System

Abstract

An object of the system according to the embodiment is to effectively plan an idea using a generated AI through a discussion by a plurality of persons.SOLUTION: A system according to an embodiment includes a discussion unit, an analysis unit, and a planning unit. The discussion section performs a discussion among a plurality of persons and proposes an idea based on the conditions of the generated AI. The analyzing unit analyzes the idea proposed by the discussion unit. The planning unit plans events and measures based on the result analyzed by the analysis 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] With conventional technology, it was difficult to effectively generate ideas using generative AI through discussions among multiple people.

[0005] The system of the embodiment aims to effectively develop ideas using generative AI through discussions among multiple people. [Means for solving the problem]

[0006] The system according to the embodiment includes a discussion unit, an analysis unit, and a planning unit. The discussion unit holds discussions among multiple people and generates ideas based on the conditions of the generation AI. The analysis unit analyzes the ideas generated by the discussion unit. The planning unit plans events and initiatives based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system of the embodiment can effectively develop ideas that utilize generative AI through discussions among multiple people. [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 sales promotion system according to an embodiment of the present invention is a system that holds discussions among multiple participants and generates ideas based on the conditions set by a generation AI. This system analyzes the content of the discussions and utilizes them to plan events and initiatives. For example, ideas for sales promotion events and new sales strategies are discussed. The content of these discussions is input into a generation AI. The generation AI analyzes the participants' comments and extracts important points and common themes. For example, if many participants agree that a particular event is effective, the generation AI proposes details for that event. Events and initiatives are planned based on the results of the analysis by the generation AI. For example, specific schedules and implementation methods are determined based on the event details proposed by the generation AI. This allows the sales promotion system to plan effective sales promotion events and initiatives. This allows the sales promotion system to improve sales performance for mobile and fixed-line devices. For example, the generation AI analyzes the content of the discussions and proposes effective events and initiatives, generating new ideas for sales promotion. Furthermore, multiple-person discussions reflect diverse perspectives and opinions, resulting in more effective initiatives.

[0029] A sales promotion system according to an embodiment includes a discussion unit, an analysis unit, and a planning unit. The discussion unit holds a discussion with multiple participants and generates ideas based on the conditions set by the generation AI. The discussion unit discusses, for example, ideas for sales promotion events and new sales strategies. The discussion unit can also select individuals with extensive knowledge and experience in sales. For example, the discussion unit can assign a participant who is knowledgeable about sales promotion as a leader and have them lead the discussion. The analysis unit uses the generation AI to analyze the ideas generated by the discussion unit. For example, the analysis unit analyzes participants' comments and extracts important points and common themes. The analysis unit can also refer to past success stories and market trends. For example, the analysis unit analyzes past success stories and extracts common elements. The planning unit uses the generation AI to plan events and initiatives based on the results of the analysis by the analysis unit. For example, the planning unit determines the specific schedule and implementation method based on the details of the event proposed by the generation AI. The planning unit can also customize the plan taking current market trends into account. For example, the planning unit analyzes current market trends and customizes the content of the plan, thereby enabling the sales promotion system according to the embodiment to plan effective events and initiatives.

[0030] The discussion section can discuss ideas for sales promotion events or new sales strategies. The discussion section, for example, discusses ideas for sales promotion events. For example, ideas for specific events such as campaigns and exhibitions are shared. The discussion section can also discuss new sales strategies. For example, strategies such as target market selection and pricing are discussed. This makes it possible to generate effective ideas through discussions of sales promotion events and new sales strategies.

[0031] The analysis unit can analyze participants' comments and extract important points and common themes. The analysis unit, for example, analyzes participants' comments and extracts important points. For example, it identifies important points based on frequently occurring keywords and the weighting of comments. The analysis unit can also extract common themes. For example, it identifies common themes using a theme classification method or common point extraction criteria. In this way, by analyzing participants' comments, it is possible to extract important points and common themes and plan effective events and initiatives.

[0032] The planning department can determine the schedule and implementation method based on the details of the event proposed by the generation AI. The planning department, for example, determines the schedule based on the details of the event proposed by the generation AI. For example, it sets a timeline and important milestones. The planning department can also determine the implementation method. For example, it determines the implementation procedures and necessary resources. In this way, by determining the specific schedule and implementation method based on the details of the event proposed by the generation AI, it is possible to realize effective events and initiatives.

[0033] The discussion group can select people with extensive knowledge or experience in sales. The discussion group can select people with extensive knowledge or experience in sales, for example, based on past achievements or professional qualifications. The discussion group can also set participants who are knowledgeable about sales promotion as leaders and have them lead the discussion. By selecting people with extensive knowledge and experience in sales, more effective discussions can be achieved.

