system

Abstract

The system according to this embodiment aims to efficiently gather the requests and conditions of each player and find the most suitable negotiating partner. [Solution] The system according to the embodiment comprises a hearing unit, a strategy generation unit, a negotiation unit, and a matching unit. The hearing unit hears the requests and conditions of each player. The strategy generation unit generates a negotiation strategy based on the information heard by the hearing unit. The negotiation unit automatically conducts negotiations based on the strategy generated by the strategy generation unit. The matching unit matches the negotiating partners found by the negotiation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of efficiently listening to the demands and conditions of each player of media advertisements and finding an optimal negotiation partner is complicated and time-consuming.

[0005] The system according to the embodiment aims to efficiently listen to the demands and conditions of each player and find an optimal negotiation partner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a hearing unit, a strategy generation unit, a negotiation unit, and a matching unit. The hearing unit hears the requests and conditions of each player. The strategy generation unit generates a negotiation strategy based on the information gathered by the hearing unit. The negotiation unit automatically conducts negotiations based on the strategy generated by the strategy generation unit. The matching unit matches the negotiating partners found by the negotiation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently gather the requests and conditions of each player and find the most suitable negotiating partner. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The automated media advertising negotiation system according to an embodiment of the present invention is a system in which a conversational AI listens to the requests and conditions of each player in media advertising (media, advertising agency, production company, advertising model), and based on those conditions, each player's representative AI automatically conducts negotiations on the automated media advertising negotiation platform, finds the most suitable negotiating partner that meets the conditions, and sets up a meeting place for negotiations between the representatives. The automated media advertising negotiation system listens to the requests and conditions of each player, and based on those conditions, each player's representative AI automatically conducts negotiations on the automated media advertising negotiation platform, finds the most suitable negotiating partner that meets the conditions, and sets up a meeting place for negotiations between the representatives. This system allows each player to proceed with negotiations efficiently and find the most suitable advertising project. For example, the conversational AI listens to the requests and conditions of each player. At this time, each player can specifically communicate their requests and conditions. For example, an advertising agency can communicate a request such as "We want to monetize with content aimed at a specific target audience." A production company can communicate a condition such as "We are looking for an advertising project that can utilize our video shooting and editing capabilities." Next, based on the information gathered, each player's AI representative conducts automated negotiations on the media advertising automated negotiation platform. The AI ​​representative reads past negotiation data and generates negotiation strategies based on information gathered from the representatives. For example, the AI ​​representative for an advertising agency searches for media outlets that can provide "content for a specific target audience" based on past negotiation data and negotiates with those media outlets. As a result of the automated negotiations, it finds the optimal negotiating partner that meets the conditions and sets up a meeting between the representatives. For example, the AI ​​representative for an advertising agency finds a media outlet that can provide "content for a specific target audience" and sets up a meeting between the media outlet's representative and the advertising agency's representative. In this way, each player can proceed with negotiations efficiently and find the optimal advertising deal. This system allows each player to proceed with negotiations efficiently and find the optimal advertising deal. In addition, the automation of the negotiation process saves resources for each player.For example, advertising agencies can use AI to automate negotiations, allowing their staff to focus on other tasks. Similarly, production companies can efficiently find the most suitable advertising projects by using AI to automate negotiations. In this way, by utilizing conversational AI and AI to automate negotiations, we provide a system that streamlines the media advertising negotiation process, enabling each player to find the most suitable advertising projects. As a result, the automated media advertising negotiation system can efficiently negotiate based on the requests and conditions of each player, and find the most suitable advertising projects.

[0029] The automated media advertising negotiation system according to this embodiment comprises a hearing unit, a strategy generation unit, a negotiation unit, and a matching unit. The hearing unit hears the requests and conditions of each player. The hearing unit uses, for example, a conversational AI to hear the requests and conditions of each player. For example, the hearing unit can hear an advertising agency's request, such as "We want to monetize with content aimed at a specific target audience." The hearing unit can also hear a production company's condition, such as "We are looking for advertising projects that can utilize our video shooting and editing capabilities." The strategy generation unit generates a negotiation strategy based on the information gathered by the hearing unit. For example, the strategy generation unit reads past negotiation data and generates a negotiation strategy based on information gathered from the person in charge. For example, based on the advertising agency's request, the strategy generation unit searches for media that can provide "content aimed at a specific target audience" and generates a strategy to negotiate with that media. The negotiation unit performs automated negotiations based on the strategy generated by the strategy generation unit. The negotiation department, for example, uses an AI acting as an agent for an advertising agency to find media outlets that can provide "content for a specific target audience" and then negotiates with those media outlets. The negotiation department can generate negotiation strategies based on past negotiation data and automatically conduct negotiations based on those strategies. For example, the negotiation department uses an AI acting as an agent for an advertising agency to find media outlets that can provide "content for a specific target audience" and then negotiates with those media outlets. The matching department matches the most suitable negotiating partners found by the negotiation department. For example, the matching department sets up a meeting between a representative of a media outlet that can provide "content for a specific target audience" found by an AI acting as an agent for an advertising agency and a representative of an advertising agency. The matching department can find the most suitable negotiating partners and set up a meeting between the representatives. For example, the matching department sets up a meeting between a representative of a media outlet that can provide "content for a specific target audience" found by an AI acting as an agent for an advertising agency and a representative of an advertising agency. As a result, the automated media advertising negotiation system according to this embodiment can efficiently proceed with negotiations based on the requests and conditions of each player and find the most suitable advertising deals.

[0030] The Hearing Department gathers information on the needs and requirements of each player. For example, the Hearing Department uses conversational AI to gather information on the needs and requirements of each player. Specifically, the conversational AI utilizes natural language processing technology to converse with representatives from advertising agencies and production companies to elicit detailed needs and requirements. For example, if an advertising agency representative wants to monetize content aimed at a specific target audience, the conversational AI will ask detailed questions about the specific attributes of that target audience (age, gender, interests, etc.) and the desired content format (video, article, banner ad, etc.) to gather information. Similarly, if a production company representative is looking for advertising projects that can leverage their video shooting and editing capabilities, the conversational AI will also inquire about the production company's preferred shooting and editing styles, past achievements, and desired budget and schedule. This allows the Hearing Department to gain a detailed understanding of the specific needs and requirements of each player and provide the information necessary for the next step: strategy generation. Furthermore, the interviewing department can store the information gathered in a database and utilize it for future negotiations and matching. For example, based on information gathered in the past, it can quickly identify players with similar requests and conditions, enabling efficient matching. This allows the interviewing department to accurately and quickly grasp the requests and conditions of each player, improving the overall efficiency and accuracy of the system.

[0031] The Strategy Generation Department generates negotiation strategies based on information gathered by the Interviewing Department. For example, the Strategy Generation Department reads past negotiation data and generates negotiation strategies based on information gathered from the relevant personnel. Specifically, the Strategy Generation Department analyzes past success and failure cases and uses algorithms to derive the optimal negotiation strategy. For example, when generating a strategy to find media outlets that can provide "content for a specific target audience" based on an advertising agency's request and negotiate with those media outlets, it refers to a list of media outlets that have previously provided similar content for the target audience and selects the most suitable media outlet from among them. The Strategy Generation Department also clarifies the points to emphasize during negotiations and the conditions under which concessions are possible, based on the information gathered. For example, if an advertising agency has budget constraints, the Strategy Generation Department selects media outlets that will be most effective within the budget and develops a strategy to emphasize the budget during negotiations with those media outlets while being flexible with other conditions (e.g., advertising period and placement). Furthermore, the Strategy Generation Department can update negotiation strategies in real time using AI. For example, the strategy can be adjusted as needed based on the progress of negotiations and the other party's reactions, leading to the optimal negotiation outcome. This allows the strategy generation unit to generate effective and flexible negotiation strategies based on the information gathered, thereby increasing the success rate of negotiations.

[0032] The Negotiation Department conducts automated negotiations based on strategies generated by the Strategy Generation Department. For example, the Negotiation Department searches for media outlets that can provide "content for a specific target audience" using an AI acting as an agent for an advertising agency, and then negotiates with those media outlets. Specifically, the Negotiation Department can generate negotiation strategies based on past negotiation data and conduct automated negotiations based on those strategies. For example, when the Negotiation Department finds media outlets that can provide "content for a specific target audience" using an AI acting as an agent for an advertising agency and negotiates with those media outlets, it refers to past negotiation data and selects the optimal negotiation method. The Negotiation Department uses AI to monitor the progress of negotiations in real time and can adjust the negotiation strategy as needed in response to the other party's reactions. For example, if the other party expresses reluctance regarding the budget, the Negotiation Department will propose concessions on other conditions (e.g., the advertising period or placement) to facilitate the negotiation. The Negotiation Department can also store the results of negotiations in a database and use them in future negotiations. As a result, the Negotiation Department can conduct effective and flexible negotiations based on past negotiation data and derive the optimal negotiation results. Furthermore, the negotiating team can use AI to monitor the progress of negotiations in real time and adjust their negotiation strategy as needed based on the other party's responses. This allows the negotiating team to conduct effective and flexible negotiations and achieve the optimal outcome.

