system

Abstract

The system according to this embodiment aims to efficiently gather and negotiate the requests and conditions of each player in the real estate industry. [Solution] The system according to this embodiment comprises a hearing unit, a negotiation unit, and a setting unit. The hearing unit hears the requests and conditions of each player. The negotiation unit conducts negotiations based on the information heard by the hearing unit. The setting unit sets up a meeting place for negotiations between the representatives based on the results of the negotiations conducted 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 persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently hear the demands and conditions of each player in the real estate industry and conduct negotiations.

[0005] The system according to the embodiment aims to efficiently hear the demands and conditions of each player in the real estate industry and conduct negotiations.

Means for Solving the Problems

[0007] The system according to this embodiment can efficiently hear the requests and conditions of each player in the real estate industry and conduct negotiations. [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, and the like. The communication I / F controls communication between a plurality of 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 real estate transaction support 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 the real estate industry (landowners, construction companies, building material manufacturers, developers, real estate sales, financiers, and consumers), and based on those conditions, each player's agent AI automatically negotiates and concludes a deal. The real estate transaction support system is a mechanism in which a conversational AI listens to the requests and conditions of each player, and based on those conditions, each player's agent AI automatically negotiates and concludes a deal. Specifically, it consists of the following steps. First, the real estate transaction support system has a conversational AI listen to the requests and conditions of each player. At this time, the conversational AI understands the requests and conditions of the players in detail based on past negotiation history and market information. For example, if a landowner has a request such as "I want to sell it for more than XX yen per tsubo within 3 years," the real estate transaction support system listens to and records that request. Next, based on the information listened to, the real estate transaction support system has each player's agent AI automatically negotiate. The proxy AI negotiates with other players' proxy AIs on an automated negotiation platform to find suitable negotiating partners. For example, if a construction company has a condition such as "we want to accept orders even at a lower price when resources are available depending on the order situation," the proxy AI will negotiate based on that condition and find a suitable negotiating partner. If a suitable negotiating partner is found as a result of the negotiations, the real estate transaction support system sets up a meeting between the representatives. For example, if the conditions of the landowner and the construction company match, the real estate transaction support system sets up a meeting between the representatives and determines the specific transaction details. In this way, real estate transactions are made more efficient, and transactions that meet the needs and conditions of each player are realized. This mechanism can create an era in which real estate transactions as a whole can be conducted safely and smoothly. By thoroughly reducing the stress and time loss associated with negotiations and revitalizing real estate transactions, it is possible to create a vibrant and optimally changing society. As a result, the real estate transaction support system can automatically negotiate based on the needs and conditions of each player and efficiently conclude deals.

[0029] The real estate transaction support system according to this embodiment comprises a hearing unit, a negotiation unit, and a setting 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 in detail. For example, if a landowner has a request such as "I want to sell it within three years if it is worth more than XX yen per tsubo," the hearing unit hears and records that request. The hearing unit can also grasp the requests and conditions of players in detail based on past negotiation history and market information. For example, the hearing unit refers to past negotiation history and hears the requests and conditions of players in detail. The negotiation unit conducts negotiations based on the information gathered by the hearing unit. For example, the negotiation unit uses an agent AI to negotiate with the agent AIs of other players on an automated negotiation platform. For example, if a construction company has a condition such as "I want to accept orders even if the unit price is low when there is an opportunity to use resources depending on the order situation," the negotiation unit conducts negotiations based on that condition and finds an appropriate negotiating partner. Furthermore, the Negotiation Department can also set up a meeting between the representatives if it finds a suitable negotiating partner. For example, if the landowner and the construction company agree on the terms, the Negotiation Department can set up a meeting between the representatives to determine the specific transaction details. The Setting Department sets up a meeting between the representatives based on the results of negotiations conducted by the Negotiation Department. The Setting Department can set up a meeting between the representatives using, for example, an online conferencing system. For example, if the landowner and the construction company agree on the terms, the Setting Department can set up a meeting between the representatives using an online conferencing system to determine the specific transaction details. The Setting Department can also set up a face-to-face meeting. For example, if the landowner and the construction company agree on the terms, the Setting Department can set up a face-to-face meeting to determine the specific transaction details. As a result, the real estate transaction support system according to this embodiment can automatically negotiate based on the requests and conditions of each player and efficiently conclude deals.

[0030] The Hearing Department gathers information on each player's needs and requirements. For example, the Hearing Department uses conversational AI to gather detailed information on each player's needs and requirements. Specifically, the conversational AI utilizes natural language processing technology to collect information through dialogue with players. For instance, if a landowner has a request such as "I want to sell it for at least X yen per tsubo (approximately 3.3 square meters) within three years," the Hearing Department gathers and records this request. The conversational AI analyzes the player's statements in real time, extracts important information, and stores it in a database. Furthermore, the Hearing Department can also gain a detailed understanding of players' needs and requirements based on past negotiation history and market information. For example, the Hearing Department can refer to past negotiation history to gather detailed information on players' needs and requirements. This allows the Hearing Department to collect more accurate information based on players' past behavioral patterns and market trends. Additionally, the Hearing Department can organize and prioritize the players' needs and requirements. For example, if a landowner has multiple requests, the importance of each request is assessed and prioritized, providing the negotiation department with the necessary foundational information to conduct negotiations efficiently. This allows the hearing department to understand the player's requests and conditions in detail and accurately, providing useful information to the negotiation and setting departments.

[0031] The Negotiation Department conducts negotiations based on information gathered by the Hearing Department. For example, the Negotiation Department uses proxy AI to negotiate with other players' proxy AIs on an automated negotiation platform. Specifically, the proxy AI optimizes the player's requests and conditions based on the information provided by the Hearing Department and formulates a negotiation strategy. For example, if a construction company has a condition such as "we want to accept orders even at a low price when we have available resources depending on the order situation," the proxy AI will negotiate based on that condition and find a suitable negotiating partner. The proxy AI negotiates with other players' proxy AIs in real time to adjust conditions and find common ground. The Negotiation Department can also set up a meeting between representatives if a suitable negotiating partner is found. For example, if the landowner and the construction company agree on the conditions, the Negotiation Department will set up a meeting between representatives to determine the specific transaction details. The Negotiation Department can monitor the progress of negotiations in real time and modify the negotiation strategy as needed. This allows the Negotiation Department to conduct negotiations efficiently and effectively, and to achieve optimal transactions based on the player's requests and conditions. Furthermore, the negotiating department can meticulously record the results of negotiations and use this information for future negotiations. This allows the negotiating department to develop more precise negotiation strategies based on past negotiation data, thereby improving the overall negotiating capabilities of the system.

[0032] The Schedule Department sets up a meeting place for negotiations between the parties involved, based on the results of negotiations conducted by the Negotiation Department. For example, the Schedule Department may use an online conferencing system to set up the meeting place. Specifically, based on the information provided by the Negotiation Department, the Schedule Department adjusts the optimal meeting date, time, and location to set up the meeting place for negotiations between the parties involved. For example, if the landowner and the construction company agree on the terms, the Schedule Department will set up a meeting place for negotiations between the parties involved using an online conferencing system to determine the specific terms of the transaction. The Schedule Department will handle tasks such as setting up the online conferencing system, sending out invitations, and managing the progress of the meeting to support smooth negotiations. The Schedule Department can also set up in-person meetings. For example, if the landowner and the construction company agree on the terms, the Schedule Department will set up an in-person meeting to determine the specific terms of the transaction. When setting up an in-person meeting, the Schedule Department will handle tasks such as booking a meeting room, arranging transportation, and preparing meeting materials to support smooth negotiations. In this way, the Schedule Department can appropriately set up online and in-person meetings and support smooth negotiations between the parties involved. Furthermore, the scheduling unit can monitor the progress of negotiations in real time and reschedule or adjust meetings as needed. This allows the scheduling unit to efficiently and effectively set up negotiation venues and improve the overall negotiation capabilities of the system.

[0033] The history section allows a conversational AI to conduct interviews based on past negotiation history and market information. For example, the history section can refer to past negotiation history to conduct detailed interviews about the player's requests and conditions. For instance, if the history section finds that a landowner has a request such as "I want to sell within three years if the price is over X yen per tsubo," based on past negotiation history, it will conduct the interview and record that request. The history section can also conduct detailed interviews about the player's requests and conditions based on market information. For example, the history section can conduct detailed interviews about the player's requests and conditions based on market prices and competitor information. This makes it possible to conduct detailed interviews based on past negotiation history and market information. Some or all of the above processing in the history section may be performed using AI, or not. For example, the history section can input past negotiation history and market information into a generating AI and have the generating AI execute the interview content.

