the success rate of negotiations. Furthermore, the cultural analysis unit can analyze the impact of cultural background on negotiations based on past negotiation data and propose optimal strategies.

[0059] The AI ​​negotiation assistant can also be equipped with a health management unit that monitors the negotiator's health condition and provides advice based on it. The health management unit, for example, monitors the negotiator's heart rate and stress level in real time and provides advice based on that data. For example, if stress is high, it can suggest ways to relax. The health management unit can also provide appropriate advice based on the negotiator's physical condition. For example, if the heart rate is elevated, it can encourage deep breathing. Furthermore, the health management unit can learn the negotiator's physical condition based on past data and provide advice based on that.

[0060] The AI ​​negotiation assistant may further include a social media analysis unit that analyzes the social media activity of the negotiating partner and extracts information advantageous to the negotiation. The social media analysis unit may, for example, analyze the social media activity of the negotiating partner to identify their interests. For example, it may analyze what topics the negotiating partner is interested in. The social media analysis unit may also propose strategies advantageous to the negotiation based on information obtained from the negotiating partner's social media activity. For example, it may make proposals related to the negotiating partner's interests. Furthermore, the social media analysis unit may identify the negotiating partner's personality and values ​​and propose strategies based on them.

[0061] The AI ​​negotiation assistant can also be equipped with an industry analysis unit that analyzes negotiation data from different industries and proposes industry-specific negotiation strategies. The industry analysis unit, for example, collects negotiation data from different industries and analyzes the negotiation styles and patterns of each industry. For example, it analyzes the differences in negotiation styles between the technology industry and the financial industry. The industry analysis unit can also propose industry-specific negotiation strategies. For example, in the technology industry, it might propose a strategy that emphasizes technical details, while in the financial industry, it might propose a strategy that emphasizes risk management. Furthermore, the industry analysis unit can analyze past successes and failures to propose optimal strategies.

[0062] The AI ​​negotiation assistant can also be equipped with a report generation unit that records the progress of negotiations in real time and automatically generates a detailed report after the negotiations are completed. The report generation unit, for example, records statements and decisions made at each stage of the negotiations. The report generation unit can also collect data in real time during negotiations and generate a report based on that data after the negotiations are completed. For example, it can create a report summarizing the progress and results of the negotiations. Furthermore, the report generation unit can record the progress of negotiations based on past data and create a report based on that.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The data analysis unit analyzes the data. For example, the data analysis unit collects and analyzes data on the past behavior of the negotiating partner. It can also analyze market trends and economic indicators, and can analyze the data using machine learning algorithms. Step 2: The prediction unit predicts future negotiation results based on the data analyzed by the data analysis unit. For example, it can predict what strategies will be effective in the next negotiation based on past negotiation data, and can also predict negotiation results using time series prediction and regression analysis. Step 3: The advisory unit provides advice to the negotiator based on the results predicted by the prediction unit. For example, it provides advice on the negotiator's choice of remarks and timing, as well as advice on how to improve the negotiation strategy. Advice can also be provided via text message or voice assistant. Step 4: The feedback unit provides feedback to the negotiator based on the advice provided by the advice unit. For example, the feedback unit can provide real-time feedback based on the negotiator's statements and actions, or provide feedback at a later date. It can also provide real-time text message or voice feedback. Step 5: The information sharing section shares information necessary for the negotiation. For example, it can share the negotiation goals and conditions, documents, and database entries. It can also share information on the cloud, enabling real-time collaborative editing. Step 6: The document creation section creates documents based on the information shared by the information sharing section. For example, it can automatically create documents outlining the goals and conditions of negotiations, and can also create contracts and meeting minutes. It can also create presentations and videos. Step 7: The virtual presence unit supports dialogue with the negotiating partner through a virtual or voice-based interface. For example, it analyzes the negotiating partner's facial expressions and tone of voice to generate responses based on their emotions. It can also infer the negotiating partner's intentions and ask appropriate questions or make suggestions to the negotiator based on those. It can also support virtual presence using VR or AR. Step 8: The security department protects the security and privacy of the negotiation data. For example, it can encrypt data, control access, analyze the access history of the negotiation data, and detect signs of unauthorized access. It can also automatically back up the negotiation data to minimize the risk of data loss.

[0065] (Example 2) The AI ​​negotiation assistant according to the embodiment of the present invention is a system that analyzes data, makes predictions, provides advice, gives feedback, shares information, creates documents, provides virtual presence, and protects security, thereby enabling the AI ​​negotiation assistant to conduct negotiations efficiently and strategically.