[0033] The matching department matches the most suitable negotiating partners identified by the negotiation department. For example, the matching department sets up a meeting between a media representative who can provide "content for a specific target audience" (identified by the advertising agency's AI) and a representative from an advertising agency. Specifically, the matching department can find the most suitable negotiating partners and set up a meeting between the two parties. For example, when setting up a meeting between a media representative who can provide "content for a specific target audience" (identified by the advertising agency's AI) and a representative from an advertising agency, the matching department coordinates the schedules of both parties and sets up the meeting using an online conferencing system. The matching department can also monitor the progress of the negotiations and provide support as needed. For example, if negotiations become difficult, the matching department will propose the best solution for both parties based on past negotiation data to facilitate the negotiations. The matching department can also store the results of negotiations in a database and use them for future matching. This allows the matching department to find the most suitable negotiating partners and provide an efficient and effective negotiation environment. Furthermore, the matching department can use AI to monitor the progress of negotiations in real time and adjust the negotiation strategy as needed based on the other party's responses. This allows the matching department to conduct effective and flexible negotiations and arrive at the optimal negotiation outcome.

[0034] The automated media advertising negotiation system includes a data storage unit that stores past negotiation data. The data storage unit stores, for example, past negotiation data. The data storage unit needs to clarify the specific content and storage method of the past negotiation data. For example, the data storage unit can store negotiation results and negotiation processes. By storing past negotiation data, the data storage unit can be used to help future negotiations. For example, the data storage unit can store past negotiation data and generate future negotiation strategies based on that data. In this way, by storing past negotiation data, it can be used to help future negotiations. Some or all of the above processing in the data storage unit may be performed using, for example, AI, or not using AI. For example, the data storage unit can input past negotiation data into a generating AI and have the generating AI perform the data storage.

[0035] The automated media advertising negotiation system includes a data analysis unit that generates negotiation strategies based on data stored by a data storage unit. The data analysis unit generates negotiation strategies based on data stored by the data storage unit, for example. The data analysis unit can analyze past negotiation data and generate negotiation strategies based on that data. By analyzing stored data, the data analysis unit can generate more accurate negotiation strategies. For example, the data analysis unit analyzes past negotiation data and generates negotiation strategies based on that data. This allows for the generation of more accurate negotiation strategies by analyzing stored data. Some or all of the above-described processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input past negotiation data into a generation AI and have the generation AI perform the data analysis.

[0036] The automated media advertising negotiation system includes a matching unit that finds negotiating partners and a setting unit that sets up a negotiation forum between the representatives. The setting unit can, for example, find negotiating partners and set up a negotiation forum between the representatives. The setting unit can find the optimal negotiating partners and set up a negotiation forum between the representatives. For example, the setting unit sets up a negotiation forum between a media representative who can provide "content for a specific target audience" found by the advertising agency's AI and a representative of the advertising agency. This allows for the finding of the optimal negotiating partners and the setting up of negotiation forums efficiently. Some or all of the above processing in the setting unit may be performed using AI, for example, or not using AI. For example, the setting unit can input information about negotiating partners into a generating AI and have the generating AI perform the setting up of the negotiation forum.

[0037] The Hearing Department can specifically gather information on the requests and conditions of each player. For example, the Hearing Department can use a conversational AI to specifically gather information on the requests and conditions of each player. For example, the Hearing Department can specifically gather information on an advertising agency's request, such as "We want to monetize with content targeted at a specific audience." The Hearing Department can also specifically gather information on a production company's condition, such as "We are looking for advertising projects that can utilize our video shooting and editing capabilities." By gathering specific requests and conditions, a more appropriate negotiation strategy can be generated. Some or all of the above processing in the Hearing Department may be performed using AI, for example, or not. For example, the Hearing Department can input the requests and conditions of each player into a generating AI and have the generating AI perform the request and condition gathering.

[0038] The strategy generation unit can generate negotiation strategies based on the information gathered through interviews. For example, the strategy generation unit generates negotiation strategies based on the information gathered through interviews. By generating negotiation strategies based on the information gathered through interviews, the strategy generation unit enables more effective negotiations. For example, based on the advertising agency's request, the strategy generation unit searches for media outlets that can provide "content for a specific target audience" and generates a strategy to negotiate with those media outlets. This enables more effective negotiations by generating negotiation strategies based on the information gathered through interviews. Some or all of the above-described processes in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input the information gathered through interviews into a generation AI and have the generation AI execute the generation of negotiation strategies.

[0039] The interviewing unit can analyze the user's past interview history and select the most suitable interview method. For example, the interviewing unit can prioritize interview formats that the user has preferred in the past (such as dialogue or questionnaire formats). For example, the interviewing unit can reconfirm questions that the user has answered in detail in the past and ask additional questions. The interviewing unit can also conduct efficient interviews by adopting question formats that the user has answered quickly in the past. In this way, by analyzing past interview history, a more effective interview method can be selected. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the user's past interview history into a generating AI and have the generating AI select the most suitable interview method.

[0040] The interviewing unit can customize the questions asked during the interview based on the user's current projects and areas of interest. For example, the interviewing unit can prioritize questions related to the user's current projects. For example, the interviewing unit can ask questions about relevant advertising opportunities based on the user's areas of interest. The interviewing unit can also ask relevant questions by referring to the user's past project history. By customizing the questions based on the user's current projects and areas of interest, a more effective interview becomes possible. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input information about the user's projects and areas of interest into a generating AI and have the generating AI customize the questions.

[0041] The interviewing unit can prioritize asking highly relevant questions during interviews, taking into account the user's geographical location. For example, if the user is in a specific region, the interviewing unit can ask questions related to advertising opportunities in that region. For example, based on the user's geographical location, the interviewing unit can ask questions about region-specific needs. Furthermore, if the user is on the move, the interviewing unit can ask questions related to advertising opportunities in their destination. This allows for more effective interviews by asking highly relevant questions while considering the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the user's geographical location into a generating AI and have the generating AI select highly relevant questions.

[0042] The interviewing unit can analyze the user's social media activity during the interview and ask relevant questions. For example, the interviewing unit can ask questions based on topics the user has shown interest in on social media. For example, the interviewing unit can ask questions related to advertising campaigns based on the user's social media activity. The interviewing unit can also ask relevant questions based on the accounts the user follows on social media. This allows for more effective interviews by analyzing the user's social media activity and asking relevant questions. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's social media activity data into a generating AI and have the generating AI select relevant questions.

[0043] The strategy generation unit can adjust the level of detail of a strategy based on past negotiation results when generating a strategy. For example, if past negotiations were successful, the strategy generation unit can reproduce that strategy in detail. For example, if past negotiations were unsuccessful, the strategy generation unit can simplify the strategy. The strategy generation unit can also analyze past negotiation results and provide a strategy with the optimal level of detail. By adjusting the level of detail of a strategy based on past negotiation results, it can provide a more effective strategy. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input past negotiation result data into a generation AI and have the generation AI perform the adjustment of the level of detail of the strategy.

[0044] The strategy generation unit can apply different strategic algorithms depending on the attributes of the negotiating partner during strategy generation. For example, if the negotiating partner is a large corporation, the strategy generation unit can provide a detailed strategy. For example, if the negotiating partner is a small or medium-sized enterprise, the strategy generation unit can provide a concise strategy. The strategy generation unit can also apply the most suitable strategic algorithm depending on the industry of the negotiating partner. This allows for the provision of more effective strategies by applying different strategic algorithms according to the attributes of the negotiating partner. Some or all of the above-described processes in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input the negotiating partner's attribute information into a generation AI and have the generation AI execute the application of strategic algorithms.

[0045] The strategy generation unit can determine strategy priorities based on the timing of negotiation submissions when generating strategies. For example, if the negotiation submission is early, the strategy generation unit can provide a detailed strategy. For example, if the negotiation submission is late, the strategy generation unit can provide a concise strategy. The strategy generation unit can also determine the optimal strategy priority based on the negotiation submission timing. This allows for the provision of more effective strategies by prioritizing strategies based on the negotiation submission timing. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input negotiation submission timing data into a generation AI and have the generation AI perform the determination of strategy priorities.

[0046] The strategy generation unit can adjust the order of strategies based on the relevance of negotiations during strategy generation. For example, if the relevance of negotiations is high, the strategy generation unit can provide a detailed strategy. For example, if the relevance of negotiations is low, the strategy generation unit can provide a concise strategy. The strategy generation unit can also determine the optimal order of strategies based on the relevance of negotiations. This allows for the provision of more effective strategies by adjusting the order of strategies based on the relevance of negotiations. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input negotiation relevance data into a generation AI and have the generation AI perform the adjustment of the order of strategies.

