System

Abstract

To provide a system according to the embodiment which analyzes a content and tone of voice of a party concerned, compiles issues, and proposes an appropriate solution in arbitration of a quarrel or dispute.SOLUTION: A system according to the embodiment includes a reception part, an analysis part, a compilation part, and a proposition part. The reception part inputs a story content. The analysis part analyzes the story content and tone of voice inputted by the reception part. The compilation part compiles issues of the story analyzed by the analysis part. The proposition part proposes appropriate solutions on the basis of the issues compiled by the compilation part.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in mediating arguments or disputes in that it was difficult to properly analyze the content and tone of voice of the parties involved, summarize the issues, and propose a solution.

[0005] The system according to the embodiment aims to analyze the content and tone of voice of the parties involved in arbitrating a quarrel or dispute, summarize the points of contention, and propose an appropriate solution. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a summarization unit, and a proposal unit. The reception unit inputs the content of the talk. The analysis unit analyzes the content of the talk and tone of voice input by the reception unit. The summarization unit summarizes the issues in the talk analyzed by the analysis unit. The proposal unit proposes an appropriate solution based on the issues summarized by the summarization unit. Effect of the Invention

[0007] When arbitrating a quarrel or dispute, the system according to the embodiment can analyze the content and tone of voice of the parties involved, summarize the issues, and propose an appropriate solution. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The quarrel arbitration system according to the embodiment of the present invention is a system that arbitrates quarrels using a generation AI. In this system, the parties input the contents of the talk, and the generation AI analyzes the contents and tone of voice, summarizes the points of dispute, and proposes an appropriate solution. For example, the parties input the contents of the talk into an app. At this time, the parties can freely input their own opinions and feelings. For example, the parties input the contents of the talk, such as "I am angry that he did not keep his promise." This information is input into the generation AI. Next, the generation AI analyzes the input contents of the talk and the tone of voice. The generation AI analyzes the contents of the talk and identifies which part is the point of dispute. In addition, by analyzing the tone of voice, it is possible to grasp the strength of the emotions and the degree of tension of the parties. For example, the analysis result shows that the part "he did not keep his promise" is the point of dispute, and that strong anger can be felt from the tone of voice. Furthermore, the generation AI considers an appropriate solution. The generation AI considers the points of dispute in the talk and the emotions of the parties, and proposes the most appropriate solution. For example, it can propose a solution such as "ask him to apologize." This proposal is displayed to the parties. This mechanism allows the parties to proceed with the discussion calmly. The generative AI analyzes the content of the conversation from a third-party perspective and proposes appropriate solutions, allowing the parties to proceed with the discussion without becoming emotional. In addition, by analyzing the tone of voice, the generative AI can grasp changes in the parties' emotions and propose solutions at the appropriate time. For example, proposing a solution when the parties have calmed down will help the discussion proceed smoothly. In this way, the use of generative AI makes it possible to realize a system that can efficiently mediate arguments. As a result, the argument mediation system can analyze the content and tone of voice of the parties, summarize the points of contention, and propose appropriate solutions.

[0029] The dispute arbitration system according to the embodiment includes a reception unit, an analysis unit, a summary unit, and a proposal unit. The reception unit is used by the parties to input the contents of the talk. The contents of the talk include, for example, the opinions and feelings of the parties, but are not limited to such examples. The reception unit can receive, for example, text input and voice input. The analysis unit uses a generation AI to analyze the contents of the talk and the tone of voice input by the reception unit. The analysis unit, for example, analyzes the contents of the talk and identifies which part is the point of dispute. The analysis unit can also grasp the intensity of the emotions and the degree of tension of the parties by analyzing the tone of voice. For example, the generation AI can analyze the contents of the talk using a text generation AI (for example, LLM) and identify the points of dispute. The generation AI can also analyze the tone of voice using voice analysis technology and grasp the intensity of the emotions and the degree of tension. The summary unit uses the generation AI to summarize the points of dispute in the talk analyzed by the analysis unit. The summary unit, for example, extracts important parts of the contents of the talk and organizes the points of dispute. The proposal unit uses the generation AI to propose an appropriate solution based on the points of dispute summarized by the summary unit. The suggestion unit, for example, considers the points of contention in the story and the emotions of the parties, and proposes the most appropriate solution. For example, the generation AI can propose a solution by referring to past success stories and the opinions of experts. As a result, the fight arbitration system according to the embodiment can analyze the content of the parties' stories and the tone of voice, summarize the points of contention, and propose an appropriate solution. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit displays a solution to the parties based on the solution proposed by the generation AI. Furthermore, the suggestion unit has a function of evaluating the reliability of the solution. For example, the suggestion unit can evaluate the reliability of the solution proposed by the generation AI and preferentially propose a highly reliable solution. In addition, the suggestion unit has a function of accepting feedback from the user. For example, the suggestion unit can collect feedback from the parties and use it to improve the system. As a result, the fight arbitration system can analyze the content of the parties' stories and the tone of voice, summarize the points of contention, and propose an appropriate solution.

[0030] The reception unit receives input of the contents of the talk by the parties. The contents of the talk include, for example, the opinions and feelings of the parties, but are not limited to such examples. The reception unit can receive, for example, text input and voice input. Specifically, in the case of text input, the parties can input their opinions and feelings using a keyboard or a touch screen. In the case of voice input, the parties speak through a microphone, and the system converts the voice into text. Voice input is particularly effective for maintaining a natural flow of conversation, and can capture the nuances of emotions more accurately. Furthermore, the reception unit has a function of temporarily storing the input data and transmitting it to the subsequent analysis unit and summary unit. This allows the reception unit to efficiently collect the contents of the talk of the parties and smoothly proceed with the processing of the entire system. In addition, the reception unit also has a security function for protecting the privacy of the input data, and can safely manage the information of the parties by encrypting data and controlling access. This allows the reception unit to provide an environment in which the parties can input the contents of the talk with peace of mind, and improve the reliability of the entire system.

[0031] The analysis unit uses the generation AI to analyze the contents of the conversation and the tone of voice input by the reception unit. For example, the analysis unit analyzes the contents of the conversation to identify which part is the point of contention. In addition, the analysis unit can grasp the strength of the emotions and the degree of tension of the parties by analyzing the tone of voice. Specifically, the generation AI analyzes the contents of the conversation using a text generation AI (for example, LLM) to identify the points of contention. The text generation AI has learned a large amount of text data, and can extract important keywords and phrases from the contents of the conversation input and clarify the points of contention. In addition, the generation AI can analyze the tone of voice using voice analysis technology to grasp the strength of emotions and the degree of tension. The voice analysis technology analyzes characteristics such as the pitch, tempo, and volume of the voice to evaluate the emotional state of the parties. For example, if the voice becomes higher or faster, it is determined that the tension or anger is strong. As a result, the analysis unit can comprehensively analyze both the contents of the conversation and the tone of voice and accurately grasp the emotions and points of contention of the parties. Furthermore, the analysis unit can perform more accurate analysis by referring to past data and similar cases. This enables the analysis department to accurately understand the content and emotions of the parties involved and support the arbitration process throughout the system.

[0032] The summary unit uses the generation AI to summarize the issues in the story analyzed by the analysis unit. For example, the summary unit extracts important parts of the content of the story and organizes the issues. Specifically, the generation AI extracts important keywords and phrases from the content of the story and organizes them to clarify the issues. The generation AI has learned a large amount of text data and can efficiently summarize the content of the story. For example, it organizes the opinions and feelings of the parties and identifies which parts are the most important. The summary unit also has a function to visually display the issues and provide information in a format that is easy for the parties to understand. For example, it visualizes the issues using graphs and charts so that the parties can understand them at a glance. Furthermore, the summary unit can organize the issues by referring to past data and similar cases. This allows the summary unit to efficiently organize the content of the story and provide information in a format that is easy for the parties to understand. This allows the summary unit to efficiently organize the content of the story of the parties and support the arbitration process of the entire system.

[0033] The proposal unit uses the generation AI to propose an appropriate solution based on the issues summarized by the summary unit. The proposal unit, for example, considers the issues in the story and the emotions of the parties and proposes the most appropriate solution. Specifically, the generation AI can propose a solution by referring to past success cases and expert opinions. The generation AI has learned a large amount of data and can identify the most effective solution based on similar past cases and expert opinions. For example, by referring to past success cases, it proposes a solution that was effective in a similar situation. In addition, the proposal unit has a function to evaluate the reliability of the solution, and can evaluate the reliability of the solution proposed by the generation AI and preferentially propose a highly reliable solution. Furthermore, the proposal unit has a function to accept feedback from users, and can collect feedback from the parties and use it to improve the system. For example, it evaluates whether the proposed solution was actually effective and reflects it in the next proposal. As a result, the proposal unit can always provide a highly accurate solution based on the latest information and improve the satisfaction of the parties. As a result, the proposal unit can consider the content and emotions of the parties' stories and propose the most appropriate solution.

[0034] The quarrel arbitration system includes a reference section that refers to specific past cases or success cases. The reference section refers to past cases and success cases using a generation AI. For example, the reference section can search a database for similar past cases and refer to them. The reference section can also improve the accuracy of the solution based on successful projects and performance data. For example, the reference section analyzes past success cases and proposes the most effective solution. As a result, the accuracy of the solution is improved by referring to past cases and success cases. Some or all of the above-mentioned processing in the reference section may be performed, for example, using AI, or may be performed without using AI. For example, the reference section can propose a solution based on past cases and success cases searched by the generation AI. Furthermore, the reference section has a function of updating past cases and success cases in real time. For example, the reference section can automatically collect the latest cases and success cases and update the database. As a result, the reference section can always propose a solution based on the latest information.

[0035] The quarrel arbitration system includes an evaluation unit that evaluates the reliability of the solution. The evaluation unit uses a generation AI to evaluate the reliability of the solution. For example, the evaluation unit can evaluate the reliability of the solution proposed by the generation AI and preferentially propose a highly reliable solution. The evaluation unit evaluates the reliability of the solution, for example, based on performance data or a third party evaluation. For example, the evaluation unit can evaluate the reliability of the solution by referring to past success stories and expert opinions. As a result, the reliability of the proposal is improved by evaluating the reliability of the solution. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit displays the solution to the parties based on the reliability of the solution evaluated by the generation AI. Furthermore, the evaluation unit has a function of evaluating the reliability of the solution in real time. For example, the evaluation unit can evaluate the reliability of the solution based on the latest data and propose a highly reliable solution. As a result, the evaluation unit can always evaluate the reliability of the solution based on the latest information.

[0036] The fight arbitration system includes a feedback unit that accepts feedback from users. The feedback unit accepts feedback from users using a generation AI. For example, the feedback unit can collect feedback from the parties and use it to improve the system. The feedback unit collects feedback, for example, through questionnaires and user reviews. For example, the feedback unit can conduct a questionnaire for the parties and collect opinions on the usability of the system and the effectiveness of the solution. This allows the system to be improved by accepting feedback from users. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can identify improvements to the system based on the feedback collected by the generation AI. Furthermore, the feedback unit has a function of adjusting the feedback collection method in real time. For example, the feedback unit can select the optimal collection method depending on the situation of the parties. This allows the feedback unit to always collect feedback in the optimal way and use it to improve the system.