[0034] The analysis unit can refer to past success stories or market trends. The analysis unit, for example, refers to past success stories. For example, it analyzes past success stories based on the definition of success and evaluation criteria. The analysis unit can also refer to market trends. For example, it analyzes market trends using trend data sources and analytical methods. In this way, by referring to past success stories and market trends, the accuracy of the analysis can be improved, and effective events and initiatives can be planned.

[0035] During a discussion, the discussion section can analyze the participants' past speech history and select an appropriate topic. The discussion section, for example, analyzes the participants' past speech history and selects an appropriate topic. For example, it can select a related topic based on ideas that participants have previously proposed. It can also extract topics that participants are particularly interested in from their past speeches and set them as topics. It can also analyze the participants' past speech history and select topics that are likely to lead to in-depth discussion. In this way, by analyzing the participants' past speech history, it is possible to select the most appropriate topic and hold effective discussions.

[0036] During the discussion, the discussion section can assign roles based on the participants' expertise and experience. For example, a participant who is knowledgeable about sales promotion can be set as the leader and lead the discussion. A participant who is knowledgeable about market analysis can be in charge of data analysis and provide the basis for the discussion. A participant who has experience in event planning can be in charge of coming up with specific ideas. In this way, by assigning roles based on the participants' expertise and experience, effective discussions can be held.

[0037] The discussion section can summarize what participants say in real time during a discussion to support the progress of the discussion. The discussion section can, for example, summarize what participants say in real time to support the progress of the discussion. For example, it can summarize what participants say in real time to clarify the key points of the discussion. It can also highlight important comments to indicate the direction of the discussion. It can also summarize what participants say in real time to smooth the progress of the discussion. In this way, by summarizing what participants say in real time, it is possible to smooth the progress of the discussion.

[0038] During a discussion, the discussion section can prioritize the discussion of highly relevant topics by taking into account the geographic location information of the participants. The discussion section prioritizes the discussion of highly relevant topics by taking into account the geographic location information of the participants. For example, if participants are concentrated in a specific region, topics related to that region can be prioritized for discussion. Also, if participants are from different regions, topics of common interest can be set as topics. Also, sales strategies for each region can be discussed based on the geographic location information of the participants. In this way, highly relevant topics can be prioritized for discussion by taking into account the geographic location information of the participants.

[0039] The discussion section can analyze the social media activity of participants during a discussion and suggest related topics. The discussion section can, for example, analyze the social media activity of participants and suggest related topics. For example, it can analyze the content of participants' social media posts and suggest topics of interest. It can also set related topics based on the participants' social media activity history. It can also suggest related topics by taking into account the activities of participants' friends on social media. In this way, by analyzing the participants' social media activity, related topics can be suggested, enabling effective discussions.

[0040] The discussion section can customize the discussion method by reflecting the participants' past feedback during the discussion. The discussion section, for example, customizes the discussion method by reflecting the participants' past feedback. For example, the discussion method can be adjusted based on the participants' past feedback. The discussion can also be made to proceed smoothly by reflecting the participants' past feedback. The discussion can also be customized by referring to the participants' past feedback. In this way, the discussion method can be customized by reflecting the participants' past feedback, enabling effective discussions.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between statements. The analysis unit, for example, analyzes the interrelationships between statements and extracts important points. For example, the analysis unit analyzes the interrelationships between statements using co-occurrence network analysis or correlation analysis. It can also extract common themes by taking the interrelationships between statements into consideration. For example, it identifies common themes based on the interrelationships between statements. It can also improve the accuracy of the analysis based on the interrelationships between statements. In this way, the accuracy of the analysis is improved by taking the interrelationships between statements into consideration.

[0042] The analysis unit can perform analysis based on the attribute information of the participants during analysis. The analysis unit performs analysis, for example, taking into account the attribute information of the participants (age, gender, occupation, etc.). For example, the importance of comments can be adjusted based on the attribute information of the participants. The accuracy of the analysis can also be improved by taking into account the attribute information of the participants. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the participants.

[0043] During analysis, the analysis unit can weight the analysis based on the frequency of statements. For example, the analysis unit performs analysis by placing emphasis on themes that are frequently stated. For example, the frequency of statements is measured based on the number of statements and the duration of statements. Important points can also be extracted based on the frequency of statements. For example, the analysis is weighted taking into account the frequency of statements. In this way, weighting the analysis based on the frequency of statements improves the accuracy of the analysis.

[0044] During analysis, the analysis unit can perform analysis based on the geographic distribution of comments. For example, the analysis unit analyzes the geographic distribution of comments and extracts important points for each region. For example, the analysis unit analyzes the geographic distribution of comments using a geographic information system (GIS) or location information data. It can also extract common themes by taking the geographic distribution of comments into consideration. For example, it can identify common themes based on the geographic distribution of comments. It can also improve the accuracy of the analysis based on the geographic distribution of comments. In this way, the accuracy of the analysis is improved by taking the geographic distribution of comments into consideration.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature. The analysis unit, for example, refers to related literature and adjusts the importance of statements. For example, related literature is selected using a literature database or citation criteria. Common themes can also be extracted based on the related literature. For example, the accuracy of the analysis is improved by referring to related literature. In this way, the accuracy of the analysis is improved by referring to related literature.