[0047] The negotiation department can improve the accuracy of negotiations by referring to past negotiation data during negotiations. For example, the negotiation department can provide the optimal negotiation method based on past negotiation data. For example, the negotiation department can analyze past negotiation data to improve the success rate of negotiations. The negotiation department can also adjust the level of detail in negotiations by referring to past negotiation data. In this way, the accuracy of negotiations can be improved by referring to past negotiation data. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input past negotiation data into a generating AI and have the generating AI perform the task of improving the accuracy of negotiations.

[0048] The negotiation department can conduct negotiations while considering the attribute information of the negotiating party. For example, if the negotiating party is a large corporation, the department can conduct detailed negotiations. For example, if the negotiating party is a small or medium-sized enterprise, the department can conduct concise negotiations. The negotiation department can also provide the optimal negotiation method depending on the industry of the negotiating party. This makes it possible to conduct more effective negotiations by considering the attribute information of the negotiating party. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input the attribute information of the negotiating party into a generating AI and have the generating AI select the negotiation method.

[0049] The negotiating department can conduct negotiations while considering the geographical distribution of the negotiations. For example, if the negotiating partner is in a specific region, the negotiating department will conduct negotiations relevant to that region. For example, based on the geographical distribution of negotiations, the negotiating department can conduct negotiations that meet region-specific needs. Furthermore, the negotiating department can provide the optimal negotiation method by considering the geographical distribution of negotiations. This makes it possible to conduct negotiations that meet region-specific needs by considering the geographical distribution of negotiations. Some or all of the above processing in the negotiating department may be performed using AI, for example, or not using AI. For example, the negotiating department can input geographical distribution data of negotiations into a generating AI and have the generating AI select a negotiation method.

[0050] The negotiation department can improve the accuracy of negotiations by referring to relevant negotiation literature during negotiations. For example, the negotiation department can provide the optimal negotiation method based on relevant negotiation literature. For example, the negotiation department can analyze relevant negotiation literature to improve the success rate of negotiations. The negotiation department can also adjust the level of detail of negotiations by referring to relevant negotiation literature. In this way, the accuracy of negotiations can be improved by referring to relevant negotiation literature. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input negotiation literature data into a generating AI and have the generating AI perform negotiation accuracy improvements.

[0051] The matching unit can improve the accuracy of matching by referring to past matching data during the matching process. For example, the matching unit can provide the optimal matching method based on past matching data. For example, the matching unit can analyze past matching data to improve the success rate of matching. The matching unit can also adjust the level of detail of matching by referring to past matching data. This allows for improved matching accuracy by referring to past matching data. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input past matching data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0052] The matching unit can perform matching while considering the attribute information of the negotiating partner. For example, if the negotiating partner is a large company, the matching unit can perform detailed matching. For example, if the negotiating partner is a small or medium-sized enterprise, the matching unit can perform simple matching. The matching unit can also provide the optimal matching method depending on the industry of the negotiating partner. This makes more effective matching possible by considering the attribute information of the negotiating partner. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the attribute information of the negotiating partner into a generating AI and have the generating AI select a matching method.

[0053] The matching unit can perform matching while considering the geographical distribution of negotiations. For example, if a negotiating partner is in a specific region, the matching unit will perform matching related to that region. For example, the matching unit can perform matching that meets region-specific needs based on the geographical distribution of negotiations. Furthermore, the matching unit can also provide the optimal matching method while considering the geographical distribution of negotiations. This makes it possible to perform matching that meets region-specific needs by considering the geographical distribution of negotiations. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input geographical distribution data of negotiations into a generating AI and have the generating AI select a matching method.

[0054] The matching unit can improve the accuracy of matching by referring to relevant negotiation literature during the matching process. For example, the matching unit can provide the optimal matching method based on relevant negotiation literature. For example, the matching unit can analyze relevant negotiation literature to improve the success rate of matching. The matching unit can also adjust the level of detail of matching by referring to relevant negotiation literature. This improves the accuracy of matching by referring to relevant negotiation literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input negotiation literature data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0055] The data storage unit can optimize the storage algorithm by referring to past stored data when saving data. For example, the data storage unit can provide the optimal storage method based on past stored data. For example, the data storage unit can analyze past stored data to improve the success rate of storage. The data storage unit can also adjust the level of detail of storage by referring to past stored data. This allows the storage algorithm to be optimized by referring to past stored data, thereby improving the success rate of storage. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without using AI. For example, the data storage unit can input past stored data into a generating AI and have the generating AI perform the optimization of the storage algorithm.

[0056] The data storage unit can weight the stored data based on when the negotiation history was submitted. For example, if the negotiation history was submitted early, the data storage unit can store detailed data. For example, if the negotiation history was submitted late, the data storage unit can store only the essential data. The data storage unit can also weight the stored data based on when the negotiation history was submitted. This allows for more effective data storage by weighting the stored data based on when the negotiation history was submitted. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input negotiation history submission date data into a generating AI and have the generating AI perform the weighting of the stored data.

[0057] The data analysis unit can optimize its analysis algorithm by referring to past analysis data during data analysis. For example, the data analysis unit can provide the optimal analysis method based on past analysis data. For example, the data analysis unit can analyze past analysis data to improve the success rate of the analysis. The data analysis unit can also adjust the level of detail of the analysis by referring to past analysis data. In this way, by referring to past analysis data, the analysis algorithm can be optimized and the success rate of the analysis can be improved. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without using AI. For example, the data analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0058] The data analysis unit can weight the analysis data based on when the negotiation history was submitted. For example, if the negotiation history was submitted early, the data analysis unit can analyze detailed data. For example, if the negotiation history was submitted late, the data analysis unit can analyze data that focuses on the key points. The data analysis unit can also weight the analysis data based on when the negotiation history was submitted. This allows for more effective data analysis by weighting the analysis data based on when the negotiation history was submitted. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input negotiation history submission date data into a generating AI and have the generating AI perform the weighting of the analysis data.

[0059] The setting unit can select the optimal setting method by referring to past negotiation history when setting a setting. For example, the setting unit can provide the optimal setting method based on past negotiation history. For example, the setting unit can analyze past negotiation history and improve the success rate of setting a setting. The setting unit can also adjust the level of detail of the setting by referring to past negotiation history. This allows the setting unit to select the optimal setting method by referring to past negotiation history and improve the success rate of negotiations. Some or all of the above processing in the setting unit may be performed using AI, for example, or without using AI. For example, the setting unit can input past negotiation history data into a generating AI and have the generating AI perform the selection of a setting method.

[0060] The venue setting unit can select the optimal venue setting method when setting the venue, taking into account the geographical location information of the negotiation. For example, if the negotiating partner is in a specific region, the venue setting unit will set the venue in a region relevant to that region. For example, the venue setting unit can set the venue in a way that meets the specific needs of the region based on the geographical location information of the negotiation. The venue setting unit can also provide the optimal venue setting method, taking into account the geographical location information of the negotiation. In this way, by taking into account the geographical location information of the negotiation, the optimal venue setting method that meets the specific needs of the region can be provided. Some or all of the above processing in the venue setting unit may be performed using AI, for example, or without using AI. For example, the venue setting unit can input the geographical location information of the negotiation into a generating AI and have the generating AI select the venue setting method.

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

[0062] The automated media advertising negotiation system can analyze a user's past negotiation history and select the optimal negotiation method. For example, it can prioritize negotiation methods that have been successful for the user in the past. It can also avoid negotiation methods that have been unsuccessful for the user in the past. Furthermore, it can adjust the level of detail in the negotiation based on the user's past negotiation history. This allows for the selection of a more effective negotiation method by analyzing past negotiation history. Some or all of the above processes in the negotiation section may be performed using AI or not. For example, the negotiation section can input the user's past negotiation history into a generating AI and have the generating AI select the optimal negotiation method.

[0063] The automated media advertising negotiation system can adjust the negotiation process by considering the attribute information of the negotiating party. For example, if the negotiating party is a large corporation, it can conduct detailed negotiations. Conversely, if the negotiating party is a small or medium-sized enterprise, it can conduct concise negotiations. Furthermore, it can provide the optimal negotiation method depending on the industry of the negotiating party. This makes more effective negotiations possible by considering the attribute information of the negotiating party. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input the attribute information of the negotiating party into a generating AI and have the generating AI select the negotiation method.