[0037] The analysis unit can analyze the content of the talk and specifically identify which part is the point of contention. The analysis unit uses the generation AI to analyze the content of the talk and identify which part is the point of contention. For example, the analysis unit uses a text generation AI (e.g., LLM) to analyze the content of the talk and identify the point of contention. The generation AI can analyze the content of the talk and identify the points of contention and important points of discussion. For example, the generation AI can analyze the content of the talk and identify the part that "the promise was not kept" as the point of contention. As a result, by analyzing the content of the talk and identifying the point of contention, an appropriate solution can be proposed. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can propose a solution to the parties based on the point of contention identified by the generation AI. Furthermore, the analysis unit has a function of adjusting the method of identifying the point of contention in real time. For example, the analysis unit can select the optimal identification method depending on the situation of the parties. As a result, the analysis unit can always identify the point of contention in the optimal way and propose an appropriate solution.

[0038] The analysis unit can analyze the tone of voice and specifically grasp the intensity of the emotions and the degree of tension of the parties. The analysis unit uses the generation AI to analyze the tone of voice and grasp the intensity of the emotions and the degree of tension of the parties. For example, the analysis unit can analyze the tone of voice using voice analysis technology and grasp the intensity of the emotions and the degree of tension. The generation AI can analyze the tone, pitch, speed, etc. of the tone of voice and identify the intensity of the emotions and the degree of tension of the parties. For example, the generation AI can analyze the tone of voice and identify the intensity of the emotions when the parties feel strong anger. In addition, the generation AI can analyze the tone of voice and grasp the degree of tension when the parties are nervous. In this way, the intensity of the emotions and the degree of tension of the parties can be grasped by analyzing the tone of voice. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can identify the intensity of the emotions and the degree of tension of the parties based on the data of the tone of voice analyzed by the generation AI. Furthermore, the analysis unit has a function of adjusting the analysis method of the tone of voice in real time. For example, the analysis unit can select the optimal analysis method according to the situation of the parties. This enables the analysis unit to always analyze the tone of voice in the most optimal way and grasp the intensity of the person's emotions and level of tension.

[0039] The reception unit can analyze the user's past input history and provide an optimal input interface. The reception unit uses the generation AI to analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display phrases and keywords that have been frequently input in the past as candidates. In addition, the reception unit can preferentially suggest input methods (such as voice and text) that have been used in the past. Furthermore, the reception unit can predict and suggest phrases and keywords that will be used in a specific time period from the past input history. As a result, the reception unit can provide an optimal input interface to the user by analyzing the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide an optimal input interface based on the past input history analyzed by the generation AI. Furthermore, the reception unit has a function of adjusting the method of providing the input interface in real time. For example, the reception unit can select the optimal providing method depending on the user's situation. As a result, the reception unit can always provide an input interface in an optimal manner, improving user convenience.

[0040] The reception unit can filter the input contents based on the user's current situation or interests when inputting the contents of the talk. The reception unit uses the generation AI to filter the input contents based on the user's current situation and interests when inputting the contents of the talk. For example, the reception unit can automatically suggest related keywords when the user inputs the current situation. The reception unit can also filter the input contents based on the user's interests and preferentially display highly relevant information. Furthermore, the reception unit can simplify the input contents according to the user's current situation and allow only necessary information to be input. This makes it possible to provide highly relevant information by filtering the input contents based on the user's current situation and interests. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide optimal information to the user based on the input contents filtered by the generation AI. Furthermore, the reception unit has a function of adjusting the filtering method of the input contents in real time. For example, the reception unit can select the optimal filtering method according to the user's situation. This makes it possible for the reception unit to always filter the input contents in the optimal manner and provide highly relevant information.

[0041] The reception unit can preferentially input highly relevant content by specifically considering the geographical location information of the user when inputting the content of the talk. The reception unit uses the generation AI to preferentially input highly relevant content by considering the geographical location information of the user when inputting the content of the talk. For example, when the user is in a specific location, the reception unit can preferentially input information related to the location. Furthermore, the reception unit can automatically suggest related issues based on the geographical location information of the user. Furthermore, when the user is moving, the reception unit can preferentially input information related to the current location. As a result, highly relevant information can be preferentially input by considering the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide optimal information to the user based on the geographical location information considered by the generation AI. Furthermore, the reception unit has a function of adjusting the method of considering the geographical location information in real time. For example, the reception unit can select the optimal method of considering according to the user's situation. As a result, the reception unit can always consider the geographical location information in the optimal manner and provide highly relevant information.

[0042] The reception unit can analyze the social media activity of the user when inputting the content of the talk, and input the related content. The reception unit can use the generation AI to analyze the social media activity of the user when inputting the content of the talk, and input the related content. For example, the reception unit can extract related keywords from the user's social media posts and reflect them in the input content. The reception unit can also analyze the user's social media activity and automatically suggest related issues. Furthermore, the reception unit can analyze the user's emotional expression on social media and adjust the input content. This makes it possible to provide highly relevant information by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can provide optimal information to the user based on the social media activity analyzed by the generation AI. Furthermore, the reception unit has a function of adjusting the analysis method of social media activity in real time. For example, the reception unit can select the optimal analysis method according to the user's situation. This makes it possible for the reception unit to always analyze social media activity in the optimal way and input the related content.

[0043] The analysis unit can improve the accuracy of the analysis by referring to similar cases in the past when analyzing the content of the talk. The analysis unit can improve the accuracy of the analysis by referring to similar cases in the past when analyzing the content of the talk using the generation AI. For example, the analysis unit can search for similar cases in the past from a database and reflect them in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to successful examples of similar cases. Furthermore, the analysis unit can reduce the risk of the analysis by referring to failed examples of similar cases. As a result, the accuracy of the analysis is improved by referring to similar cases in the past. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on similar cases in the past searched by the generation AI. Furthermore, the analysis unit has a function of adjusting the method of referring to similar cases in real time. For example, the analysis unit can select the optimal reference method according to the user's situation. As a result, the analysis unit can always refer to similar cases in the past in the optimal way and reflect them in the analysis of the content of the talk.

[0044] The analysis unit can perform the analysis by taking into account the attribute information of the user when analyzing the content of the talk. The analysis unit performs the analysis by taking into account the attribute information of the user using the generation AI when analyzing the content of the talk. For example, the analysis unit can adjust the viewpoint of the analysis by taking into account the age and gender of the user. In addition, the analysis unit can adjust the content of the analysis by taking into account the occupation and social status of the user. Furthermore, the analysis unit can adjust the method of analysis by taking into account the cultural background of the user. As a result, more appropriate analysis is possible by taking into account the attribute information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on the attribute information of the user taken into account by the generation AI. Furthermore, the analysis unit has a function of adjusting the method of taking into account the attribute information in real time. For example, the analysis unit can select the optimal method of taking into account depending on the situation of the user. As a result, the analysis unit can always take into account the attribute information of the user in the optimal method and analyze the content of the talk.

[0045] The analysis unit can perform the analysis by taking into account the geographical distribution when analyzing the content of the talk. The analysis unit performs the analysis by taking into account the geographical distribution using the generation AI when analyzing the content of the talk. For example, the analysis unit can analyze issues specific to a region based on the location of the user. In addition, the analysis unit can perform an analysis that reflects the characteristics of each region by taking into account the geographical distribution. Furthermore, the analysis unit can propose a solution for each region based on the geographical distribution. This makes it possible to analyze issues specific to a region by taking into account the geographical distribution. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on the geographical distribution taken into account by the generation AI. Furthermore, the analysis unit has a function of adjusting the method of taking into account the geographical distribution in real time. For example, the analysis unit can select the optimal method of taking into account depending on the user's situation. This makes it possible for the analysis unit to always take into account the geographical distribution in the optimal manner and analyze the content of the talk.

[0046] The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing the content of the story. The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing the content of the story using the generation AI. For example, the analysis unit can search for related literature from a database and reflect it in the analysis. In addition, the analysis unit can improve the accuracy of the analysis by referring to successful examples of related literature. Furthermore, the analysis unit can reduce the risk of the analysis by referring to unsuccessful examples of related literature. As a result, the accuracy of the analysis is improved by referring to related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on the related literature searched by the generation AI. Furthermore, the analysis unit has a function of adjusting the reference method of related literature in real time. For example, the analysis unit can select the optimal reference method according to the user's situation. As a result, the analysis unit can always refer to related literature in an optimal manner and reflect it in the analysis of the content of the story.

[0047] The summarizing unit can select the optimal method by referring to past summarizing methods when summarizing the issues. The summarizing unit uses the generation AI to select the optimal method by referring to past summarizing methods when summarizing the issues. For example, the summarizing unit can select the optimal way to summarize the issues by referring to past successful cases. Also, the summarizing unit can select a summarizing method that reduces risk by referring to past failure cases. Furthermore, the summarizing unit can search a database for similar past cases and select the optimal summarizing method. As a result, the optimal summarizing method can be selected by referring to past summarizing methods. A part or all of the above-mentioned processing in the summarizing unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarizing unit can provide the optimal summarizing method based on the past summarizing methods searched by the generation AI. Furthermore, the summarizing unit has a function of adjusting the reference method of the summarizing method in real time. For example, the summarizing unit can select the optimal reference method according to the user's situation. As a result, the summarizing unit can always refer to past summarizing methods in the optimal way and reflect them in the summarization of the issues.

[0048] The summarizing unit can summarize the issues by considering the attribute information of the user. The summarizing unit uses the generation AI to summarize the issues by considering the attribute information of the user. For example, the summarizing unit can adjust the way the issues are summarized by considering the age and sex of the user. The summarizing unit can also adjust the way the issues are summarized by considering the occupation and social status of the user. Furthermore, the summarizing unit can adjust the way the issues are summarized by considering the cultural background of the user. This allows for more appropriate summarization by considering the attribute information of the user. A part or all of the above-mentioned processing in the summarizing unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarizing unit can provide an optimal way of summarizing based on the attribute information of the user considered by the generation AI. Furthermore, the summarizing unit has a function of adjusting the way the attribute information is considered in real time. For example, the summarizing unit can select the optimal way of considering depending on the situation of the user. This allows the summarizing unit to always consider the attribute information of the user in the optimal way and summarize the issues.

[0049] The summarizing unit can summarize issues taking into account the geographical distribution when summarizing issues. The summarizing unit uses the generation AI to summarize issues taking into account the geographical distribution when summarizing issues. For example, the summarizing unit can summarize issues specific to a region based on the user's location. In addition, the summarizing unit can summarize issues reflecting the characteristics of each region by taking into account the geographical distribution. Furthermore, the summarizing unit can propose solutions for each region based on the geographical distribution. This allows issues specific to each region to be summarized by taking into account the geographical distribution. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the summarizing unit can provide an optimal summarizing method based on the geographical distribution taken into account by the generation AI. Furthermore, the summarizing unit has a function of adjusting the method of considering the geographical distribution in real time. For example, the summarizing unit can select the optimal method of considering depending on the user's situation. This allows the summarizing unit to always consider the geographical distribution in the optimal way and summarize issues.

