System

Abstract

An object of a system according to an embodiment is to collect and analyze opinions and emotions of related persons and to smoothly progress a dialogue.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and an interaction unit. The collection unit collects an opinion or an emotion of a related person. The analysis unit analyzes the information collected by the collection unit. The proposal unit generates a proposal for the dialogue on the basis of the analysis result obtained by the analysis unit. The dialogue unit proceeds with a dialogue on the basis of the proposal generated by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to effectively gather the opinions and feelings of those involved in disputes and discussions and to facilitate smooth dialogue.

[0005] The system according to the embodiment aims to collect and analyze the opinions and feelings of the parties involved and facilitate smooth dialogue. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a dialogue unit. The collection unit collects opinions or emotions of stakeholders. The analysis unit analyzes the information collected by the collection unit. The proposal unit generates proposals for dialogue based on the analysis results obtained by the analysis unit. The dialogue unit progresses the dialogue based on the proposals generated by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment collects and analyzes the opinions and feelings of the parties involved, enabling smooth dialogue. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to promote dialogue and understanding in conflict zones. This system collects the opinions and emotions of the parties involved, analyzes the collected information, generates appropriate proposals for dialogue, and advances the dialogue based on the generated proposals. For example, the system collects the opinions and emotions of the parties involved, analyzes them, and generates proposed ways to proceed with the dialogue and solutions. A dialogue between the parties is conducted based on the generated proposals, promoting dialogue and understanding. This is expected to contribute to conflict resolution. This system can also be used in situations such as disputes, discussions, and education. For example, the generative AI can suggest ways to exchange opinions and advance discussions in educational settings, thereby promoting smooth communication. Furthermore, the generative AI can make appropriate suggestions in corporate discussions and decision-making, thereby realizing efficient dialogue. As a result, a system utilizing generative AI can promote dialogue and understanding in conflict zones and contribute to conflict resolution. For example, detailed collection of the opinions and emotions of the parties involved, analysis of which generates proposed ways to proceed with the dialogue and solutions, can facilitate smooth dialogue. This system can also be used in situations such as disputes, discussions, and education, enabling smooth communication and efficient dialogue.

[0029] A dialogue promotion system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a dialogue unit. The collection unit collects opinions or emotions of participants. The opinions and emotions of participants include, but are not limited to, positive opinions and negative emotions. The collection unit can accept, for example, voice input or text input. The collection unit can also estimate the emotions of participants and adjust the timing of collecting opinions and emotions based on the estimated emotions. For example, if a participant is nervous, the collection unit waits until the participant relaxes before collecting their opinions and emotions. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, for example, text analysis or emotion analysis, but is not limited to these examples. The analysis unit generates a dialogue progress and solution proposal based on the collected information. For example, a generation AI generates a dialogue progress and solution proposal taking into account the opinions and emotions of the participants. The proposal unit generates a dialogue proposal based on the analysis results obtained by the analysis unit. The proposal can include, for example, a dialogue progress and solution proposal, but is not limited to these examples. The suggestion unit presents the generated proposal to the parties involved. The dialogue unit advances a dialogue based on the proposal generated by the suggestion unit. The dialogue unit may, for example, advance a dialogue between the parties involved based on the generated proposal and support the advancement of the dialogue. As a result, the dialogue promotion system according to the embodiment can promote dialogue and understanding by collecting and analyzing opinions and emotions of the parties involved, generating proposals, and advancing the dialogue.

[0030] The collection unit can accept voice input or text input. The collection unit, for example, uses a microphone to accept voice input. For example, voice data is collected when a participant speaks into the microphone. The collection unit can also use a keyboard to accept text input. For example, text data is collected when a participant inputs their opinions or feelings using a keyboard. Furthermore, the collection unit can convert voice data into text data using voice recognition technology. For example, the content spoken by the participant is analyzed using voice recognition technology and saved as text data. In this way, by accepting voice input or text input, the opinions and feelings of the participants can be collected in a variety of ways. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI.

[0031] The analysis unit can generate a dialogue procedure or a proposed solution based on the collected information. The analysis unit, for example, analyzes the collected information using text analysis technology. For example, the analysis unit analyzes the opinions and emotions of the parties involved using text analysis technology and generates a dialogue procedure or a proposed solution. The analysis unit can also analyze the collected information using emotion analysis technology. For example, the analysis unit analyzes the emotions of the parties involved using emotion analysis technology and generates a dialogue procedure or a proposed solution. The analysis unit can also analyze the collected information using a generation AI and generate a dialogue procedure or a proposed solution. For example, the generation AI generates a dialogue procedure or a proposed solution taking into account the opinions and emotions of the parties involved. This can improve the quality of the dialogue by generating a dialogue procedure or a proposed solution based on the collected information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI.

[0032] The proposal unit can present the generated proposal to the parties involved. For example, the proposal unit presents the generated proposal to the parties involved in text format. For example, the proposal unit sends the generated proposal to the parties involved as a text message. The proposal unit can also present the generated proposal to the parties involved in audio format. For example, the proposal unit sends the generated proposal to the parties involved as an audio message. The proposal unit can also present the generated proposal to the parties involved in visual format. For example, the generated proposal is presented to the parties involved as a graph or chart. In this way, presenting the generated proposal to the parties involved can facilitate the progress of the dialogue. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0033] The dialogue unit can proceed with the dialogue based on the generated proposal. The dialogue unit, for example, proceeds with the dialogue between the parties based on the generated proposal. For example, the parties exchange opinions and deepen their understanding in accordance with the generated proposal. The dialogue unit can also support the progress of the dialogue based on the generated proposal. For example, the dialogue unit instructs the parties on how to proceed with the dialogue and supports the dialogue to proceed smoothly. Furthermore, the dialogue unit can monitor the progress of the dialogue based on the generated proposal and adjust the method of proceeding with the dialogue as necessary. For example, the dialogue unit monitors the progress of the dialogue in real time and makes appropriate suggestions if the dialogue stagnates. In this way, the effectiveness of the dialogue can be improved by proceeding with the dialogue based on the generated proposal. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without using AI.

[0034] The dialogue unit can support the progress of the dialogue. For example, the dialogue unit provides appropriate feedback to the participants to support the progress of the dialogue. For example, the dialogue unit provides feedback to the participants about the progress of the dialogue and the next step. The dialogue unit can also monitor the opinions and emotions of the participants in real time to support the progress of the dialogue. For example, the dialogue unit monitors the opinions and emotions of the participants in real time and supports the dialogue to proceed smoothly. Furthermore, the dialogue unit can analyze the opinions and emotions of the participants to support the progress of the dialogue and adjust the way the dialogue proceeds. For example, the dialogue unit analyzes the opinions and emotions of the participants and appropriately adjusts the way the dialogue proceeds. This supports the progress of the dialogue and helps the dialogue to proceed smoothly. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without using AI.