[0066] An AI negotiation assistant according to an embodiment includes a data analysis unit, a prediction unit, an advice unit, a feedback unit, an information sharing unit, a document creation unit, a virtual presence unit, and a security unit. The data analysis unit analyzes data. For example, the data analysis unit collects and analyzes past behavioral data of a negotiating partner. The data analysis unit can also analyze market trends and economic indicators. The data analysis unit can also analyze data using a machine learning algorithm. The prediction unit predicts future negotiation results based on the data analyzed by the data analysis unit. For example, the prediction unit predicts what strategy will be effective in the next negotiation based on past negotiation data. The prediction unit can also predict future negotiation results using time series prediction. The prediction unit can also predict negotiation results using regression analysis. The advice unit provides advice to the negotiator based on the results predicted by the prediction unit. For example, the advice unit provides advice to the negotiator regarding the selection and timing of statements. The advice unit can also provide advice on improvements to the negotiation strategy. The advice unit can also provide advice via text message or voice assistant. The feedback unit provides feedback to the negotiator based on the advice provided by the advice unit. For example, the feedback unit provides feedback in real time based on the negotiator's statements and actions. The feedback unit can also provide feedback at a later date. The feedback unit can also provide real-time text message or voice feedback. The information sharing unit shares information necessary for the negotiation. For example, the information sharing unit shares negotiation goals and conditions. The information sharing unit can also share documents and database entries. The information sharing unit can also share information on the cloud to enable real-time collaborative editing. The document creation unit creates documents based on the information shared by the information sharing unit. For example, the document creation unit automatically creates documents that summarize the negotiation goals and conditions. The document creation unit can also create contracts and minutes. The document creation unit can also create presentations and videos.The virtual presence unit supports dialogue with the negotiating counterpart through a virtual or voice-based interface. For example, the virtual presence unit may analyze the negotiating counterpart's facial expressions and tone of voice to generate responses based on their emotions. The virtual presence unit may also infer the negotiating counterpart's intentions and, based on that, ask appropriate questions or make appropriate suggestions to the negotiator. The virtual presence unit may also support virtual presence using VR or AR. The security unit protects the security and privacy of negotiation data. For example, the security unit may encrypt data and perform access control. The security unit may also analyze the access history of negotiation data to detect signs of unauthorized access. The security unit may also automatically back up negotiation data to minimize the risk of data loss. This allows the AI ​​negotiation assistant according to the embodiment to proceed with negotiations efficiently and strategically. For example, the AI ​​negotiation assistant may provide advice and feedback to negotiators in real time to increase the success rate of negotiations. The AI ​​negotiation assistant may also enhance the security of negotiation data and minimize the risk of data loss. The AI ​​negotiation assistant may also quickly provide information necessary for negotiations to support negotiators' preparations.

[0067] The data analysis unit analyzes the negotiating partner's past behavioral data to understand their negotiation style and patterns. For example, the data analysis unit uses the generation AI to analyze the negotiating partner's past statements and behavioral data to identify emotional patterns. For example, it analyzes how the negotiating partner feels about specific topics. The data analysis unit also monitors the other party's emotional changes in real time during negotiations and dynamically adjusts the negotiation strategy based on that data. For example, if the other party shows anger, it suggests a calm response. The data analysis unit also allows the generation AI to learn the negotiating partner's emotional patterns and predict future emotional changes based on past data. For example, it predicts how the other party's emotions will change as the negotiation progresses and suggests an appropriate strategy. This allows the negotiating partner's style and patterns to be understood, making it possible to develop a more effective negotiation strategy.

[0068] The advice section can analyze the negotiator's statements and behavioral data and generate appropriate advice. For example, the generative AI in the advice section collects data on the cultural background of the negotiating partner and analyzes it in comparison with past negotiation data. For example, it understands the negotiation style and values ​​of a particular culture. In addition, the generative AI in the advice section proposes a strategy that takes into account the negotiating partner's values ​​and cultural background. For example, by incorporating culturally important elements into negotiations, the other party's trust can be gained. In addition, the generative AI in the advice section proposes appropriate communication methods and negotiation strategies based on the negotiating partner's cultural background. For example, it provides advice on avoiding cultural taboos. This allows negotiators to receive appropriate advice, thereby increasing the success rate of negotiations.

[0069] The feedback unit can provide real-time feedback based on the negotiator's statements and actions. For example, the generation AI analyzes the gestures and posture of the negotiating partner to identify non-verbal behavioral patterns. For example, it determines whether the partner is nervous or relaxed. The feedback unit also monitors the partner's non-verbal behavior in real time during negotiations and predicts the progress of the negotiation based on that data. For example, it determines whether the partner is showing interest. The feedback unit also learns the partner's non-verbal behavioral patterns based on past data and predicts future behavior. For example, it analyzes how specific gestures affect the success of the negotiation. This allows the negotiator's behavior to be immediately improved by providing feedback in real time.

[0070] The information sharing unit can collect information necessary for negotiations and provide it to negotiators. In the information sharing unit, for example, the generation AI collects negotiation data from different industries and analyzes the negotiation styles and patterns of each industry. For example, it analyzes the differences in negotiation styles between the technology industry and the financial industry. The information sharing unit also has the generation AI propose industry-specific negotiation strategies. For example, in the technology industry, it would propose a strategy that emphasizes technical details, while in the financial industry, it would propose a strategy that emphasizes risk management. The information sharing unit also analyzes success stories and failure stories for each industry based on negotiation data from different industries and proposes the optimal strategy. For example, it proposes a strategy that takes past success stories into consideration. This makes it possible to support negotiators' preparation by quickly providing the information necessary for negotiations.