[0050] The summarizing unit can improve the accuracy of the summary by referring to related literature when summarizing the issues. The summarizing unit uses the generation AI to improve the accuracy of the summary by referring to related literature when summarizing the issues. For example, the summarizing unit can search for related literature from a database and reflect it in the summary. In addition, the summarizing unit can improve the accuracy of the summary by referring to successful examples of related literature. Furthermore, the summarizing unit can reduce the risk of summarization by referring to unsuccessful examples of related literature. As a result, the accuracy of the summary is improved by referring to related literature. A part or all of the above-mentioned processing in the summarizing unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarizing unit can provide an optimal summarizing method based on the related literature searched by the generation AI. Furthermore, the summarizing unit has a function of adjusting the reference method of related literature in real time. For example, the summarizing unit can select the optimal reference method according to the user's situation. As a result, the summarizing unit can always refer to related literature in the optimal way and reflect it in the summary of the issues.

[0051] The suggestion unit can make an optimal proposal by referring to past success cases when proposing a solution. The suggestion unit uses the generation AI to make an optimal proposal by referring to past success cases when proposing a solution. For example, the suggestion unit can search for past success cases from a database and propose an optimal solution. In addition, the suggestion unit can improve the accuracy of the solution by referring to success cases of similar cases. Furthermore, the suggestion unit can analyze past success cases and propose the most effective solution. As a result, the optimal solution can be proposed by referring to past success cases. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can provide an optimal solution based on past success cases searched by the generation AI. Furthermore, the suggestion unit has a function of adjusting the reference method of the success cases in real time. For example, the suggestion unit can select the optimal reference method according to the user's situation. As a result, the suggestion unit can always refer to past success cases in the optimal way and reflect them in the solution proposal.

[0052] The suggestion unit can make a suggestion taking into account the attribute information of the user when proposing a solution. The suggestion unit makes a suggestion taking into account the attribute information of the user using the generation AI when proposing a solution. For example, the suggestion unit can propose an optimal solution taking into account the age and sex of the user. In addition, the suggestion unit can adjust the content of the solution taking into account the occupation and social status of the user. Furthermore, the suggestion unit can adjust the method of the solution taking into account the cultural background of the user. As a result, a more appropriate solution can be proposed by taking into account the attribute information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide an optimal solution based on the attribute information of the user taken into account by the generation AI. Furthermore, the suggestion unit has a function of adjusting the method of considering the attribute information in real time. For example, the suggestion unit can select the optimal consideration method according to the user's situation. As a result, the suggestion unit can always consider the attribute information of the user in the optimal method and propose a solution.

[0053] The suggestion unit can make a proposal taking into consideration the geographical distribution when proposing a solution. The suggestion unit makes a proposal taking into consideration the geographical distribution using the generation AI when proposing a solution. For example, the suggestion unit can propose a solution specific to a region based on the location of the user. Also, the suggestion unit can propose a solution reflecting the characteristics of each region by taking into consideration the geographical distribution. Furthermore, the suggestion unit can propose a solution for each region based on the geographical distribution. As a result, a solution specific to a region can be proposed by taking into consideration the geographical distribution. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can provide an optimal solution based on the geographical distribution taken into consideration by the generation AI. Furthermore, the suggestion unit has a function of adjusting the method of taking into consideration the geographical distribution in real time. For example, the suggestion unit can select the optimal method of taking into consideration according to the user's situation. As a result, the suggestion unit can always take into consideration the geographical distribution in the optimal manner and propose a solution.

[0054] The suggestion unit can improve the accuracy of the proposal by referring to related literature when proposing a solution. The suggestion unit uses the generation AI to improve the accuracy of the proposal by referring to related literature when proposing a solution. For example, the suggestion unit can search for related literature from a database and reflect it in the proposal. In addition, the suggestion unit can improve the accuracy of the proposal by referring to successful examples of related literature. Furthermore, the suggestion unit can reduce the risk of the proposal by referring to failure examples of related literature. As a result, the accuracy of the proposal is improved by referring to related literature. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide an optimal proposal method based on the related literature searched by the generation AI. Furthermore, the suggestion unit has a function of adjusting the reference method of related literature in real time. For example, the suggestion unit can select the optimal reference method according to the user's situation. As a result, the suggestion unit can always refer to related literature in an optimal manner and reflect it in the proposal of the solution.

[0055] The reference unit can select the optimal case by referring to past success cases when selecting a reference case. The reference unit uses the generation AI to select the optimal case by referring to past success cases when selecting a reference case. For example, the reference unit can search a database for past success cases and select the optimal reference case. The reference unit can also improve the accuracy of the reference case by referring to success cases of similar cases. Furthermore, the reference unit can analyze past success cases and select the most effective reference case. As a result, the optimal reference case can be selected by referring to past success cases. A part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can provide the optimal reference case based on past success cases searched by the generation AI. Furthermore, the reference unit has a function of adjusting the reference method for success cases in real time. For example, the reference unit can select the optimal reference method according to the user's situation. As a result, the reference unit can always refer to past success cases in the optimal way and reflect them in the selection of the reference case.

[0056] The reference unit can select the optimal case by considering the geographical distribution when selecting the reference case. The reference unit uses the generation AI to select the optimal case by considering the geographical distribution when selecting the reference case. For example, the reference unit can select a region-specific reference case based on the location of the user. In addition, the reference unit can select a reference case that reflects the characteristics of each region by considering the geographical distribution. Furthermore, the reference unit can select a reference case for each region based on the geographical distribution. As a result, a region-specific reference case can be selected by considering the geographical distribution. A part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can provide the optimal reference case based on the geographical distribution considered by the generation AI. Furthermore, the reference unit has a function of adjusting the method of considering the geographical distribution in real time. For example, the reference unit can select the optimal method of considering according to the user's situation. As a result, the reference unit can always consider the geographical distribution in the optimal manner and select the reference case.

[0057] The evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data when evaluating a solution. The evaluation unit uses the generation AI to improve the accuracy of the evaluation by referring to past evaluation data when evaluating a solution. For example, the evaluation unit can search for past evaluation data from a database and reflect it in the evaluation. Also, the evaluation unit can improve the accuracy of the evaluation by referring to evaluation data of similar cases. Furthermore, the evaluation unit can analyze past evaluation data and select the most effective evaluation method. As a result, the accuracy of the evaluation is improved by referring to the past evaluation data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can provide an optimal evaluation method based on the past evaluation data searched by the generation AI. Furthermore, the evaluation unit has a function of adjusting the method of referring to the evaluation data in real time. For example, the evaluation unit can select the optimal reference method according to the user's situation. As a result, the evaluation unit can always refer to past evaluation data in an optimal manner and reflect it in the evaluation of the solution.

[0058] The evaluation unit can perform evaluation taking into account the geographical distribution when evaluating the solution. The evaluation unit performs evaluation taking into account the geographical distribution using the generation AI when evaluating the solution. For example, the evaluation unit can perform a region-specific evaluation based on the location of the user. In addition, the evaluation unit can perform evaluation reflecting the characteristics of each region by taking into account the geographical distribution. Furthermore, the evaluation unit can perform evaluation for each region based on the geographical distribution. As a result, region-specific evaluation is possible by taking into account the geographical distribution. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can provide an optimal evaluation method based on the geographical distribution taken into account by the generation AI. Furthermore, the evaluation unit has a function of adjusting the method of taking into account the geographical distribution in real time. For example, the evaluation unit can select the optimal consideration method according to the user's situation. As a result, the evaluation unit can always take into account the geographical distribution in the optimal manner and evaluate the solution.

[0059] The feedback unit can improve the accuracy of collection by referring to past feedback data when collecting feedback. The feedback unit uses the generation AI to improve the accuracy of collection by referring to past feedback data when collecting feedback. For example, the feedback unit can search for past feedback data from a database and reflect it in the collection. Also, the feedback unit can improve the accuracy of collection by referring to feedback data of similar cases. Furthermore, the feedback unit can analyze past feedback data and select the most effective collection method. As a result, the accuracy of collection is improved by referring to past feedback data. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can provide an optimal collection method based on past feedback data searched by the generation AI. Furthermore, the feedback unit has a function of adjusting the reference method of feedback data in real time. For example, the feedback unit can select the optimal reference method according to the user's situation. As a result, the feedback unit can always refer to past feedback data in an optimal manner and reflect it in the collection of feedback.

[0060] The feedback unit can collect feedback while taking into account the geographical distribution. The feedback unit uses the generation AI to collect feedback while taking into account the geographical distribution. For example, the feedback unit can collect region-specific feedback based on the user's location. Also, the feedback unit can collect feedback reflecting the characteristics of each region while taking into account the geographical distribution. Furthermore, the feedback unit can collect feedback for each region based on the geographical distribution. As a result, region-specific feedback can be collected by taking into account the geographical distribution. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can provide an optimal collection method based on the geographical distribution taken into account by the generation AI. Furthermore, the feedback unit has a function of adjusting the method of taking into account the geographical distribution in real time. For example, the feedback unit can select the optimal consideration method according to the user's situation. As a result, the feedback unit can always take into account the geographical distribution in the optimal manner and collect feedback.

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

[0062] The conflict arbitration system may further include an hobby analysis unit that considers the user's hobbies and interests. The hobby analysis unit may analyze the user's hobbies and interests and propose a solution taking these into consideration. For example, if the user has a specific hobby, a solution related to the hobby may be proposed. The hobby analysis unit may also suggest activities that allow the user to relax and reduce stress. This allows a more personalized solution to be proposed by taking into consideration the user's hobbies and interests.

[0063] The dispute arbitration system may further include an economic analysis unit that takes into account the user's economic situation. The economic analysis unit may analyze the user's income and expenditure and propose a solution taking these into account. For example, if the user is in a financially difficult situation, the system may propose a cost-free solution. The economic analysis unit may also provide appropriate support depending on the user's economic situation. This allows a more realistic solution to be proposed by taking into account the user's economic situation.

[0064] The dispute mediation system may further include a learning analysis unit that takes into account the learning style of the user. The learning analysis unit may analyze the learning style and level of understanding of the user and propose a solution taking these into account. For example, if the user is a visual learner, the system may provide visual materials. If the user is an auditory learner, the system may provide audio explanations. This allows for a more effective solution to be proposed by taking into account the learning style of the user.

[0065] The dispute arbitration system may further include a time management unit that supports the user's time management. The time management unit may analyze the user's schedule and how he or she uses time, and may propose a solution by taking these into consideration. For example, if the user has a busy schedule, the system may propose a solution that can be implemented in a short time. The time management unit may also propose a solution at an optimal timing according to the user's schedule. This may support the user's time management, thereby making it possible to propose a more efficient solution.