[0035] The collection unit can analyze the participant's past speech history and select the optimal collection method. The collection unit, for example, uses text analysis technology to analyze the participant's past speech history. For example, the collection unit analyzes the content of the participant's past speech using text analysis technology and selects the optimal collection method. The collection unit can also prioritize a collection method that the participant has preferred in the past. For example, if the participant has preferred text input in the past, the collection unit prioritizes text input. Furthermore, if the participant has frequently used voice input in the past, the collection unit can also prioritize voice input. For example, if the participant has expressed their opinions using images or videos in the past, the collection unit prioritizes image and video collection. In this way, the optimal collection method can be selected by analyzing the participant's past speech history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The collection unit can filter opinions and emotions based on the relevant person's current situation and areas of interest when collecting them. The collection unit, for example, uses sensors and location information technology to grasp the relevant person's current situation. For example, the collection unit can grasp the relevant person's current situation using sensors and location information technology and filter the opinions and emotions when collecting them. The collection unit can also analyze past data to grasp the relevant person's areas of interest. For example, the collection unit can filter based on areas in which the relevant person has previously shown interest. Furthermore, the collection unit can estimate the relevant person's current emotions and filter based on those emotions. For example, if the relevant person is interested in a specific issue in their current situation, the collection unit can preferentially collect opinions and emotions related to that issue. This makes it possible to collect highly relevant information by filtering based on the relevant person's current situation and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI.

[0037] When collecting opinions and emotions, the collection unit can select the optimal collection means depending on the input method of the participants. The collection unit, for example, uses an input device to understand the input method of the participants. For example, if the participants prefer voice input, the collection unit can prioritize voice input using a microphone. Also, if the participants prefer text input, the collection unit can prioritize text input using a keyboard. Furthermore, if the participants express their opinions using images or videos, the collection unit can prioritize image or video collection using a camera. For example, if the participants express their opinions using images or videos, the collection unit can use a camera to collect the images or videos. In this way, by selecting the optimal collection means depending on the input method of the participants, information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0038] When collecting opinions and emotions, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the relevant person. The collection unit, for example, uses GPS technology to acquire the geographical location information of the relevant person. For example, if the relevant person is in a specific area, the collection unit prioritizes collecting opinions and emotions related to that area. In addition, if the relevant person is moving, the collection unit can also prioritize collecting opinions and emotions related to the current location. Furthermore, if the relevant person stays in a specific location for a long period of time, the collection unit can also prioritize collecting opinions and emotions related to that location. For example, if the relevant person is in a specific area, the collection unit prioritizes collecting opinions and emotions related to that area. In this way, by taking into account the geographical location information of the relevant person, highly relevant information can be prioritized. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0039] When collecting opinions and emotions, the collection unit can analyze the social media activities of the relevant person and collect related information. The collection unit, for example, uses text analysis technology to analyze the social media activities of the relevant person. For example, it collects opinions and emotions posted by the relevant person on social media. The collection unit can also analyze the social media activities of the relevant person and collect related opinions and emotions. Furthermore, the collection unit can collect the opinions and emotions of the relevant person's friends and followers on social media. For example, it collects opinions and emotions posted by the relevant person on social media. In this way, by analyzing the social media activities of the relevant person, related information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0040] When collecting opinions and emotions, the collection unit can customize the collection method by reflecting the participant's past feedback. The collection unit, for example, uses text analysis technology to analyze the participant's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the participant in the past. The collection unit can also prioritize and use collection methods that the participant has preferred in the past. For example, the collection unit prioritizes and uses collection methods that the participant has preferred in the past. Furthermore, the collection unit can avoid collection methods that the participant has been dissatisfied with in the past. For example, the collection unit avoids collection methods that the participant has been dissatisfied with in the past. In this way, the collection method can be optimized by reflecting the participant's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, uses text analysis technology to evaluate the importance of the information. For example, the analysis unit evaluates the importance of the opinions and emotions of the parties involved using text analysis technology and adjusts the level of detail of the analysis. The analysis unit can also perform a detailed analysis of information with high importance. For example, a detailed analysis is performed on information with high importance. Furthermore, the analysis unit can also perform a simplified analysis of information with low importance. For example, a simplified analysis is performed on information with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. The analysis unit, for example, uses text analysis technology to classify the categories of information. For example, it classifies the opinions and emotions of stakeholders into categories and applies different analysis algorithms to them. The analysis unit can also apply an emotion analysis algorithm to information related to emotions. For example, it applies an emotion analysis algorithm to information related to emotions. The analysis unit can also apply an opinion analysis algorithm to information related to opinions. For example, it applies an opinion analysis algorithm to information related to opinions. In this way, by applying different analysis algorithms depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the relevant person's past analysis results. The analysis unit, for example, uses text analysis technology to analyze the relevant person's past analysis results. For example, the current analysis result is adjusted based on the analysis results provided by the relevant person in the past. The analysis unit can also optimize the analysis algorithm by referring to the relevant person's past analysis results. For example, the analysis algorithm is optimized by referring to the relevant person's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by using the relevant person's past analysis results. For example, the analysis accuracy is improved by referring to the relevant person's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, uses text analysis technology to evaluate the time of submission of information. For example, the analysis unit evaluates the time of information submitted by the relevant person and determines the priority of analysis. The analysis unit can also prioritize analysis of recently submitted information. For example, recently submitted information is analyzed first. Furthermore, the analysis unit can also postpone analysis of older information. For example, older information is analyzed first. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, uses text analysis technology to evaluate the relevance of the information. For example, the analysis unit evaluates the relevance of the opinions and emotions of the parties involved using text analysis technology and adjusts the order of analysis. The analysis unit can also prioritize analysis of information with high relevance. For example, it prioritizes analysis of information with high relevance. Furthermore, the analysis unit can also postpone analysis of information with low relevance. For example, it analyzes information with low relevance later. In this way, by adjusting the order of analysis based on the relevance of the information, important information can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the participants. The analysis unit, for example, uses text analysis technology to evaluate the expertise level of the participants. For example, the analysis unit evaluates the expertise level of the participants using text analysis technology and adjusts the use of technical terms in the analysis. The analysis unit can also use a lot of technical terms if the participants have specialized knowledge. For example, if the participants have specialized knowledge, the analysis unit uses a lot of technical terms. Furthermore, the analysis unit can avoid technical terms if the participants do not have specialized knowledge. For example, if the participants do not have specialized knowledge, the analysis unit avoids technical terms. In this way, by adjusting the use of technical terms according to the expertise level of the participants, it is possible to provide analysis results that are easy to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the information when making a suggestion. The suggestion unit, for example, uses text analysis technology to evaluate the importance of the information. For example, the suggestion unit evaluates the importance of the opinions and emotions of stakeholders using text analysis technology and adjusts the level of detail of the suggestion. The suggestion unit can also make detailed suggestions for information with high importance. For example, detailed suggestions are made for information with high importance. The suggestion unit can also make simplified suggestions for information with low importance. For example, simplified suggestions are made for information with low importance. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of information. The proposal unit, for example, uses text analysis technology to classify the category of information. For example, it classifies the opinions and emotions of stakeholders into categories and applies different proposal algorithms to them. The proposal unit can also apply a sentiment analysis algorithm to information related to emotions. For example, it applies a sentiment analysis algorithm to information related to emotions. The proposal unit can also apply an opinion analysis algorithm to information related to opinions. For example, it applies an opinion analysis algorithm to information related to opinions. This applies different proposal algorithms depending on the category of information, thereby improving the accuracy of the proposal. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0049] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the relevant person's past proposal results. The proposal unit, for example, uses text analysis technology to analyze the relevant person's past proposal results. For example, the proposal unit adjusts the current proposal based on the relevant person's past proposal results. The proposal unit can also optimize the proposal algorithm by referring to the relevant person's past proposal results. For example, the proposal algorithm is optimized by referring to the relevant person's past proposal results. Furthermore, the proposal unit can also improve the accuracy of the proposal by using the relevant person's past proposal results. For example, the proposal accuracy is improved by referring to the relevant person's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the information. The suggestion unit, for example, uses text analysis technology to evaluate the time of submission of the information. For example, the suggestion unit evaluates the time of information submitted by the relevant person and determines the priority of the proposal. The suggestion unit can also prioritize recently submitted information. For example, recently submitted information is prioritized. Furthermore, the suggestion unit can also postpone proposal of older information. For example, older information is proposed later. This enables efficient proposals by determining the priority of the proposal based on the time of submission of the information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0051] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the information. The proposal unit, for example, uses text analysis technology to evaluate the relevance of the information. For example, the proposal unit evaluates the relevance of the opinions and emotions of stakeholders using text analysis technology and adjusts the order of proposals. The proposal unit can also prioritize proposing information with high relevance. For example, it prioritizes proposing information with high relevance. Furthermore, the proposal unit can also postpone proposing information with low relevance. For example, it proposes information with low relevance later. In this way, by adjusting the order of proposals based on the relevance of the information, important information can be prioritized. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the expertise level of the relevant person. The suggestion unit, for example, uses text analysis technology to evaluate the expertise level of the relevant person. For example, the suggestion unit evaluates the expertise level of the relevant person using text analysis technology and adjusts the use of technical terminology in the proposal. The suggestion unit can also use a lot of technical terminology if the relevant person has expertise. For example, if the relevant person has expertise, the suggestion unit uses a lot of technical terminology. Furthermore, the suggestion unit can avoid technical terminology if the relevant person does not have expertise. For example, if the relevant person does not have expertise, the suggestion unit avoids technical terminology. In this way, by adjusting the use of technical terminology according to the expertise level of the relevant person, it is possible to provide a proposal that is easy to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0053] When proceeding with a dialogue, the dialogue unit can select an optimal dialogue proceeding method by referring to the past dialogue history of the parties involved. The dialogue unit, for example, uses text analysis technology to analyze the past dialogue history of the parties involved. For example, the dialogue unit prioritizes the use of a dialogue proceeding method that the parties involved preferred in the past. The dialogue unit can also avoid a dialogue proceeding method that the parties involved were dissatisfied with in the past. For example, the dialogue unit avoids a dialogue proceeding method that the parties involved were dissatisfied with in the past. Furthermore, the dialogue unit can select an optimal dialogue proceeding method based on the past dialogue history of the parties involved. For example, the dialogue unit selects an optimal dialogue proceeding method based on the past dialogue history of the parties involved. In this way, the optimal dialogue proceeding method can be selected by referring to the past dialogue history of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without using AI.