[0046] The analysis unit can perform analysis based on market value during analysis. For example, the analysis unit adjusts the importance of comments taking market value into account. For example, the analysis unit evaluates market value based on market research data and sales forecasts. It can also extract common themes based on market value. For example, the accuracy of the analysis can be improved by taking market value into consideration.

[0047] When planning, the planning unit can analyze past success cases and select the optimal planning method. The planning unit, for example, analyzes past success cases and extracts common elements. For example, it analyzes past success cases based on the definition of success and evaluation criteria. It can also select the optimal planning method based on past success cases. For example, it can improve the accuracy of planning by referring to past success cases. In this way, by analyzing past success cases, it is possible to select the optimal planning method and perform effective planning.

[0048] The planning unit can customize the content of the plan based on current market trends when planning. The planning unit, for example, analyzes current market trends and customizes the content of the plan. For example, the planning unit analyzes market trends using trend data sources and analysis methods. The planning unit can also select the optimal planning method based on market trends. For example, the planning accuracy can be improved by referring to market trends. This makes it possible to customize the content of the plan by taking current market trends into consideration, thereby enabling effective planning.

[0049] The planning unit can improve the planning method by reflecting the feedback of the participants when planning. The planning unit improves the planning method, for example, based on the feedback of the participants. For example, feedback is collected based on survey results and evaluation comments. The planning unit can also improve the accuracy of the planning by reflecting the feedback of the participants. For example, the content of the planning can be customized by referring to the feedback of the participants. In this way, by reflecting the feedback of the participants, the planning method can be improved and effective planning becomes possible.

[0050] The planning unit can select an appropriate planning method based on the geographical location information when planning. For example, the planning unit plans sales strategies for each region based on the geographical location information of participants. For example, the planning unit collects geographical location information using GPS data or location information services. It can also propose events and initiatives unique to each region by taking the geographical location information into consideration. For example, it selects the optimal planning method by taking the characteristics of each region into consideration. This makes it possible to select the optimal planning method and to plan effectively by taking the geographical location information into consideration.

[0051] When planning, the planning unit can analyze social media activity and propose plan contents. For example, the planning unit analyzes participants' social media posts and proposes events and activities that are of high interest. For example, the planning unit analyzes social media activity based on an analysis of the posts and the frequency of activity. It can also set related plan contents based on social media activity history. It can also propose related events and activities by referring to the activities of friends on social media. In this way, by analyzing social media activity, it is possible to propose related plan contents and make effective plans.

[0052] The planning unit can customize the planning method by reflecting past feedback when planning. The planning unit adjusts the planning method based on, for example, past feedback from participants. For example, feedback is collected based on survey results and evaluation comments. The planning unit can also improve the accuracy of planning by reflecting past feedback from participants. For example, the planning method is customized by referring to past feedback from participants. In this way, by reflecting past feedback, the planning method can be customized and effective planning becomes possible.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The discussion section translates what participants say in real time, facilitating communication between participants who speak different languages. For example, if an English-speaking participant and a Japanese-speaking participant are simultaneously participating in a discussion, the content can be instantly translated to ensure that both parties can understand. The translated content can also be displayed as text so that participants can check it later. Furthermore, to improve translation accuracy, technical terms and industry-specific expressions can be registered in advance. This facilitates communication between participants who speak different languages ​​and enables more effective discussions.

[0055] Based on the results of the discussion, the planning department can propose sales promotion events using virtual reality (VR). For example, they can propose an event where product demonstrations are conducted using VR. They can also propose an event where a virtual store is set up where customers can try out products using VR. They can also propose an event where VR is used to communicate in real time with customers in remote locations. This makes it possible to propose new sales promotion events that utilize VR and attract customer interest.

[0056] The analysis unit can analyze the content of the discussion and evaluate the reliability of the participants' comments. For example, it can evaluate the reliability of comments based on past comment history and expertise. It can also evaluate reliability by checking whether the content of a comment is consistent with the opinions of other participants. It can also evaluate reliability by referring to the data and materials that form the basis of the comment. This allows for more accurate analysis results by evaluating the reliability of comments.

[0057] Based on the results of the discussions, the planning department can propose personalized marketing strategies using artificial intelligence (AI). For example, by analyzing customer purchase history and behavioral data, it can propose the best products for each individual customer. It can also deliver customized advertisements based on customer interests. Furthermore, it can adjust marketing strategies in real time based on customer feedback. This makes it possible to propose personalized marketing strategies using AI and improve customer satisfaction.