[0064] The automated media advertising negotiation system can conduct negotiations while considering the geographical distribution of negotiations. For example, if the negotiating partner is in a specific region, it will conduct negotiations related to that region. It can also conduct negotiations that are tailored to region-specific needs based on the geographical distribution of negotiations. Furthermore, it can provide the optimal negotiation method while considering the geographical distribution of negotiations. This makes it possible to conduct negotiations that are tailored to region-specific needs by considering the geographical distribution of negotiations. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input geographical distribution data of negotiations into a generating AI and have the generating AI select a negotiation method.

[0065] The automated media advertising negotiation system can improve the accuracy of negotiations by referring to relevant negotiation literature. For example, it can provide the optimal negotiation method based on relevant negotiation literature. It can also analyze relevant negotiation literature to improve the success rate of negotiations. Furthermore, it can adjust the level of detail of negotiations by referring to relevant negotiation literature. In this way, the accuracy of negotiations can be improved by referring to relevant negotiation literature. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input relevant negotiation literature data into a generating AI and have the generating AI perform the task of improving negotiation accuracy.

[0066] The automated media advertising negotiation system can select the optimal setting method by referring to past negotiation history. For example, it can provide the optimal setting method based on past negotiation history. It can also analyze past negotiation history to improve the success rate of setting. Furthermore, it can adjust the level of detail of setting by referring to past negotiation history. This allows the system to select the optimal setting method by referring to past negotiation history and improve the success rate of negotiations. Some or all of the above processing in the setting unit may be performed using AI or not. For example, the setting unit can input past negotiation history data into a generating AI and have the generating AI perform the selection of a setting method.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The hearing department gathers information about the needs and requirements of each player. For example, a conversational AI can be used to gather information about the needs of advertising agencies and the requirements of production companies. Step 2: The strategy generation unit generates a negotiation strategy based on the information gathered by the interviewing unit. For example, it reads past negotiation data and generates a negotiation strategy based on information gathered from interviews with the relevant personnel. Step 3: The Negotiation Department automatically conducts negotiations based on the strategies generated by the Strategy Generation Department. For example, an AI acting as an agent for an advertising agency searches for media outlets that can provide "content for a specific target audience" and negotiates with those media outlets. Step 4: The matching department matches the most suitable negotiating partners found by the negotiation department. For example, it sets up a meeting between a media representative who can provide "content for a specific target audience" found by the advertising agency's AI and a representative from the advertising agency.

[0069] (Example of form 2) The automated media advertising negotiation system according to an embodiment of the present invention is a system in which a conversational AI listens to the requests and conditions of each player in media advertising (media, advertising agency, production company, advertising model), and based on those conditions, each player's representative AI automatically conducts negotiations on the automated media advertising negotiation platform, finds the most suitable negotiating partner that meets the conditions, and sets up a meeting place for negotiations between the representatives. The automated media advertising negotiation system listens to the requests and conditions of each player, and based on those conditions, each player's representative AI automatically conducts negotiations on the automated media advertising negotiation platform, finds the most suitable negotiating partner that meets the conditions, and sets up a meeting place for negotiations between the representatives. This system allows each player to proceed with negotiations efficiently and find the most suitable advertising project. For example, the conversational AI listens to the requests and conditions of each player. At this time, each player can specifically communicate their requests and conditions. For example, an advertising agency can communicate a request such as "We want to monetize with content aimed at a specific target audience." A production company can communicate a condition such as "We are looking for an advertising project that can utilize our video shooting and editing capabilities." Next, based on the information gathered, each player's AI representative conducts automated negotiations on the media advertising automated negotiation platform. The AI ​​representative reads past negotiation data and generates negotiation strategies based on information gathered from the representatives. For example, the AI ​​representative for an advertising agency searches for media outlets that can provide "content for a specific target audience" based on past negotiation data and negotiates with those media outlets. As a result of the automated negotiations, it finds the optimal negotiating partner that meets the conditions and sets up a meeting between the representatives. For example, the AI ​​representative for an advertising agency finds a media outlet that can provide "content for a specific target audience" and sets up a meeting between the media outlet's representative and the advertising agency's representative. In this way, each player can proceed with negotiations efficiently and find the optimal advertising deal. This system allows each player to proceed with negotiations efficiently and find the optimal advertising deal. In addition, the automation of the negotiation process saves resources for each player.For example, advertising agencies can use AI to automate negotiations, allowing their staff to focus on other tasks. Similarly, production companies can efficiently find the most suitable advertising projects by using AI to automate negotiations. In this way, by utilizing conversational AI and AI to automate negotiations, we provide a system that streamlines the media advertising negotiation process, enabling each player to find the most suitable advertising projects. As a result, the automated media advertising negotiation system can efficiently negotiate based on the requests and conditions of each player, and find the most suitable advertising projects.

[0070] The automated media advertising negotiation system according to this embodiment comprises a hearing unit, a strategy generation unit, a negotiation unit, and a matching unit. The hearing unit hears the requests and conditions of each player. The hearing unit uses, for example, a conversational AI to hear the requests and conditions of each player. For example, the hearing unit can hear an advertising agency's request, such as "We want to monetize with content aimed at a specific target audience." The hearing unit can also hear a production company's condition, such as "We are looking for advertising projects that can utilize our video shooting and editing capabilities." The strategy generation unit generates a negotiation strategy based on the information gathered by the hearing unit. For example, the strategy generation unit reads past negotiation data and generates a negotiation strategy based on information gathered from the person in charge. For example, based on the advertising agency's request, the strategy generation unit searches for media that can provide "content aimed at a specific target audience" and generates a strategy to negotiate with that media. The negotiation unit performs automated negotiations based on the strategy generated by the strategy generation unit. The negotiation department, for example, uses an AI acting as an agent for an advertising agency to find media outlets that can provide "content for a specific target audience" and then negotiates with those media outlets. The negotiation department can generate negotiation strategies based on past negotiation data and automatically conduct negotiations based on those strategies. For example, the negotiation department uses an AI acting as an agent for an advertising agency to find media outlets that can provide "content for a specific target audience" and then negotiates with those media outlets. The matching department matches the most suitable negotiating partners found by the negotiation department. For example, the matching department sets up a meeting between a representative of a media outlet that can provide "content for a specific target audience" found by an AI acting as an agent for an advertising agency and a representative of an advertising agency. The matching department can find the most suitable negotiating partners and set up a meeting between the representatives. For example, the matching department sets up a meeting between a representative of a media outlet that can provide "content for a specific target audience" found by an AI acting as an agent for an advertising agency and a representative of an advertising agency. As a result, the automated media advertising negotiation system according to this embodiment can efficiently proceed with negotiations based on the requests and conditions of each player and find the most suitable advertising deals.

[0071] The Hearing Department gathers information on the needs and requirements of each player. For example, the Hearing Department uses conversational AI to gather information on the needs and requirements of each player. Specifically, the conversational AI utilizes natural language processing technology to converse with representatives from advertising agencies and production companies to elicit detailed needs and requirements. For example, if an advertising agency representative wants to monetize content aimed at a specific target audience, the conversational AI will ask detailed questions about the specific attributes of that target audience (age, gender, interests, etc.) and the desired content format (video, article, banner ad, etc.) to gather information. Similarly, if a production company representative is looking for advertising projects that can leverage their video shooting and editing capabilities, the conversational AI will also inquire about the production company's preferred shooting and editing styles, past achievements, and desired budget and schedule. This allows the Hearing Department to gain a detailed understanding of the specific needs and requirements of each player and provide the information necessary for the next step: strategy generation. Furthermore, the interviewing department can store the information gathered in a database and utilize it for future negotiations and matching. For example, based on information gathered in the past, it can quickly identify players with similar requests and conditions, enabling efficient matching. This allows the interviewing department to accurately and quickly grasp the requests and conditions of each player, improving the overall efficiency and accuracy of the system.

[0072] The Strategy Generation Department generates negotiation strategies based on information gathered by the Interviewing Department. For example, the Strategy Generation Department reads past negotiation data and generates negotiation strategies based on information gathered from the relevant personnel. Specifically, the Strategy Generation Department analyzes past success and failure cases and uses algorithms to derive the optimal negotiation strategy. For example, when generating a strategy to find media outlets that can provide "content for a specific target audience" based on an advertising agency's request and negotiate with those media outlets, it refers to a list of media outlets that have previously provided similar content for the target audience and selects the most suitable media outlet from among them. The Strategy Generation Department also clarifies the points to emphasize during negotiations and the conditions under which concessions are possible, based on the information gathered. For example, if an advertising agency has budget constraints, the Strategy Generation Department selects media outlets that will be most effective within the budget and develops a strategy to emphasize the budget during negotiations with those media outlets while being flexible with other conditions (e.g., advertising period and placement). Furthermore, the Strategy Generation Department can update negotiation strategies in real time using AI. For example, the strategy can be adjusted as needed based on the progress of negotiations and the other party's reactions, leading to the optimal negotiation outcome. This allows the strategy generation unit to generate effective and flexible negotiation strategies based on the information gathered, thereby increasing the success rate of negotiations.