[0034] The platform unit allows proxy AIs to conduct negotiations on the automated negotiation platform. For example, the platform unit can use proxy AIs to negotiate with proxy AIs of other players on the automated negotiation platform. For example, if a construction company has a condition such as "we want to accept orders even if the unit price is low when we have available resources depending on the order situation," the platform unit will negotiate based on that condition and find a suitable negotiating partner. The platform unit can also set up a meeting between the representatives once a suitable negotiating partner is found. For example, if the conditions of the landowner and the construction company match, the platform unit will set up a meeting between the representatives and determine the specific transaction details. This allows for efficient negotiation on the automated negotiation platform. Some or all of the above processes in the platform unit may be performed using AI, or not using AI. For example, the platform unit can input the negotiation content when the proxy AI conducts negotiations on the automated negotiation platform into a generation AI and have the generation AI execute the negotiation process.

[0035] The strategy generation unit can generate negotiation strategies based on the requests and conditions of each player. For example, if a landowner has a request such as "I want to sell my land for more than XX yen per tsubo within 3 years," the strategy generation unit will generate a negotiation strategy based on that request. Similarly, if a construction company has a condition such as "I want to accept orders even at a low price when there are resource vacancies depending on the order situation," the strategy generation unit can also generate a negotiation strategy based on that condition. In this way, the optimal negotiation strategy can be generated based on the requests and conditions of each player. 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 the requests and conditions of each player into a generation AI and have the generation AI execute the generation of negotiation strategies.

[0036] The recording unit can record the results of negotiations. For example, the recording unit can record the results of negotiations in detail. For example, the recording unit can record the results of negotiations between landowners and construction companies for later reference and analysis. The recording unit can also record the negotiation process. For example, the recording unit can record the progress and content of negotiations for later reference and analysis. This makes it possible to refer to and analyze the results of negotiations later by recording them. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the negotiation results into a generating AI and have the generating AI execute the recorded content.

[0037] The interviewing unit can analyze the user's past requests and conditions and select the optimal interviewing method. For example, the interviewing unit can customize the interview questions based on requests and conditions that the user has frequently submitted in the past. For example, the interviewing unit can analyze patterns in the user's past requests and conditions and propose the optimal interviewing method. The interviewing unit can also refer to the user's past requests and conditions and highlight important points during the interview. In this way, the optimal interviewing method can be selected by analyzing the user's past requests and conditions. Some or all of the above processes 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 past requests and conditions data into a generating AI and have the generating AI select the optimal interviewing method.

[0038] 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 may prioritize questions related to the user's current projects. For example, the interviewing unit may add relevant questions based on the user's areas of interest. The interviewing unit can also select appropriate questions according to the progress of the user's current projects. This allows for the collection of more relevant information by customizing the questions based on the user's current projects and areas of interest. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not. For example, the interviewing unit can input the user's current project and area of ​​interest data into a generating AI and have the generating AI customize the questions.

[0039] The interviewing unit can prioritize asking highly relevant questions during the interview, taking into account the user's geographical location. For example, if the user is in a specific region, the interviewing unit will prioritize questions related to that region. For instance, based on the user's geographical location, the interviewing unit will ask questions about region-specific needs and conditions. Furthermore, if the user is on the move, the interviewing unit can ask questions related to their current location in real time. This allows for more relevant questions to be asked by 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 data into a generating AI and have the generating AI prioritize the questions.

[0040] The interviewing unit can analyze the user's social media activity during the interview and ask relevant questions. For example, the interviewing unit can analyze the user's social media posts and ask questions related to their recent interests. For example, the interviewing unit can ask questions related to current trends based on the user's social media activity. The interviewing unit can also refer to the activities of the user's social media followers and friends and ask relevant questions. This allows for more relevant questions to be asked by analyzing the user's social media activity. 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 generate the questions.

[0041] The negotiating department can adjust the level of detail in negotiations based on the importance of the conditions. For example, the negotiating department can conduct detailed negotiations on important conditions, confirming every detail. For example, the negotiating department can conduct concise negotiations on less important conditions, proceeding quickly. The negotiating department can also adjust the level of detail in negotiations in stages according to the importance of the conditions. This allows for efficient negotiations by adjusting the level of detail in negotiations based on the importance of the conditions. Some or all of the above processes in the negotiating department may be performed using AI, for example, or not. For example, the negotiating department can input condition importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in negotiations.

[0042] The negotiation department can apply different negotiation algorithms depending on the category of conditions during negotiations. For example, in the case of price negotiations, the negotiation department applies a negotiation algorithm specialized for price. For example, in the case of delivery date negotiations, the negotiation department applies a negotiation algorithm specialized for delivery date. Furthermore, in the case of quality negotiations, the negotiation department can also apply a negotiation algorithm specialized for quality. This enables efficient negotiation by applying the appropriate negotiation algorithm according to the category of conditions. Some or all of the above-described processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input condition category data into a generating AI and have the generating AI execute the application of negotiation algorithms.

[0043] The negotiating department can determine negotiation priorities based on the timing of the submission of terms. For example, the negotiating department may prioritize negotiations on terms submitted early, or postpone negotiations on terms submitted later. The negotiating department can also adjust negotiation priorities in stages according to the submission timing. This allows for efficient negotiations by determining negotiation priorities based on the timing of the submission of terms. Some or all of the above processes in the negotiating department may be performed using AI, or not. For example, the negotiating department can input data on the timing of the submission of terms into a generating AI and have the generating AI determine the negotiation priorities.

[0044] The negotiating department can adjust the order of negotiations based on the relevance of the conditions. For example, the negotiating department may prioritize negotiations on highly relevant conditions, or postpone negotiations on less relevant conditions. The negotiating department can also adjust the order of negotiations in stages according to the relevance of the conditions. This allows for efficient negotiations by adjusting the order of negotiations based on the relevance of the conditions. Some or all of the above processes in the negotiating department may be performed using AI, or not. For example, the negotiating department can input condition relevance data into a generating AI and have the generating AI perform the adjustment of the negotiation order.

[0045] The configuration unit can select the optimal configuration method by referring to past negotiation history during configuration. For example, the configuration unit can set the optimal negotiation setting based on past negotiation history. For example, the configuration unit can refer to the configuration methods of successful negotiations from past negotiation history. The configuration unit can also analyze past negotiation history and select the optimal configuration method. In this way, the optimal negotiation setting can be set by referring to past negotiation history. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without using AI. For example, the configuration unit can input past negotiation history data into a generating AI and have the generating AI select the optimal configuration method.

[0046] The configuration unit can apply different configuration methods depending on the negotiation category during configuration. For example, in the case of price negotiations, the configuration unit applies a configuration method specialized for price. For example, in the case of delivery date negotiations, the configuration unit applies a configuration method specialized for delivery date. Furthermore, in the case of quality negotiations, the configuration unit can also apply a configuration method specialized for quality. This allows for the creation of an efficient negotiation environment by applying the appropriate configuration method according to the negotiation category. Some or all of the above-described processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input negotiation category data into a generating AI and have the generating AI execute the application of configuration methods.

[0047] The configuration unit can determine the priority of configurations based on the timing of negotiation submissions. For example, the configuration unit can prioritize configurations for negotiations submitted early, or postpone configurations for negotiations submitted later. The configuration unit can also adjust the priority of configurations in stages according to the submission timing. This allows for the creation of an efficient negotiation environment by determining the priority of configurations based on the timing of negotiation submissions. Some or all of the above processing in the configuration unit may be performed using AI, or not. For example, the configuration unit can input negotiation submission timing data into a generating AI and have the generating AI determine the priority of configurations.

[0048] The configuration unit can adjust the order of configurations based on the relevance of the negotiations during configuration. For example, the configuration unit can prioritize configurations for highly relevant negotiations. For example, it can postpone configurations for less relevant negotiations. The configuration unit can also adjust the order of configurations in stages according to the relevance of the negotiations. This allows for the creation of an efficient negotiation environment by adjusting the order of configurations based on the relevance of the negotiations. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input negotiation relevance data into a generating AI and have the generating AI perform the adjustment of the configuration order.