[0066] The conflict mediation system may further include a communication analysis unit that analyzes the communication style of the user. The communication analysis unit may analyze the communication style and patterns of the user and propose a solution by taking these into consideration. For example, if the user prefers direct communication, the system may propose a direct solution. Also, if the user prefers indirect communication, the system may propose an indirect solution. In this way, a more appropriate solution may be proposed by taking the user's communication style into consideration.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The reception unit receives input of the contents of the conversation by the parties. The contents of the conversation include, for example, the opinions and feelings of the parties, but are not limited to such examples. The reception unit can receive, for example, text input or voice input. Step 2: The analysis unit uses the generation AI to analyze the content of the conversation and tone of voice input by the reception unit. The analysis unit, for example, analyzes the content of the conversation to identify which parts are the points of contention. The analysis unit can also grasp the intensity of the emotions and level of tension of the parties by analyzing the tone of voice. For example, the generation AI can analyze the content of the conversation using a text generation AI (e.g., LLM) and identify the points of contention. The generation AI can also analyze the tone of voice using voice analysis technology to grasp the intensity of emotions and level of tension. Step 3: The summary section uses the generation AI to summarize the points of contention in the story analyzed by the analysis section. For example, the summary section extracts important parts of the content of the story and organizes the points of contention. Step 4: The suggestion unit uses the generation AI to propose an appropriate solution based on the issues summarized by the summary unit. The suggestion unit, for example, considers the issues in the discussion and the feelings of the parties involved and proposes the most appropriate solution. For example, the generation AI can propose a solution by referring to past success stories and the opinions of experts. Furthermore, the suggestion unit has a function to evaluate the reliability of the solution and also has a function to accept feedback from users.

[0069] (Example 2) The quarrel arbitration system according to the embodiment of the present invention is a system that arbitrates quarrels using a generation AI. In this system, the parties input the contents of the talk, and the generation AI analyzes the contents and tone of voice, summarizes the points of dispute, and proposes an appropriate solution. For example, the parties input the contents of the talk into an app. At this time, the parties can freely input their own opinions and feelings. For example, the parties input the contents of the talk, such as "I am angry that he did not keep his promise." This information is input into the generation AI. Next, the generation AI analyzes the input contents of the talk and the tone of voice. The generation AI analyzes the contents of the talk and identifies which part is the point of dispute. In addition, by analyzing the tone of voice, it is possible to grasp the strength of the emotions and the degree of tension of the parties. For example, the analysis result shows that the part "he did not keep his promise" is the point of dispute, and that strong anger can be felt from the tone of voice. Furthermore, the generation AI considers an appropriate solution. The generation AI considers the points of dispute in the talk and the emotions of the parties, and proposes the most appropriate solution. For example, it can propose a solution such as "ask him to apologize." This proposal is displayed to the parties. This mechanism allows the parties to proceed with the discussion calmly. The generative AI analyzes the content of the conversation from a third-party perspective and proposes appropriate solutions, allowing the parties to proceed with the discussion without becoming emotional. In addition, by analyzing the tone of voice, the generative AI can grasp changes in the parties' emotions and propose solutions at the appropriate time. For example, proposing a solution when the parties have calmed down will help the discussion proceed smoothly. In this way, the use of generative AI makes it possible to realize a system that can efficiently mediate arguments. As a result, the argument mediation system can analyze the content and tone of voice of the parties, summarize the points of contention, and propose appropriate solutions.

[0070] The dispute arbitration system according to the embodiment includes a reception unit, an analysis unit, a summary unit, and a proposal unit. The reception unit is used by the parties to input the contents of the talk. The contents of the talk include, for example, the opinions and feelings of the parties, but are not limited to such examples. The reception unit can receive, for example, text input and voice input. The analysis unit uses a generation AI to analyze the contents of the talk and the tone of voice input by the reception unit. The analysis unit, for example, analyzes the contents of the talk and identifies which part is the point of dispute. The analysis unit can also grasp the intensity of the emotions and the degree of tension of the parties by analyzing the tone of voice. For example, the generation AI can analyze the contents of the talk using a text generation AI (for example, LLM) and identify the points of dispute. The generation AI can also analyze the tone of voice using voice analysis technology and grasp the intensity of the emotions and the degree of tension. The summary unit uses the generation AI to summarize the points of dispute in the talk analyzed by the analysis unit. The summary unit, for example, extracts important parts of the contents of the talk and organizes the points of dispute. The proposal unit uses the generation AI to propose an appropriate solution based on the points of dispute summarized by the summary unit. The suggestion unit, for example, considers the points of contention in the story and the emotions of the parties, and proposes the most appropriate solution. For example, the generation AI can propose a solution by referring to past success stories and the opinions of experts. As a result, the fight arbitration system according to the embodiment can analyze the content of the parties' stories and the tone of voice, summarize the points of contention, and propose an appropriate solution. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit displays a solution to the parties based on the solution proposed by the generation AI. Furthermore, the suggestion unit has a function of evaluating the reliability of the solution. For example, the suggestion unit can evaluate the reliability of the solution proposed by the generation AI and preferentially propose a highly reliable solution. In addition, the suggestion unit has a function of accepting feedback from the user. For example, the suggestion unit can collect feedback from the parties and use it to improve the system. As a result, the fight arbitration system can analyze the content of the parties' stories and the tone of voice, summarize the points of contention, and propose an appropriate solution.

[0071] The reception unit receives input of the contents of the talk by the parties. The contents of the talk include, for example, the opinions and feelings of the parties, but are not limited to such examples. The reception unit can receive, for example, text input and voice input. Specifically, in the case of text input, the parties can input their opinions and feelings using a keyboard or a touch screen. In the case of voice input, the parties speak through a microphone, and the system converts the voice into text. Voice input is particularly effective for maintaining a natural flow of conversation, and can capture the nuances of emotions more accurately. Furthermore, the reception unit has a function of temporarily storing the input data and transmitting it to the subsequent analysis unit and summary unit. This allows the reception unit to efficiently collect the contents of the talk of the parties and smoothly proceed with the processing of the entire system. In addition, the reception unit also has a security function for protecting the privacy of the input data, and can safely manage the information of the parties by encrypting data and controlling access. This allows the reception unit to provide an environment in which the parties can input the contents of the talk with peace of mind, and improve the reliability of the entire system.

[0072] The analysis unit uses the generation AI to analyze the contents of the conversation and the tone of voice input by the reception unit. For example, the analysis unit analyzes the contents of the conversation to identify which part is the point of contention. In addition, the analysis unit can grasp the strength of the emotions and the degree of tension of the parties by analyzing the tone of voice. Specifically, the generation AI analyzes the contents of the conversation using a text generation AI (for example, LLM) to identify the points of contention. The text generation AI has learned a large amount of text data, and can extract important keywords and phrases from the contents of the conversation input and clarify the points of contention. In addition, the generation AI can analyze the tone of voice using voice analysis technology to grasp the strength of emotions and the degree of tension. The voice analysis technology analyzes characteristics such as the pitch, tempo, and volume of the voice to evaluate the emotional state of the parties. For example, if the voice becomes higher or faster, it is determined that the tension or anger is strong. As a result, the analysis unit can comprehensively analyze both the contents of the conversation and the tone of voice and accurately grasp the emotions and points of contention of the parties. Furthermore, the analysis unit can perform more accurate analysis by referring to past data and similar cases. This enables the analysis department to accurately understand the content and emotions of the parties involved and support the arbitration process throughout the system.

[0073] The summary unit uses the generation AI to summarize the issues in the story analyzed by the analysis unit. For example, the summary unit extracts important parts of the content of the story and organizes the issues. Specifically, the generation AI extracts important keywords and phrases from the content of the story and organizes them to clarify the issues. The generation AI has learned a large amount of text data and can efficiently summarize the content of the story. For example, it organizes the opinions and feelings of the parties and identifies which parts are the most important. The summary unit also has a function to visually display the issues and provide information in a format that is easy for the parties to understand. For example, it visualizes the issues using graphs and charts so that the parties can understand them at a glance. Furthermore, the summary unit can organize the issues by referring to past data and similar cases. This allows the summary unit to efficiently organize the content of the story and provide information in a format that is easy for the parties to understand. This allows the summary unit to efficiently organize the content of the story of the parties and support the arbitration process of the entire system.

[0074] The proposal unit uses the generation AI to propose an appropriate solution based on the issues summarized by the summary unit. The proposal unit, for example, considers the issues in the story and the emotions of the parties and proposes the most appropriate solution. Specifically, the generation AI can propose a solution by referring to past success cases and expert opinions. The generation AI has learned a large amount of data and can identify the most effective solution based on similar past cases and expert opinions. For example, by referring to past success cases, it proposes a solution that was effective in a similar situation. In addition, the proposal unit has a function to evaluate the reliability of the solution, and can evaluate the reliability of the solution proposed by the generation AI and preferentially propose a highly reliable solution. Furthermore, the proposal unit has a function to accept feedback from users, and can collect feedback from the parties and use it to improve the system. For example, it evaluates whether the proposed solution was actually effective and reflects it in the next proposal. As a result, the proposal unit can always provide a highly accurate solution based on the latest information and improve the satisfaction of the parties. As a result, the proposal unit can consider the content and emotions of the parties' stories and propose the most appropriate solution.

[0075] The quarrel arbitration system includes a reference section that refers to specific past cases or success cases. The reference section refers to past cases and success cases using a generation AI. For example, the reference section can search a database for similar past cases and refer to them. The reference section can also improve the accuracy of the solution based on successful projects and performance data. For example, the reference section analyzes past success cases and proposes the most effective solution. As a result, the accuracy of the solution is improved by referring to past cases and success cases. Some or all of the above-mentioned processing in the reference section may be performed, for example, using AI, or may be performed without using AI. For example, the reference section can propose a solution based on past cases and success cases searched by the generation AI. Furthermore, the reference section has a function of updating past cases and success cases in real time. For example, the reference section can automatically collect the latest cases and success cases and update the database. As a result, the reference section can always propose a solution based on the latest information.

[0076] The quarrel arbitration system includes an evaluation unit that evaluates the reliability of the solution. The evaluation unit uses a generation AI to evaluate the reliability of the solution. For example, the evaluation unit can evaluate the reliability of the solution proposed by the generation AI and preferentially propose a highly reliable solution. The evaluation unit evaluates the reliability of the solution, for example, based on performance data or a third party evaluation. For example, the evaluation unit can evaluate the reliability of the solution by referring to past success stories and expert opinions. As a result, the reliability of the proposal is improved by evaluating the reliability of the solution. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit displays the solution to the parties based on the reliability of the solution evaluated by the generation AI. Furthermore, the evaluation unit has a function of evaluating the reliability of the solution in real time. For example, the evaluation unit can evaluate the reliability of the solution based on the latest data and propose a highly reliable solution. As a result, the evaluation unit can always evaluate the reliability of the solution based on the latest information.

[0077] The fight arbitration system includes a feedback unit that accepts feedback from users. The feedback unit accepts feedback from users using a generation AI. For example, the feedback unit can collect feedback from the parties and use it to improve the system. The feedback unit collects feedback, for example, through questionnaires and user reviews. For example, the feedback unit can conduct a questionnaire for the parties and collect opinions on the usability of the system and the effectiveness of the solution. This allows the system to be improved by accepting feedback from users. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can identify improvements to the system based on the feedback collected by the generation AI. Furthermore, the feedback unit has a function of adjusting the feedback collection method in real time. For example, the feedback unit can select the optimal collection method depending on the situation of the parties. This allows the feedback unit to always collect feedback in the optimal way and use it to improve the system.

[0078] The analysis unit can analyze the content of the talk and specifically identify which part is the point of contention. The analysis unit uses the generation AI to analyze the content of the talk and identify which part is the point of contention. For example, the analysis unit uses a text generation AI (e.g., LLM) to analyze the content of the talk and identify the point of contention. The generation AI can analyze the content of the talk and identify the points of contention and important points of discussion. For example, the generation AI can analyze the content of the talk and identify the part that "the promise was not kept" as the point of contention. As a result, by analyzing the content of the talk and identifying the point of contention, an appropriate solution can be proposed. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can propose a solution to the parties based on the point of contention identified by the generation AI. Furthermore, the analysis unit has a function of adjusting the method of identifying the point of contention in real time. For example, the analysis unit can select the optimal identification method depending on the situation of the parties. As a result, the analysis unit can always identify the point of contention in the optimal way and propose an appropriate solution.