[0073] The Negotiation Department conducts automated negotiations based on strategies generated by the Strategy Generation Department. For example, the Negotiation Department searches for media outlets that can provide "content for a specific target audience" using an AI acting as an agent for an advertising agency, and then negotiates with those media outlets. Specifically, the Negotiation Department can generate negotiation strategies based on past negotiation data and conduct automated negotiations based on those strategies. For example, when the Negotiation Department finds media outlets that can provide "content for a specific target audience" using an AI acting as an agent for an advertising agency and negotiates with those media outlets, it refers to past negotiation data and selects the optimal negotiation method. The Negotiation Department uses AI to monitor the progress of negotiations in real time and can adjust the negotiation strategy as needed in response to the other party's reactions. For example, if the other party expresses reluctance regarding the budget, the Negotiation Department will propose concessions on other conditions (e.g., the advertising period or placement) to facilitate the negotiation. The Negotiation Department can also store the results of negotiations in a database and use them in future negotiations. As a result, the Negotiation Department can conduct effective and flexible negotiations based on past negotiation data and derive the optimal negotiation results. Furthermore, the negotiating team can use AI to monitor the progress of negotiations in real time and adjust their negotiation strategy as needed based on the other party's responses. This allows the negotiating team to conduct effective and flexible negotiations and achieve the optimal outcome.

[0074] The matching department matches the most suitable negotiating partners identified by the negotiation department. For example, the matching department sets up a meeting between a media representative who can provide "content for a specific target audience" (identified by the advertising agency's AI) and a representative from an advertising agency. Specifically, the matching department can find the most suitable negotiating partners and set up a meeting between the two parties. For example, when setting up a meeting between a media representative who can provide "content for a specific target audience" (identified by the advertising agency's AI) and a representative from an advertising agency, the matching department coordinates the schedules of both parties and sets up the meeting using an online conferencing system. The matching department can also monitor the progress of the negotiations and provide support as needed. For example, if negotiations become difficult, the matching department will propose the best solution for both parties based on past negotiation data to facilitate the negotiations. The matching department can also store the results of negotiations in a database and use them for future matching. This allows the matching department to find the most suitable negotiating partners and provide an efficient and effective negotiation environment. Furthermore, the matching department can use AI to monitor the progress of negotiations in real time and adjust the negotiation strategy as needed based on the other party's responses. This allows the matching department to conduct effective and flexible negotiations and arrive at the optimal negotiation outcome.

[0075] The automated media advertising negotiation system includes a data storage unit that stores past negotiation data. The data storage unit stores, for example, past negotiation data. The data storage unit needs to clarify the specific content and storage method of the past negotiation data. For example, the data storage unit can store negotiation results and negotiation processes. By storing past negotiation data, the data storage unit can be used to help future negotiations. For example, the data storage unit can store past negotiation data and generate future negotiation strategies based on that data. In this way, by storing past negotiation data, it can be used to help future negotiations. Some or all of the above processing in the data storage unit may be performed using, for example, AI, or not using AI. For example, the data storage unit can input past negotiation data into a generating AI and have the generating AI perform the data storage.

[0076] The automated media advertising negotiation system includes a data analysis unit that generates negotiation strategies based on data stored by a data storage unit. The data analysis unit generates negotiation strategies based on data stored by the data storage unit, for example. The data analysis unit can analyze past negotiation data and generate negotiation strategies based on that data. By analyzing stored data, the data analysis unit can generate more accurate negotiation strategies. For example, the data analysis unit analyzes past negotiation data and generates negotiation strategies based on that data. This allows for the generation of more accurate negotiation strategies by analyzing stored data. Some or all of the above-described processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input past negotiation data into a generation AI and have the generation AI perform the data analysis.

[0077] The automated media advertising negotiation system includes a matching unit that finds negotiating partners and a setting unit that sets up a negotiation forum between the representatives. The setting unit can, for example, find negotiating partners and set up a negotiation forum between the representatives. The setting unit can find the optimal negotiating partners and set up a negotiation forum between the representatives. For example, the setting unit sets up a negotiation forum between a media representative who can provide "content for a specific target audience" found by the advertising agency's AI and a representative of the advertising agency. This allows for the finding of the optimal negotiating partners and the setting up of negotiation forums efficiently. Some or all of the above processing in the setting unit may be performed using AI, for example, or not using AI. For example, the setting unit can input information about negotiating partners into a generating AI and have the generating AI perform the setting up of the negotiation forum.

[0078] The Hearing Department can specifically gather information on the requests and conditions of each player. For example, the Hearing Department can use a conversational AI to specifically gather information on the requests and conditions of each player. For example, the Hearing Department can specifically gather information on an advertising agency's request, such as "We want to monetize with content targeted at a specific audience." The Hearing Department can also specifically gather information on a production company's condition, such as "We are looking for advertising projects that can utilize our video shooting and editing capabilities." By gathering specific requests and conditions, a more appropriate negotiation strategy can be generated. Some or all of the above processing in the Hearing Department may be performed using AI, for example, or not. For example, the Hearing Department can input the requests and conditions of each player into a generating AI and have the generating AI perform the request and condition gathering.

[0079] The strategy generation unit can generate negotiation strategies based on the information gathered through interviews. For example, the strategy generation unit generates negotiation strategies based on the information gathered through interviews. By generating negotiation strategies based on the information gathered through interviews, the strategy generation unit enables more effective negotiations. For example, based on the advertising agency's request, the strategy generation unit searches for media outlets that can provide "content for a specific target audience" and generates a strategy to negotiate with those media outlets. This enables more effective negotiations by generating negotiation strategies based on the information gathered through interviews. Some or all of the above-described processes in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input the information gathered through interviews into a generation AI and have the generation AI execute the generation of negotiation strategies.

[0080] The interview unit can estimate the user's emotions and adjust the timing of the interview based on the estimated emotions. For example, if the user is feeling stressed, the interview unit can conduct the interview during a time when the user is relaxed. For example, if the user is focused, the interview unit can leverage that focus to conduct a detailed interview. Also, if the user is tired, the interview unit can conduct a short, concise interview. By adjusting the timing of the interview according to the user's emotions, more effective interviews become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using AI, or not using AI. For example, the interview unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0081] The interviewing unit can analyze the user's past interview history and select the most suitable interview method. For example, the interviewing unit can prioritize interview formats that the user has preferred in the past (such as dialogue or questionnaire formats). For example, the interviewing unit can reconfirm questions that the user has answered in detail in the past and ask additional questions. The interviewing unit can also conduct efficient interviews by adopting question formats that the user has answered quickly in the past. In this way, by analyzing past interview history, a more effective interview method can be selected. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the user's past interview history into a generating AI and have the generating AI select the most suitable interview method.

[0082] The interviewing unit can customize the questions asked during the interview based on the user's current projects and areas of interest. For example, the interviewing unit can prioritize questions related to the user's current projects. For example, the interviewing unit can ask questions about relevant advertising opportunities based on the user's areas of interest. The interviewing unit can also ask relevant questions by referring to the user's past project history. By customizing the questions based on the user's current projects and areas of interest, a more effective interview becomes possible. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input information about the user's projects and areas of interest into a generating AI and have the generating AI customize the questions.

[0083] The interview unit can estimate the user's emotions and determine the priority of the interview based on the estimated emotions. For example, if the user is nervous, the interview unit can start with questions that help them relax. For example, if the user is relaxed, the interview unit can prioritize asking detailed questions. Also, if the user is in a hurry, the interview unit can prioritize asking important questions. This allows for more effective interviews by determining the priority of the interview according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using AI, or not using AI. For example, the interview unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The interviewing unit can prioritize asking highly relevant questions during interviews, taking into account the user's geographical location. For example, if the user is in a specific region, the interviewing unit can ask questions related to advertising opportunities in that region. For example, based on the user's geographical location, the interviewing unit can ask questions about region-specific needs. Furthermore, if the user is on the move, the interviewing unit can ask questions related to advertising opportunities in their destination. This allows for more effective interviews by asking highly relevant questions while considering the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the user's geographical location into a generating AI and have the generating AI select highly relevant questions.

[0085] The interviewing unit can analyze the user's social media activity during the interview and ask relevant questions. For example, the interviewing unit can ask questions based on topics the user has shown interest in on social media. For example, the interviewing unit can ask questions related to advertising campaigns based on the user's social media activity. The interviewing unit can also ask relevant questions based on the accounts the user follows on social media. This allows for more effective interviews by analyzing the user's social media activity and asking relevant questions. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's social media activity data into a generating AI and have the generating AI select relevant questions.