[0049] The history unit can select the optimal recording method by referring to past history data when recording history. For example, the history unit selects the optimal recording method based on past history data. For example, the history unit refers to successful recording methods from past history data. The history unit can also analyze past history data and select the optimal recording method. In this way, the optimal recording method can be selected by referring to past history data. Some or all of the above processing in the history unit may be performed using AI, for example, or without using AI. For example, the history unit can input past history data into a generating AI and have the generating AI perform the selection of the optimal recording method.

[0050] The history unit can apply different recording methods depending on the category of the history when recording history. For example, the history unit can apply a price-specific recording method to the history of price negotiations. For example, the history unit can apply a delivery date-specific recording method to the history of delivery date negotiations. Furthermore, the history unit can also apply a quality-specific recording method to the history of quality negotiations. This enables efficient recording by applying the appropriate recording method according to the category of the history. Some or all of the above processing in the history unit may be performed using AI, for example, or without AI. For example, the history unit can input history category data into a generating AI and have the generating AI execute the application of the recording method.

[0051] The history section can determine the priority of records based on the submission date of each history entry. For example, the history section prioritizes recording history entries submitted early. For example, it postpones recording history entries submitted later. The history section can also adjust the priority of records in stages according to the submission date. This enables efficient record-keeping by determining the priority of records based on the submission date. Some or all of the above processing in the history section may be performed using AI, for example, or not using AI. For example, the history section can input history submission date data into a generating AI and have the generating AI determine the priority of records.

[0052] The history unit can adjust the order of records based on the relevance of the history entries when recording history. For example, the history unit prioritizes recording highly relevant history entries. For example, it postpones recording less relevant history entries. The history unit can also adjust the order of records in stages according to the relevance of the history entries. This allows for efficient recording by adjusting the order of records based on the relevance of the history entries. Some or all of the above processing in the history unit may be performed using AI, for example, or without AI. For example, the history unit can input history relevance data into a generating AI and have the generating AI perform the adjustment of the recording order.

[0053] The platform unit can select the optimal display method by referring to the user's past operation history when displaying the platform. For example, the platform unit selects the optimal display method based on the user's past operation history. For example, the platform unit refers to successful display methods from the user's past operation history. The platform unit can also analyze the user's past operation history and select the optimal display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the platform unit may be performed using AI, for example, or without using AI. For example, the platform unit can input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0054] The platform unit can customize the displayed content based on the user's current projects and areas of interest when displaying the platform. For example, the platform unit may prioritize displaying information related to the user's current ongoing projects. For example, the platform unit may add relevant information based on the user's areas of interest. The platform unit can also display appropriate information according to the progress of the user's current projects. By customizing the displayed content based on the user's current projects and areas of interest, it is possible to provide more relevant information. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit may input the user's current project and area of ​​interest data into a generating AI and have the generating AI perform the customization of the displayed content.

[0055] The platform unit can prioritize the display of highly relevant information when displaying the platform, taking into account the user's geographical location. For example, if the user is in a specific region, the platform unit will prioritize the display of information related to that region. For example, the platform unit will display region-specific information based on the user's geographical location. Furthermore, if the user is on the move, the platform unit can display information related to their current location in real time. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input the user's geographical location data into a generating AI and have the generating AI prioritize the display content.

[0056] The platform unit can analyze the user's social media activity and display relevant information when the platform is displayed. For example, the platform unit can analyze the user's social media posts and display information related to recent interests. For example, the platform unit can display information related to current trends based on the user's social media activity. The platform unit can also display relevant information by referring to the activities of the user's social media followers and friends. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input the user's social media activity data into a generating AI and have the generating AI generate the display content.

[0057] The strategy generation unit can select the optimal generation method by referring to past strategy data when generating a strategy. For example, the strategy generation unit can select the optimal generation method based on past strategy data. For example, the strategy generation unit can refer to the generation methods of successful strategies from past strategy data. The strategy generation unit can also analyze past strategy data and select the optimal generation method. In this way, the optimal generation method can be selected by referring to past strategy data. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without using AI. For example, the strategy generation unit can input past strategy data into a generation AI and have the generation AI perform the selection of the optimal generation method.

[0058] The strategy generation unit can apply different generation methods depending on the strategy category when generating strategies. For example, in the case of a pricing strategy, the strategy generation unit applies a generation method specialized for pricing. For example, in the case of a delivery strategy, the strategy generation unit applies a generation method specialized for delivery time. Furthermore, in the case of a quality strategy, the strategy generation unit can also apply a generation method specialized for quality. This enables efficient strategy generation by applying the appropriate generation method according to the strategy category. 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 strategy category data into a generation AI and have the generation AI execute the application of generation methods.

[0059] The strategy generation unit can determine the generation priority based on the submission timing of strategies during strategy generation. For example, the strategy generation unit prioritizes the generation of strategies submitted early. For example, it postpones the generation of strategies submitted later. The strategy generation unit can also adjust the generation priority in stages according to the submission timing. This enables efficient strategy generation by determining the generation priority based on the submission timing of strategies. 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 strategy submission timing data into a generation AI and have the generation AI determine the generation priority.

[0060] The strategy generation unit can adjust the generation order based on the relevance of the strategies during strategy generation. For example, the strategy generation unit can prioritize the generation of highly relevant strategies. For example, it can postpone the generation of less relevant strategies. The strategy generation unit can also adjust the generation order in stages according to the relevance of the strategies. This allows for efficient strategy generation by adjusting the generation order based on the relevance of the strategies. 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 strategy relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0061] The recording unit can select the optimal recording method by referring to past recording data during recording. For example, the recording unit can select the optimal recording method based on past recording data. For example, the recording unit can refer to successful recording methods from past recording data. The recording unit can also analyze past recording data and select the optimal recording method. In this way, the optimal recording method can be selected by referring to past recording data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI. For example, the recording unit can input past recording data into a generating AI and have the generating AI perform the selection of the optimal recording method.

[0062] The recording unit can apply different recording methods depending on the category of the record. For example, for price negotiations, the recording unit can apply a recording method specialized for price. For example, for delivery date negotiations, the recording unit can apply a recording method specialized for delivery date. Furthermore, for quality negotiations, the recording unit can also apply a recording method specialized for quality. This enables efficient recording by applying the appropriate recording method according to the category of the record. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the category data of the record into a generating AI and have the generating AI execute the application of the recording method.

[0063] The recording unit can determine the priority of records based on the submission date at the time of recording. For example, the recording unit will prioritize recording records that are submitted early. For example, the recording unit will postpone recording records that are submitted later. The recording unit can also adjust the priority of records in stages according to the submission date. This enables efficient recording by determining the priority of records based on the submission date. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the submission date data of records into a generating AI and have the generating AI perform the determination of the record priority.

[0064] The recording unit can adjust the order of records based on their relevance during recording. For example, the recording unit may prioritize recording highly relevant records, or postpone recording less relevant records. The recording unit can also adjust the order of records in stages according to their relevance. This allows for efficient recording by adjusting the order of records based on their relevance. Some or all of the above processing in the recording unit may be performed using AI, or not. For example, the recording unit can input relevance data of records into a generating AI and have the generating AI perform the adjustment of the order of records.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The history section allows a conversational AI to conduct interviews based on past negotiation history and market information. For example, it can refer to past negotiation history to gather detailed information about the player's requests and conditions. It can also gather detailed information about the player's requests and conditions based on market information. Furthermore, past negotiation history and market information can be input into a generating AI, which can then execute the interview content. This enables detailed interviews based on past negotiation history and market information.

[0067] The platform allows proxy AIs to conduct negotiations on an automated negotiation platform. For example, a proxy AI can negotiate with another player's proxy AI on the automated negotiation platform. Furthermore, if a suitable negotiating partner is found, a meeting between the representatives can be arranged. Additionally, the proxy AI can input the negotiation details into a generating AI, allowing the generating AI to manage the negotiation process. This enables efficient negotiation on the automated negotiation platform.

[0068] The strategy generation unit can generate negotiation strategies based on each player's requests and conditions. For example, it can generate a negotiation strategy based on a landowner's request such as "I want to sell my land for at least X yen per tsubo (approximately 3.3 square meters) within three years." It can also generate a negotiation strategy based on a construction company's condition such as "I want to accept orders even at a low price when I have available resources depending on the order situation." Furthermore, the requests and conditions of each player can be input into the generation AI, which then generates the negotiation strategy. This allows for the generation of the optimal negotiation strategy based on each player's requests and conditions.