[0054] The dialogue unit can customize the dialogue means based on the current situations of the parties involved as the dialogue progresses. The dialogue unit, for example, uses sensors or location information technology to grasp the current situations of the parties involved. For example, if the parties involved are on the move, voice dialogue is prioritized. The dialogue unit can also prioritize text dialogue if the parties involved are in a quiet place. Furthermore, the dialogue unit can also prioritize non-face-to-face dialogue means if the parties involved are in a meeting. For example, if the parties involved are in a meeting, non-face-to-face dialogue means is prioritized. This enables efficient dialogue by customizing the dialogue means based on the current situations of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI.

[0055] The dialogue unit can improve the dialogue progression method by reflecting the feedback of the participants during the dialogue. The dialogue unit, for example, uses text analysis technology to analyze the feedback of the participants. For example, the dialogue unit adjusts the progression method based on the feedback provided by the participants during the dialogue. The dialogue unit can also improve the next progression method based on the feedback provided by the participants after the dialogue. For example, the dialogue unit improves the next progression method based on the feedback provided by the participants after the dialogue. Furthermore, the dialogue unit can reflect the feedback of the participants in real time and dynamically adjust the dialogue progression method. For example, the dialogue unit reflects the feedback of the participants in real time and dynamically adjusts the dialogue progression method. In this way, the dialogue progression method can be improved by reflecting the feedback of the participants. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI.

[0056] When proceeding with a dialogue, the dialogue unit can select an optimal dialogue proceeding method by taking into account the geographical location information of the parties involved. The dialogue unit, for example, uses GPS technology to acquire the geographical location information of the parties involved. For example, if the parties involved are in a specific area, the dialogue unit adopts a dialogue proceeding method related to that area. Furthermore, if the parties involved are on the move, the dialogue unit can also adopt a dialogue proceeding method suitable for movement. Furthermore, if the parties involved are staying in a specific location for a long period of time, the dialogue unit can also adopt a dialogue proceeding method related to that location. For example, if the parties involved are in a specific area, the dialogue unit adopts a dialogue proceeding method related to that area. In this way, the optimal dialogue proceeding method can be selected by taking into account the geographical location information of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without using AI.

[0057] The dialogue unit can analyze the social media activities of the parties involved and suggest a means of dialogue as the dialogue progresses. The dialogue unit, for example, uses text analysis technology to analyze the social media activities of the parties involved. For example, the dialogue unit can suggest a means of dialogue based on the opinions and emotions expressed by the parties involved on social media. The dialogue unit can also analyze the social media activities of the parties involved and suggest the optimal means of dialogue. Furthermore, the dialogue unit can also suggest a means of dialogue based on the opinions and emotions of the parties involved's friends and followers on social media. For example, the dialogue unit suggests a means of dialogue based on the opinions and emotions expressed by the parties involved on social media. In this way, the optimal means of dialogue can be suggested by analyzing the social media activities of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI.

[0058] The dialogue unit can customize the dialogue progression method by reflecting the participants' past feedback during the dialogue progression. The dialogue unit, for example, uses text analysis technology to analyze the participants' past feedback. For example, the dialogue unit adjusts the dialogue progression method based on feedback provided by the participants in the past. The dialogue unit can also prioritize and use dialogue progression methods that the participants have preferred in the past. Furthermore, the dialogue unit can avoid dialogue progression methods that the participants have been dissatisfied with in the past. For example, the dialogue progression method is adjusted based on feedback provided by the participants in the past. In this way, the dialogue progression method can be optimized by reflecting the participants' past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze the participant's past speech history and select the optimal collection method. For example, the collection unit can analyze the participant's past speech content using text analysis technology and select the optimal collection method. The collection unit can also prioritize a collection method that the participant has preferred in the past. For example, if the participant has preferred text input in the past, the collection unit can prioritize text input. Furthermore, if the participant has frequently used voice input in the past, the collection unit can also prioritize voice input. For example, if the participant has previously used images or videos to express their opinions, the collection unit can prioritize image and video collection. In this way, the optimal collection method can be selected by analyzing the participant's past speech history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0061] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the information. For example, the suggestion unit evaluates the importance of the opinions and emotions of the relevant parties using text analysis technology and adjusts the level of detail of the proposal. The suggestion unit can also make detailed proposals for information with high importance. For example, detailed proposals are made for information with high importance. Furthermore, the suggestion unit can also make simplified proposals for information with low importance. For example, simplified proposals are made for information with low importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0062] When proceeding with a dialogue, the dialogue unit can select an optimal dialogue proceeding method by referring to the past dialogue history of the participants. For example, text analysis technology can be used to analyze the past dialogue history of the participants. For example, a dialogue proceeding method that the participants preferred in the past can be used preferentially. The dialogue unit can also avoid a dialogue proceeding method that the participants were dissatisfied with in the past. For example, a dialogue proceeding method that the participants were dissatisfied with in the past can be avoided. Furthermore, the dialogue unit can select an optimal dialogue proceeding method based on the past dialogue history of the participants. For example, the optimal dialogue proceeding method is selected based on the past dialogue history of the participants. In this way, the optimal dialogue proceeding method can be selected by referring to the past dialogue history of the participants. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI.