[0071] The document creation unit can automatically create documents that organize the goals and conditions of negotiations. In this document creation unit, for example, the generation AI analyzes the social media activity of the negotiating partner to identify their interests. For example, it analyzes what topics the partner is interested in. The document creation unit also proposes strategies that are advantageous to the negotiation based on information obtained from the negotiating partner's social media activity. For example, it makes proposals related to the partner's interests. In addition, the generation AI analyzes social media activity to identify the partner's personality and values. For example, it understands what values ​​the partner holds and proposes a strategy based on those. This makes it possible to support negotiators' preparations by automatically creating documents that organize the goals and conditions of negotiations.

[0072] The virtual presence unit can analyze the facial expressions and tone of voice of the negotiating partner and generate a response that corresponds to their emotions. For example, the generation AI in the virtual presence unit monitors the negotiating partner's emotions in real time and detects changes in their emotions. For example, if the partner shows anger, the generation AI will suggest a calm response. The virtual presence unit also uses the emotion estimation function to have the generation AI suggest a strategy that corresponds to the negotiating partner's emotions. For example, if the partner is relaxed, the generation AI will make an aggressive proposal. The virtual presence unit also predicts changes in the negotiating partner's emotions based on the emotion estimation data and suggests an appropriate strategy. For example, it can predict when the partner's emotions will change and suggest a strategy that matches that timing. This allows for more effective communication by generating responses that correspond to the negotiating partner's emotions.

[0073] The security unit can dynamically adjust the encryption level of the negotiation data to provide optimal security. For example, the generation AI analyzes the encryption level of the negotiation data in real time to provide optimal security. For example, the encryption level is adjusted according to the importance of the data. The security unit also provides optimal security based on the encryption level of the negotiation data, with the generation AI applying strong encryption for highly confidential data. The security unit also learns the encryption level of the negotiation data based on past data, with the generation AI dynamically adjusting the encryption level based on specific conditions. This makes it possible to optimize the security of the negotiation data by dynamically adjusting the encryption level.

[0074] The security department can assess the privacy risks of the negotiation data and propose countermeasures according to the risks. For example, the security department has the generation AI assess the privacy risks of the negotiation data in real time and propose countermeasures according to the risks. For example, it provides guidelines on data handling. The security department also has the generation AI propose appropriate countermeasures based on the privacy risks of the negotiation data. For example, it proposes data anonymization and access control. The security department also has the generation AI learn the privacy risks of the negotiation data based on past data and propose countermeasures according to the risks. For example, it provides effective countermeasures for specific risks. In this way, the privacy of the negotiation data can be protected by assessing privacy risks and proposing appropriate countermeasures.

[0075] The data analysis unit can analyze the emotional patterns of the negotiating partner and dynamically adjust the negotiation strategy based on changes in emotion. For example, the data analysis unit uses the generation AI to analyze the negotiating partner's past statements and behavioral data to identify emotional patterns. For example, it analyzes the emotions the negotiating partner shows toward specific topics. The data analysis unit also monitors the other party's emotional changes in real time during negotiations and dynamically adjusts the negotiation strategy based on that data. For example, if the other party shows anger, it suggests a calm response. The data analysis unit also uses the generation AI to learn the negotiating partner's emotional patterns and predict future emotional changes based on past data. For example, it predicts how the other party's emotions will change as the negotiation progresses and proposes an appropriate strategy. This makes it possible to dynamically adjust strategies based on the other party's emotional changes, thereby achieving more effective negotiations.

[0076] The data analysis unit can propose a strategy that takes into account the cultural background and values ​​of the negotiating partner. For example, the generative AI collects data on the cultural background of the negotiating partner and analyzes it in comparison with past negotiation data. For example, it understands the negotiation style and values ​​of a particular culture. The generative AI then proposes a strategy that takes into account the values ​​and cultural background of the negotiating partner. For example, it can gain the trust of the other party by incorporating culturally important elements into negotiations. The data analysis unit also allows the generative AI to propose appropriate communication methods and negotiation strategies based on the cultural background of the negotiating partner. For example, it can provide advice on avoiding cultural taboos. This makes it possible to propose a more effective negotiation strategy by taking into account the cultural background and values ​​of the negotiating partner.

[0077] The data analysis unit analyzes the nonverbal behavior of the negotiating partner and can predict the progress of the negotiation based on that. For example, the data analysis unit allows the generation AI to analyze the gestures and posture of the negotiating partner and identify nonverbal behavior patterns. For example, it determines whether the partner is nervous or relaxed. The data analysis unit also allows the generation AI to monitor the partner's nonverbal behavior in real time during negotiations and predict the progress of the negotiation based on that data. For example, it determines whether the partner is showing interest. The data analysis unit also allows the generation AI to learn the negotiating partner's nonverbal behavior patterns based on past data and predict future behavior. For example, it analyzes how specific gestures affect the success of the negotiation. This makes it possible to predict the progress of the negotiation and take appropriate measures by analyzing nonverbal behavior.

[0078] The data analysis unit can analyze negotiation data from different industries and propose industry-specific negotiation strategies. For example, the generation AI collects negotiation data from different industries and analyzes the negotiation styles and patterns of each industry. For example, it analyzes the differences in negotiation styles between the technology industry and the financial industry. The data analysis unit then has the generation AI propose industry-specific negotiation strategies. For example, it proposes a strategy that emphasizes technical details in the technology industry, while emphasizing risk management in the financial industry. The data analysis unit also has the generation AI analyze success stories and failure stories for each industry based on negotiation data from different industries and propose an optimal strategy. For example, it proposes a strategy that takes past success stories into consideration. In this way, by analyzing negotiation data from different industries, it is possible to propose industry-specific negotiation strategies.