[0069] The recording unit can record the results of negotiations. For example, it can record the results of negotiations between landowners and construction companies for later reference and analysis. It can also record the negotiation process. Furthermore, the negotiation results can be input into a generating AI, and the AI ​​can execute the recorded content. This makes it possible to record the negotiation results for later reference and analysis.

[0070] The interviewing department can analyze users' past requests and conditions to select the most suitable interviewing method. For example, it can customize interview questions based on requests and conditions that users have frequently submitted in the past. It can also analyze patterns in users' past requests and conditions to propose the most suitable interviewing method. Furthermore, it can refer to users' past requests and conditions to highlight important points during the interview. In this way, by analyzing users' past requests and conditions, the most suitable interviewing method can be selected.

[0071] The interview function can customize the questions asked during the interview based on the user's current projects and areas of interest. For example, it can prioritize questions related to the user's current projects. It can also add relevant questions based on the user's areas of interest. Furthermore, it can select appropriate questions according to the progress of the user's current projects. By customizing the questions based on the user's current projects and areas of interest, more relevant information can be collected.

[0072] The interview function can prioritize asking highly relevant questions during interviews, taking into account the user's geographical location. For example, if a user is in a specific region, it can prioritize questions related to that region. It can also ask questions about region-specific needs and conditions based on the user's geographical location. Furthermore, if a user is on the move, it can ask questions related to their current location in real time. This allows for more relevant questions to be asked by considering the user's geographical location.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The interviewing department interviews each player to understand their needs and requirements. For example, they use conversational AI to interview and record each player's needs and requirements in detail. Furthermore, they can also gain a detailed understanding of players' needs and requirements based on past negotiation history and market information. Step 2: The Negotiation Department conducts negotiations based on the information gathered by the Hearing Department. For example, they use proxy AI to negotiate with other players' proxy AI on an automated negotiation platform to find a suitable negotiating partner. If a suitable negotiating partner is found, a meeting between the representatives can also be arranged. Step 3: The Setup Department sets up a meeting place for negotiations between the parties involved, based on the results of negotiations conducted by the Negotiation Department. For example, they set up an online meeting system or an in-person meeting to determine the specific terms of the transaction.

[0075] (Example of form 2) The real estate transaction support 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 the real estate industry (landowners, construction companies, building material manufacturers, developers, real estate sales, financiers, and consumers), and based on those conditions, each player's agent AI automatically negotiates and concludes a deal. The real estate transaction support system is a mechanism in which a conversational AI listens to the requests and conditions of each player, and based on those conditions, each player's agent AI automatically negotiates and concludes a deal. Specifically, it consists of the following steps. First, the real estate transaction support system has a conversational AI listen to the requests and conditions of each player. At this time, the conversational AI understands the requests and conditions of the players in detail based on past negotiation history and market information. For example, if a landowner has a request such as "I want to sell it for more than XX yen per tsubo within 3 years," the real estate transaction support system listens to and records that request. Next, based on the information listened to, the real estate transaction support system has each player's agent AI automatically negotiate. The proxy AI negotiates with other players' proxy AIs on an automated negotiation platform to find suitable negotiating partners. For example, if a construction company has a condition such as "we want to accept orders even at a lower price when resources are available depending on the order situation," the proxy AI will negotiate based on that condition and find a suitable negotiating partner. If a suitable negotiating partner is found as a result of the negotiations, the real estate transaction support system sets up a meeting between the representatives. For example, if the conditions of the landowner and the construction company match, the real estate transaction support system sets up a meeting between the representatives and determines the specific transaction details. In this way, real estate transactions are made more efficient, and transactions that meet the needs and conditions of each player are realized. This mechanism can create an era in which real estate transactions as a whole can be conducted safely and smoothly. By thoroughly reducing the stress and time loss associated with negotiations and revitalizing real estate transactions, it is possible to create a vibrant and optimally changing society. As a result, the real estate transaction support system can automatically negotiate based on the needs and conditions of each player and efficiently conclude deals.

[0076] The real estate transaction support system according to this embodiment comprises a hearing unit, a negotiation unit, and a setting 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 in detail. For example, if a landowner has a request such as "I want to sell it within three years if it is worth more than XX yen per tsubo," the hearing unit hears and records that request. The hearing unit can also grasp the requests and conditions of players in detail based on past negotiation history and market information. For example, the hearing unit refers to past negotiation history and hears the requests and conditions of players in detail. The negotiation unit conducts negotiations based on the information gathered by the hearing unit. For example, the negotiation unit uses an agent AI to negotiate with the agent AIs of other players on an automated negotiation platform. For example, if a construction company has a condition such as "I want to accept orders even if the unit price is low when there is an opportunity to use resources depending on the order situation," the negotiation unit conducts negotiations based on that condition and finds an appropriate negotiating partner. Furthermore, the Negotiation Department can also set up a meeting between the representatives if it finds a suitable negotiating partner. For example, if the landowner and the construction company agree on the terms, the Negotiation Department can set up a meeting between the representatives to determine the specific transaction details. The Setting Department sets up a meeting between the representatives based on the results of negotiations conducted by the Negotiation Department. The Setting Department can set up a meeting between the representatives using, for example, an online conferencing system. For example, if the landowner and the construction company agree on the terms, the Setting Department can set up a meeting between the representatives using an online conferencing system to determine the specific transaction details. The Setting Department can also set up a face-to-face meeting. For example, if the landowner and the construction company agree on the terms, the Setting Department can set up a face-to-face meeting to determine the specific transaction details. As a result, the real estate transaction support system according to this embodiment can automatically negotiate based on the requests and conditions of each player and efficiently conclude deals.

[0077] The Hearing Department gathers information on each player's needs and requirements. For example, the Hearing Department uses conversational AI to gather detailed information on each player's needs and requirements. Specifically, the conversational AI utilizes natural language processing technology to collect information through dialogue with players. For instance, if a landowner has a request such as "I want to sell it for at least X yen per tsubo (approximately 3.3 square meters) within three years," the Hearing Department gathers and records this request. The conversational AI analyzes the player's statements in real time, extracts important information, and stores it in a database. Furthermore, the Hearing Department can also gain a detailed understanding of players' needs and requirements based on past negotiation history and market information. For example, the Hearing Department can refer to past negotiation history to gather detailed information on players' needs and requirements. This allows the Hearing Department to collect more accurate information based on players' past behavioral patterns and market trends. Additionally, the Hearing Department can organize and prioritize the players' needs and requirements. For example, if a landowner has multiple requests, the importance of each request is assessed and prioritized, providing the negotiation department with the necessary foundational information to conduct negotiations efficiently. This allows the hearing department to understand the player's requests and conditions in detail and accurately, providing useful information to the negotiation and setting departments.

[0078] The Negotiation Department conducts negotiations based on information gathered by the Hearing Department. For example, the Negotiation Department uses proxy AI to negotiate with other players' proxy AIs on an automated negotiation platform. Specifically, the proxy AI optimizes the player's requests and conditions based on the information provided by the Hearing Department and formulates a negotiation strategy. For example, if a construction company has a condition such as "we want to accept orders even at a low price when we have available resources depending on the order situation," the proxy AI will negotiate based on that condition and find a suitable negotiating partner. The proxy AI negotiates with other players' proxy AIs in real time to adjust conditions and find common ground. The Negotiation Department can also set up a meeting between representatives if a suitable negotiating partner is found. For example, if the landowner and the construction company agree on the conditions, the Negotiation Department will set up a meeting between representatives to determine the specific transaction details. The Negotiation Department can monitor the progress of negotiations in real time and modify the negotiation strategy as needed. This allows the Negotiation Department to conduct negotiations efficiently and effectively, and to achieve optimal transactions based on the player's requests and conditions. Furthermore, the negotiating department can meticulously record the results of negotiations and use this information for future negotiations. This allows the negotiating department to develop more precise negotiation strategies based on past negotiation data, thereby improving the overall negotiating capabilities of the system.