[0079] The analysis unit can analyze the tone of voice and specifically grasp the intensity of the emotions and the degree of tension of the parties. The analysis unit uses the generation AI to analyze the tone of voice and grasp the intensity of the emotions and the degree of tension of the parties. For example, the analysis unit can analyze the tone of voice using voice analysis technology and grasp the intensity of the emotions and the degree of tension. The generation AI can analyze the tone, pitch, speed, etc. of the tone of voice and identify the intensity of the emotions and the degree of tension of the parties. For example, the generation AI can analyze the tone of voice and identify the intensity of the emotions when the parties feel strong anger. In addition, the generation AI can analyze the tone of voice and grasp the degree of tension when the parties are nervous. In this way, the intensity of the emotions and the degree of tension of the parties can be grasped by analyzing the tone of voice. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can identify the intensity of the emotions and the degree of tension of the parties based on the data of the tone of voice analyzed by the generation AI. Furthermore, the analysis unit has a function of adjusting the analysis method of the tone of voice in real time. For example, the analysis unit can select the optimal analysis method according to the situation of the parties. This enables the analysis unit to always analyze the tone of voice in the most optimal way and grasp the intensity of the person's emotions and level of tension.

[0080] The reception unit can estimate the user's emotions and adjust the input method of the content of the talk based on the estimated user's emotions. The reception unit can estimate the user's emotions using the generation AI and adjust the input method of the content of the talk based on the estimated user's emotions. For example, the reception unit can estimate the user's emotions using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expressions to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user is stressed and provide a simple interface. In addition, the generation AI can identify the emotion when the user is relaxed and provide detailed input options. Furthermore, the generation AI can identify the emotion when the user is in a hurry and prioritize voice input. This can reduce the burden on the user by adjusting the input method according to the user's emotions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can provide an optimal input method based on the user's emotions estimated by the generation AI. Furthermore, the reception unit has a function of adjusting the estimation method of the user's emotions in real time. For example, the reception unit can select an optimal estimation method depending on the user's situation. This allows the reception unit to always estimate the user's emotions using an optimal method and adjust the method for inputting the content of the conversation.

[0081] The reception unit can analyze the user's past input history and provide an optimal input interface. The reception unit uses the generation AI to analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display phrases and keywords that have been frequently input in the past as candidates. In addition, the reception unit can preferentially suggest input methods (such as voice and text) that have been used in the past. Furthermore, the reception unit can predict and suggest phrases and keywords that will be used in a specific time period from the past input history. As a result, the reception unit can provide an optimal input interface to the user by analyzing the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide an optimal input interface based on the past input history analyzed by the generation AI. Furthermore, the reception unit has a function of adjusting the method of providing the input interface in real time. For example, the reception unit can select the optimal providing method depending on the user's situation. As a result, the reception unit can always provide an input interface in an optimal manner, improving user convenience.

[0082] The reception unit can filter the input contents based on the user's current situation or interests when inputting the contents of the talk. The reception unit uses the generation AI to filter the input contents based on the user's current situation and interests when inputting the contents of the talk. For example, the reception unit can automatically suggest related keywords when the user inputs the current situation. The reception unit can also filter the input contents based on the user's interests and preferentially display highly relevant information. Furthermore, the reception unit can simplify the input contents according to the user's current situation and allow only necessary information to be input. This makes it possible to provide highly relevant information by filtering the input contents based on the user's current situation and interests. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide optimal information to the user based on the input contents filtered by the generation AI. Furthermore, the reception unit has a function of adjusting the filtering method of the input contents in real time. For example, the reception unit can select the optimal filtering method according to the user's situation. This makes it possible for the reception unit to always filter the input contents in the optimal manner and provide highly relevant information.

[0083] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. The reception unit can estimate the user's emotion using the generation AI and determine the priority of the input contents based on the estimated user's emotion. For example, the reception unit can estimate the user's emotion using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user is feeling strong anger and allow the user to input important points of contention with priority. In addition, the generation AI can identify the emotion when the user is calm and allow the user to input detailed information. Furthermore, the generation AI can identify the emotion when the user is confused and allow the user to input concise input content with priority. As a result, important information can be input with priority by determining the priority of the input contents according to the user's emotion. Part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can determine the priority of the input contents based on the user's emotion estimated by the generation AI. Furthermore, the reception unit has a function of adjusting the priority of input contents in real time. For example, the reception unit can select the optimal priority according to the user's situation. This allows the reception unit to always determine the priority of input contents in the optimal way, and to input important information preferentially.

[0084] The reception unit can preferentially input highly relevant content by specifically considering the geographical location information of the user when inputting the content of the talk. The reception unit uses the generation AI to preferentially input highly relevant content by considering the geographical location information of the user when inputting the content of the talk. For example, when the user is in a specific location, the reception unit can preferentially input information related to the location. Furthermore, the reception unit can automatically suggest related issues based on the geographical location information of the user. Furthermore, when the user is moving, the reception unit can preferentially input information related to the current location. As a result, highly relevant information can be preferentially input by considering the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide optimal information to the user based on the geographical location information considered by the generation AI. Furthermore, the reception unit has a function of adjusting the method of considering the geographical location information in real time. For example, the reception unit can select the optimal method of considering according to the user's situation. As a result, the reception unit can always consider the geographical location information in the optimal manner and provide highly relevant information.

[0085] The reception unit can analyze the social media activity of the user when inputting the content of the talk, and input the related content. The reception unit can use the generation AI to analyze the social media activity of the user when inputting the content of the talk, and input the related content. For example, the reception unit can extract related keywords from the user's social media posts and reflect them in the input content. The reception unit can also analyze the user's social media activity and automatically suggest related issues. Furthermore, the reception unit can analyze the user's emotional expression on social media and adjust the input content. This makes it possible to provide highly relevant information by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can provide optimal information to the user based on the social media activity analyzed by the generation AI. Furthermore, the reception unit has a function of adjusting the analysis method of social media activity in real time. For example, the reception unit can select the optimal analysis method according to the user's situation. This makes it possible for the reception unit to always analyze social media activity in the optimal way and input the related content.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis method of the content of the talk based on the estimated user's emotions. The analysis unit can estimate the user's emotions using the generation AI and adjust the analysis method of the content of the talk based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expressions to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user feels strong anger and perform analysis with emphasis on the strength of the emotion. In addition, the generation AI can identify the emotion when the user is calm and prioritize logical analysis. Furthermore, the generation AI can identify the emotion when the user is confused and perform a concise and clear analysis. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on the user's emotions estimated by the generation AI. Furthermore, the analysis unit has a function of adjusting the analysis method in real time. For example, the analysis unit can select the optimal analysis method according to the user's situation. This allows the analysis unit to always analyze the content of the conversation in the optimal way and adjust the analysis method based on the user's emotions.

[0087] The analysis unit can improve the accuracy of the analysis by referring to similar cases in the past when analyzing the content of the talk. The analysis unit can improve the accuracy of the analysis by referring to similar cases in the past when analyzing the content of the talk using the generation AI. For example, the analysis unit can search for similar cases in the past from a database and reflect them in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to successful examples of similar cases. Furthermore, the analysis unit can reduce the risk of the analysis by referring to failed examples of similar cases. As a result, the accuracy of the analysis is improved by referring to similar cases in the past. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on similar cases in the past searched by the generation AI. Furthermore, the analysis unit has a function of adjusting the method of referring to similar cases in real time. For example, the analysis unit can select the optimal reference method according to the user's situation. As a result, the analysis unit can always refer to similar cases in the past in the optimal way and reflect them in the analysis of the content of the talk.

[0088] The analysis unit can perform the analysis by taking into account the attribute information of the user when analyzing the content of the talk. The analysis unit performs the analysis by taking into account the attribute information of the user using the generation AI when analyzing the content of the talk. For example, the analysis unit can adjust the viewpoint of the analysis by taking into account the age and gender of the user. In addition, the analysis unit can adjust the content of the analysis by taking into account the occupation and social status of the user. Furthermore, the analysis unit can adjust the method of analysis by taking into account the cultural background of the user. As a result, more appropriate analysis is possible by taking into account the attribute information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on the attribute information of the user taken into account by the generation AI. Furthermore, the analysis unit has a function of adjusting the method of taking into account the attribute information in real time. For example, the analysis unit can select the optimal method of taking into account depending on the situation of the user. As a result, the analysis unit can always take into account the attribute information of the user in the optimal method and analyze the content of the talk.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit can estimate the user's emotions using the generation AI and adjust the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expressions to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user is nervous and provide a simple and highly visible display method. In addition, the generation AI can identify the emotion when the user is relaxed and provide a display method including detailed information. Furthermore, the generation AI can identify the emotion when the user is in a hurry and provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can provide an optimal display method based on the user's emotions estimated by the generation AI. Furthermore, the analysis unit has a function of adjusting the display method in real time. For example, the analysis unit can select the optimal adjustment method according to the user's situation. This allows the analysis unit to always adjust the display method of the analysis results in the optimal way and provide a display method based on the user's emotions.

[0090] The analysis unit can perform the analysis by taking into account the geographical distribution when analyzing the content of the talk. The analysis unit performs the analysis by taking into account the geographical distribution using the generation AI when analyzing the content of the talk. For example, the analysis unit can analyze issues specific to a region based on the location of the user. In addition, the analysis unit can perform an analysis that reflects the characteristics of each region by taking into account the geographical distribution. Furthermore, the analysis unit can propose a solution for each region based on the geographical distribution. This makes it possible to analyze issues specific to a region by taking into account the geographical distribution. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on the geographical distribution taken into account by the generation AI. Furthermore, the analysis unit has a function of adjusting the method of taking into account the geographical distribution in real time. For example, the analysis unit can select the optimal method of taking into account depending on the user's situation. This makes it possible for the analysis unit to always take into account the geographical distribution in the optimal manner and analyze the content of the talk.

[0091] The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing the content of the story. The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing the content of the story using the generation AI. For example, the analysis unit can search for related literature from a database and reflect it in the analysis. In addition, the analysis unit can improve the accuracy of the analysis by referring to successful examples of related literature. Furthermore, the analysis unit can reduce the risk of the analysis by referring to unsuccessful examples of related literature. As a result, the accuracy of the analysis is improved by referring to related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide an optimal analysis method based on the related literature searched by the generation AI. Furthermore, the analysis unit has a function of adjusting the reference method of related literature in real time. For example, the analysis unit can select the optimal reference method according to the user's situation. As a result, the analysis unit can always refer to related literature in an optimal manner and reflect it in the analysis of the content of the story.