[0063] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, text analysis technology is used to evaluate the importance of the information. For example, the importance of the opinions and emotions of the parties involved is evaluated using text analysis technology, and the level of detail of the analysis is adjusted. The analysis unit can also perform a detailed analysis of information with high importance. For example, a detailed analysis is performed on information with high importance. Furthermore, the analysis unit can also perform a simplified analysis of information with low importance. For example, a simplified analysis is performed on information with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0064] The dialogue unit can customize the dialogue means based on the current situations of the parties involved as the dialogue progresses. For example, sensors and location information technology can be used to grasp the current situations of the parties involved. For example, if the parties involved are on the move, voice dialogue can be prioritized. The dialogue unit can also prioritize text dialogue if the parties involved are in a quiet place. Furthermore, the dialogue unit can also prioritize non-face-to-face dialogue means if the parties involved are in a meeting. For example, if the parties involved are in a meeting, non-face-to-face dialogue means can be prioritized. This enables efficient dialogue by customizing the dialogue means based on the current situations of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI.

[0065] When collecting opinions and emotions, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the relevant person. For example, GPS technology can be used to acquire the geographical location information of the relevant person. For example, if the relevant person is in a specific area, opinions and emotions related to that area can be prioritized. In addition, if the relevant person is moving, the collection unit can prioritize collecting opinions and emotions related to the current location. Furthermore, if the relevant person stays in a specific place for a long period of time, the collection unit can prioritize collecting opinions and emotions related to that place. For example, if the relevant person is in a specific area, opinions and emotions related to that area are prioritized. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the relevant person. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects the opinions or emotions of the participants. The opinions and emotions of the participants can include positive opinions, negative emotions, etc. The collection unit can accept voice input or text input, estimate the emotions of the participants, and adjust the timing of collecting opinions and emotions based on the estimated emotions. For example, if the participants are nervous, the collection unit waits until they are relaxed before collecting their opinions and emotions. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as text analysis and sentiment analysis. Based on the collected information, the analysis unit generates suggestions for how to proceed with the dialogue and solutions. Step 3: The proposal unit generates a proposal for the dialogue based on the analysis results obtained by the analysis unit. The proposal includes a method for proceeding with the dialogue and a proposed solution. The proposal unit presents the generated proposal to the parties involved. Step 4: The dialogue unit advances the dialogue based on the proposal generated by the proposal unit. The dialogue unit advances the dialogue between the parties based on the generated proposal, and can also support the progress of the dialogue.

[0068] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to promote dialogue and understanding in conflict zones. This system collects the opinions and emotions of the parties involved, analyzes the collected information, generates appropriate proposals for dialogue, and advances the dialogue based on the generated proposals. For example, the system collects the opinions and emotions of the parties involved, analyzes them, and generates proposed ways to proceed with the dialogue and solutions. A dialogue between the parties is conducted based on the generated proposals, promoting dialogue and understanding. This is expected to contribute to conflict resolution. This system can also be used in situations such as disputes, discussions, and education. For example, the generative AI can suggest ways to exchange opinions and advance discussions in educational settings, thereby promoting smooth communication. Furthermore, the generative AI can make appropriate suggestions in corporate discussions and decision-making, thereby realizing efficient dialogue. As a result, a system utilizing generative AI can promote dialogue and understanding in conflict zones and contribute to conflict resolution. For example, detailed collection of the opinions and emotions of the parties involved, analysis of which generates proposed ways to proceed with the dialogue and solutions, can facilitate smooth dialogue. This system can also be used in situations such as disputes, discussions, and education, enabling smooth communication and efficient dialogue.

[0069] A dialogue promotion system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a dialogue unit. The collection unit collects opinions or emotions of participants. The opinions and emotions of participants include, but are not limited to, positive opinions and negative emotions. The collection unit can accept, for example, voice input or text input. The collection unit can also estimate the emotions of participants and adjust the timing of collecting opinions and emotions based on the estimated emotions. For example, if a participant is nervous, the collection unit waits until the participant relaxes before collecting their opinions and emotions. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, for example, text analysis or emotion analysis, but is not limited to these examples. The analysis unit generates a dialogue progress and solution proposal based on the collected information. For example, a generation AI generates a dialogue progress and solution proposal taking into account the opinions and emotions of the participants. The proposal unit generates a dialogue proposal based on the analysis results obtained by the analysis unit. The proposal can include, for example, a dialogue progress and solution proposal, but is not limited to these examples. The suggestion unit presents the generated proposal to the parties involved. The dialogue unit advances a dialogue based on the proposal generated by the suggestion unit. The dialogue unit may, for example, advance a dialogue between the parties involved based on the generated proposal and support the advancement of the dialogue. As a result, the dialogue promotion system according to the embodiment can promote dialogue and understanding by collecting and analyzing opinions and emotions of the parties involved, generating proposals, and advancing the dialogue.

[0070] The collection unit can accept voice input or text input. The collection unit, for example, uses a microphone to accept voice input. For example, voice data is collected when a participant speaks into the microphone. The collection unit can also use a keyboard to accept text input. For example, text data is collected when a participant inputs their opinions or feelings using a keyboard. Furthermore, the collection unit can convert voice data into text data using voice recognition technology. For example, the content spoken by the participant is analyzed using voice recognition technology and saved as text data. In this way, by accepting voice input or text input, the opinions and feelings of the participants can be collected in a variety of ways. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI.

[0071] The analysis unit can generate a dialogue procedure or a proposed solution based on the collected information. The analysis unit, for example, analyzes the collected information using text analysis technology. For example, the analysis unit analyzes the opinions and emotions of the parties involved using text analysis technology and generates a dialogue procedure or a proposed solution. The analysis unit can also analyze the collected information using emotion analysis technology. For example, the analysis unit analyzes the emotions of the parties involved using emotion analysis technology and generates a dialogue procedure or a proposed solution. The analysis unit can also analyze the collected information using a generation AI and generate a dialogue procedure or a proposed solution. For example, the generation AI generates a dialogue procedure or a proposed solution taking into account the opinions and emotions of the parties involved. This can improve the quality of the dialogue by generating a dialogue procedure or a proposed solution based on the collected information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI.

[0072] The proposal unit can present the generated proposal to the parties involved. For example, the proposal unit presents the generated proposal to the parties involved in text format. For example, the proposal unit sends the generated proposal to the parties involved as a text message. The proposal unit can also present the generated proposal to the parties involved in audio format. For example, the proposal unit sends the generated proposal to the parties involved as an audio message. The proposal unit can also present the generated proposal to the parties involved in visual format. For example, the generated proposal is presented to the parties involved as a graph or chart. In this way, presenting the generated proposal to the parties involved can facilitate the progress of the dialogue. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0073] The dialogue unit can proceed with the dialogue based on the generated proposal. The dialogue unit, for example, proceeds with the dialogue between the parties based on the generated proposal. For example, the parties exchange opinions and deepen their understanding in accordance with the generated proposal. The dialogue unit can also support the progress of the dialogue based on the generated proposal. For example, the dialogue unit instructs the parties on how to proceed with the dialogue and supports the dialogue to proceed smoothly. Furthermore, the dialogue unit can monitor the progress of the dialogue based on the generated proposal and adjust the method of proceeding with the dialogue as necessary. For example, the dialogue unit monitors the progress of the dialogue in real time and makes appropriate suggestions if the dialogue stagnates. In this way, the effectiveness of the dialogue can be improved by proceeding with the dialogue based on the generated proposal. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without using AI.