[0058] The analysis unit can analyze the content of the discussion and evaluate the influence of participants' comments. For example, the influence of a comment can be evaluated based on the frequency of the comment and the reactions of other participants. It can also evaluate the impact of a comment on the direction of the discussion. Furthermore, it can evaluate the extent to which the content of a comment influenced the opinions of other participants. In this way, by evaluating the influence of comments, it is possible to maximize the effectiveness of the discussion.

[0059] Based on the results of the discussion, the planning department can propose a sales promotion event using blockchain technology. For example, blockchain can be used to manage event participant information and ensure transparency. Blockchain can also be used to issue rewards to event participants as tokens. Furthermore, blockchain can also be used to record the results of the event so that they can be verified later. This makes it possible to propose new sales promotion events that utilize blockchain technology and improve reliability and transparency.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The discussion section involves multiple people holding a discussion and coming up with ideas based on the conditions set by the generation AI. For example, they can discuss ideas for sales promotion events or new sales strategies. It is also possible to select someone with extensive knowledge and experience in sales and set them as a leader. Step 2: The analysis part uses generative AI to analyze the ideas generated by the discussion part. For example, it analyzes participants' comments and extracts important points and common themes. It can also refer to past success stories and market trends. Step 3: The planning department uses the generation AI to plan events and initiatives based on the results of the analysis by the analysis department. For example, the planning department determines the specific schedule and implementation method based on the event details proposed by the generation AI. The planning content can also be customized taking into account current market trends.

[0062] (Example 2) A sales promotion system according to an embodiment of the present invention is a system that holds discussions among multiple participants and generates ideas based on the conditions set by a generation AI. This system analyzes the content of the discussions and utilizes them to plan events and initiatives. For example, ideas for sales promotion events and new sales strategies are discussed. The content of these discussions is input into a generation AI. The generation AI analyzes the participants' comments and extracts important points and common themes. For example, if many participants agree that a particular event is effective, the generation AI proposes details for that event. Events and initiatives are planned based on the results of the analysis by the generation AI. For example, specific schedules and implementation methods are determined based on the event details proposed by the generation AI. This allows the sales promotion system to plan effective sales promotion events and initiatives. This allows the sales promotion system to improve sales performance for mobile and fixed-line devices. For example, the generation AI analyzes the content of the discussions and proposes effective events and initiatives, generating new ideas for sales promotion. Furthermore, multiple-person discussions reflect diverse perspectives and opinions, resulting in more effective initiatives.

[0063] A sales promotion system according to an embodiment includes a discussion unit, an analysis unit, and a planning unit. The discussion unit holds a discussion with multiple participants and generates ideas based on the conditions set by the generation AI. The discussion unit discusses, for example, ideas for sales promotion events and new sales strategies. The discussion unit can also select individuals with extensive knowledge and experience in sales. For example, the discussion unit can assign a participant who is knowledgeable about sales promotion as a leader and have them lead the discussion. The analysis unit uses the generation AI to analyze the ideas generated by the discussion unit. For example, the analysis unit analyzes participants' comments and extracts important points and common themes. The analysis unit can also refer to past success stories and market trends. For example, the analysis unit analyzes past success stories and extracts common elements. The planning unit uses the generation AI to plan events and initiatives based on the results of the analysis by the analysis unit. For example, the planning unit determines the specific schedule and implementation method based on the details of the event proposed by the generation AI. The planning unit can also customize the plan taking current market trends into account. For example, the planning unit analyzes current market trends and customizes the content of the plan, thereby enabling the sales promotion system according to the embodiment to plan effective events and initiatives.

[0064] The discussion section can discuss ideas for sales promotion events or new sales strategies. The discussion section, for example, discusses ideas for sales promotion events. For example, ideas for specific events such as campaigns and exhibitions are shared. The discussion section can also discuss new sales strategies. For example, strategies such as target market selection and pricing are discussed. This makes it possible to generate effective ideas through discussions of sales promotion events and new sales strategies.

[0065] The analysis unit can analyze participants' comments and extract important points and common themes. The analysis unit, for example, analyzes participants' comments and extracts important points. For example, it identifies important points based on frequently occurring keywords and the weighting of comments. The analysis unit can also extract common themes. For example, it identifies common themes using a theme classification method or common point extraction criteria. In this way, by analyzing participants' comments, it is possible to extract important points and common themes and plan effective events and initiatives.

[0066] The planning department can determine the schedule and implementation method based on the details of the event proposed by the generation AI. The planning department, for example, determines the schedule based on the details of the event proposed by the generation AI. For example, it sets a timeline and important milestones. The planning department can also determine the implementation method. For example, it determines the implementation procedures and necessary resources. In this way, by determining the specific schedule and implementation method based on the details of the event proposed by the generation AI, it is possible to realize effective events and initiatives.