[0086] The strategy generation unit can estimate the user's emotions and adjust the way the strategy is presented based on the estimated emotions. For example, if the user is relaxed, the strategy generation unit can provide a detailed strategy. For example, if the user is in a hurry, the strategy generation unit can provide a concise strategy. Furthermore, if the user is excited, the strategy generation unit can provide a visually stimulating strategy. In this way, by adjusting the way the strategy is presented according to the user's emotions, a more effective strategy can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or not using AI. For example, the strategy generation unit can input user emotion data into the generative AI and have the generative AI adjust the way the strategy is presented.

[0087] The strategy generation unit can adjust the level of detail of a strategy based on past negotiation results when generating a strategy. For example, if past negotiations were successful, the strategy generation unit can reproduce that strategy in detail. For example, if past negotiations were unsuccessful, the strategy generation unit can simplify the strategy. The strategy generation unit can also analyze past negotiation results and provide a strategy with the optimal level of detail. By adjusting the level of detail of a strategy based on past negotiation results, it can provide a more effective strategy. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input past negotiation result data into a generation AI and have the generation AI perform the adjustment of the level of detail of the strategy.

[0088] The strategy generation unit can apply different strategic algorithms depending on the attributes of the negotiating partner during strategy generation. For example, if the negotiating partner is a large corporation, the strategy generation unit can provide a detailed strategy. For example, if the negotiating partner is a small or medium-sized enterprise, the strategy generation unit can provide a concise strategy. The strategy generation unit can also apply the most suitable strategic algorithm depending on the industry of the negotiating partner. This allows for the provision of more effective strategies by applying different strategic algorithms according to the attributes of the negotiating partner. Some or all of the above-described processes in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input the negotiating partner's attribute information into a generation AI and have the generation AI execute the application of strategic algorithms.

[0089] The strategy generation unit can estimate the user's emotions and adjust the length of the strategy based on the estimated emotions. For example, if the user is in a hurry, the strategy generation unit can provide a short, concise strategy. For example, if the user is relaxed, the strategy generation unit can provide a detailed strategy. Furthermore, if the user is excited, the strategy generation unit can provide a visually stimulating strategy. By adjusting the length of the strategy according to the user's emotions, a more effective strategy can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the strategy.

[0090] The strategy generation unit can determine strategy priorities based on the timing of negotiation submissions when generating strategies. For example, if the negotiation submission is early, the strategy generation unit can provide a detailed strategy. For example, if the negotiation submission is late, the strategy generation unit can provide a concise strategy. The strategy generation unit can also determine the optimal strategy priority based on the negotiation submission timing. This allows for the provision of more effective strategies by prioritizing strategies based on the negotiation submission timing. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input negotiation submission timing data into a generation AI and have the generation AI perform the determination of strategy priorities.

[0091] The strategy generation unit can adjust the order of strategies based on the relevance of negotiations during strategy generation. For example, if the relevance of negotiations is high, the strategy generation unit can provide a detailed strategy. For example, if the relevance of negotiations is low, the strategy generation unit can provide a concise strategy. The strategy generation unit can also determine the optimal order of strategies based on the relevance of negotiations. This allows for the provision of more effective strategies by adjusting the order of strategies based on the relevance of negotiations. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without AI. For example, the strategy generation unit can input negotiation relevance data into a generation AI and have the generation AI perform the adjustment of the order of strategies.

[0092] The negotiation unit can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is relaxed, the negotiation unit can conduct a detailed negotiation. If the user is in a hurry, the negotiation unit can conduct a concise negotiation. If the user is excited, the negotiation unit can also conduct a visually stimulating negotiation. By adjusting the negotiation process according to the user's emotions, more effective negotiations become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the negotiation process.

[0093] The negotiation department can improve the accuracy of negotiations by referring to past negotiation data during negotiations. For example, the negotiation department can provide the optimal negotiation method based on past negotiation data. For example, the negotiation department can analyze past negotiation data to improve the success rate of negotiations. The negotiation department can also adjust the level of detail in negotiations by referring to past negotiation data. In this way, the accuracy of negotiations can be improved by referring to past negotiation data. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input past negotiation data into a generating AI and have the generating AI perform the task of improving the accuracy of negotiations.

[0094] The negotiation department can conduct negotiations while considering the attribute information of the negotiating party. For example, if the negotiating party is a large corporation, the department can conduct detailed negotiations. For example, if the negotiating party is a small or medium-sized enterprise, the department can conduct concise negotiations. The negotiation department can also provide the optimal negotiation method depending on the industry of the negotiating party. This makes it possible to conduct more effective negotiations by considering the attribute information of the negotiating party. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input the attribute information of the negotiating party into a generating AI and have the generating AI select the negotiation method.

[0095] The negotiation unit can estimate the user's emotions and adjust the order in which negotiation results are displayed based on the estimated emotions. For example, if the user is relaxed, the negotiation unit may prioritize displaying detailed results. For example, if the user is in a hurry, the negotiation unit may prioritize displaying concise results. Furthermore, if the user is excited, the negotiation unit may prioritize displaying visually stimulating results. This allows for a more effective presentation of negotiation results by adjusting the order in which negotiation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the display order of negotiation results.

[0096] The negotiating department can conduct negotiations while considering the geographical distribution of the negotiations. For example, if the negotiating partner is in a specific region, the negotiating department will conduct negotiations relevant to that region. For example, based on the geographical distribution of negotiations, the negotiating department can conduct negotiations that meet region-specific needs. Furthermore, the negotiating department can provide the optimal negotiation method by considering the geographical distribution of negotiations. This makes it possible to conduct negotiations that meet region-specific needs by considering the geographical distribution of negotiations. Some or all of the above processing in the negotiating department may be performed using AI, for example, or not using AI. For example, the negotiating department can input geographical distribution data of negotiations into a generating AI and have the generating AI select a negotiation method.

[0097] The negotiation department can improve the accuracy of negotiations by referring to relevant negotiation literature during negotiations. For example, the negotiation department can provide the optimal negotiation method based on relevant negotiation literature. For example, the negotiation department can analyze relevant negotiation literature to improve the success rate of negotiations. The negotiation department can also adjust the level of detail of negotiations by referring to relevant negotiation literature. In this way, the accuracy of negotiations can be improved by referring to relevant negotiation literature. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input negotiation literature data into a generating AI and have the generating AI perform negotiation accuracy improvements.

[0098] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit can provide detailed matching criteria. For example, if the user is in a hurry, the matching unit can provide concise matching criteria. Furthermore, if the user is excited, the matching unit can provide visually stimulating matching criteria. This allows for more effective matching by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using AI or not using AI. For example, the matching unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the matching criteria.

[0099] The matching unit can improve the accuracy of matching by referring to past matching data during the matching process. For example, the matching unit can provide the optimal matching method based on past matching data. For example, the matching unit can analyze past matching data to improve the success rate of matching. The matching unit can also adjust the level of detail of matching by referring to past matching data. This allows for improved matching accuracy by referring to past matching data. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input past matching data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0100] The matching unit can perform matching while considering the attribute information of the negotiating partner. For example, if the negotiating partner is a large company, the matching unit can perform detailed matching. For example, if the negotiating partner is a small or medium-sized enterprise, the matching unit can perform simple matching. The matching unit can also provide the optimal matching method depending on the industry of the negotiating partner. This makes more effective matching possible by considering the attribute information of the negotiating partner. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the attribute information of the negotiating partner into a generating AI and have the generating AI select a matching method.

[0101] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is relaxed, the matching unit may prioritize displaying detailed results. For example, if the user is in a hurry, the matching unit may prioritize displaying concise results. Also, if the user is excited, the matching unit may prioritize displaying visually stimulating results. By adjusting the order in which matching results are displayed according to the user's emotions, it becomes possible to present more effective matching results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, or not using AI. For example, the matching unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the matching results.

[0102] The matching unit can perform matching while considering the geographical distribution of negotiations. For example, if a negotiating partner is in a specific region, the matching unit will perform matching related to that region. For example, the matching unit can perform matching that meets region-specific needs based on the geographical distribution of negotiations. Furthermore, the matching unit can also provide the optimal matching method while considering the geographical distribution of negotiations. This makes it possible to perform matching that meets region-specific needs by considering the geographical distribution of negotiations. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input geographical distribution data of negotiations into a generating AI and have the generating AI select a matching method.

[0103] The matching unit can improve the accuracy of matching by referring to relevant negotiation literature during the matching process. For example, the matching unit can provide the optimal matching method based on relevant negotiation literature. For example, the matching unit can analyze relevant negotiation literature to improve the success rate of matching. The matching unit can also adjust the level of detail of matching by referring to relevant negotiation literature. This improves the accuracy of matching by referring to relevant negotiation literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input negotiation literature data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0104] The data storage unit can estimate the user's emotions and select data to store based on the estimated emotions. For example, if the user is relaxed, the data storage unit can store detailed data. For example, if the user is in a hurry, the data storage unit can store concise data. Also, if the user is excited, the data storage unit can store visually stimulating data. This allows for more effective data storage by selecting data to store according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data storage unit may be performed using AI, or not using AI. For example, the data storage unit can input user emotion data into a generative AI and have the generative AI select the data to store.