[0074] The dialogue unit can support the progress of the dialogue. For example, the dialogue unit provides appropriate feedback to the participants to support the progress of the dialogue. For example, the dialogue unit provides feedback to the participants about the progress of the dialogue and the next step. The dialogue unit can also monitor the opinions and emotions of the participants in real time to support the progress of the dialogue. For example, the dialogue unit monitors the opinions and emotions of the participants in real time and supports the dialogue to proceed smoothly. Furthermore, the dialogue unit can analyze the opinions and emotions of the participants to support the progress of the dialogue and adjust the way the dialogue proceeds. For example, the dialogue unit analyzes the opinions and emotions of the participants and appropriately adjusts the way the dialogue proceeds. This supports the progress of the dialogue and helps the dialogue to proceed smoothly. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without using AI.

[0075] The collection unit can estimate the emotions of the participants and adjust the timing of collecting opinions and emotions based on the estimated emotions. The collection unit, for example, uses emotion recognition technology to estimate the emotions of the participants. For example, the collection unit estimates emotions by analyzing the participants' facial expressions and voices. The collection unit can also adjust the timing of collecting opinions and emotions based on the estimated emotions. For example, if the participants are nervous, the collection unit waits until they relax before collecting their opinions and emotions. Furthermore, if the participants are angry, the collection unit can wait until they calm down before collecting their opinions and emotions. For example, if the participants are calm, the collection unit collects their opinions and emotions immediately. This allows for adjusting the collection timing according to the participants' emotions, thereby enabling more appropriate opinions and emotions to be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI.

[0076] The collection unit can analyze the participant's past speech history and select the optimal collection method. The collection unit, for example, uses text analysis technology to analyze the participant's past speech history. For example, the collection unit analyzes the content of the participant's past speech using text analysis technology and selects the optimal collection method. The collection unit can also prioritize a collection method that the participant has preferred in the past. For example, if the participant has preferred text input in the past, the collection unit prioritizes text input. Furthermore, if the participant has frequently used voice input in the past, the collection unit can also prioritize voice input. For example, if the participant has expressed their opinions using images or videos in the past, the collection unit prioritizes image and video collection. In this way, the optimal collection method can be selected by analyzing the participant's past speech history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The collection unit can filter opinions and emotions based on the relevant person's current situation and areas of interest when collecting them. The collection unit, for example, uses sensors and location information technology to grasp the relevant person's current situation. For example, the collection unit can grasp the relevant person's current situation using sensors and location information technology and filter the opinions and emotions when collecting them. The collection unit can also analyze past data to grasp the relevant person's areas of interest. For example, the collection unit can filter based on areas in which the relevant person has previously shown interest. Furthermore, the collection unit can estimate the relevant person's current emotions and filter based on those emotions. For example, if the relevant person is interested in a specific issue in their current situation, the collection unit can preferentially collect opinions and emotions related to that issue. This makes it possible to collect highly relevant information by filtering based on the relevant person's current situation and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI.

[0078] When collecting opinions and emotions, the collection unit can select the optimal collection means depending on the input method of the participants. The collection unit, for example, uses an input device to understand the input method of the participants. For example, if the participants prefer voice input, the collection unit can prioritize voice input using a microphone. Also, if the participants prefer text input, the collection unit can prioritize text input using a keyboard. Furthermore, if the participants express their opinions using images or videos, the collection unit can prioritize image or video collection using a camera. For example, if the participants express their opinions using images or videos, the collection unit can use a camera to collect the images or videos. In this way, by selecting the optimal collection means depending on the input method of the participants, information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0079] The collection unit can estimate the emotions of the participants and determine the priority of the opinions and emotions to be collected based on the estimated emotions. The collection unit, for example, uses emotion recognition technology to estimate the emotions of the participants. For example, the emotion is estimated by analyzing the participants' facial expressions and voices. The collection unit can also determine the priority of the opinions and emotions to be collected based on the estimated emotions. For example, if the participants are feeling strong emotions, opinions related to those emotions are collected with priority. Furthermore, if the participants are feeling calm, the collection unit can postpone opinions related to those emotions. For example, if the participants are feeling neutral emotions, opinions related to those emotions are collected with medium priority. In this way, by determining the priority of the opinions and emotions to be collected based on the participants' emotions, important information can be collected with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0080] When collecting opinions and emotions, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the relevant person. The collection unit, for example, uses GPS technology to acquire the geographical location information of the relevant person. For example, if the relevant person is in a specific area, the collection unit prioritizes collecting opinions and emotions related to that area. In addition, if the relevant person is moving, the collection unit can also prioritize collecting opinions and emotions related to the current location. Furthermore, if the relevant person stays in a specific location for a long period of time, the collection unit can also prioritize collecting opinions and emotions related to that location. For example, if the relevant person is in a specific area, the collection unit prioritizes collecting opinions and emotions related to that area. In this way, by taking into account the geographical location information of the relevant person, highly relevant information can be prioritized. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0081] When collecting opinions and emotions, the collection unit can analyze the social media activities of the relevant person and collect related information. The collection unit, for example, uses text analysis technology to analyze the social media activities of the relevant person. For example, it collects opinions and emotions posted by the relevant person on social media. The collection unit can also analyze the social media activities of the relevant person and collect related opinions and emotions. Furthermore, the collection unit can collect the opinions and emotions of the relevant person's friends and followers on social media. For example, it collects opinions and emotions posted by the relevant person on social media. In this way, by analyzing the social media activities of the relevant person, related information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0082] When collecting opinions and emotions, the collection unit can customize the collection method by reflecting the participant's past feedback. The collection unit, for example, uses text analysis technology to analyze the participant's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the participant in the past. The collection unit can also prioritize and use collection methods that the participant has preferred in the past. For example, the collection unit prioritizes and uses collection methods that the participant has preferred in the past. Furthermore, the collection unit can avoid collection methods that the participant has been dissatisfied with in the past. For example, the collection unit avoids collection methods that the participant has been dissatisfied with in the past. In this way, the collection method can be optimized by reflecting the participant's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0083] The analysis unit can estimate the emotions of the participants and adjust the presentation method of the analysis based on the estimated emotions. The analysis unit, for example, uses emotion recognition technology to estimate the emotions of the participants. For example, the analysis unit analyzes the participants' facial expressions and voices to estimate their emotions. The analysis unit can also adjust the presentation method of the analysis based on the estimated emotions. For example, if the participants are nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the participants are relaxed, the analysis unit can provide detailed analysis results. For example, if the participants are in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows for adjusting the presentation method of the analysis based on the participants' emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, uses text analysis technology to evaluate the importance of the information. For example, the analysis unit evaluates the importance of the opinions and emotions of the parties involved using text analysis technology and adjusts the level of detail of the analysis. The analysis unit can also perform a detailed analysis of information with high importance. For example, a detailed analysis is performed on information with high importance. Furthermore, the analysis unit can also perform a simplified analysis of information with low importance. For example, a simplified analysis is performed on information with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. The analysis unit, for example, uses text analysis technology to classify the categories of information. For example, it classifies the opinions and emotions of stakeholders into categories and applies different analysis algorithms to them. The analysis unit can also apply an emotion analysis algorithm to information related to emotions. For example, it applies an emotion analysis algorithm to information related to emotions. The analysis unit can also apply an opinion analysis algorithm to information related to opinions. For example, it applies an opinion analysis algorithm to information related to opinions. In this way, by applying different analysis algorithms depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the relevant person's past analysis results. The analysis unit, for example, uses text analysis technology to analyze the relevant person's past analysis results. For example, the current analysis result is adjusted based on the analysis results provided by the relevant person in the past. The analysis unit can also optimize the analysis algorithm by referring to the relevant person's past analysis results. For example, the analysis algorithm is optimized by referring to the relevant person's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by using the relevant person's past analysis results. For example, the analysis accuracy is improved by referring to the relevant person's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0087] The analysis unit can estimate the emotions of the participants and adjust the length of the analysis based on the estimated emotions. The analysis unit, for example, uses emotion recognition technology to estimate the emotions of the participants. For example, the analysis unit estimates emotions by analyzing the participants' facial expressions and voices. The analysis unit can also adjust the length of the analysis based on the estimated emotions. For example, if the participants are nervous, the analysis unit can provide a short and concise analysis result. Furthermore, if the participants are relaxed, the analysis unit can provide a detailed analysis result. For example, if the participants are in a hurry, the analysis unit can provide a concise analysis result. By adjusting the length of the analysis based on the participants' emotions, an analysis result of an appropriate length can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, uses text analysis technology to evaluate the time of submission of information. For example, the analysis unit evaluates the time of information submitted by the relevant person and determines the priority of analysis. The analysis unit can also prioritize analysis of recently submitted information. For example, recently submitted information is analyzed first. Furthermore, the analysis unit can also postpone analysis of older information. For example, older information is analyzed first. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, uses text analysis technology to evaluate the relevance of the information. For example, the analysis unit evaluates the relevance of the opinions and emotions of the parties involved using text analysis technology and adjusts the order of analysis. The analysis unit can also prioritize analysis of information with high relevance. For example, it prioritizes analysis of information with high relevance. Furthermore, the analysis unit can also postpone analysis of information with low relevance. For example, it analyzes information with low relevance later. In this way, by adjusting the order of analysis based on the relevance of the information, important information can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the participants. The analysis unit, for example, uses text analysis technology to evaluate the expertise level of the participants. For example, the analysis unit evaluates the expertise level of the participants using text analysis technology and adjusts the use of technical terms in the analysis. The analysis unit can also use a lot of technical terms if the participants have specialized knowledge. For example, if the participants have specialized knowledge, the analysis unit uses a lot of technical terms. Furthermore, the analysis unit can avoid technical terms if the participants do not have specialized knowledge. For example, if the participants do not have specialized knowledge, the analysis unit avoids technical terms. In this way, by adjusting the use of technical terms according to the expertise level of the participants, it is possible to provide analysis results that are easy to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0091] The suggestion unit can estimate the emotions of the participants and adjust the way the proposal is presented based on the estimated emotions. The suggestion unit, for example, uses emotion recognition technology to estimate the emotions of the participants. For example, the suggestion unit can analyze the participants' facial expressions and voices to estimate their emotions. The suggestion unit can also adjust the way the proposal is presented based on the estimated emotions. For example, if the participants are nervous, the suggestion unit can provide a simple, highly visible proposal. Furthermore, if the participants are relaxed, the suggestion unit can provide a detailed proposal. For example, if the participants are in a hurry, the suggestion unit can provide a proposal that focuses on the main points. This allows the suggestion unit to adjust the way the proposal is presented based on the participants' emotions, thereby providing a more appropriate proposal. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or without the generation AI.