[0067] The discussion group can select people with extensive knowledge or experience in sales. The discussion group can select people with extensive knowledge or experience in sales, for example, based on past achievements or professional qualifications. The discussion group can also set participants who are knowledgeable about sales promotion as leaders and have them lead the discussion. By selecting people with extensive knowledge and experience in sales, more effective discussions can be achieved.

[0068] The analysis unit can refer to past success stories or market trends. The analysis unit, for example, refers to past success stories. For example, it analyzes past success stories based on the definition of success and evaluation criteria. The analysis unit can also refer to market trends. For example, it analyzes market trends using trend data sources and analytical methods. In this way, by referring to past success stories and market trends, the accuracy of the analysis can be improved, and effective events and initiatives can be planned.

[0069] The discussion unit can estimate the emotions of the participants and adjust the way the discussion proceeds based on the estimated emotions of the participants. The discussion unit, for example, estimates the emotions of the participants and adjusts the way the discussion proceeds based on the estimated emotions of the participants. For example, if the participants are nervous, the discussion can be slowed down to create a relaxed atmosphere. If the participants are excited, the discussion can be slowed down to allow them to reach a consensus and prevent the discussion from going off track. If the participants are tired, the discussion can be continued with breaks to maintain concentration. This allows for more effective discussions by adjusting the way the discussion proceeds based on the emotions of the participants. Emotion estimation is achieved 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.

[0070] During a discussion, the discussion section can analyze the participants' past speech history and select an appropriate topic. The discussion section, for example, analyzes the participants' past speech history and selects an appropriate topic. For example, it can select a related topic based on ideas that participants have previously proposed. It can also extract topics that participants are particularly interested in from their past speeches and set them as topics. It can also analyze the participants' past speech history and select topics that are likely to lead to in-depth discussion. In this way, by analyzing the participants' past speech history, it is possible to select the most appropriate topic and hold effective discussions.

[0071] During the discussion, the discussion section can assign roles based on the participants' expertise and experience. For example, a participant who is knowledgeable about sales promotion can be set as the leader and lead the discussion. A participant who is knowledgeable about market analysis can be in charge of data analysis and provide the basis for the discussion. A participant who has experience in event planning can be in charge of coming up with specific ideas. In this way, by assigning roles based on the participants' expertise and experience, effective discussions can be held.

[0072] The discussion section can summarize what participants say in real time during a discussion to support the progress of the discussion. The discussion section can, for example, summarize what participants say in real time to support the progress of the discussion. For example, it can summarize what participants say in real time to clarify the key points of the discussion. It can also highlight important comments to indicate the direction of the discussion. It can also summarize what participants say in real time to smooth the progress of the discussion. In this way, by summarizing what participants say in real time, it is possible to smooth the progress of the discussion.

[0073] The discussion unit can estimate the emotions of the participants and determine the priority of comments based on the estimated emotions of the participants. The discussion unit, for example, estimates the emotions of the participants and determines the priority of comments based on the estimated emotions of the participants. For example, if a participant is excited, the priority of the comment can be set high and their opinion can be raised early. Also, if a participant is nervous, the priority of the comment can be set low to give them time to relax. Also, if a participant is tired, the priority of the comment can be adjusted and the discussion can proceed with breaks. In this way, by determining the priority of comments based on the emotions of the participants, effective discussions can be achieved. 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.

[0074] During a discussion, the discussion section can prioritize the discussion of highly relevant topics by taking into account the geographic location information of the participants. The discussion section prioritizes the discussion of highly relevant topics by taking into account the geographic location information of the participants. For example, if participants are concentrated in a specific region, topics related to that region can be prioritized for discussion. Also, if participants are from different regions, topics of common interest can be set as topics. Also, sales strategies for each region can be discussed based on the geographic location information of the participants. In this way, highly relevant topics can be prioritized for discussion by taking into account the geographic location information of the participants.

[0075] The discussion section can analyze the social media activity of participants during a discussion and suggest related topics. The discussion section can, for example, analyze the social media activity of participants and suggest related topics. For example, it can analyze the content of participants' social media posts and suggest topics of interest. It can also set related topics based on the participants' social media activity history. It can also suggest related topics by taking into account the activities of participants' friends on social media. In this way, by analyzing the participants' social media activity, related topics can be suggested, enabling effective discussions.

[0076] The discussion section can customize the discussion method by reflecting the participants' past feedback during the discussion. The discussion section, for example, customizes the discussion method by reflecting the participants' past feedback. For example, the discussion method can be adjusted based on the participants' past feedback. The discussion can also be made to proceed smoothly by reflecting the participants' past feedback. The discussion can also be customized by referring to the participants' past feedback. In this way, the discussion method can be customized by reflecting the participants' past feedback, enabling effective discussions.