[0105] The data storage unit can optimize the storage algorithm by referring to past stored data when saving data. For example, the data storage unit can provide the optimal storage method based on past stored data. For example, the data storage unit can analyze past stored data to improve the success rate of storage. The data storage unit can also adjust the level of detail of storage by referring to past stored data. This allows the storage algorithm to be optimized by referring to past stored data, thereby improving the success rate of storage. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without using AI. For example, the data storage unit can input past stored data into a generating AI and have the generating AI perform the optimization of the storage algorithm.

[0106] The data storage unit can estimate the user's emotions and adjust the storage frequency based on the estimated emotions. For example, the data storage unit saves data frequently when the user is relaxed. For example, when the user is in a hurry, the data storage unit can save only the minimum necessary data. Also, when the user is excited, the data storage unit can save visually stimulating data. This allows for more effective data storage by adjusting the storage frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data storage unit may be performed using AI or not using AI. For example, the data storage unit can input user emotion data into the generative AI and have the generative AI adjust the storage frequency.

[0107] The data storage unit can weight the stored data based on when the negotiation history was submitted. For example, if the negotiation history was submitted early, the data storage unit can store detailed data. For example, if the negotiation history was submitted late, the data storage unit can store only the essential data. The data storage unit can also weight the stored data based on when the negotiation history was submitted. This allows for more effective data storage by weighting the stored data based on when the negotiation history was submitted. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input negotiation history submission date data into a generating AI and have the generating AI perform the weighting of the stored data.

[0108] The data analysis unit can estimate the user's emotions and select analysis data based on the estimated emotions. For example, if the user is relaxed, the data analysis unit can analyze detailed data. For example, if the user is in a hurry, the data analysis unit can analyze concise data. Furthermore, if the user is excited, the data analysis unit can analyze visually stimulating data. This allows for more effective data analysis by selecting analysis data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, or not using AI. For example, the data analysis unit can input user emotion data into a generative AI and have the generative AI select the analysis data.

[0109] The data analysis unit can optimize its analysis algorithm by referring to past analysis data during data analysis. For example, the data analysis unit can provide the optimal analysis method based on past analysis data. For example, the data analysis unit can analyze past analysis data to improve the success rate of the analysis. The data analysis unit can also adjust the level of detail of the analysis by referring to past analysis data. In this way, by referring to past analysis data, the analysis algorithm can be optimized and the success rate of the analysis can be improved. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without using AI. For example, the data analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0110] The data analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated emotions. For example, if the user is relaxed, the data analysis unit will analyze data frequently. For example, if the user is in a hurry, the data analysis unit can analyze only the minimum necessary data. Also, if the user is excited, the data analysis unit can analyze visually stimulating data. By adjusting the frequency of analysis according to the user's emotions, more effective data analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis frequency.

[0111] The data analysis unit can weight the analysis data based on when the negotiation history was submitted. For example, if the negotiation history was submitted early, the data analysis unit can analyze detailed data. For example, if the negotiation history was submitted late, the data analysis unit can analyze data that focuses on the key points. The data analysis unit can also weight the analysis data based on when the negotiation history was submitted. This allows for more effective data analysis by weighting the analysis data based on when the negotiation history was submitted. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input negotiation history submission date data into a generating AI and have the generating AI perform the weighting of the analysis data.

[0112] The setting unit can estimate the user's emotions and adjust the setting of the negotiation environment based on the estimated emotions. For example, if the user is relaxed, the setting unit can create a detailed setting. For example, if the user is in a hurry, the setting unit can create a concise setting. Furthermore, if the user is excited, the setting unit can create a visually stimulating setting. By adjusting the setting of the negotiation environment according to the user's emotions, a more effective negotiation environment can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the setting unit may be performed using AI, or not using AI. For example, the setting unit can input user emotion data into the generative AI and have the generative AI adjust the setting method.

[0113] The setting unit can select the optimal setting method by referring to past negotiation history when setting a setting. For example, the setting unit can provide the optimal setting method based on past negotiation history. For example, the setting unit can analyze past negotiation history and improve the success rate of setting a setting. The setting unit can also adjust the level of detail of the setting by referring to past negotiation history. This allows the setting unit to select the optimal setting method by referring to past negotiation history and improve the success rate of negotiations. Some or all of the above processing in the setting unit may be performed using AI, for example, or without using AI. For example, the setting unit can input past negotiation history data into a generating AI and have the generating AI perform the selection of a setting method.

[0114] The setting unit can estimate the user's emotions and determine the priority of negotiation settings based on the estimated emotions. For example, if the user is relaxed, the setting unit may prioritize a detailed setting. For example, if the user is in a hurry, the setting unit may prioritize a concise setting. Furthermore, if the user is excited, the setting unit may prioritize a visually stimulating setting. This allows for a more effective negotiation setting by determining the priority of negotiation settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the setting unit may be performed using AI, or not using AI. For example, the setting unit can input user emotion data into the generative AI and have the generative AI determine the priority of negotiation settings.

[0115] The venue setting unit can select the optimal venue setting method when setting the venue, taking into account the geographical location information of the negotiation. For example, if the negotiating partner is in a specific region, the venue setting unit will set the venue in a region relevant to that region. For example, the venue setting unit can set the venue in a way that meets the specific needs of the region based on the geographical location information of the negotiation. The venue setting unit can also provide the optimal venue setting method, taking into account the geographical location information of the negotiation. In this way, by taking into account the geographical location information of the negotiation, the optimal venue setting method that meets the specific needs of the region can be provided. Some or all of the above processing in the venue setting unit may be performed using AI, for example, or without using AI. For example, the venue setting unit can input the geographical location information of the negotiation into a generating AI and have the generating AI select the venue setting method.

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

[0117] The automated media advertising negotiation system can estimate the user's emotions and adjust the negotiation pace based on those emotions. For example, if the user is relaxed, the negotiation pace can be slowed down, and detailed explanations can be provided. If the user is in a hurry, the negotiation pace can be sped up, and the key points can be conveyed concisely. Furthermore, if the user is excited, the negotiation can be conducted using visually stimulating presentations. By adjusting the negotiation pace according to the user's emotions, more effective negotiations become possible. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into the generative AI and have the generative AI adjust the negotiation pace.

[0118] The automated media advertising negotiation system can analyze a user's past negotiation history and select the optimal negotiation method. For example, it can prioritize negotiation methods that have been successful for the user in the past. It can also avoid negotiation methods that have been unsuccessful for the user in the past. Furthermore, it can adjust the level of detail in the negotiation based on the user's past negotiation history. This allows for the selection of a more effective negotiation method by analyzing past negotiation history. Some or all of the above processes in the negotiation section may be performed using AI or not. For example, the negotiation section can input the user's past negotiation history into a generating AI and have the generating AI select the optimal negotiation method.

[0119] The automated media advertising negotiation system can estimate the user's emotions and determine negotiation priorities based on those emotions. For example, if the user is relaxed, detailed negotiations can be prioritized. If the user is in a hurry, concise negotiations can be prioritized. Furthermore, if the user is excited, visually stimulating negotiations can be prioritized. This allows for more effective negotiations by prioritizing negotiations according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI determine negotiation priorities.

[0120] The automated media advertising negotiation system can adjust the negotiation process by considering the attribute information of the negotiating party. For example, if the negotiating party is a large corporation, it can conduct detailed negotiations. Conversely, if the negotiating party is a small or medium-sized enterprise, it can conduct concise negotiations. Furthermore, it can provide the optimal negotiation method depending on the industry of the negotiating party. This makes more effective negotiations possible by considering the attribute information of the negotiating party. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input the attribute information of the negotiating party into a generating AI and have the generating AI select the negotiation method.

[0121] The automated media advertising negotiation system can estimate the user's emotions and adjust the order in which negotiation results are displayed based on those emotions. For example, if the user is relaxed, detailed results can be prioritized. If the user is in a hurry, concise results can be prioritized. Furthermore, if the user is excited, visually stimulating results can be prioritized. By adjusting the order in which negotiation results are displayed according to the user's emotions, it becomes possible to present negotiation results more effectively. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into the generative AI and have the generative AI adjust the display order of negotiation results.

[0122] The automated media advertising negotiation system can conduct negotiations while considering the geographical distribution of negotiations. For example, if the negotiating partner is in a specific region, it will conduct negotiations related to that region. It can also conduct negotiations that are tailored to region-specific needs based on the geographical distribution of negotiations. Furthermore, it can provide the optimal negotiation method while considering the geographical distribution of negotiations. This makes it possible to conduct negotiations that are tailored to region-specific needs by considering the geographical distribution of negotiations. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input geographical distribution data of negotiations into a generating AI and have the generating AI select a negotiation method.