[0092] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the information when making a suggestion. The suggestion unit, for example, uses text analysis technology to evaluate the importance of the information. For example, the suggestion unit evaluates the importance of the opinions and emotions of stakeholders using text analysis technology and adjusts the level of detail of the suggestion. The suggestion unit can also make detailed suggestions for information with high importance. For example, detailed suggestions are made for information with high importance. The suggestion unit can also make simplified suggestions for information with low importance. For example, simplified suggestions are made for information with low importance. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0093] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of information. The proposal unit, for example, uses text analysis technology to classify the category of information. For example, it classifies the opinions and emotions of stakeholders into categories and applies different proposal algorithms to them. The proposal unit can also apply a sentiment analysis algorithm to information related to emotions. For example, it applies a sentiment analysis algorithm to information related to emotions. The proposal unit can also apply an opinion analysis algorithm to information related to opinions. For example, it applies an opinion analysis algorithm to information related to opinions. This applies different proposal algorithms depending on the category of information, thereby improving the accuracy of the proposal. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0094] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the relevant person's past proposal results. The proposal unit, for example, uses text analysis technology to analyze the relevant person's past proposal results. For example, the proposal unit adjusts the current proposal based on the relevant person's past proposal results. The proposal unit can also optimize the proposal algorithm by referring to the relevant person's past proposal results. For example, the proposal algorithm is optimized by referring to the relevant person's past proposal results. Furthermore, the proposal unit can also improve the accuracy of the proposal by using the relevant person's past proposal results. For example, the proposal accuracy is improved by referring to the relevant person's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0095] The suggestion unit can estimate the emotion of the participant and adjust the length of the proposal based on the estimated emotion. The suggestion unit, for example, uses emotion recognition technology to estimate the emotion of the participant. For example, the suggestion unit can analyze the participant's facial expressions and voice to estimate the emotion. The suggestion unit can also adjust the length of the proposal based on the estimated emotion. For example, if the participant is nervous, the suggestion unit can provide a short and to-the-point proposal. Furthermore, if the participant is relaxed, the suggestion unit can provide a detailed proposal. For example, if the participant is in a hurry, the suggestion unit can provide a concise proposal. In this way, by adjusting the length of the proposal based on the participant's emotion, a proposal of appropriate length can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using the generation AI, or can be performed without using the generation AI.

