System

Abstract

The system according to the embodiment aims to streamline the negotiation process between the parties involved in a real estate development case.SOLUTION: A system includes a site selection part, a project planning part, a financing part, a construction management part, a sales strategy part, and a self-learning part. The site selection part hears the requests and conditions of the persons concerned and proposes an optimum site on the basis of them. A project planning part hears the requests and conditions of the persons concerned and makes an optimum plan on the basis of them. The financial procurement part hears the requests and conditions of the persons concerned and proposes an optimum financial procurement method on the basis of them. The construction management part hears the requests and conditions of the persons concerned and proposes an optimum management method on the basis of them. A sales strategy part hears the requests and conditions of the persons concerned and proposes an optimum sales strategy according to them. The self-learning part learns from past success examples and failure examples and utilizes them for the next negotiation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the negotiation process between parties involved in real estate development projects was complex, making it difficult to proceed efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of the negotiation process between the parties involved in a real estate development project. [Means for solving the problem]

[0006] The system according to the embodiment includes a site selection unit, a project planning unit, a financing unit, a construction management unit, a sales strategy unit, and a self-learning unit. The site selection unit listens to the requests and conditions of the parties involved and proposes the most suitable site based on that. The project planning unit listens to the requests and conditions of the parties involved and creates the most suitable plan based on that. The financing unit listens to the requests and conditions of the parties involved and proposes the most suitable financing method based on that. The construction management unit listens to the requests and conditions of the parties involved and proposes the most suitable management method based on that. The sales strategy unit listens to the requests and conditions of the parties involved and proposes the most suitable sales strategy based on that. The self-learning unit learns from past successes and failures and applies that knowledge to future negotiations. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the negotiation process between the parties involved in a real estate development project. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The proxy AI system according to the embodiment of the present invention is a system that efficiently supports complex development projects and negotiation processes in the real estate industry. As a result, the proxy AI system can efficiently and effectively support complex development projects and negotiation processes in the real estate industry.

[0029] The proxy AI system according to the embodiment includes a site selection unit, a project planning unit, a financing unit, a construction management unit, a sales strategy unit, and a self-learning unit. The site selection unit listens to the requests and conditions of stakeholders and proposes the optimal site based on the information. For example, the generation AI receives input prompts such as conditions for a location with good transportation access or proximity to commercial facilities, and lists candidate sites based on the input prompts. The generation AI also learns from past successes and failures to make more appropriate proposals. The project planning unit listens to the requests and conditions of stakeholders and develops an optimal plan based on the input prompts. For example, the generation AI receives input prompts such as a plan to maximize profits within a budget or an environmentally friendly plan, and creates a plan based on the input prompts. The generation AI also learns from past project data to propose more effective plans. The financing unit listens to the requests and conditions of stakeholders and proposes the optimal financing method based on the input prompts. For example, the generation AI receives input prompts such as conditions for low-interest loans and quick fundraising, and lists financial institutions and investors based on the input prompts. The generation AI also learns from past financial procurement data to make more appropriate proposals. The construction management department listens to the requests and conditions of stakeholders and proposes optimal management methods based on them. For example, the generation AI receives input prompts such as shortening construction time and reducing costs, and creates construction schedules and cost management plans based on those. The generation AI also learns from past construction project data to propose more effective management methods. The sales strategy department listens to the requests and conditions of stakeholders and proposes optimal sales strategies based on those. For example, the generation AI receives input prompts such as selling at a high price or selling quickly, and creates sales plans based on those. The generation AI also learns from past sales data to propose more effective sales strategies. The self-learning department learns from past successes and failures and applies that knowledge to future negotiations. For example, the generation AI analyzes past negotiation data to learn what conditions led to success and what failures occurred. This is expected to improve the agent AI system's negotiation skills, as well as the speed and quality of deal completions.

[0030] The site selection unit can take into account not only geographical data, but also local social trends and future development plans. For example, the site selection unit uses generative AI to select the optimal site by considering local social trends and future development plans in addition to geographical data. For example, it can analyze local demographic trends and infrastructure development plans and suggest sites that are likely to increase in value in the future. This makes it possible to select sites that take into account local social trends and future development plans.

[0031] The site selection unit can collect the opinions of local residents in real time and reflect them. For example, the site selection unit uses a generation AI to collect the opinions of local residents in real time and reflect them in the site selection. For example, it analyzes online surveys and social media posts to select the optimal site based on the opinions of residents. This makes it possible to select a site that reflects the opinions of local residents.

[0032] The site selection unit can automatically perform environmental impact assessments and make environmentally friendly site selections. For example, the generation AI can automatically perform environmental impact assessments and make environmentally friendly site selections. For example, it can analyze the land's ecosystem, water quality, air quality, etc. to select environmentally friendly sites. This makes it possible to select sites that are environmentally friendly.