[0079] The data analysis unit can analyze the social media activity of the negotiating partner and extract information advantageous to the negotiation. In the data analysis unit, for example, the generation AI analyzes the social media activity of the negotiating partner to identify their interests. For example, it analyzes what topics the other party is interested in. The data analysis unit also proposes a strategy advantageous to the negotiation based on the information obtained from the social media activity of the negotiating partner. For example, it makes proposals related to the other party's interests. In addition, the data analysis unit analyzes the social media activity of the generation AI to identify the personality and values ​​of the negotiating partner. For example, it understands what values ​​the other party holds and proposes a strategy based on those values. In this way, by analyzing the social media activity of the negotiating partner, it is possible to extract information advantageous to the negotiation and formulate a strategy.

[0080] The data analysis unit can use the emotion estimation function to monitor changes in the emotions of the negotiating partner in real time and propose a strategy based on those emotions. In the data analysis unit, for example, the generation AI monitors the emotions of the negotiating partner in real time and detects changes in emotions. For example, if the partner shows anger, it proposes a calm response. In addition, the data analysis unit uses the emotion estimation function to have the generation AI propose a strategy based on the emotions of the negotiating partner. For example, if the partner is relaxed, it makes an aggressive proposal. In addition, the data analysis unit uses the emotion estimation data to predict changes in the emotions of the negotiating partner and propose an appropriate strategy. For example, it predicts the timing when the partner's emotions will change and proposes a strategy that matches that timing. In this way, by monitoring changes in the emotions of the negotiating partner in real time and proposing an appropriate strategy, it is possible to increase the success rate of negotiations.

[0081] The advice unit can analyze the negotiator's past success stories and provide optimal advice based on that. In the advice unit, for example, the generation AI collects the negotiator's past success stories and analyzes the factors behind their success. For example, it identifies the factors that led to the success of a particular strategy or approach. In addition, the advice unit has the generation AI provide optimal advice to the negotiator based on past success stories. For example, it provides specific advice for recreating a successful strategy. In addition, the advice unit has the generation AI learn from past success stories and provide advice that is tailored to the negotiator's current situation. For example, it compares past success stories with the current situation and proposes the optimal strategy. In this way, by analyzing past success stories, it is possible to provide optimal advice to the negotiator.

[0082] The advisory unit can analyze the tone and speed of a negotiator's voice and suggest the optimal timing to speak. For example, the generation AI of the advisory unit analyzes the tone and speed of a negotiator's voice in real time to optimize the timing of speaking. For example, it adjusts the timing of speaking while watching the other party's reaction. The advisory unit also has the generation AI suggest the optimal timing to speak based on the tone and speed of the negotiator's voice. For example, it will speak when the other party has finished speaking. The advisory unit also has the generation AI learn the patterns of the negotiator's tone and speed of voice based on past data and predict the optimal timing to speak. For example, it will identify the timing when a particular tone and speed are effective. In this way, by analyzing the negotiator's tone and speed of voice, it can suggest the optimal timing to speak and increase the effectiveness of negotiations.

[0083] The advice unit supports negotiations in different languages ​​and can provide advice that transcends language barriers. For example, the generative AI in the advice unit supports negotiations in different languages ​​and performs real-time translation. For example, it translates what a negotiator says in English into Japanese. The advice unit also allows the generative AI to provide appropriate advice in negotiations in different languages. For example, it suggests statements that take cultural nuances into consideration. The advice unit also allows the generative AI to learn data in different languages ​​and provide advice that transcends language barriers. For example, it suggests strategies that are effective in negotiations in a specific language. In this way, by supporting negotiations in different languages, it is possible to provide advice that transcends language barriers and increase the effectiveness of negotiations.

[0084] The advice unit can monitor the negotiator's physical condition and provide advice based on that. For example, the generation AI monitors the negotiator's heart rate and stress level in real time and provides advice based on that data. For example, if stress is high, it will suggest ways to relax. The advice unit also provides appropriate advice based on the negotiator's physical condition. For example, if the heart rate is elevated, it will encourage deep breathing. The advice unit also learns the negotiator's physical condition based on past data and provides advice based on that. For example, it provides advice that is effective for specific conditions. In this way, by monitoring the negotiator's physical condition, appropriate advice can be provided and the effectiveness of negotiations can be improved.

[0085] The advice unit can use the emotion estimation function to suggest relaxation methods and stress reduction measures according to the negotiator's emotions. In the advice unit, for example, the generation AI analyzes the negotiator's emotions in real time and suggests relaxation methods according to the emotions. For example, if the negotiator is nervous, it may suggest deep breathing or meditation. In addition, the advice unit uses the emotion estimation function to have the generation AI suggest stress reduction measures according to the negotiator's emotions. For example, if stress is high, it may suggest listening to relaxing music. In addition, the advice unit has the generation AI learn the negotiator's emotional state based on past data and provide relaxation methods and stress reduction measures according to the emotions. For example, it may suggest methods that are effective for specific emotional states. In this way, by suggesting relaxation methods and stress reduction measures according to the negotiator's emotions, it is possible to reduce the negotiator's psychological burden and improve the effectiveness of negotiations.