[0096] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the information. The suggestion unit, for example, uses text analysis technology to evaluate the time of submission of the information. For example, the suggestion unit evaluates the time of information submitted by the relevant person and determines the priority of the proposal. The suggestion unit can also prioritize recently submitted information. For example, recently submitted information is prioritized. Furthermore, the suggestion unit can also postpone proposal of older information. For example, older information is proposed later. This enables efficient proposals by determining the priority of the proposal based on the time of submission of the information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0097] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the information. The proposal unit, for example, uses text analysis technology to evaluate the relevance of the information. For example, the proposal unit evaluates the relevance of the opinions and emotions of stakeholders using text analysis technology and adjusts the order of proposals. The proposal unit can also prioritize proposing information with high relevance. For example, it prioritizes proposing information with high relevance. Furthermore, the proposal unit can also postpone proposing information with low relevance. For example, it proposes information with low relevance later. In this way, by adjusting the order of proposals based on the relevance of the information, important information can be prioritized. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0098] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the expertise level of the relevant person. The suggestion unit, for example, uses text analysis technology to evaluate the expertise level of the relevant person. For example, the suggestion unit evaluates the expertise level of the relevant person using text analysis technology and adjusts the use of technical terminology in the proposal. The suggestion unit can also use a lot of technical terminology if the relevant person has expertise. For example, if the relevant person has expertise, the suggestion unit uses a lot of technical terminology. Furthermore, the suggestion unit can avoid technical terminology if the relevant person does not have expertise. For example, if the relevant person does not have expertise, the suggestion unit avoids technical terminology. In this way, by adjusting the use of technical terminology according to the expertise level of the relevant person, it is possible to provide a proposal that is easy to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0099] The dialogue unit can estimate the emotions of the participants and adjust the dialogue progress method based on the estimated emotions. The dialogue unit, for example, uses emotion recognition technology to estimate the emotions of the participants. For example, the dialogue unit estimates emotions by analyzing the facial expressions and voices of the participants. The dialogue unit can also adjust the dialogue progress method based on the estimated emotions. For example, if the participants are nervous, the dialogue unit adopts a dialogue progress method that helps them relax. Furthermore, if the participants are angry, the dialogue unit can adopt a dialogue progress method that helps them calm down. For example, if the participants are calm, the dialogue unit adopts a method that smoothly progresses the dialogue. In this way, by adjusting the dialogue progress method based on the emotions of the participants, a more appropriate dialogue can be progressed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or without AI.

[0100] When proceeding with a dialogue, the dialogue unit can select an optimal dialogue proceeding method by referring to the past dialogue history of the parties involved. The dialogue unit, for example, uses text analysis technology to analyze the past dialogue history of the parties involved. For example, the dialogue unit prioritizes the use of a dialogue proceeding method that the parties involved preferred in the past. The dialogue unit can also avoid a dialogue proceeding method that the parties involved were dissatisfied with in the past. For example, the dialogue unit avoids a dialogue proceeding method that the parties involved were dissatisfied with in the past. Furthermore, the dialogue unit can select an optimal dialogue proceeding method based on the past dialogue history of the parties involved. For example, the dialogue unit selects an optimal dialogue proceeding method based on the past dialogue history of the parties involved. In this way, the optimal dialogue proceeding method can be selected by referring to the past dialogue history of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without using AI.

[0101] The dialogue unit can customize the dialogue means based on the current situations of the parties involved as the dialogue progresses. The dialogue unit, for example, uses sensors or location information technology to grasp the current situations of the parties involved. For example, if the parties involved are on the move, voice dialogue is prioritized. The dialogue unit can also prioritize text dialogue if the parties involved are in a quiet place. Furthermore, the dialogue unit can also prioritize non-face-to-face dialogue means if the parties involved are in a meeting. For example, if the parties involved are in a meeting, non-face-to-face dialogue means is prioritized. This enables efficient dialogue by customizing the dialogue means based on the current situations of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI.

[0102] The dialogue unit can improve the dialogue progression method by reflecting the feedback of the participants during the dialogue. The dialogue unit, for example, uses text analysis technology to analyze the feedback of the participants. For example, the dialogue unit adjusts the progression method based on the feedback provided by the participants during the dialogue. The dialogue unit can also improve the next progression method based on the feedback provided by the participants after the dialogue. For example, the dialogue unit improves the next progression method based on the feedback provided by the participants after the dialogue. Furthermore, the dialogue unit can reflect the feedback of the participants in real time and dynamically adjust the dialogue progression method. For example, the dialogue unit reflects the feedback of the participants in real time and dynamically adjusts the dialogue progression method. In this way, the dialogue progression method can be improved by reflecting the feedback of the participants. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI.

[0103] The dialogue unit can estimate the emotions of the participants and determine the priority of the dialogue based on the estimated emotions. The dialogue unit, for example, uses emotion recognition technology to estimate the emotions of the participants. For example, the dialogue unit estimates emotions by analyzing the participants' facial expressions and voices. The dialogue unit can also determine the priority of the dialogue based on the estimated emotions. For example, if the participants are feeling strong emotions, the dialogue can be prioritized. Furthermore, if the participants are feeling calm, the dialogue can be postponed. For example, if the participants are feeling neutral emotions, the dialogue can be prioritized. In this way, by determining the priority of the dialogue based on the participants' emotions, important dialogues can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit can be performed, for example, using AI or without AI.

[0104] When proceeding with a dialogue, the dialogue unit can select an optimal dialogue proceeding method by taking into account the geographical location information of the parties involved. The dialogue unit, for example, uses GPS technology to acquire the geographical location information of the parties involved. For example, if the parties involved are in a specific area, the dialogue unit adopts a dialogue proceeding method related to that area. Furthermore, if the parties involved are on the move, the dialogue unit can also adopt a dialogue proceeding method suitable for movement. Furthermore, if the parties involved are staying in a specific location for a long period of time, the dialogue unit can also adopt a dialogue proceeding method related to that location. For example, if the parties involved are in a specific area, the dialogue unit adopts a dialogue proceeding method related to that area. In this way, the optimal dialogue proceeding method can be selected by taking into account the geographical location information of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without using AI.

[0105] The dialogue unit can analyze the social media activities of the parties involved and suggest a means of dialogue as the dialogue progresses. The dialogue unit, for example, uses text analysis technology to analyze the social media activities of the parties involved. For example, the dialogue unit can suggest a means of dialogue based on the opinions and emotions expressed by the parties involved on social media. The dialogue unit can also analyze the social media activities of the parties involved and suggest the optimal means of dialogue. Furthermore, the dialogue unit can also suggest a means of dialogue based on the opinions and emotions of the parties involved's friends and followers on social media. For example, the dialogue unit suggests a means of dialogue based on the opinions and emotions expressed by the parties involved on social media. In this way, the optimal means of dialogue can be suggested by analyzing the social media activities of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI.

[0106] The dialogue unit can customize the dialogue progression method by reflecting the participants' past feedback during the dialogue progression. The dialogue unit, for example, uses text analysis technology to analyze the participants' past feedback. For example, the dialogue unit adjusts the dialogue progression method based on feedback provided by the participants in the past. The dialogue unit can also prioritize and use dialogue progression methods that the participants have preferred in the past. Furthermore, the dialogue unit can avoid dialogue progression methods that the participants have been dissatisfied with in the past. For example, the dialogue progression method is adjusted based on feedback provided by the participants in the past. In this way, the dialogue progression method can be optimized by reflecting the participants' past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and dialogue unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect opinions and emotions of the parties involved using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates proposals for how to proceed with the dialogue and solutions. The proposal unit presents the generated proposals to the parties involved via the display 40A and speaker 40B of the smart device 14. The dialogue unit progresses the dialogue based on the proposals generated by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and dialogue unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect opinions and emotions of the parties involved using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates proposals for how to proceed with the dialogue and solutions. The suggestion unit presents the generated proposals to the parties involved through the speaker 240 of the smart glasses 214. The dialogue unit proceeds with the dialogue based on the proposals generated by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and dialogue unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect opinions and emotions of the parties involved using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates proposals for how to proceed with the dialogue and solutions. The proposal unit presents the generated proposals to the parties involved via the display 343 and speaker 240 of the headset type terminal 314. The dialogue unit progresses the dialogue based on the proposals generated by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and dialogue unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect opinions and emotions of the parties using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates proposals for how to proceed with the dialogue and solutions. The proposal unit presents the generated proposals to the parties via the speaker 240 of the robot 414. The dialogue unit progresses the dialogue based on the proposals generated by the control unit 46A of the robot 414.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The dialogue unit can estimate the emotions of the participants and adjust the dialogue progress method based on the estimated emotions. For example, if the participants are nervous, a dialogue progress method that helps them relax can be adopted. Furthermore, if the participants are angry, a dialogue progress method that helps them calm down can be adopted. For example, if the participants are calm, a method that smoothly progresses the dialogue can be adopted. In this way, by adjusting the dialogue progress method based on the emotions of the participants, a more appropriate dialogue can be progressed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI.