[0079] The Schedule Department sets up a meeting place for negotiations between the parties involved, based on the results of negotiations conducted by the Negotiation Department. For example, the Schedule Department may use an online conferencing system to set up the meeting place. Specifically, based on the information provided by the Negotiation Department, the Schedule Department adjusts the optimal meeting date, time, and location to set up the meeting place for negotiations between the parties involved. For example, if the landowner and the construction company agree on the terms, the Schedule Department will set up a meeting place for negotiations between the parties involved using an online conferencing system to determine the specific terms of the transaction. The Schedule Department will handle tasks such as setting up the online conferencing system, sending out invitations, and managing the progress of the meeting to support smooth negotiations. The Schedule Department can also set up in-person meetings. For example, if the landowner and the construction company agree on the terms, the Schedule Department will set up an in-person meeting to determine the specific terms of the transaction. When setting up an in-person meeting, the Schedule Department will handle tasks such as booking a meeting room, arranging transportation, and preparing meeting materials to support smooth negotiations. In this way, the Schedule Department can appropriately set up online and in-person meetings and support smooth negotiations between the parties involved. Furthermore, the scheduling unit can monitor the progress of negotiations in real time and reschedule or adjust meetings as needed. This allows the scheduling unit to efficiently and effectively set up negotiation venues and improve the overall negotiation capabilities of the system.

[0080] The history section allows a conversational AI to conduct interviews based on past negotiation history and market information. For example, the history section can refer to past negotiation history to conduct detailed interviews about the player's requests and conditions. For instance, if the history section finds that a landowner has a request such as "I want to sell within three years if the price is over X yen per tsubo," based on past negotiation history, it will conduct the interview and record that request. The history section can also conduct detailed interviews about the player's requests and conditions based on market information. For example, the history section can conduct detailed interviews about the player's requests and conditions based on market prices and competitor information. This makes it possible to conduct detailed interviews based on past negotiation history and market information. Some or all of the above processing in the history section may be performed using AI, or not. For example, the history section can input past negotiation history and market information into a generating AI and have the generating AI execute the interview content.

[0081] The platform unit allows proxy AIs to conduct negotiations on the automated negotiation platform. For example, the platform unit can use proxy AIs to negotiate with proxy AIs of other players on the automated negotiation platform. For example, if a construction company has a condition such as "we want to accept orders even if the unit price is low when we have available resources depending on the order situation," the platform unit will negotiate based on that condition and find a suitable negotiating partner. The platform unit can also set up a meeting between the representatives once a suitable negotiating partner is found. For example, if the conditions of the landowner and the construction company match, the platform unit will set up a meeting between the representatives and determine the specific transaction details. This allows for efficient negotiation on the automated negotiation platform. Some or all of the above processes in the platform unit may be performed using AI, or not using AI. For example, the platform unit can input the negotiation content when the proxy AI conducts negotiations on the automated negotiation platform into a generation AI and have the generation AI execute the negotiation process.

[0082] The strategy generation unit can generate negotiation strategies based on the requests and conditions of each player. For example, if a landowner has a request such as "I want to sell my land for more than XX yen per tsubo within 3 years," the strategy generation unit will generate a negotiation strategy based on that request. Similarly, if a construction company has a condition such as "I want to accept orders even at a low price when there are resource vacancies depending on the order situation," the strategy generation unit can also generate a negotiation strategy based on that condition. In this way, the optimal negotiation strategy can be generated based on the requests and conditions of each player. 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 the requests and conditions of each player into a generation AI and have the generation AI execute the generation of negotiation strategies.

[0083] The recording unit can record the results of negotiations. For example, the recording unit can record the results of negotiations in detail. For example, the recording unit can record the results of negotiations between landowners and construction companies for later reference and analysis. The recording unit can also record the negotiation process. For example, the recording unit can record the progress and content of negotiations for later reference and analysis. This makes it possible to refer to and analyze the results of negotiations later by recording them. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the negotiation results into a generating AI and have the generating AI execute the recorded content.

[0084] 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 stressed, the interview unit can delay the interview to allow the user to relax. For example, if the user is relaxed, the interview unit can immediately begin the interview to collect detailed information. Conversely, if the user is in a hurry, the interview unit can advance the interview to quickly collect requests and conditions. By adjusting the timing of the interview according to the user's emotions, a more appropriate interview becomes possible. 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 interview unit may be performed using AI or not. For example, the interview unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the interview.

[0085] The interviewing unit can analyze the user's past requests and conditions and select the optimal interviewing method. For example, the interviewing unit can customize the interview questions based on requests and conditions that the user has frequently submitted in the past. For example, the interviewing unit can analyze patterns in the user's past requests and conditions and propose the optimal interviewing method. The interviewing unit can also refer to the user's past requests and conditions and highlight important points during the interview. In this way, the optimal interviewing method can be selected by analyzing the user's past requests and conditions. Some or all of the above processes 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 past requests and conditions data into a generating AI and have the generating AI select the optimal interviewing method.

[0086] 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 may prioritize questions related to the user's current projects. For example, the interviewing unit may add relevant questions based on the user's areas of interest. The interviewing unit can also select appropriate questions according to the progress of the user's current projects. This allows for the collection of more relevant information by customizing the questions based on the user's current projects and areas of interest. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not. For example, the interviewing unit can input the user's current project and area of ​​interest data into a generating AI and have the generating AI customize the questions.

[0087] The interviewing 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 stressed, the interviewing unit will set a low priority and prioritize other tasks. For example, if the user is relaxed, the interviewing unit will set a high priority and quickly collect information. Also, if the user is in a hurry, the interviewing unit can set the interview as the highest priority and respond quickly. This allows information to be collected at a more appropriate time 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 interviewing unit may be performed using AI, or not using AI. For example, the interviewing unit can input user emotion data into the generative AI and have the generative AI determine the priority of the interview.

[0088] The interviewing unit can prioritize asking highly relevant questions during the interview, taking into account the user's geographical location. For example, if the user is in a specific region, the interviewing unit will prioritize questions related to that region. For instance, based on the user's geographical location, the interviewing unit will ask questions about region-specific needs and conditions. Furthermore, if the user is on the move, the interviewing unit can ask questions related to their current location in real time. This allows for more relevant questions to be asked by 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 data into a generating AI and have the generating AI prioritize the questions.

[0089] The interviewing unit can analyze the user's social media activity during the interview and ask relevant questions. For example, the interviewing unit can analyze the user's social media posts and ask questions related to their recent interests. For example, the interviewing unit can ask questions related to current trends based on the user's social media activity. The interviewing unit can also refer to the activities of the user's social media followers and friends and ask relevant questions. This allows for more relevant questions to be asked by analyzing the user's social media activity. 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 generate the questions.

[0090] The negotiation unit can estimate the user's emotions and adjust the negotiation's expression based on those emotions. For example, if the user is stressed, the negotiation unit will use a calm expression. For example, if the user is relaxed, the negotiation unit will use a more detailed and informative expression. If the user is in a hurry, the negotiation unit can also use a concise and rapid expression. By adjusting the negotiation's expression 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 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 negotiation's expression.

[0091] The negotiating department can adjust the level of detail in negotiations based on the importance of the conditions. For example, the negotiating department can conduct detailed negotiations on important conditions, confirming every detail. For example, the negotiating department can conduct concise negotiations on less important conditions, proceeding quickly. The negotiating department can also adjust the level of detail in negotiations in stages according to the importance of the conditions. This allows for efficient negotiations by adjusting the level of detail in negotiations based on the importance of the conditions. Some or all of the above processes in the negotiating department may be performed using AI, for example, or not. For example, the negotiating department can input condition importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in negotiations.

[0092] The negotiation department can apply different negotiation algorithms depending on the category of conditions during negotiations. For example, in the case of price negotiations, the negotiation department applies a negotiation algorithm specialized for price. For example, in the case of delivery date negotiations, the negotiation department applies a negotiation algorithm specialized for delivery date. Furthermore, in the case of quality negotiations, the negotiation department can also apply a negotiation algorithm specialized for quality. This enables efficient negotiation by applying the appropriate negotiation algorithm according to the category of conditions. Some or all of the above-described processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input condition category data into a generating AI and have the generating AI execute the application of negotiation algorithms.

[0093] The negotiation unit can estimate the user's emotions and adjust the length of the negotiation based on the estimated emotions. For example, if the user is stressed, the negotiation unit can shorten the negotiation length and proceed quickly. For example, if the user is relaxed, the negotiation unit can lengthen the negotiation length to gather detailed information. The negotiation unit can also set the negotiation length to the minimum possible and respond quickly if the user is in a hurry. This allows for more effective negotiation by adjusting the negotiation length 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 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 negotiation unit may be performed using AI or not using AI. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the negotiation.