[0086] The information sharing unit can record the progress of negotiations in real time and automatically generate a detailed report after the negotiations are completed. In the information sharing unit, for example, the generation AI records the progress of negotiations in real time and automatically generates a detailed report after the negotiations are completed. For example, it records statements and decisions made at each stage of the negotiations. In addition, the information sharing unit has the generation AI collect data in real time during negotiations and generate a report based on that data after the negotiations are completed. For example, it creates a report summarizing the progress and results of the negotiations. In addition, the information sharing unit builds a system in which the generation AI records the progress of negotiations and automatically generates a detailed report after the negotiations are completed. For example, it records statements and decisions made at each stage of the negotiations and creates a report based on that. In this way, the transparency and efficiency of negotiations can be improved by recording the progress of negotiations in real time and automatically generating detailed reports.

[0087] The information sharing unit automatically generates legal documents related to negotiations, minimizing legal risks. For example, the information sharing unit allows the generation AI to automatically generate legal documents related to negotiations, minimizing legal risks. For example, it automatically generates contracts and agreements. The information sharing unit also allows the generation AI to analyze legal risks during negotiations and generate appropriate legal documents based on that analysis. For example, it creates contracts based on specific conditions. The information sharing unit also allows the generation AI to learn from past legal documents and automatically generate legal documents related to negotiations. For example, it creates new contracts based on past contracts. In this way, legal risks can be minimized by automatically generating legal documents related to negotiations.

[0088] The information sharing unit can record emotional elements that arise during the negotiation process and create documents based on them. For example, the information sharing unit allows the generation AI to record emotional elements that arise during the negotiation process in real time and create documents based on them. For example, it records emotional changes during negotiations and creates reports that reflect them. The information sharing unit also allows the generation AI to analyze emotional elements during negotiations and create documents based on that data. For example, it records emotional changes as the negotiation progresses and creates reports based on that. The information sharing unit also allows the generation AI to learn emotional elements that arise during the negotiation process based on past data and create documents based on that. For example, it records reactions to specific emotional states and creates reports based on that. In this way, by recording emotional elements that arise during the negotiation process and creating documents based on that, the transparency and efficiency of negotiations can be improved.

[0089] The information sharing unit can support the creation of documents in different formats. For example, the information sharing unit supports the generation AI in creating documents in different formats and automatically generates presentations and videos. For example, creating a presentation summarizing the progress of negotiations. The information sharing unit also allows the generation AI to collect data during negotiations and create documents in different formats based on that data. For example, creating a video summarizing the results of the negotiations. The information sharing unit also supports the generation AI in creating documents in different formats based on past data. For example, creating a new presentation based on past presentations. In this way, by supporting the creation of documents in different formats, the transparency and efficiency of negotiations can be improved.

[0090] The information sharing unit can share data related to negotiations on the cloud, enabling real-time collaborative editing. The information sharing unit, for example, allows the generation AI to share data related to negotiations on the cloud, enabling real-time collaborative editing. For example, the progress of the negotiations can be shared on the cloud, allowing multiple users to edit simultaneously. The information sharing unit also allows the generation AI to share data on the cloud during negotiations, supporting real-time collaborative editing. For example, the results of the negotiations can be shared on the cloud, allowing multiple users to edit simultaneously. The information sharing unit also supports collaborative editing on the cloud by the generation AI based on past data. For example, past negotiation data can be shared on the cloud, allowing multiple users to edit simultaneously. This allows data related to negotiations to be shared on the cloud, enabling real-time collaborative editing, thereby improving the transparency and efficiency of negotiations.

[0091] The information sharing unit can use the emotion estimation function to adjust the tone and style of a document according to the negotiator's emotions. In the information sharing unit, for example, the generation AI analyzes the negotiator's emotions in real time and adjusts the tone and style of a document according to the emotions. For example, if the negotiator is nervous, the generation AI creates a document with a relaxed tone. In addition, the information sharing unit uses the emotion estimation function to have the generation AI adjust the tone and style of a document according to the negotiator's emotions. For example, the generation AI creates a document that includes encouraging words that will help the negotiator feel confident. In addition, the information sharing unit has the generation AI learn the negotiator's emotional state based on past data and provide the tone and style of a document according to the emotion. For example, the generation AI creates a document using a tone and style that is effective for a particular emotional state. In this way, the effectiveness of negotiations can be improved by adjusting the tone and style of a document according to the negotiator's emotions.

[0092] The virtual presence unit can infer the intentions of the negotiating partner and, based on that, ask appropriate questions and make appropriate proposals to the negotiator. For example, the generation AI of the virtual presence unit analyzes the negotiating partner's remarks and actions to infer their intentions. For example, it understands what the other party wants and asks appropriate questions and makes appropriate proposals based on that. The virtual presence unit also analyzes the other party's intentions in real time during negotiations and, based on that, asks appropriate questions and makes appropriate proposals to the negotiator. For example, if the other party wants specific conditions, it asks questions related to those conditions. The virtual presence unit also learns the intentions of the negotiating partner based on past data and, based on that, asks appropriate questions and makes appropriate proposals. For example, it asks questions and makes effective proposals for specific intentions. This makes it possible to infer the intentions of the negotiating partner and, based on that, improve the effectiveness of negotiations.