[0092] The summary unit can estimate the user's emotions and adjust the way of summarizing the issues based on the estimated user's emotions. The summary unit uses the generation AI to estimate the user's emotions and adjust the way of summarizing the issues based on the estimated user's emotions. For example, the summary unit can estimate the user's emotions using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user feels strong anger and summarize the issues by emphasizing the strength of the emotion. In addition, the generation AI can identify the emotion when the user is calm and summarize the issues from a logical perspective. Furthermore, the generation AI can identify the emotion when the user is confused and summarize the issues in a concise and clear manner. This allows for more appropriate summarization by adjusting the way of summarizing the issues according to the user's emotions. Some or all of the above-mentioned processing in the summary unit may be performed using, for example, AI, or may be performed without using AI. For example, the summary unit can provide an optimal summary based on the user's emotions estimated by the generation AI. Furthermore, the summarizing unit has a function of adjusting the summarizing adjustment method in real time. For example, the summarizing unit can select the optimal adjustment method according to the user's situation. This allows the summarizing unit to always adjust the summarization of the issues in the optimal way and provide a summarization based on the user's feelings.

[0093] The summarizing unit can select the optimal method by referring to past summarizing methods when summarizing the issues. The summarizing unit uses the generation AI to select the optimal method by referring to past summarizing methods when summarizing the issues. For example, the summarizing unit can select the optimal way to summarize the issues by referring to past successful cases. Also, the summarizing unit can select a summarizing method that reduces risk by referring to past failure cases. Furthermore, the summarizing unit can search a database for similar past cases and select the optimal summarizing method. As a result, the optimal summarizing method can be selected by referring to past summarizing methods. A part or all of the above-mentioned processing in the summarizing unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarizing unit can provide the optimal summarizing method based on the past summarizing methods searched by the generation AI. Furthermore, the summarizing unit has a function of adjusting the reference method of the summarizing method in real time. For example, the summarizing unit can select the optimal reference method according to the user's situation. As a result, the summarizing unit can always refer to past summarizing methods in the optimal way and reflect them in the summarization of the issues.

[0094] The summarizing unit can summarize the issues by considering the attribute information of the user. The summarizing unit uses the generation AI to summarize the issues by considering the attribute information of the user. For example, the summarizing unit can adjust the way the issues are summarized by considering the age and sex of the user. The summarizing unit can also adjust the way the issues are summarized by considering the occupation and social status of the user. Furthermore, the summarizing unit can adjust the way the issues are summarized by considering the cultural background of the user. This allows for more appropriate summarization by considering the attribute information of the user. A part or all of the above-mentioned processing in the summarizing unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarizing unit can provide an optimal way of summarizing based on the attribute information of the user considered by the generation AI. Furthermore, the summarizing unit has a function of adjusting the way the attribute information is considered in real time. For example, the summarizing unit can select the optimal way of considering depending on the situation of the user. This allows the summarizing unit to always consider the attribute information of the user in the optimal way and summarize the issues.

[0095] The summary unit can estimate the user's emotion and adjust the display method of the summary result based on the estimated user's emotion. The summary unit uses the generation AI to estimate the user's emotion and adjust the display method of the summary result based on the estimated user's emotion. For example, the summary unit can estimate the user's emotion using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user is nervous and provide a simple and highly visible display method. In addition, the generation AI can identify the emotion when the user is relaxed and provide a display method including detailed information. Furthermore, the generation AI can identify the emotion when the user is in a hurry and provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method according to the user's emotion. Part or all of the above-mentioned processing in the summary unit may be performed using, for example, AI, or may be performed without using AI. For example, the summary unit can provide an optimal display method based on the user's emotion estimated by the generation AI. Furthermore, the summarizing unit has a function of adjusting the display method adjustment method in real time. For example, the summarizing unit can select the optimal adjustment method according to the user's situation. This allows the summarizing unit to always adjust the display method of the summary result in the optimal way and provide a display method based on the user's feelings.

[0096] The summarizing unit can summarize issues taking into account the geographical distribution when summarizing issues. The summarizing unit uses the generation AI to summarize issues taking into account the geographical distribution when summarizing issues. For example, the summarizing unit can summarize issues specific to a region based on the user's location. In addition, the summarizing unit can summarize issues reflecting the characteristics of each region by taking into account the geographical distribution. Furthermore, the summarizing unit can propose solutions for each region based on the geographical distribution. This allows issues specific to each region to be summarized by taking into account the geographical distribution. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the summarizing unit can provide an optimal summarizing method based on the geographical distribution taken into account by the generation AI. Furthermore, the summarizing unit has a function of adjusting the method of considering the geographical distribution in real time. For example, the summarizing unit can select the optimal method of considering depending on the user's situation. This allows the summarizing unit to always consider the geographical distribution in the optimal way and summarize issues.

[0097] The summarizing unit can improve the accuracy of the summary by referring to related literature when summarizing the issues. The summarizing unit uses the generation AI to improve the accuracy of the summary by referring to related literature when summarizing the issues. For example, the summarizing unit can search for related literature from a database and reflect it in the summary. In addition, the summarizing unit can improve the accuracy of the summary by referring to successful examples of related literature. Furthermore, the summarizing unit can reduce the risk of summarization by referring to unsuccessful examples of related literature. As a result, the accuracy of the summary is improved by referring to related literature. A part or all of the above-mentioned processing in the summarizing unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarizing unit can provide an optimal summarizing method based on the related literature searched by the generation AI. Furthermore, the summarizing unit has a function of adjusting the reference method of related literature in real time. For example, the summarizing unit can select the optimal reference method according to the user's situation. As a result, the summarizing unit can always refer to related literature in the optimal way and reflect it in the summary of the issues.

[0098] The suggestion unit can estimate the user's emotion and adjust the solution proposal method based on the estimated user's emotion. The suggestion unit uses the generation AI to estimate the user's emotion and adjust the solution proposal method based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user feels strong anger and propose a solution with emphasis on the intensity of the emotion. In addition, the generation AI can identify the emotion when the user is calm and propose a solution from a logical perspective. Furthermore, the generation AI can identify the emotion when the user is confused and propose a concise and clear solution. As a result, a more appropriate solution can be proposed by adjusting the proposal method according to the user's emotion. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide an optimal proposal method based on the user's emotion estimated by the generation AI. Furthermore, the suggestion unit has a function of adjusting the adjustment method of the proposed method in real time. For example, the suggestion unit can select the optimal adjustment method according to the user's situation. This allows the suggestion unit to always adjust the solution proposal method in the optimal way and provide the proposal method based on the user's feelings.

[0099] The suggestion unit can make an optimal proposal by referring to past success cases when proposing a solution. The suggestion unit uses the generation AI to make an optimal proposal by referring to past success cases when proposing a solution. For example, the suggestion unit can search for past success cases from a database and propose an optimal solution. In addition, the suggestion unit can improve the accuracy of the solution by referring to success cases of similar cases. Furthermore, the suggestion unit can analyze past success cases and propose the most effective solution. As a result, the optimal solution can be proposed by referring to past success cases. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can provide an optimal solution based on past success cases searched by the generation AI. Furthermore, the suggestion unit has a function of adjusting the reference method of the success cases in real time. For example, the suggestion unit can select the optimal reference method according to the user's situation. As a result, the suggestion unit can always refer to past success cases in the optimal way and reflect them in the solution proposal.

[0100] The suggestion unit can make a suggestion taking into account the attribute information of the user when proposing a solution. The suggestion unit makes a suggestion taking into account the attribute information of the user using the generation AI when proposing a solution. For example, the suggestion unit can propose an optimal solution taking into account the age and sex of the user. In addition, the suggestion unit can adjust the content of the solution taking into account the occupation and social status of the user. Furthermore, the suggestion unit can adjust the method of the solution taking into account the cultural background of the user. As a result, a more appropriate solution can be proposed by taking into account the attribute information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide an optimal solution based on the attribute information of the user taken into account by the generation AI. Furthermore, the suggestion unit has a function of adjusting the method of considering the attribute information in real time. For example, the suggestion unit can select the optimal consideration method according to the user's situation. As a result, the suggestion unit can always consider the attribute information of the user in the optimal method and propose a solution.

[0101] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. The suggestion unit can estimate the user's emotions using the generation AI and determine the priority of suggestions based on the estimated user's emotions. For example, the suggestion unit can estimate the user's emotions using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user feels strong anger and determine the priority by emphasizing the strength of the emotion. In addition, the generation AI can identify the emotion when the user is calm and determine the priority from a logical perspective. Furthermore, the generation AI can identify the emotion when the user is confused and determine a concise and clear priority. As a result, by determining the priority of suggestions according to the user's emotions, important solutions can be preferentially proposed. A part or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can provide an optimal priority based on the user's emotions estimated by the generation AI. Furthermore, the suggestion unit has a function of adjusting the priority determination method in real time. For example, the suggestion unit can select the optimal determination method depending on the user's situation. This allows the suggestion unit to always determine the priority of suggestions in an optimal manner and provide priorities based on the user's emotions.

[0102] The suggestion unit can make a proposal taking into consideration the geographical distribution when proposing a solution. The suggestion unit makes a proposal taking into consideration the geographical distribution using the generation AI when proposing a solution. For example, the suggestion unit can propose a solution specific to a region based on the location of the user. Also, the suggestion unit can propose a solution reflecting the characteristics of each region by taking into consideration the geographical distribution. Furthermore, the suggestion unit can propose a solution for each region based on the geographical distribution. As a result, a solution specific to a region can be proposed by taking into consideration the geographical distribution. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can provide an optimal solution based on the geographical distribution taken into consideration by the generation AI. Furthermore, the suggestion unit has a function of adjusting the method of taking into consideration the geographical distribution in real time. For example, the suggestion unit can select the optimal method of taking into consideration according to the user's situation. As a result, the suggestion unit can always take into consideration the geographical distribution in the optimal manner and propose a solution.

[0103] The suggestion unit can improve the accuracy of the proposal by referring to related literature when proposing a solution. The suggestion unit uses the generation AI to improve the accuracy of the proposal by referring to related literature when proposing a solution. For example, the suggestion unit can search for related literature from a database and reflect it in the proposal. In addition, the suggestion unit can improve the accuracy of the proposal by referring to successful examples of related literature. Furthermore, the suggestion unit can reduce the risk of the proposal by referring to failure examples of related literature. As a result, the accuracy of the proposal is improved by referring to related literature. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide an optimal proposal method based on the related literature searched by the generation AI. Furthermore, the suggestion unit has a function of adjusting the reference method of related literature in real time. For example, the suggestion unit can select the optimal reference method according to the user's situation. As a result, the suggestion unit can always refer to related literature in an optimal manner and reflect it in the proposal of the solution.

[0104] The reference unit can estimate the user's emotion and select a reference case based on the estimated user's emotion. The reference unit can estimate the user's emotion using the generation AI and select a reference case based on the estimated user's emotion. For example, the reference unit can estimate the user's emotion using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user feels strong anger and select a reference case with emphasis on the strength of the emotion. In addition, the generation AI can identify the emotion when the user is calm and select a reference case from a logical perspective. Furthermore, the generation AI can identify the emotion when the user is confused and select a concise and clear reference case. As a result, by selecting a reference case according to the user's emotion, a more appropriate case can be provided. A part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI or may be performed without using AI. For example, the reference unit can provide an optimal reference case based on the user's emotion estimated by the generation AI. Furthermore, the reference section has a function of adjusting the selection method of the reference cases in real time. For example, the reference section can select the optimal selection method according to the user's situation. This allows the reference section to always select reference cases in the optimal method and provide reference cases based on the user's emotions.