[0077] The analysis unit can estimate the emotions of the participants and adjust the analysis criteria based on the estimated emotions of the participants. For example, the analysis unit estimates the emotions of the participants and adjusts the analysis criteria based on the estimated emotions of the participants. For example, if a participant is excited, the importance of the participants' comments can be set high and the analysis criteria can be adjusted. Also, if a participant is nervous, the importance of the participants' comments can be set low and the analysis criteria can be adjusted. Also, if a participant is tired, the importance of the participants' comments can be adjusted and the analysis criteria can be adjusted. In this way, by adjusting the analysis criteria based on the emotions of the participants, the accuracy of the analysis can be improved. 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.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between statements. The analysis unit, for example, analyzes the interrelationships between statements and extracts important points. For example, the analysis unit analyzes the interrelationships between statements using co-occurrence network analysis or correlation analysis. It can also extract common themes by taking the interrelationships between statements into consideration. For example, it identifies common themes based on the interrelationships between statements. It can also improve the accuracy of the analysis based on the interrelationships between statements. In this way, the accuracy of the analysis is improved by taking the interrelationships between statements into consideration.

[0079] The analysis unit can perform analysis based on the attribute information of the participants during analysis. The analysis unit performs analysis, for example, taking into account the attribute information of the participants (age, gender, occupation, etc.). For example, the importance of comments can be adjusted based on the attribute information of the participants. The accuracy of the analysis can also be improved by taking into account the attribute information of the participants. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the participants.

[0080] During analysis, the analysis unit can weight the analysis based on the frequency of statements. For example, the analysis unit performs analysis by placing emphasis on themes that are frequently stated. For example, the frequency of statements is measured based on the number of statements and the duration of statements. Important points can also be extracted based on the frequency of statements. For example, the analysis is weighted taking into account the frequency of statements. In this way, weighting the analysis based on the frequency of statements improves the accuracy of the analysis.

[0081] The analysis unit can estimate the emotions of the participants and adjust the display method of the analysis results based on the estimated emotions of the participants. For example, the analysis unit can estimate the emotions of the participants and adjust the display method of the analysis results based on the estimated emotions of the participants. For example, if a participant is excited, a visually stimulating display method can be provided. If a participant is nervous, a simple and highly visible display method can be provided. If a participant is tired, a visually relaxing display method can be provided. This allows for a more effective display by adjusting the display method of the analysis results based on the emotions of the participants. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] During analysis, the analysis unit can perform analysis based on the geographic distribution of comments. For example, the analysis unit analyzes the geographic distribution of comments and extracts important points for each region. For example, the analysis unit analyzes the geographic distribution of comments using a geographic information system (GIS) or location information data. It can also extract common themes by taking the geographic distribution of comments into consideration. For example, it can identify common themes based on the geographic distribution of comments. It can also improve the accuracy of the analysis based on the geographic distribution of comments. In this way, the accuracy of the analysis is improved by taking the geographic distribution of comments into consideration.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature. The analysis unit, for example, refers to related literature and adjusts the importance of statements. For example, related literature is selected using a literature database or citation criteria. Common themes can also be extracted based on the related literature. For example, the accuracy of the analysis is improved by referring to related literature. In this way, the accuracy of the analysis is improved by referring to related literature.

[0084] The analysis unit can perform analysis based on market value during analysis. For example, the analysis unit adjusts the importance of comments taking market value into account. For example, the analysis unit evaluates market value based on market research data and sales forecasts. It can also extract common themes based on market value. For example, the accuracy of the analysis can be improved by taking market value into consideration.

[0085] The planning unit can estimate the emotions of the participants and adjust the planning method based on the estimated emotions of the participants. For example, the planning unit estimates the emotions of the participants and adjusts the planning method based on the estimated emotions of the participants. For example, if the participants are excited, a planning method that incorporates proactive ideas can be adopted. If the participants are nervous, planning can be carried out in a relaxed environment. If the participants are tired, planning can be carried out with breaks in between. In this way, by adjusting the planning method based on the emotions of the participants, more effective planning is possible. Emotion estimation is realized using an emotion estimation function using, 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.

[0086] When planning, the planning unit can analyze past success cases and select the optimal planning method. The planning unit, for example, analyzes past success cases and extracts common elements. For example, it analyzes past success cases based on the definition of success and evaluation criteria. It can also select the optimal planning method based on past success cases. For example, it can improve the accuracy of planning by referring to past success cases. In this way, by analyzing past success cases, it is possible to select the optimal planning method and perform effective planning.

[0087] The planning unit can customize the content of the plan based on current market trends when planning. The planning unit, for example, analyzes current market trends and customizes the content of the plan. For example, the planning unit analyzes market trends using trend data sources and analysis methods. The planning unit can also select the optimal planning method based on market trends. For example, the planning accuracy can be improved by referring to market trends. This makes it possible to customize the content of the plan by taking current market trends into consideration, thereby enabling effective planning.