[0093] The virtual presence unit can provide a culturally appropriate virtual presence by taking into account the cultural background of the negotiating partner. For example, the generation AI collects data on the cultural background of the negotiating partner and provides a virtual presence based on that data. For example, it creates an avatar that reflects the etiquette and manners of a particular culture. The virtual presence unit also analyzes the cultural background of the negotiating partner in real time during negotiations and adjusts the virtual presence based on that analysis. For example, it uses culturally appropriate expressions and gestures. The virtual presence unit also learns the cultural background of the negotiating partner based on past data and provides a virtual presence based on that analysis. For example, it creates an avatar that reflects the communication style of a particular culture. This allows the generation AI to take into account the cultural background of the negotiating partner and provide a culturally appropriate virtual presence, thereby improving the effectiveness of negotiations.

[0094] The virtual presence unit can support virtual presence on different platforms. For example, the generation AI in the virtual presence unit supports virtual presence on different platforms, enabling negotiations using VR or AR. For example, it creates an avatar that supports negotiations in a VR space. The virtual presence unit also supports virtual presence on different platforms in real time during negotiations using the generation AI. For example, in negotiations using AR, it provides an avatar that reflects the other party's facial expressions and gestures. The virtual presence unit also supports virtual presence on different platforms using past data from the generation AI. For example, in negotiations using VR or AR, it creates an effective avatar. This allows the effectiveness of negotiations to be improved by supporting virtual presence on different platforms.

[0095] The virtual presence unit can analyze the negotiating partner's past virtual presence data and propose an optimal communication strategy. In the virtual presence unit, for example, the generation AI collects and analyzes the negotiating partner's past virtual presence data. For example, it analyzes the avatar's reactions and actions in past negotiations. In addition, the virtual presence unit proposes an optimal communication strategy during negotiations based on the past virtual presence data. For example, it proposes an effective strategy with reference to past data. In addition, the virtual presence unit learns the negotiating partner's virtual presence patterns based on past data and proposes an optimal communication strategy based on that. For example, it proposes an effective strategy for a specific pattern. In this way, by analyzing the negotiating partner's past virtual presence data, it is possible to propose an optimal communication strategy and improve the effectiveness of negotiations.

[0096] The virtual presence unit uses an emotion estimation function to adjust the facial expressions and behavior of the virtual avatar in real time according to the emotions of the negotiating partner. For example, the generation AI of the virtual presence unit analyzes the emotions of the negotiating partner in real time and adjusts the facial expressions and behavior of the virtual avatar accordingly. For example, if the partner smiles, the avatar smiles back. The virtual presence unit also uses the emotion estimation function to adjust the facial expressions and behavior of the virtual avatar in real time according to the emotions of the negotiating partner. For example, if the partner shows anger, the avatar responds calmly. The virtual presence unit also uses the generation AI to learn the emotional patterns of the negotiating partner based on past data and adjust the facial expressions and behavior of the virtual avatar based on that. For example, it creates an avatar that responds effectively to a specific emotional state. This allows the facial expressions and behavior of the virtual avatar to be adjusted in real time according to the emotions of the negotiating partner, supporting more effective communication.

[0097] The security department can analyze the access history of the negotiation data and detect signs of unauthorized access. For example, the generation AI analyzes the access history of the negotiation data in real time to detect signs of unauthorized access. For example, it detects abnormal access patterns and issues an alert. The security department also has the generation AI identify signs of unauthorized access based on the access history of the negotiation data. For example, it detects access from unusual times or locations. The security department also has the generation AI learn the access history of the negotiation data based on past data and predict signs of unauthorized access. For example, it detects unauthorized access based on specific patterns. This makes it possible to strengthen the security of the negotiation data by detecting signs of unauthorized access.

[0098] The security unit can dynamically adjust the encryption level of the negotiation data to provide optimal security. For example, the generation AI analyzes the encryption level of the negotiation data in real time to provide optimal security. For example, the encryption level is adjusted according to the importance of the data. The security unit also provides optimal security based on the encryption level of the negotiation data, with the generation AI applying strong encryption for highly confidential data. The security unit also learns the encryption level of the negotiation data based on past data, with the generation AI dynamically adjusting the encryption level based on specific conditions. This makes it possible to optimize the security of the negotiation data by dynamically adjusting the encryption level.

[0099] The security department can assess the privacy risks of the negotiation data and propose countermeasures according to the risks. For example, the security department has the generation AI assess the privacy risks of the negotiation data in real time and propose countermeasures according to the risks. For example, it provides guidelines on data handling. The security department also has the generation AI propose appropriate countermeasures based on the privacy risks of the negotiation data. For example, it proposes data anonymization and access control. The security department also has the generation AI learn the privacy risks of the negotiation data based on past data and propose countermeasures according to the risks. For example, it provides effective countermeasures for specific risks. In this way, the privacy of the negotiation data can be protected by assessing privacy risks and proposing appropriate countermeasures.