[0094] The negotiating department can determine negotiation priorities based on the timing of the submission of terms. For example, the negotiating department may prioritize negotiations on terms submitted early, or postpone negotiations on terms submitted later. The negotiating department can also adjust negotiation priorities in stages according to the submission timing. This allows for efficient negotiations by determining negotiation priorities based on the timing of the submission of terms. Some or all of the above processes in the negotiating department may be performed using AI, or not. For example, the negotiating department can input data on the timing of the submission of terms into a generating AI and have the generating AI determine the negotiation priorities.

[0095] The negotiating department can adjust the order of negotiations based on the relevance of the conditions. For example, the negotiating department may prioritize negotiations on highly relevant conditions, or postpone negotiations on less relevant conditions. The negotiating department can also adjust the order of negotiations in stages according to the relevance of the conditions. This allows for efficient negotiations by adjusting the order of negotiations based on the relevance of the conditions. Some or all of the above processes in the negotiating department may be performed using AI, or not. For example, the negotiating department can input condition relevance data into a generating AI and have the generating AI perform the adjustment of the negotiation order.

[0096] The settings unit can estimate the user's emotions and adjust the negotiation setting based on the estimated emotions. For example, if the user is stressed, the settings unit will set up the negotiation in a relaxing environment. For example, if the user is relaxed, the settings unit will set up the negotiation immediately. Also, if the user is in a hurry, the settings unit can quickly set up the negotiation. By adjusting the negotiation setting 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 processing in the settings unit may be performed using AI or not using AI. For example, the settings unit can input user emotion data into the generative AI and have the generative AI adjust the negotiation setting.

[0097] The configuration unit can select the optimal configuration method by referring to past negotiation history during configuration. For example, the configuration unit can set the optimal negotiation setting based on past negotiation history. For example, the configuration unit can refer to the configuration methods of successful negotiations from past negotiation history. The configuration unit can also analyze past negotiation history and select the optimal configuration method. In this way, the optimal negotiation setting can be set by referring to past negotiation history. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without using AI. For example, the configuration unit can input past negotiation history data into a generating AI and have the generating AI select the optimal configuration method.

[0098] The configuration unit can apply different configuration methods depending on the negotiation category during configuration. For example, in the case of price negotiations, the configuration unit applies a configuration method specialized for price. For example, in the case of delivery date negotiations, the configuration unit applies a configuration method specialized for delivery date. Furthermore, in the case of quality negotiations, the configuration unit can also apply a configuration method specialized for quality. This allows for the creation of an efficient negotiation environment by applying the appropriate configuration method according to the negotiation category. Some or all of the above-described processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input negotiation category data into a generating AI and have the generating AI execute the application of configuration methods.

[0099] The settings unit can estimate the user's emotions and determine the priority of negotiation sessions based on the estimated emotions. For example, if the user is stressed, the settings unit will set the priority of the negotiation session low. For example, if the user is relaxed, the settings unit will set the priority of the negotiation session high. The settings unit can also set the priority of the negotiation session to the highest priority if the user is in a hurry. This allows for more effective negotiations by determining the priority of negotiation sessions 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 settings unit may be performed using AI, or not using AI. For example, the settings unit can input user emotion data into a generative AI and have the generative AI determine the priority of negotiation sessions.

[0100] The configuration unit can determine the priority of configurations based on the timing of negotiation submissions. For example, the configuration unit can prioritize configurations for negotiations submitted early, or postpone configurations for negotiations submitted later. The configuration unit can also adjust the priority of configurations in stages according to the submission timing. This allows for the creation of an efficient negotiation environment by determining the priority of configurations based on the timing of negotiation submissions. Some or all of the above processing in the configuration unit may be performed using AI, or not. For example, the configuration unit can input negotiation submission timing data into a generating AI and have the generating AI determine the priority of configurations.

[0101] The configuration unit can adjust the order of configurations based on the relevance of the negotiations during configuration. For example, the configuration unit can prioritize configurations for highly relevant negotiations. For example, it can postpone configurations for less relevant negotiations. The configuration unit can also adjust the order of configurations in stages according to the relevance of the negotiations. This allows for the creation of an efficient negotiation environment by adjusting the order of configurations based on the relevance of the negotiations. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input negotiation relevance data into a generating AI and have the generating AI perform the adjustment of the configuration order.

[0102] The history unit can estimate the user's emotions and adjust the history recording method based on the estimated emotions. For example, if the user is stressed, the history unit may adopt a concise recording method. For example, if the user is relaxed, the history unit may adopt a detailed recording method. The history unit may also adopt a rapid recording method if the user is in a hurry. By adjusting the history recording method according to the user's emotions, more appropriate recording 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 history unit may be performed using AI or not using AI. For example, the history unit can input user emotion data into a generative AI and have the generative AI adjust the recording method.

[0103] The history unit can select the optimal recording method by referring to past history data when recording history. For example, the history unit selects the optimal recording method based on past history data. For example, the history unit refers to successful recording methods from past history data. The history unit can also analyze past history data and select the optimal recording method. In this way, the optimal recording method can be selected by referring to past history data. Some or all of the above processing in the history unit may be performed using AI, for example, or without using AI. For example, the history unit can input past history data into a generating AI and have the generating AI perform the selection of the optimal recording method.

[0104] The history unit can apply different recording methods depending on the category of the history when recording history. For example, the history unit can apply a price-specific recording method to the history of price negotiations. For example, the history unit can apply a delivery date-specific recording method to the history of delivery date negotiations. Furthermore, the history unit can also apply a quality-specific recording method to the history of quality negotiations. This enables efficient recording by applying the appropriate recording method according to the category of the history. Some or all of the above processing in the history unit may be performed using AI, for example, or without AI. For example, the history unit can input history category data into a generating AI and have the generating AI execute the application of the recording method.

[0105] The history unit can estimate the user's emotions and determine the priority of the history based on the estimated emotions. For example, if the user is stressed, the history unit will set a lower priority for the history. For example, if the user is relaxed, the history unit will set a higher priority for the history. The history unit can also set the history to the highest priority if the user is in a hurry. This allows for more appropriate recording by determining the priority of the history 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 history unit may be performed using AI or not using AI. For example, the history unit can input user emotion data into a generative AI and have the generative AI perform the determination of history priorities.

[0106] The history section can determine the priority of records based on the submission date of each history entry. For example, the history section prioritizes recording history entries submitted early. For example, it postpones recording history entries submitted later. The history section can also adjust the priority of records in stages according to the submission date. This enables efficient record-keeping by determining the priority of records based on the submission date. Some or all of the above processing in the history section may be performed using AI, for example, or not using AI. For example, the history section can input history submission date data into a generating AI and have the generating AI determine the priority of records.

[0107] The history unit can adjust the order of records based on the relevance of the history entries when recording history. For example, the history unit prioritizes recording highly relevant history entries. For example, it postpones recording less relevant history entries. The history unit can also adjust the order of records in stages according to the relevance of the history entries. This allows for efficient recording by adjusting the order of records based on the relevance of the history entries. Some or all of the above processing in the history unit may be performed using AI, for example, or without AI. For example, the history unit can input history relevance data into a generating AI and have the generating AI perform the adjustment of the recording order.

[0108] The platform unit can estimate the user's emotions and adjust the platform's display method based on the estimated emotions. For example, if the user is stressed, the platform unit provides a simple and highly visible display method. For example, if the user is relaxed, the platform unit provides a display method that includes detailed information. The platform unit can also provide a concise display method if the user is in a hurry. By adjusting the platform's display method according to the user's emotions, a more appropriate display 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 platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0109] The platform unit can select the optimal display method by referring to the user's past operation history when displaying the platform. For example, the platform unit selects the optimal display method based on the user's past operation history. For example, the platform unit refers to successful display methods from the user's past operation history. The platform unit can also analyze the user's past operation history and select the optimal display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the platform unit may be performed using AI, for example, or without using AI. For example, the platform unit can input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0110] The platform unit can customize the displayed content based on the user's current projects and areas of interest when displaying the platform. For example, the platform unit may prioritize displaying information related to the user's current ongoing projects. For example, the platform unit may add relevant information based on the user's areas of interest. The platform unit can also display appropriate information according to the progress of the user's current projects. By customizing the displayed content based on the user's current projects and areas of interest, it is possible to provide more relevant information. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit may input the user's current project and area of ​​interest data into a generating AI and have the generating AI perform the customization of the displayed content.