[0088] The planning unit can improve the planning method by reflecting the feedback of the participants when planning. The planning unit improves the planning method, for example, based on the feedback of the participants. For example, feedback is collected based on survey results and evaluation comments. The planning unit can also improve the accuracy of the planning by reflecting the feedback of the participants. For example, the content of the planning can be customized by referring to the feedback of the participants. In this way, by reflecting the feedback of the participants, the planning method can be improved and effective planning becomes possible.

[0089] The planning unit can estimate the emotions of the participants and determine the priority of planning based on the estimated emotions of the participants. The planning unit, for example, estimates the emotions of the participants and determines the priority of planning based on the estimated emotions of the participants. For example, if the participants are excited, proactive ideas can be prioritized. Also, if the participants are nervous, planning can be carried out in a relaxed environment. Also, if the participants are tired, planning can be carried out with breaks in between. In this way, by determining the priority of planning based on the emotions of the participants, effective planning is possible. Emotion estimation is realized using an emotion estimation function using, 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.

[0090] The planning unit can select an appropriate planning method based on the geographical location information when planning. For example, the planning unit plans sales strategies for each region based on the geographical location information of participants. For example, the planning unit collects geographical location information using GPS data or location information services. It can also propose events and initiatives unique to each region by taking the geographical location information into consideration. For example, it selects the optimal planning method by taking the characteristics of each region into consideration. This makes it possible to select the optimal planning method and to plan effectively by taking the geographical location information into consideration.

[0091] When planning, the planning unit can analyze social media activity and propose plan contents. For example, the planning unit analyzes participants' social media posts and proposes events and activities that are of high interest. For example, the planning unit analyzes social media activity based on an analysis of the posts and the frequency of activity. It can also set related plan contents based on social media activity history. It can also propose related events and activities by referring to the activities of friends on social media. In this way, by analyzing social media activity, it is possible to propose related plan contents and make effective plans.

[0092] The planning unit can customize the planning method by reflecting past feedback when planning. The planning unit adjusts the planning method based on, for example, past feedback from participants. For example, feedback is collected based on survey results and evaluation comments. The planning unit can also improve the accuracy of planning by reflecting past feedback from participants. For example, the planning method is customized by referring to past feedback from participants. In this way, by reflecting past feedback, the planning method can be customized and effective planning becomes possible. === Hard Collateral 1-1 === Each of the multiple elements, including the discussion unit, analysis unit, and planning 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 discussion unit is realized by the control unit 46A of the smart device 14 and collects comments from participants. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected comments to extract important points and common themes. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and determines details of events and initiatives based on the analysis results. The discussion unit may be realized by the specific processing unit 290 of the data processing device 12, for example, and the analysis unit and planning unit may be realized by the control unit 46A of the smart device 14, for example. === Hard Collateral 1-2 === Each of the multiple elements, including the discussion unit, analysis unit, and planning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the discussion unit is realized by the control unit 46A of the smart glasses 214 and collects participants' comments. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected comments to extract important points and common themes. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines details of events and initiatives based on the analysis results. The discussion unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and planning unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned discussion unit, analysis unit, and planning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the discussion unit is realized by the control unit 46A of the headset-type terminal 314 and collects comments from participants. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected comments to extract important points and common themes. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and determines details of events and initiatives based on the analysis results. The discussion unit may be realized by the specific processing unit 290 of the data processing device 12, for example, and the analysis unit and planning unit may be realized by the control unit 46A of the headset-type terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned discussion unit, analysis unit, and planning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the discussion unit is realized by the control unit 46A of the robot 414 and collects comments from participants. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected comments to extract important points and common themes. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines details of events and initiatives based on the analysis results. The discussion unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and planning unit may be realized, for example, by the control unit 46A of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The discussion section translates what participants say in real time, facilitating communication between participants who speak different languages. For example, if an English-speaking participant and a Japanese-speaking participant are simultaneously participating in a discussion, the content can be instantly translated to ensure that both parties can understand. The translated content can also be displayed as text so that participants can check it later. Furthermore, to improve translation accuracy, technical terms and industry-specific expressions can be registered in advance. This facilitates communication between participants who speak different languages ​​and enables more effective discussions.

[0095] The analysis unit analyzes the audio data of the discussion and can estimate the emotions of participants based on the tone and speed of speech. For example, if the tone of speech is high and the speed is fast, it can be estimated that the participant is excited. On the other hand, if the tone is low and the speed is slow, it can be estimated that the participant is calm. Furthermore, if there are long silences between speeches, it can be estimated that the participant is nervous. This makes it possible to more accurately estimate the emotions of participants by analyzing the audio data and adjust the way the discussion proceeds.