[0100] The security unit can integrate different security protocols and provide multiple layers of security. For example, the generation AI can integrate different security protocols and provide multiple layers of security. For example, it can combine data encryption, access control, and a monitoring system. In addition, the generation AI can integrate multiple security protocols to enhance the security of the negotiation data. For example, it can simultaneously apply data encryption and access control. In addition, the generation AI can learn different security protocols based on past data and provide multiple layers of security. For example, it can dynamically adjust security protocols based on specific conditions. This allows the security of the negotiation data to be enhanced by integrating different security protocols and providing multiple layers of security.

[0101] The security department can use the emotion estimation function to provide security alerts that correspond to the negotiator's emotions, thereby increasing the sense of security. For example, the generation AI in the security department analyzes the negotiator's emotions in real time and provides security alerts that correspond to the emotions. For example, if the negotiator is feeling anxious, an alert to strengthen security is issued. The security department also uses the emotion estimation function to provide security alerts that correspond to the negotiator's emotions. For example, it proposes security measures that will make the negotiator feel secure. The security department also uses the generation AI to learn the negotiator's emotional state based on past data and provide security alerts that correspond to the emotions. For example, it provides security measures that are effective for specific emotional states. In this way, by providing security alerts that correspond to the negotiator's emotions, it is possible to increase the sense of security and improve the effectiveness of negotiations.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The AI ​​negotiation assistant can also be equipped with a cultural analysis unit that proposes strategies that take into account the cultural background of the negotiating partner. The cultural analysis unit, for example, collects cultural characteristics of the negotiating partner's country or region and adjusts the negotiation strategy based on that information. For example, it makes proposals that take into account the etiquette and manners of a particular culture. The cultural analysis unit can also suggest appropriate communication methods based on the negotiating partner's cultural background. For example, using culturally appropriate expressions and gestures can increase the success rate of negotiations. Furthermore, the cultural analysis unit can analyze the impact of cultural background on negotiations based on past negotiation data and propose optimal strategies.

[0104] The AI ​​negotiation assistant can also be equipped with a health management unit that monitors the negotiator's health condition and provides advice based on it. The health management unit, for example, monitors the negotiator's heart rate and stress level in real time and provides advice based on that data. For example, if stress is high, it can suggest ways to relax. The health management unit can also provide appropriate advice based on the negotiator's physical condition. For example, if the heart rate is elevated, it can encourage deep breathing. Furthermore, the health management unit can learn the negotiator's physical condition based on past data and provide advice based on that.

[0105] The AI ​​negotiation assistant may further include a social media analysis unit that analyzes the social media activity of the negotiating partner and extracts information advantageous to the negotiation. The social media analysis unit may, for example, analyze the social media activity of the negotiating partner to identify their interests. For example, it may analyze what topics the negotiating partner is interested in. The social media analysis unit may also propose strategies advantageous to the negotiation based on information obtained from the negotiating partner's social media activity. For example, it may make proposals related to the negotiating partner's interests. Furthermore, the social media analysis unit may identify the negotiating partner's personality and values ​​and propose strategies based on them.

[0106] The AI ​​negotiation assistant can also be equipped with an industry analysis unit that analyzes negotiation data from different industries and proposes industry-specific negotiation strategies. The industry analysis unit, for example, collects negotiation data from different industries and analyzes the negotiation styles and patterns of each industry. For example, it analyzes the differences in negotiation styles between the technology industry and the financial industry. The industry analysis unit can also propose industry-specific negotiation strategies. For example, in the technology industry, it might propose a strategy that emphasizes technical details, while in the financial industry, it might propose a strategy that emphasizes risk management. Furthermore, the industry analysis unit can analyze past successes and failures to propose optimal strategies.

[0107] The AI ​​negotiation assistant can also be equipped with a report generation unit that records the progress of negotiations in real time and automatically generates a detailed report after the negotiations are completed. The report generation unit, for example, records statements and decisions made at each stage of the negotiations. The report generation unit can also collect data in real time during negotiations and generate a report based on that data after the negotiations are completed. For example, it can create a report summarizing the progress and results of the negotiations. Furthermore, the report generation unit can record the progress of negotiations based on past data and create a report based on that.

[0108] The AI ​​negotiation assistant can also be equipped with an emotional care unit that suggests relaxation methods and stress reduction measures according to the negotiator's emotions. The emotional care unit, for example, analyzes the negotiator's emotions in real time and suggests relaxation methods according to the emotions. For example, if the negotiator is nervous, it may suggest deep breathing or meditation. The emotional care unit can also use an emotion estimation function to suggest stress reduction measures according to the negotiator's emotions. For example, if stress is high, it may suggest listening to relaxing music. Furthermore, the emotional care unit can learn the negotiator's emotional state based on past data and provide relaxation methods and stress reduction measures according to the emotions.

[0109] The AI ​​negotiation assistant can also be equipped with an emotional response unit that adjusts the facial expressions and behavior of the virtual avatar in real time according to the emotions of the negotiating partner. The emotional response unit, for example, analyzes the emotions of the negotiating partner in real time and adjusts the facial expressions and behavior of the virtual avatar accordingly. For example, if the negotiating partner smiles, the avatar smiles back. The emotional response unit can also use an emotion estimation function to adjust the facial expressions and behavior of the virtual avatar in real time according to the emotions of the negotiating partner. For example, if the negotiating partner shows anger, the avatar will respond calmly. Furthermore, the emotional response unit can learn the emotional patterns of the negotiating partner based on past data and adjust the facial expressions and behavior of the virtual avatar based on that.