[0111] The platform unit can estimate the user's emotions and adjust the platform's operating procedures based on the estimated emotions. For example, if the user is stressed, the platform unit can simplify the operating procedures to allow for quick operation. For example, if the user is relaxed, the platform unit can provide detailed operating procedures and suggest customizable operating methods. The platform unit can also set the operating procedures to the shortest possible length to allow for quick operation if the user is in a hurry. This allows for more appropriate operation by adjusting the platform's operating procedures 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 platform unit may be performed using AI, or not using AI. For example, the platform unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the operating procedures.

[0112] The platform unit can prioritize the display of highly relevant information when displaying the platform, taking into account the user's geographical location. For example, if the user is in a specific region, the platform unit will prioritize the display of information related to that region. For example, the platform unit will display region-specific information based on the user's geographical location. Furthermore, if the user is on the move, the platform unit can display information related to their current location in real time. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input the user's geographical location data into a generating AI and have the generating AI prioritize the display content.

[0113] The platform unit can analyze the user's social media activity and display relevant information when the platform is displayed. For example, the platform unit can analyze the user's social media posts and display information related to recent interests. For example, the platform unit can display information related to current trends based on the user's social media activity. The platform unit can also display relevant information by referring to the activities of the user's social media followers and friends. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input the user's social media activity data into a generating AI and have the generating AI generate the display content.

[0114] The strategy generation unit can estimate the user's emotions and adjust the strategy generation method based on the estimated user emotions. For example, if the user is stressed, the strategy generation unit can generate a simple and quick strategy. For example, if the user is relaxed, the strategy generation unit can generate a detailed and comprehensive strategy. Also, if the user is in a hurry, the strategy generation unit can generate a quickly actionable strategy. In this way, by adjusting the strategy generation method according to the user's emotions, a more appropriate strategy can be generated. 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 strategy generation method.

[0115] The strategy generation unit can select the optimal generation method by referring to past strategy data when generating a strategy. For example, the strategy generation unit can select the optimal generation method based on past strategy data. For example, the strategy generation unit can refer to the generation methods of successful strategies from past strategy data. The strategy generation unit can also analyze past strategy data and select the optimal generation method. In this way, the optimal generation method can be selected by referring to past strategy data. Some or all of the above processing in the strategy generation unit may be performed using AI, for example, or without using AI. For example, the strategy generation unit can input past strategy data into a generation AI and have the generation AI perform the selection of the optimal generation method.

[0116] The strategy generation unit can apply different generation methods depending on the strategy category when generating strategies. For example, in the case of a pricing strategy, the strategy generation unit applies a generation method specialized for pricing. For example, in the case of a delivery strategy, the strategy generation unit applies a generation method specialized for delivery time. Furthermore, in the case of a quality strategy, the strategy generation unit can also apply a generation method specialized for quality. This enables efficient strategy generation by applying the appropriate generation method according to the strategy category. 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 strategy category data into a generation AI and have the generation AI execute the application of generation methods.

[0117] The strategy generation unit can determine the generation priority based on the submission timing of strategies during strategy generation. For example, the strategy generation unit prioritizes the generation of strategies submitted early. For example, it postpones the generation of strategies submitted later. The strategy generation unit can also adjust the generation priority in stages according to the submission timing. This enables efficient strategy generation by determining the generation priority based on the submission timing of strategies. 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 strategy submission timing data into a generation AI and have the generation AI determine the generation priority.

[0118] The strategy generation unit can adjust the generation order based on the relevance of the strategies during strategy generation. For example, the strategy generation unit can prioritize the generation of highly relevant strategies. For example, it can postpone the generation of less relevant strategies. The strategy generation unit can also adjust the generation order in stages according to the relevance of the strategies. This allows for efficient strategy generation by adjusting the generation order based on the relevance of the strategies. 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 strategy relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0119] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit may adopt a concise recording method. For example, if the user is relaxed, the recording unit may adopt a detailed recording method. The recording unit may also adopt a rapid recording method if the user is in a hurry. By adjusting the recording method according to the user's emotions, more appropriate recording 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 recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input user emotion data into the generative AI and have the generative AI adjust the recording method.

[0120] The recording unit can select the optimal recording method by referring to past recording data during recording. For example, the recording unit can select the optimal recording method based on past recording data. For example, the recording unit can refer to successful recording methods from past recording data. The recording unit can also analyze past recording data and select the optimal recording method. In this way, the optimal recording method can be selected by referring to past recording data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI. For example, the recording unit can input past recording data into a generating AI and have the generating AI perform the selection of the optimal recording method.

[0121] The recording unit can apply different recording methods depending on the category of the record. For example, for price negotiations, the recording unit can apply a recording method specialized for price. For example, for delivery date negotiations, the recording unit can apply a recording method specialized for delivery date. Furthermore, for quality negotiations, the recording unit can also apply a recording method specialized for quality. This enables efficient recording by applying the appropriate recording method according to the category of the record. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the category data of the record into a generating AI and have the generating AI execute the application of the recording method.

[0122] The recording unit can estimate the user's emotions and determine recording priorities based on the estimated emotions. For example, if the user is stressed, the recording unit will set a low recording priority. For example, if the user is relaxed, the recording unit will set a high recording priority. The recording unit can also set the recording priority to the highest priority if the user is in a hurry. This allows for more appropriate recording by determining recording priorities 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 recording unit may be performed using AI, or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI determine the recording priorities.

[0123] The recording unit can determine the priority of records based on the submission date at the time of recording. For example, the recording unit will prioritize recording records that are submitted early. For example, the recording unit will postpone recording records that are submitted later. The recording unit can also adjust the priority of records in stages according to the submission date. This enables efficient recording by determining the priority of records based on the submission date. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the submission date data of records into a generating AI and have the generating AI perform the determination of the record priority.

[0124] The recording unit can adjust the order of records based on their relevance during recording. For example, the recording unit may prioritize recording highly relevant records, or postpone recording less relevant records. The recording unit can also adjust the order of records in stages according to their relevance. This allows for efficient recording by adjusting the order of records based on their relevance. Some or all of the above processing in the recording unit may be performed using AI, or not. For example, the recording unit can input relevance data of records into a generating AI and have the generating AI perform the adjustment of the order of records.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The interview function can estimate the user's emotions and adjust the timing of the interview based on those estimates. For example, if the user is stressed, the interview can be delayed to allow for a more relaxed interview. Conversely, if the user is relaxed, the interview can begin immediately to gather detailed information. Furthermore, if the user is in a hurry, the interview can be started earlier to quickly collect requests and conditions. By adjusting the timing of the interview according to the user's emotions, a more appropriate interview becomes possible.

[0127] The history section allows a conversational AI to conduct interviews based on past negotiation history and market information. For example, it can refer to past negotiation history to gather detailed information about the player's requests and conditions. It can also gather detailed information about the player's requests and conditions based on market information. Furthermore, past negotiation history and market information can be input into a generating AI, which can then execute the interview content. This enables detailed interviews based on past negotiation history and market information.

[0128] The platform allows proxy AIs to conduct negotiations on an automated negotiation platform. For example, a proxy AI can negotiate with another player's proxy AI on the automated negotiation platform. Furthermore, if a suitable negotiating partner is found, a meeting between the representatives can be arranged. Additionally, the proxy AI can input the negotiation details into a generating AI, allowing the generating AI to manage the negotiation process. This enables efficient negotiation on the automated negotiation platform.

[0129] The strategy generation unit can generate negotiation strategies based on each player's requests and conditions. For example, it can generate a negotiation strategy based on a landowner's request such as "I want to sell my land for at least X yen per tsubo (approximately 3.3 square meters) within three years." It can also generate a negotiation strategy based on a construction company's condition such as "I want to accept orders even at a low price when I have available resources depending on the order situation." Furthermore, the requests and conditions of each player can be input into the generation AI, which then generates the negotiation strategy. This allows for the generation of the optimal negotiation strategy based on each player's requests and conditions.

[0130] The recording unit can record the results of negotiations. For example, it can record the results of negotiations between landowners and construction companies for later reference and analysis. It can also record the negotiation process. Furthermore, the negotiation results can be input into a generating AI, and the AI ​​can execute the recorded content. This makes it possible to record the negotiation results for later reference and analysis.