[0109] The collection unit can analyze the participant's past speech history and select the optimal collection method. For example, the collection unit can analyze the participant's past speech content using text analysis technology and select the optimal collection method. The collection unit can also prioritize a collection method that the participant has preferred in the past. For example, if the participant has preferred text input in the past, the collection unit can prioritize text input. Furthermore, if the participant has frequently used voice input in the past, the collection unit can also prioritize voice input. For example, if the participant has previously used images or videos to express their opinions, the collection unit can prioritize image and video collection. In this way, the optimal collection method can be selected by analyzing the participant's past speech history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0110] The analysis unit can estimate the emotions of the participants and adjust the way the analysis is presented based on the estimated emotions. For example, if the participants are nervous, a simple and highly visible analysis result can be provided. Furthermore, if the participants are relaxed, a detailed analysis result can be provided. For example, if the participants are in a hurry, an analysis result that focuses on the main points can be provided. By adjusting the way the analysis is presented based on the participants' emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI.

[0111] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the information. For example, the suggestion unit evaluates the importance of the opinions and emotions of the relevant parties using text analysis technology and adjusts the level of detail of the proposal. The suggestion unit can also make detailed proposals for information with high importance. For example, detailed proposals are made for information with high importance. Furthermore, the suggestion unit can also make simplified proposals for information with low importance. For example, simplified proposals are made for information with low importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0112] When proceeding with a dialogue, the dialogue unit can select an optimal dialogue proceeding method by referring to the past dialogue history of the participants. For example, text analysis technology can be used to analyze the past dialogue history of the participants. For example, a dialogue proceeding method that the participants preferred in the past can be used preferentially. The dialogue unit can also avoid a dialogue proceeding method that the participants were dissatisfied with in the past. For example, a dialogue proceeding method that the participants were dissatisfied with in the past can be avoided. Furthermore, the dialogue unit can select an optimal dialogue proceeding method based on the past dialogue history of the participants. For example, the optimal dialogue proceeding method is selected based on the past dialogue history of the participants. In this way, the optimal dialogue proceeding method can be selected by referring to the past dialogue history of the participants. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI.

[0113] The collection unit can estimate the emotions of the participants and determine the priority of the opinions and emotions to be collected based on the estimated emotions. For example, emotion recognition technology can be used to estimate the emotions of the participants. For example, emotions can be estimated by analyzing the participants' facial expressions and voices. The collection unit can also determine the priority of the opinions and emotions to be collected based on the estimated emotions. For example, if the participants are feeling strong emotions, opinions related to those emotions can be collected preferentially. Furthermore, if the participants are feeling calm, opinions related to those emotions can be postponed. For example, if the participants are feeling neutral emotions, opinions related to those emotions can be collected with medium priority. In this way, by determining the priority of the opinions and emotions to be collected based on the participants' emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI.

[0114] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, text analysis technology is used to evaluate the importance of the information. For example, the importance of the opinions and emotions of the parties involved is evaluated using text analysis technology, and the level of detail of the analysis is adjusted. The analysis unit can also perform a detailed analysis of information with high importance. For example, a detailed analysis is performed on information with high importance. Furthermore, the analysis unit can also perform a simplified analysis of information with low importance. For example, a simplified analysis is performed on information with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0115] The suggestion unit can estimate the emotions of the participants and adjust the way the proposal is presented based on the estimated emotions. For example, emotion recognition technology can be used to estimate the emotions of the participants. For example, emotions can be estimated by analyzing the participants' facial expressions and voices. The suggestion unit can also adjust the way the proposal is presented based on the estimated emotions. For example, if the participants are nervous, a simple and highly visible proposal can be provided. Furthermore, if the participants are relaxed, the suggestion unit can provide a detailed proposal. For example, if the participants are in a hurry, a proposal that focuses on the main points can be provided. This allows for adjusting the way the proposal is presented based on the participants' emotions, thereby providing a more appropriate proposal. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, a generation AI, or without a generation AI.

[0116] The dialogue unit can customize the dialogue means based on the current situations of the parties involved as the dialogue progresses. For example, sensors and location information technology can be used to grasp the current situations of the parties involved. For example, if the parties involved are on the move, voice dialogue can be prioritized. The dialogue unit can also prioritize text dialogue if the parties involved are in a quiet place. Furthermore, the dialogue unit can also prioritize non-face-to-face dialogue means if the parties involved are in a meeting. For example, if the parties involved are in a meeting, non-face-to-face dialogue means can be prioritized. This enables efficient dialogue by customizing the dialogue means based on the current situations of the parties involved. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI.

[0117] When collecting opinions and emotions, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the relevant person. For example, GPS technology can be used to acquire the geographical location information of the relevant person. For example, if the relevant person is in a specific area, opinions and emotions related to that area can be prioritized. In addition, if the relevant person is moving, the collection unit can prioritize collecting opinions and emotions related to the current location. Furthermore, if the relevant person stays in a specific place for a long period of time, the collection unit can prioritize collecting opinions and emotions related to that place. For example, if the relevant person is in a specific area, opinions and emotions related to that area are prioritized. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the relevant person. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects the opinions or emotions of the participants. The opinions and emotions of the participants can include positive opinions, negative emotions, etc. The collection unit can accept voice input or text input, estimate the emotions of the participants, and adjust the timing of collecting opinions and emotions based on the estimated emotions. For example, if the participants are nervous, the collection unit waits until they are relaxed before collecting their opinions and emotions. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as text analysis and sentiment analysis. Based on the collected information, the analysis unit generates suggestions for how to proceed with the dialogue and solutions. Step 3: The proposal unit generates a proposal for the dialogue based on the analysis results obtained by the analysis unit. The proposal includes a method for proceeding with the dialogue and a proposed solution. The proposal unit presents the generated proposal to the parties involved. Step 4: The dialogue unit advances the dialogue based on the proposal generated by the proposal unit. The dialogue unit advances the dialogue between the parties based on the generated proposal, and can also support the progress of the dialogue.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] 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.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 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 collection unit for collecting opinions or feelings of stakeholders; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that generates a suggestion for a dialogue based on the analysis result obtained by the analysis unit; a dialogue unit that conducts a dialogue based on the proposal generated by the proposal unit. A system characterized by:

2. The collecting unit Accepts voice or text input 2. The system of claim 1.

3. The analysis unit Generate dialogue or solution suggestions based on the information collected 2. The system of claim 1.

4. The proposal unit Present the generated proposal to stakeholders 2. The system of claim 1.

5. The dialogue unit Proceed with the dialogue based on the generated suggestions 2. The system of claim 1.

6. The dialogue unit Support the dialogue 2. The system of claim 1.

7. The collecting unit Estimate stakeholders' emotions and adjust the timing of opinion or emotion collection based on the estimated emotions.

2. The system of claim 1.

8. The collecting unit Analyze the past statements of those involved and select the most appropriate collection method 2. The system of claim 1.

Images