[0110] The AI ​​negotiation assistant can also be equipped with a document adjustment unit that adjusts the tone and style of a document according to the negotiator's emotions. The document adjustment unit, for example, analyzes the negotiator's emotions in real time and adjusts the tone and style of a document according to the emotions. For example, if the negotiator is nervous, it creates a document with a relaxed tone. The document adjustment unit can also adjust the tone and style of a document according to the negotiator's emotions using an emotion estimation function. For example, it can create a document that includes words of encouragement to help the negotiator feel confident. Furthermore, the document adjustment unit can learn the negotiator's emotional state based on past data and provide a document tone and style according to the emotion.

[0111] The AI ​​negotiation assistant can also be equipped with a security emotion unit that provides security alerts according to the negotiator's emotions, enhancing their sense of security. The security emotion unit, for example, analyzes the negotiator's emotions in real time and provides security alerts according to their emotions. For example, if the negotiator feels anxious, it may issue an alert to strengthen security. The security emotion unit can also use an emotion estimation function to provide security alerts according to the negotiator's emotions. For example, it can suggest security measures that will make the negotiator feel more secure. Furthermore, the security emotion unit can learn the negotiator's emotional state based on past data and provide security alerts according to their emotions.

[0112] The AI ​​negotiation assistant can also be equipped with an emotional strategy unit that proposes a strategy based on the emotions of the negotiating partner. The emotional strategy unit, for example, analyzes the emotions of the negotiating partner in real time and detects changes in emotion. For example, if the other party shows anger, it proposes a calm response. The emotional strategy unit can also use an emotion estimation function to propose a strategy based on the emotions of the negotiating partner. For example, if the other party is relaxed, it makes an aggressive proposal. Furthermore, the emotional strategy unit can predict changes in the emotions of the negotiating partner based on past data and propose an appropriate strategy. For example, it can predict when the other party's emotions will change and propose a strategy that matches that timing.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The data analysis unit analyzes the data. For example, the data analysis unit collects and analyzes data on the past behavior of the negotiating partner. It can also analyze market trends and economic indicators, and can analyze the data using machine learning algorithms. Step 2: The prediction unit predicts future negotiation results based on the data analyzed by the data analysis unit. For example, it can predict what strategies will be effective in the next negotiation based on past negotiation data, and can also predict negotiation results using time series prediction and regression analysis. Step 3: The advisory unit provides advice to the negotiator based on the results predicted by the prediction unit. For example, it provides advice on the negotiator's choice of remarks and timing, as well as advice on how to improve the negotiation strategy. Advice can also be provided via text message or voice assistant. Step 4: The feedback unit provides feedback to the negotiator based on the advice provided by the advice unit. For example, the feedback unit can provide real-time feedback based on the negotiator's statements and actions, or provide feedback at a later date. It can also provide real-time text message or voice feedback. Step 5: The information sharing section shares information necessary for the negotiation. For example, it can share the negotiation goals and conditions, documents, and database entries. It can also share information on the cloud, enabling real-time collaborative editing. Step 6: The document creation section creates documents based on the information shared by the information sharing section. For example, it can automatically create documents outlining the goals and conditions of negotiations, and can also create contracts and meeting minutes. It can also create presentations and videos. Step 7: The virtual presence unit supports dialogue with the negotiating partner through a virtual or voice-based interface. For example, it analyzes the negotiating partner's facial expressions and tone of voice to generate responses based on their emotions. It can also infer the negotiating partner's intentions and ask appropriate questions or make suggestions to the negotiator based on those. It can also support virtual presence using VR or AR. Step 8: The security department protects the security and privacy of the negotiation data. For example, it can encrypt data, control access, analyze the access history of the negotiation data, and detect signs of unauthorized access. It can also automatically back up the negotiation data to minimize the risk of data loss.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 AI 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.

[0132] 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 AI 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 AI 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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]

[0182] 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 analyzes the data; a prediction unit that predicts future negotiation results based on the data analyzed by the data analysis unit; an advice unit that provides advice to a negotiator based on the results predicted by the prediction unit; a feedback unit that provides feedback to the negotiator based on the advice provided by the advice unit; An information sharing department that shares information necessary for negotiations; a document creation unit that creates a document based on the information shared by the information sharing unit; a virtual presence unit that supports interaction with the negotiation partner through a virtual or voice-based interface; a security unit that protects the security and privacy of the negotiation data. A system characterized by:

2. The data analysis unit Analyze the past behavioral data of the negotiating partner to understand the negotiating style and patterns of the negotiating partner 2. The system of claim 1.

3. The advice unit Analyze the negotiator's statements and behavior data to generate appropriate advice 2. The system of claim 1.

4. The feedback unit Provide real-time feedback based on what the negotiator says and does 2. The system of claim 1.

5. The information sharing unit Collect information necessary for negotiations and provide it to the negotiators 2. The system of claim 1.

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