[0123] The automated media advertising negotiation system can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is relaxed, it can conduct a detailed negotiation. If the user is in a hurry, it can conduct a concise negotiation. Furthermore, if the user is excited, it can conduct a visually stimulating negotiation. By adjusting the negotiation process according to the user's emotions, more effective negotiations become possible. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the negotiation process.

[0124] The automated media advertising negotiation system can improve the accuracy of negotiations by referring to relevant negotiation literature. For example, it can provide the optimal negotiation method based on relevant negotiation literature. It can also analyze relevant negotiation literature to improve the success rate of negotiations. Furthermore, it can adjust the level of detail of negotiations by referring to relevant negotiation literature. In this way, the accuracy of negotiations can be improved by referring to relevant negotiation literature. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input relevant negotiation literature data into a generating AI and have the generating AI perform the task of improving negotiation accuracy.

[0125] The automated media advertising negotiation system can estimate the user's emotions and adjust the negotiation setting based on those emotions. For example, if the user is relaxed, a detailed setting can be created. If the user is in a hurry, a concise setting can be created. Furthermore, if the user is excited, a visually stimulating setting can be created. By adjusting the negotiation setting according to the user's emotions, a more effective negotiation environment can be provided. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the setting unit may be performed using AI or not. For example, the setting unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the setting.

[0126] The automated media advertising negotiation system can select the optimal setting method by referring to past negotiation history. For example, it can provide the optimal setting method based on past negotiation history. It can also analyze past negotiation history to improve the success rate of setting. Furthermore, it can adjust the level of detail of setting by referring to past negotiation history. This allows the system to select the optimal setting method by referring to past negotiation history and improve the success rate of negotiations. Some or all of the above processing in the setting unit may be performed using AI or not. For example, the setting unit can input past negotiation history data into a generating AI and have the generating AI perform the selection of a setting method.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The hearing department gathers information about the needs and requirements of each player. For example, a conversational AI can be used to gather information about the needs of advertising agencies and the requirements of production companies. Step 2: The strategy generation unit generates a negotiation strategy based on the information gathered by the interviewing unit. For example, it reads past negotiation data and generates a negotiation strategy based on information gathered from interviews with the relevant personnel. Step 3: The Negotiation Department automatically conducts negotiations based on the strategies generated by the Strategy Generation Department. For example, an AI acting as an agent for an advertising agency searches for media outlets that can provide "content for a specific target audience" and negotiates with those media outlets. Step 4: The matching department matches the most suitable negotiating partners found by the negotiation department. For example, it sets up a meeting between a media representative who can provide "content for a specific target audience" found by the advertising agency's AI and a representative from the advertising agency.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0132] Each of the multiple elements described above, including the hearing unit, strategy generation unit, negotiation unit, matching unit, data storage unit, data analysis unit, and field setting unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the hearing unit uses the conversational AI of the smart device 14 to hear the requests and conditions of each player. The strategy generation unit, negotiation unit, matching unit, data storage unit, data analysis unit, and field setting unit are implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the hearing unit, strategy generation unit, negotiation unit, matching unit, data storage unit, data analysis unit, and field setting unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the hearing unit uses the conversational AI of the smart glasses 214 to hear the requests and conditions of each player. The strategy generation unit, negotiation unit, matching unit, data storage unit, data analysis unit, and field setting unit are implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the hearing unit, strategy generation unit, negotiation unit, matching unit, data storage unit, data analysis unit, and field setting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the hearing unit uses the conversational AI of the headset terminal 314 to hear the requests and conditions of each player. The strategy generation unit, negotiation unit, matching unit, data storage unit, data analysis unit, and field setting unit are implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0166] As shown in Figure 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.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the hearing unit, strategy generation unit, negotiation unit, matching unit, data storage unit, data analysis unit, and field setting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the hearing unit uses the conversational AI of the robot 414 to hear the requests and conditions of each player. The strategy generation unit, negotiation unit, matching unit, data storage unit, data analysis unit, and field setting unit are implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) The interviewing department will gather information on each player's requests and requirements, A strategy generation unit generates a negotiation strategy based on the information gathered by the aforementioned hearing unit, A negotiation unit that performs automated negotiations based on the strategy generated by the strategy generation unit, The system includes a matching unit that matches the negotiating partner found by the aforementioned negotiation unit. A system characterized by the following features. (Note 2) It is equipped with a data storage unit that stores past negotiation data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system includes a data analysis unit that generates negotiation strategies based on the data stored by the aforementioned data storage unit. The system described in Appendix 2, characterized by the features described herein. (Note 4) The matching unit is It includes a setting unit that finds negotiating partners and arranges a meeting place for the representatives involved. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned hearing section is, We will specifically interview each player to understand their requests and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The strategy generation unit, Generate negotiation strategies based on the information gathered through interviews. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned hearing section is, The system estimates the user's emotions and adjusts the timing of interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned hearing section is, Analyze the user's past interview history and select the most suitable interview method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned hearing section is, During the interview, the questions are customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned hearing section is, The system estimates the user's emotions and determines the priority of interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned hearing section is, During the interview, we prioritize asking highly relevant questions, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned hearing section is, During the interview, we analyze the user's social media activity and ask relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The strategy generation unit, It estimates user sentiment and adjusts the way strategies are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The strategy generation unit, When generating a strategy, adjust the level of detail based on past negotiation results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The strategy generation unit, When generating a strategy, different strategic algorithms are applied depending on the attributes of the negotiating partner. The system described in Appendix 1, characterized by the features described herein. (Note 16) The strategy generation unit, The system estimates the user's emotions and adjusts the length of the strategy based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The strategy generation unit, When generating strategies, prioritize them based on when negotiations are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The strategy generation unit, When generating strategies, adjust the order of strategies based on the relevance of the negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned negotiating body said, During negotiations, refer to past negotiation data to improve the accuracy of the negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned negotiating body said, When negotiating, take into account the attributes of the other party. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the order in which negotiation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned negotiating body said, When negotiating, take into account the geographical distribution of the negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned negotiating body said, During negotiations, refer to relevant literature on negotiations to improve the accuracy of the negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is During the matching process, past matching data is referenced to improve matching accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is During the matching process, the attributes of the negotiating partner are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is During the matching process, the geographical distribution of negotiations will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature on negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned data storage unit is The system estimates the user's emotions and selects data to store based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned data storage unit is When saving data, the saving algorithm is optimized by referring to previously saved data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned data storage unit is It estimates the user's emotions and adjusts the frequency of saving based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned data storage unit is When saving data, the saved data is weighted based on when the negotiation history was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned data analysis unit, The system estimates the user's emotions and selects analysis data based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned data analysis unit, During data analysis, we optimize the analysis algorithm by referring to past analysis data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned data analysis unit, It estimates the user's emotions and adjusts the frequency of analysis based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned data analysis unit, During data analysis, the analysis data is weighted based on when the negotiation history was submitted. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned field setting unit is It estimates the user's emotions and adjusts the negotiation setting based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned field setting unit is When setting up the venue, the optimal venue setting method is selected by referring to past negotiation history. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned field setting unit is It estimates the user's emotions and determines the priority of the negotiation based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned field setting unit is When setting up the venue, the optimal venue setting method will be selected considering the geographical location information of the negotiations. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The interviewing department will gather information on each player's requests and requirements, A strategy generation unit generates a negotiation strategy based on the information gathered by the aforementioned hearing unit, A negotiation unit that performs automated negotiations based on the strategy generated by the strategy generation unit, The system includes a matching unit that matches the negotiating partner found by the aforementioned negotiation unit. A system characterized by the following features.

2. It is equipped with a data storage unit that stores past negotiation data. The system according to feature 1.

3. The system includes a data analysis unit that generates negotiation strategies based on the data stored by the aforementioned data storage unit. The system according to feature 2.

4. The matching unit is It includes a setting unit that finds negotiating partners and arranges a meeting place for the representatives involved. The system according to feature 1.

5. The aforementioned hearing section is, We will specifically interview each player to understand their requests and requirements. The system according to feature 1.

6. The strategy generation unit, Generate negotiation strategies based on the information gathered through interviews. The system according to feature 1.

7. The aforementioned hearing section is, The system estimates the user's emotions and adjusts the timing of interviews based on those estimated emotions. The system according to feature 1.

8. The aforementioned hearing section is, Analyze the user's past interview history and select the most suitable interview method. The system according to feature 1.

9. The aforementioned hearing section is, During the interview, the questions are customized based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned hearing section is, The system estimates the user's emotions and determines the priority of interviews based on those estimated emotions. The system according to feature 1.

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