[0131] The interview function can estimate the user's emotions and adjust the timing of the interview based on those estimates. For example, if the user is stressed, the interview can be delayed to allow for a more relaxed interview. Conversely, if the user is relaxed, the interview can begin immediately to gather detailed information. Furthermore, if the user is in a hurry, the interview can be started earlier to quickly collect requests and conditions. By adjusting the timing of the interview according to the user's emotions, a more appropriate interview becomes possible.

[0132] The interviewing department can analyze users' past requests and conditions to select the most suitable interviewing method. For example, it can customize interview questions based on requests and conditions that users have frequently submitted in the past. It can also analyze patterns in users' past requests and conditions to propose the most suitable interviewing method. Furthermore, it can refer to users' past requests and conditions to highlight important points during the interview. In this way, by analyzing users' past requests and conditions, the most suitable interviewing method can be selected.

[0133] The interview function can customize the questions asked during the interview based on the user's current projects and areas of interest. For example, it can prioritize questions related to the user's current projects. It can also add relevant questions based on the user's areas of interest. Furthermore, it can select appropriate questions according to the progress of the user's current projects. By customizing the questions based on the user's current projects and areas of interest, more relevant information can be collected.

[0134] The interview function can estimate the user's emotions and determine the priority of the interview based on those emotions. For example, if the user is stressed, the interview can be given a lower priority, allowing other tasks to take priority. Conversely, if the user is relaxed, the interview can be given a higher priority, enabling quick information gathering. Furthermore, if the user is in a hurry, the interview can be given the highest priority, allowing for a rapid response. By determining the priority of interviews according to the user's emotions, information can be gathered at a more appropriate time.

[0135] The interview function can prioritize asking highly relevant questions during interviews, taking into account the user's geographical location. For example, if a user is in a specific region, it can prioritize questions related to that region. It can also ask questions about region-specific needs and conditions based on the user's geographical location. Furthermore, if a user is on the move, it can ask questions related to their current location in real time. This allows for more relevant questions to be asked by considering the user's geographical location.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The interviewing department interviews each player to understand their needs and requirements. For example, they use conversational AI to interview and record each player's needs and requirements in detail. Furthermore, they can also gain a detailed understanding of players' needs and requirements based on past negotiation history and market information. Step 2: The Negotiation Department conducts negotiations based on the information gathered by the Hearing Department. For example, they use proxy AI to negotiate with other players' proxy AI on an automated negotiation platform to find a suitable negotiating partner. If a suitable negotiating partner is found, a meeting between the representatives can also be arranged. Step 3: The Setup Department sets up a meeting place for negotiations between the parties involved, based on the results of negotiations conducted by the Negotiation Department. For example, they set up an online meeting system or an in-person meeting to determine the specific terms of the transaction.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] For example, each of the multiple elements, including the hearing unit, negotiation unit, and setting unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the hearing unit is implemented by the control unit 46A of the smart device 14 and uses conversational AI to hear the user's requests and conditions. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts automatic negotiations based on the information heard. The setting unit is implemented by, for example, the control unit 46A of the smart device 14 and sets up a negotiation venue between the personnel based on the negotiation results. 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.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] For example, each of the multiple elements, including the hearing unit, negotiation unit, and setting unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the hearing unit is implemented by the control unit 46A of the smart glasses 214, and uses conversational AI to hear the user's requests and conditions. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and conducts automatic negotiations based on the information heard. The setting unit is implemented by the control unit 46A of the smart glasses 214, for example, and sets up a negotiation setting between the representatives based on the negotiation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] For example, each of the multiple elements, including the hearing unit, negotiation unit, and setting unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the hearing unit is implemented by the control unit 46A of the headset terminal 314, and uses conversational AI to hear the user's requests and conditions. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and conducts automatic negotiations based on the information heard. The setting unit is implemented by, for example, the control unit 46A of the headset terminal 314, and sets up a negotiation setting between the personnel based on the negotiation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] For example, each of the multiple elements, including the hearing unit, negotiation unit, and setting unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the hearing unit is implemented by the control unit 46A of the robot 414, and uses conversational AI to hear the user's requests and conditions. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and conducts automatic negotiations based on the information heard. The setting unit is implemented by, for example, the control unit 46A of the robot 414, and sets up a meeting place for negotiations between the parties in charge based on the negotiation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) The interviewing department will gather information on each player's requests and requirements, The Negotiation Department conducts negotiations based on the information gathered by the Hearing Department, The system includes a setting unit that sets up a forum for negotiations between the representatives based on the results of negotiations conducted by the aforementioned negotiation unit. A system characterized by the following features. (Note 2) It features a history section where a conversational AI conducts interviews based on past negotiation history and market information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The platform includes an AI proxy that conducts negotiations on an automated negotiation platform. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a strategy generation unit that generates negotiation strategies based on the requests and conditions of each player. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a recording unit for recording the results of negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned hearing section is, We analyze the user's past requests and conditions to select the most suitable interview method. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) 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 11) 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 12) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned negotiating body said, During negotiations, adjust the level of detail based on the importance of the conditions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned negotiating body said, During negotiations, different negotiation algorithms are applied depending on the category of conditions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the length of the negotiation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned negotiating body said, During negotiations, prioritize negotiations based on when the terms are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned negotiating body said, During negotiations, adjust the order of negotiations based on the relevance of the conditions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned setting unit is, It estimates the user's emotions and adjusts the negotiation setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned setting unit is, During setup, the system will refer to past negotiation history to select the optimal setup method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned setting unit is, During setup, different setup methods are applied depending on the negotiation category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned 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 1, characterized by the features described herein. (Note 22) The aforementioned setting unit is, When setting up, prioritize settings based on when negotiations are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned setting unit is, During setup, adjust the order of settings based on the relevance of the negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned history section is, The system estimates the user's emotions and adjusts the history recording method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned history section is, When recording history, the system selects the optimal recording method by referring to past historical data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned history section is, When recording history, different recording methods are applied depending on the history category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned history section is, It estimates the user's emotions and determines the priority of the history based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned history section is, When recording history, prioritize records based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned history section is, When recording history, the order of records is adjusted based on the relevance of the history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned platform unit is The platform estimates user sentiment and adjusts how it displays information based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned platform unit is When displaying the platform, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned platform unit is When displaying the platform, the displayed content is 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 33) The aforementioned platform unit is The system estimates user sentiment and adjusts the platform's operating procedures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned platform unit is When displaying the platform, the system prioritizes showing more relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned platform unit is When displaying the platform, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The strategy generation unit, We estimate user sentiment and adjust the strategy generation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The strategy generation unit, When generating strategies, the optimal generation method is selected by referring to past strategy data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The strategy generation unit, When generating strategies, different generation methods are applied depending on the strategy category. The system described in Appendix 1, characterized by the features described herein. (Note 39) The strategy generation unit, When generating strategies, prioritize their creation based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 40) The strategy generation unit, When generating strategies, adjust the generation order based on the relevance of the strategies. The system described in Appendix 1, characterized by the features described herein. (Note 41) The recording unit is, The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The recording unit is, During recording, the optimal recording method is selected by referring to past recording data. The system described in Appendix 1, characterized by the features described herein. (Note 43) The recording unit is, During recording, different recording methods are applied depending on the recording category. The system described in Appendix 1, characterized by the features described herein. (Note 44) The recording unit is, The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The recording unit is, When recording, prioritize the records based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 46) The recording unit is, During recording, the order of the records is adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0210] 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, The Negotiation Department conducts negotiations based on the information gathered by the Hearing Department, The system includes a setting unit that sets up a forum for negotiations between the representatives based on the results of negotiations conducted by the aforementioned negotiation unit. A system characterized by the following features.

2. It features a history section where a conversational AI conducts interviews based on past negotiation history and market information. The system according to feature 1.

3. The system includes a platform section where an AI agent conducts negotiations on an automated negotiation platform. The system according to feature 1.

4. It features a strategy generation unit that generates negotiation strategies based on the requests and conditions of each player. The system according to feature 1.

5. It is equipped with a recording unit for recording the results of negotiations. The system according to feature 1.

6. 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.

7. The aforementioned hearing section is, We analyze the user's past requests and conditions to select the most suitable interview method. The system according to feature 1.

8. 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.

9. 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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