[0105] The reference unit can select the optimal case by referring to past success cases when selecting a reference case. The reference unit uses the generation AI to select the optimal case by referring to past success cases when selecting a reference case. For example, the reference unit can search a database for past success cases and select the optimal reference case. The reference unit can also improve the accuracy of the reference case by referring to success cases of similar cases. Furthermore, the reference unit can analyze past success cases and select the most effective reference case. As a result, the optimal reference case can be selected by referring to past success cases. A part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can provide the optimal reference case based on past success cases searched by the generation AI. Furthermore, the reference unit has a function of adjusting the reference method for success cases in real time. For example, the reference unit can select the optimal reference method according to the user's situation. As a result, the reference unit can always refer to past success cases in the optimal way and reflect them in the selection of the reference case.

[0106] The reference unit can estimate the user's emotion and adjust the display method of the reference case based on the estimated user's emotion. The reference unit can estimate the user's emotion using the generation AI and adjust the display method of the reference case based on the estimated user's emotion. For example, the reference unit can estimate the user's emotion using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user is nervous and provide a simple and highly visible display method. In addition, the generation AI can identify the emotion when the user is relaxed and provide a display method including detailed information. Furthermore, the generation AI can identify the emotion when the user is in a hurry and provide a display method that covers the main points. This allows for more appropriate display by adjusting the display method according to the user's emotion. Part or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can provide an optimal display method based on the user's emotion estimated by the generation AI. Furthermore, the reference section has a function of adjusting the display method in real time. For example, the reference section can select the optimal adjustment method according to the user's situation. This allows the reference section to always adjust the display method of the reference case in the optimal way and provide the display method based on the user's feelings.

[0107] The reference unit can select the optimal case by considering the geographical distribution when selecting the reference case. The reference unit uses the generation AI to select the optimal case by considering the geographical distribution when selecting the reference case. For example, the reference unit can select a region-specific reference case based on the location of the user. In addition, the reference unit can select a reference case that reflects the characteristics of each region by considering the geographical distribution. Furthermore, the reference unit can select a reference case for each region based on the geographical distribution. As a result, a region-specific reference case can be selected by considering the geographical distribution. A part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can provide the optimal reference case based on the geographical distribution considered by the generation AI. Furthermore, the reference unit has a function of adjusting the method of considering the geographical distribution in real time. For example, the reference unit can select the optimal method of considering according to the user's situation. As a result, the reference unit can always consider the geographical distribution in the optimal manner and select the reference case.

[0108] The evaluation unit can estimate the user's emotions and adjust the evaluation method of the solution based on the estimated user's emotions. The evaluation unit uses the generation AI to estimate the user's emotions and adjust the evaluation method of the solution based on the estimated user's emotions. For example, the evaluation unit can estimate the user's emotions using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user feels strong anger and evaluate with emphasis on the strength of the emotion. In addition, the generation AI can identify the emotion when the user is calm and evaluate from a logical perspective. Furthermore, the generation AI can identify the emotion when the user is confused and provide a concise and clear evaluation. This allows for a more appropriate evaluation by adjusting the evaluation method according to the user's emotions. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can provide an optimal evaluation method based on the user's emotions estimated by the generation AI. Furthermore, the evaluation unit has a function of adjusting the adjustment method of the evaluation method in real time. For example, the evaluation unit can select the optimal adjustment method according to the user's situation. This allows the evaluation unit to always adjust the evaluation method of the solution in the optimal way and provide the evaluation method based on the user's feelings.

[0109] The evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data when evaluating a solution. The evaluation unit uses the generation AI to improve the accuracy of the evaluation by referring to past evaluation data when evaluating a solution. For example, the evaluation unit can search for past evaluation data from a database and reflect it in the evaluation. Also, the evaluation unit can improve the accuracy of the evaluation by referring to evaluation data of similar cases. Furthermore, the evaluation unit can analyze past evaluation data and select the most effective evaluation method. As a result, the accuracy of the evaluation is improved by referring to the past evaluation data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can provide an optimal evaluation method based on the past evaluation data searched by the generation AI. Furthermore, the evaluation unit has a function of adjusting the method of referring to the evaluation data in real time. For example, the evaluation unit can select the optimal reference method according to the user's situation. As a result, the evaluation unit can always refer to past evaluation data in an optimal manner and reflect it in the evaluation of the solution.

[0110] The evaluation unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. The evaluation unit can estimate the user's emotion using the generation AI and adjust the display method of the evaluation result based on the estimated user's emotion. For example, the evaluation unit can estimate the user's emotion using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user is nervous and provide a simple and highly visible display method. In addition, the generation AI can identify the emotion when the user is relaxed and provide a display method including detailed information. Furthermore, the generation AI can identify the emotion when the user is in a hurry and provide a display method that covers the main points. This allows for more appropriate display by adjusting the display method according to the user's emotion. Part or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can provide an optimal display method based on the user's emotion estimated by the generation AI. Furthermore, the evaluation unit has a function of adjusting the display method adjustment method in real time. For example, the evaluation unit can select the optimal adjustment method according to the user's situation. This allows the evaluation unit to always adjust the display method of the evaluation result in the optimal way and provide a display method based on the user's feelings.

[0111] The evaluation unit can perform evaluation taking into account the geographical distribution when evaluating the solution. The evaluation unit performs evaluation taking into account the geographical distribution using the generation AI when evaluating the solution. For example, the evaluation unit can perform a region-specific evaluation based on the location of the user. In addition, the evaluation unit can perform evaluation reflecting the characteristics of each region by taking into account the geographical distribution. Furthermore, the evaluation unit can perform evaluation for each region based on the geographical distribution. As a result, region-specific evaluation is possible by taking into account the geographical distribution. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can provide an optimal evaluation method based on the geographical distribution taken into account by the generation AI. Furthermore, the evaluation unit has a function of adjusting the method of taking into account the geographical distribution in real time. For example, the evaluation unit can select the optimal consideration method according to the user's situation. As a result, the evaluation unit can always take into account the geographical distribution in the optimal manner and evaluate the solution.

[0112] The feedback unit can estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. The feedback unit uses the generation AI to estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. For example, the feedback unit can estimate the user's emotion using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user feels strong anger and collect feedback with emphasis on the intensity of the emotion. In addition, the generation AI can identify the emotion when the user is calm and collect feedback from a logical perspective. Furthermore, the generation AI can identify the emotion when the user is confused and collect concise and clear feedback. As a result, by adjusting the collection method according to the user's emotion, more appropriate feedback can be collected. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can provide an optimal collection method based on the user's emotion estimated by the generation AI. Furthermore, the feedback unit has a function of adjusting the adjustment method of the collection method in real time. For example, the feedback unit can select the optimal adjustment method according to the user's situation. This allows the feedback unit to always adjust the feedback collection method in the optimal way and provide the collection method based on the user's emotions.

[0113] The feedback unit can improve the accuracy of collection by referring to past feedback data when collecting feedback. The feedback unit uses the generation AI to improve the accuracy of collection by referring to past feedback data when collecting feedback. For example, the feedback unit can search for past feedback data from a database and reflect it in the collection. Also, the feedback unit can improve the accuracy of collection by referring to feedback data of similar cases. Furthermore, the feedback unit can analyze past feedback data and select the most effective collection method. As a result, the accuracy of collection is improved by referring to past feedback data. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can provide an optimal collection method based on past feedback data searched by the generation AI. Furthermore, the feedback unit has a function of adjusting the reference method of feedback data in real time. For example, the feedback unit can select the optimal reference method according to the user's situation. As a result, the feedback unit can always refer to past feedback data in an optimal manner and reflect it in the collection of feedback.

[0114] The feedback unit can estimate the user's emotion and adjust the display method of the feedback based on the estimated user's emotion. The feedback unit can estimate the user's emotion using the generation AI and adjust the display method of the feedback based on the estimated user's emotion. For example, the feedback unit can estimate the user's emotion using voice analysis technology and facial expression recognition technology. The generation AI can analyze the user's tone of voice and facial expression to identify the strength and type of emotion. For example, the generation AI can identify the emotion when the user is nervous and provide a simple and highly visible display method. In addition, the generation AI can identify the emotion when the user is relaxed and provide a display method including detailed information. Furthermore, the generation AI can identify the emotion when the user is in a hurry and provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method according to the user's emotion. Part or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can provide an optimal display method based on the user's emotion estimated by the generation AI. Furthermore, the feedback unit has a function of adjusting the adjustment method of the display method in real time. For example, the feedback unit can select the optimal adjustment method according to the user's situation. This allows the feedback unit to always adjust the feedback display method in the optimal way and provide the display method based on the user's emotions.

[0115] The feedback unit can collect feedback while taking into account the geographical distribution. The feedback unit uses the generation AI to collect feedback while taking into account the geographical distribution. For example, the feedback unit can collect region-specific feedback based on the user's location. Also, the feedback unit can collect feedback reflecting the characteristics of each region while taking into account the geographical distribution. Furthermore, the feedback unit can collect feedback for each region based on the geographical distribution. As a result, region-specific feedback can be collected by taking into account the geographical distribution. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can provide an optimal collection method based on the geographical distribution taken into account by the generation AI. Furthermore, the feedback unit has a function of adjusting the method of taking into account the geographical distribution in real time. For example, the feedback unit can select the optimal consideration method according to the user's situation. As a result, the feedback unit can always take into account the geographical distribution in the optimal manner and collect feedback.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The dispute arbitration system may further include a history analysis unit that analyzes the user's past behavior history. The history analysis unit may analyze what emotions the user has shown in what situations in the past, and compare the results with the current situation to propose a more appropriate solution. For example, the history analysis unit may refer to what solutions the user has accepted in similar disputes in the past. The history analysis unit may also analyze what changes in emotions the user has shown in the past, and predict changes in current emotions. This may allow a more personalized solution to be proposed by taking into account the user's past behavior history.

[0118] The dispute arbitration system may further include a health monitoring unit that monitors the user's current health condition. The health monitoring unit may measure the user's heart rate and stress level in real time and suggest a solution based on this data. For example, if the user's heart rate is high, the system may provide the user with advice to relax. Also, if the stress level is high, the system may suggest temporarily postponing the resolution of the dispute. This allows a solution to be suggested at a more appropriate time by taking into account the user's health condition.

[0119] The conflict mediation system may further include a network analysis unit that analyzes the user's social network. The network analysis unit may analyze the user's relationships with friends and family and propose a solution taking these relationships into account. For example, if the user frequently communicates with certain friends and family, the opinions of these people may be taken into consideration. The network analysis unit may also evaluate the user's level of social support and provide support as necessary. This may allow a more comprehensive solution to be proposed by taking the user's social network into account.

[0120] The conflict arbitration system may further include a cultural analysis unit that considers the cultural background of the user. The cultural analysis unit may analyze the cultural background and values ​​of the user and propose a solution taking these into consideration. For example, if the user has specific cultural values, a solution based on those values ​​may be proposed. The cultural analysis unit may also consider the language and communication style of the user and propose a solution in an appropriate manner. In this way, a more appropriate solution may be proposed by taking the cultural background of the user into consideration.