[0096] Based on the results of the discussion, the planning department can propose sales promotion events using virtual reality (VR). For example, they can propose an event where product demonstrations are conducted using VR. They can also propose an event where a virtual store is set up where customers can try out products using VR. They can also propose an event where VR is used to communicate in real time with customers in remote locations. This makes it possible to propose new sales promotion events that utilize VR and attract customer interest.

[0097] The discussion section monitors participants' biometric information (heart rate, electrodermal activity, etc.) and can estimate changes in their emotions in real time. For example, if their heart rate increases, it can be assumed that they are excited. Also, if their electrodermal activity increases, it can be assumed that they are nervous. Furthermore, it can adjust the way the discussion proceeds based on this biometric information. This makes it possible to estimate emotions using participants' biometric information and enable more effective discussions.

[0098] The analysis unit can analyze the content of the discussion and evaluate the reliability of the participants' comments. For example, it can evaluate the reliability of comments based on past comment history and expertise. It can also evaluate reliability by checking whether the content of a comment is consistent with the opinions of other participants. It can also evaluate reliability by referring to the data and materials that form the basis of the comment. This allows for more accurate analysis results by evaluating the reliability of comments.

[0099] Based on the results of the discussions, the planning department can propose personalized marketing strategies using artificial intelligence (AI). For example, by analyzing customer purchase history and behavioral data, it can propose the best products for each individual customer. It can also deliver customized advertisements based on customer interests. Furthermore, it can adjust marketing strategies in real time based on customer feedback. This makes it possible to propose personalized marketing strategies using AI and improve customer satisfaction.

[0100] The discussion section can estimate the emotions of participants and change the topic of the discussion based on the estimated emotions. For example, if participants are excited, it can select a topic that is likely to lead to a more lively discussion. If participants are nervous, it can select a topic that will help them relax. Furthermore, if participants are tired, it can select a lighter topic and move the discussion forward. This allows the discussion topic to be flexibly changed based on participants' emotions, promoting effective discussions.

[0101] The analysis unit can analyze the content of the discussion and evaluate the influence of participants' comments. For example, the influence of a comment can be evaluated based on the frequency of the comment and the reactions of other participants. It can also evaluate the impact of a comment on the direction of the discussion. Furthermore, it can evaluate the extent to which the content of a comment influenced the opinions of other participants. In this way, by evaluating the influence of comments, it is possible to maximize the effectiveness of the discussion.

[0102] Based on the results of the discussion, the planning department can propose a sales promotion event using blockchain technology. For example, blockchain can be used to manage event participant information and ensure transparency. Blockchain can also be used to issue rewards to event participants as tokens. Furthermore, blockchain can also be used to record the results of the event so that they can be verified later. This makes it possible to propose new sales promotion events that utilize blockchain technology and improve reliability and transparency.

[0103] The discussion section can estimate the emotions of the participants and adjust the speed of the discussion based on the estimated emotions. For example, if the participants are excited, the speed can be increased to make the discussion more lively. If the participants are nervous, the speed can be decreased to allow them to relax. Furthermore, if the participants are tired, the speed can be adjusted and breaks can be inserted between the discussions. This allows the speed of the discussion to be adjusted based on the emotions of the participants, promoting effective discussions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The discussion section involves multiple people holding a discussion and coming up with ideas based on the conditions set by the generation AI. For example, they can discuss ideas for sales promotion events or new sales strategies. It is also possible to select someone with extensive knowledge and experience in sales and set them as a leader. Step 2: The analysis part uses generative AI to analyze the ideas generated by the discussion part. For example, it analyzes participants' comments and extracts important points and common themes. It can also refer to past success stories and market trends. Step 3: The planning department uses the generation AI to plan events and initiatives based on the results of the analysis by the analysis department. For example, the planning department determines the specific schedule and implementation method based on the event details proposed by the generation AI. The planning content can also be customized taking into account current market trends.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 discussion section where multiple people discuss and come up with ideas based on the conditions of the generative AI; an analysis unit that analyzes the ideas submitted by the discussion unit; a planning unit that plans events and initiatives based on the results of the analysis by the analysis unit; Equipped with A system characterized by:

2. The discussion section includes: Discuss ideas for promotional events or new sales strategies 2. The system of claim 1.

3. The analysis unit Analyze participants' comments and extract key points and common themes 2. The system of claim 1.

4. The planning unit Determine the schedule and implementation method based on the event details proposed by the generative AI 2. The system of claim 1.

5. The discussion section includes: Select someone with extensive sales knowledge or experience 2. The system of claim 1.

6. The analysis unit Refer to past success stories or market trends 2. The system of claim 1.

7. The discussion section includes: Estimate participants' emotions and adjust the way the discussion proceeds based on the estimated emotions of the participants 2. The system of claim 1.

8. The discussion section includes: During discussions, analyze participants' past comment history and select appropriate topics.

2. The system of claim 1.

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