[0121] The fight arbitration system may further include an environment monitoring unit that monitors the user's current environment. The environment monitoring unit may measure environmental factors such as noise levels and temperature in the user's location in real time and suggest solutions based on this data. For example, if the user is in a noisy location, the system may suggest moving to a quieter location. Also, if the temperature is high, the system may suggest cooling methods. This allows a more appropriate solution to be proposed by taking the user's environment into account.

[0122] The conflict arbitration system may further include an hobby analysis unit that considers the user's hobbies and interests. The hobby analysis unit may analyze the user's hobbies and interests and propose a solution taking these into consideration. For example, if the user has a specific hobby, a solution related to the hobby may be proposed. The hobby analysis unit may also suggest activities that allow the user to relax and reduce stress. This allows a more personalized solution to be proposed by taking into consideration the user's hobbies and interests.

[0123] The dispute arbitration system may further include an economic analysis unit that takes into account the user's economic situation. The economic analysis unit may analyze the user's income and expenditure and propose a solution taking these into account. For example, if the user is in a financially difficult situation, the system may propose a cost-free solution. The economic analysis unit may also provide appropriate support depending on the user's economic situation. This allows a more realistic solution to be proposed by taking into account the user's economic situation.

[0124] The dispute mediation system may further include a learning analysis unit that takes into account the learning style of the user. The learning analysis unit may analyze the learning style and level of understanding of the user and propose a solution taking these into account. For example, if the user is a visual learner, the system may provide visual materials. If the user is an auditory learner, the system may provide audio explanations. This allows for a more effective solution to be proposed by taking into account the learning style of the user.

[0125] The dispute arbitration system may further include a time management unit that supports the user's time management. The time management unit may analyze the user's schedule and how he or she uses time, and may propose a solution by taking these into consideration. For example, if the user has a busy schedule, the system may propose a solution that can be implemented in a short time. The time management unit may also propose a solution at an optimal timing according to the user's schedule. This may support the user's time management, thereby making it possible to propose a more efficient solution.

[0126] The conflict mediation system may further include a communication analysis unit that analyzes the communication style of the user. The communication analysis unit may analyze the communication style and patterns of the user and propose a solution by taking these into consideration. For example, if the user prefers direct communication, the system may propose a direct solution. Also, if the user prefers indirect communication, the system may propose an indirect solution. In this way, a more appropriate solution may be proposed by taking the user's communication style into consideration.

[0127] The process flow of the second embodiment will be briefly described below.

[0128] Step 1: The reception unit receives input of the contents of the conversation by the parties. The contents of the conversation include, for example, the opinions and feelings of the parties, but are not limited to such examples. The reception unit can receive, for example, text input or voice input. Step 2: The analysis unit uses the generation AI to analyze the content of the conversation and tone of voice input by the reception unit. The analysis unit, for example, analyzes the content of the conversation to identify which parts are the points of contention. The analysis unit can also grasp the intensity of the emotions and level of tension of the parties by analyzing the tone of voice. For example, the generation AI can analyze the content of the conversation using a text generation AI (e.g., LLM) and identify the points of contention. The generation AI can also analyze the tone of voice using voice analysis technology to grasp the intensity of emotions and level of tension. Step 3: The summary section uses the generation AI to summarize the points of contention in the story analyzed by the analysis section. For example, the summary section extracts important parts of the content of the story and organizes the points of contention. Step 4: The suggestion unit uses the generation AI to propose an appropriate solution based on the issues summarized by the summary unit. The suggestion unit, for example, considers the issues in the discussion and the feelings of the parties involved and proposes the most appropriate solution. For example, the generation AI can propose a solution by referring to past success stories and the opinions of experts. Furthermore, the suggestion unit has a function to evaluate the reliability of the solution and also has a function to accept feedback from users.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0131] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned reception unit, analysis unit, summary unit, proposal unit, reference unit, evaluation unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the parties can input the contents of the talk. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents and tone of voice of the talk. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the points of contention in the talk. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an appropriate solution. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and refers to past cases and successful examples. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the reliability of the solution. The feedback unit is realized, for example, by the control unit 46A of the smart device 14, and accepts feedback from the user. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[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, the RAM 30, and the 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 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.

[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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0140] Fig. 4 shows an example of 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.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned reception unit, analysis unit, summary unit, proposal unit, reference unit, evaluation unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the parties can input the contents of the talk. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents and tone of voice of the talk. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the points of contention in the talk. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an appropriate solution. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and refers to past cases and successful examples. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the reliability of the solution. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214, and accepts feedback from the user. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0155] 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 performed in a secure state.

[0156] 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.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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 identification 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements including the above-mentioned reception unit, analysis unit, summary unit, proposal unit, reference unit, evaluation unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the parties can input the contents of the talk. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the talk and the tone of voice. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the points of contention in the talk. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an appropriate solution. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and refers to past cases and successful examples. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the reliability of the solution. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314, and accepts feedback from the user. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, a 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0168] 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.

[0169] 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 the voice according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0171] 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 performed in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0173] 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.

[0174] 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.

[0175] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0176] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0177] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0178] 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 a voice indicating a user input for 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.

[0179] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0180] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0181] Each of the multiple elements including the above-mentioned reception unit, analysis unit, summary unit, proposal unit, reference unit, evaluation unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the parties can input the contents of the talk. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents and tone of voice of the talk. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the points of contention in the talk. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an appropriate solution. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and refers to past cases and successful examples. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the reliability of the solution. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and accepts feedback from the user. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0183] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0185] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0187] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0189] 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.

[0190] 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 Universal Serial Bus (USB) 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.

[0191] In addition, 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 upon request from the data processing device 12.

[0192] 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.

[0193] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0194] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0195] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0196] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0198] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0200] (Appendix 1) A reception section for inputting the contents of the talk; an analysis unit that analyzes the contents of the speech and the tone of voice input by the reception unit; a summary section for summarizing the issues of the story analyzed by the analysis section; A proposal unit that proposes an appropriate solution based on the issues summarized by the summary unit. A system characterized by: (Appendix 2) Have a reference section for specific past cases or success stories 2. The system of claim 1, (Appendix 3) Equipping an evaluation department to evaluate the reliability of the solution 2. The system of claim 1, (Appendix 4) A feedback unit is provided for receiving feedback from the user. 2. The system of claim 1, (Appendix 5) The analysis unit includes: Analyze the content of the talk and pinpoint the specific points at issue 2. The system of claim 1, (Appendix 6) The analysis unit includes: Analyze the tone of voice to specifically grasp the intensity of the person's emotions and level of tension 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and adjust the way the speech is input based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past input history and provide the optimal input interface 2. The system of claim 1, (Appendix 9) The reception unit is As you type your story, filter it based specifically on your current situation or interests 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The reception unit is Prioritize relevant content by specifically taking into account the user's geographic location when typing the content of the conversation 2. The system of claim 1, (Appendix 12) The reception unit is As you type your story, it analyzes your social media activity and fills in relevant content for you 2. The system of claim 1, (Appendix 13) The analysis unit includes: Infer user emotions and adjust how the content of a conversation is analyzed based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit includes: When analyzing the content of a conversation, improve the accuracy of the analysis by referring to similar cases from the past. 2. The system of claim 1, (Appendix 15) The analysis unit includes: When analyzing the content of conversations, the analysis is performed taking into account the user's attribute information. 2. The system of claim 1, (Appendix 16) The analysis unit includes: Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit includes: When analyzing the content of stories, take geographical distribution into account. 2. The system of claim 1, (Appendix 18) The analysis unit includes: When analyzing the content of a story, refer to related literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 19) The summary section includes: Infer user sentiment and adjust argument structure based on inferred user sentiment 2. The system of claim 1, (Appendix 20) The summary section includes: When summarizing issues, refer to past methods of summarizing to select the most appropriate method 2. The system of claim 1, (Appendix 21) The summary section includes: When summarizing issues, consider user attribute information 2. The system of claim 1, (Appendix 22) The summary section includes: Infer user sentiment and adjust the way summary results are displayed based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The summary section includes: When summarizing the issues, take geographical distribution into account. 2. The system of claim 1, (Appendix 24) The summary section includes: When summarizing the issues, refer to related literature to improve the accuracy of your summary. 2. The system of claim 1, (Appendix 25) The suggestion unit, Infer user emotions and adjust the solution proposal method based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The suggestion unit, When proposing a solution, make the best proposal by referring to past success stories. 2. The system of claim 1, (Appendix 27) The suggestion unit, When proposing solutions, the system takes into account the user's attribute information. 2. The system of claim 1, (Appendix 28) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 29) The suggestion unit, When proposing solutions, consider geographical distribution when making proposals. 2. The system of claim 1, (Appendix 30) The suggestion unit, When proposing solutions, refer to related literature to improve the accuracy of the proposal 2. The system of claim 1, (Appendix 31) The reference part is Estimate the user's emotions and select reference cases based on the estimated user emotions. 3. The system of claim 2, (Appendix 32) The reference part is When selecting reference cases, refer to past success stories to select the most suitable case. 3. The system of claim 2, (Appendix 33) The reference part is Inferring user emotion and adjusting the display method of reference cases based on the estimated user emotion 3. The system of claim 2, (Appendix 34) The reference part is When selecting reference cases, select the most appropriate case taking into account geographical distribution. 3. The system of claim 2, (Appendix 35) The evaluation unit is Inferring user sentiment and adjusting the solution evaluation method based on the inferred user sentiment 4. The system of claim 3. (Appendix 36) The evaluation unit is When evaluating solutions, refer to past evaluation data to improve the accuracy of the evaluation 4. The system of claim 3. (Appendix 37) The evaluation unit is Inferring user emotion and adjusting the way rating results are displayed based on the estimated user emotion 4. The system of claim 3. (Appendix 38) The evaluation unit is When evaluating solutions, consider geographic distribution 4. The system of claim 3. (Appendix 39) The feedback unit is Infer user sentiment and adjust feedback collection methods based on the inferred user sentiment 5. The system of claim 4. (Appendix 40) The feedback unit is When collecting feedback, refer to past feedback data to improve the accuracy of the collection 5. The system of claim 4. (Appendix 41) The feedback unit is Inferring a user's emotions and adjusting the way feedback is presented based on the inferred user emotions 5. The system of claim 4. (Appendix 42) The feedback unit is When collecting feedback, do so with geographical distribution in mind. 5. The system of claim 4. [Explanation of symbols]

[0201] 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 reception section for inputting the contents of the talk; an analysis unit that analyzes the contents of the speech and the tone of voice input by the reception unit; a summary section for summarizing the issues of the story analyzed by the analysis section; A proposal unit that proposes an appropriate solution based on the issues summarized by the summary unit. A system characterized by:

2. Have a reference section for specific past cases or success stories 2. The system of claim 1 .

3. Equipping an evaluation department to evaluate the reliability of the solution 2. The system of claim 1 .

4. A feedback unit is provided for receiving feedback from the user.

2. The system of claim 1 .

5. The analysis unit includes: Analyze the tone of voice to specifically grasp the intensity of the person's emotions and level of tension 2. The system of claim 1 .

6. The reception unit is Estimate the user's emotions and adjust the way the speech is input based on the estimated user emotions 2. The system of claim 1 .

7. The reception unit is Analyze the user's past input history and provide the optimal input interface 2. The system of claim 1 .

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