[0033] The site selection unit can compare data from different cities and countries and select the most suitable site globally. For example, the generation AI can compare data from different cities and countries and select the most suitable site globally. For example, it can analyze the economic situation and infrastructure development status of each city and select the most suitable site. This makes it possible to select sites from a global perspective.

[0034] The project planning department can perform a detailed analysis of the factors that led to success and failure of past projects and create optimal plans. For example, the project planning department uses generative AI to perform a detailed analysis of past project data and identify the factors that led to success and failure. For example, it analyzes the commonalities between successful projects and the problems of unsuccessful projects and creates optimal plans. This makes it possible to create optimal plans based on past project data.

[0035] The project planning department can adjust the schedules and resources of the people involved in the project in real time. For example, the generative AI adjusts the schedules and resources of the people involved in the project in real time to create an optimal project plan. For example, it analyzes the free time and resource usage status of the people involved and proposes an efficient plan. This allows for efficient project planning by adjusting the schedules and resources of the people involved in the project in real time.

[0036] The project planning department can incorporate best practices from different industries. For example, generative AI can incorporate best practices from different industries to create optimal project plans. For example, it can propose plans that incorporate agile methods from the IT industry and lean production methods from the manufacturing industry. This makes it possible to create project plans that incorporate best practices from different industries.

[0037] The project planning department can simulate different scenarios and select the optimal plan. For example, the generation AI simulates different scenarios and selects the optimal project plan. For example, it simulates multiple budget scenarios and schedule scenarios and proposes the optimal plan. This makes it possible to select the optimal plan by simulating different scenarios.

[0038] The Financial Procurement Department can perform detailed analysis of past financial market trends and propose optimal financing methods. For example, the Financial Procurement Department uses generative AI to perform detailed analysis of past financial market data and propose optimal financing methods. For example, it analyzes past interest rate trends and investor trends to create a list of optimal borrowers and investors. This makes it possible to propose optimal financing methods based on past financial market data.

[0039] The Financial Procurement Department can collect the opinions of financial institutions and investors in real time and reflect them. For example, the Financial Procurement Department uses generative AI to collect the opinions of financial institutions and investors in real time and reflect them in the financial procurement process. For example, it analyzes online surveys and social media posts and proposes the optimal method of procurement based on investor opinions. This makes it possible to carry out financial procurement that reflects the opinions of financial institutions and investors.

[0040] The Financial Procurement Department can compare financial markets in different countries and propose optimal financing methods globally. For example, the Financial Procurement Department's AI can compare financial market data from different countries and propose optimal financing methods globally. For example, it can analyze interest rates and investor trends in each country and propose optimal financing methods. This makes it possible to raise funds from a global perspective.

[0041] The Financial Procurement Department can combine different financial products to propose the optimal portfolio. For example, the Financial Procurement Department uses a generative AI to combine different financial products to propose the optimal portfolio. For example, it proposes a portfolio that combines stocks, bonds, real estate investment trusts (REITs), etc. This makes it possible to propose the optimal portfolio that combines different financial products.

[0042] The construction management department can perform detailed analysis of data from past construction projects and propose optimal management methods. For example, the generation AI can perform detailed analysis of data from past construction projects and propose optimal management methods. For example, it can analyze commonalities between successful projects and problems in unsuccessful projects and propose optimal management methods. This makes it possible to propose optimal management methods based on past construction project data.

[0043] The construction management department can monitor the situation at the construction site in real time and take immediate action. For example, the generation AI can monitor the situation at the construction site in real time and take immediate action. For example, it can analyze the progress and problems at the site and propose immediate countermeasures. This makes it possible to monitor the situation at the construction site in real time and take immediate action.

[0044] The construction management department can incorporate different construction technologies and propose the optimal management method. For example, the generative AI can incorporate different construction technologies and propose the optimal management method. For example, it can propose management methods that utilize 3D printing technology or drone technology. This makes it possible to propose the optimal management method that incorporates different construction technologies.

[0045] The construction management department can compare data from different construction projects and select the optimal management method. For example, the generation AI compares data from different construction projects and selects the optimal management method. For example, it analyzes the commonalities between successful projects and the problems of unsuccessful projects and selects the optimal management method. This makes it possible to select the optimal management method by comparing data from different construction projects.

[0046] The sales strategy department can analyze past sales data in detail and propose the optimal strategy. For example, the sales strategy department uses generative AI to analyze past sales data in detail and propose the optimal sales strategy. For example, it analyzes the commonalities of successful sales strategies and the problems of unsuccessful strategies and proposes the optimal strategy. This makes it possible to propose the optimal sales strategy based on past sales data.

[0047] The sales strategy department can monitor market trends in real time and respond immediately. For example, the sales strategy department can use generative AI to monitor market trends in real time and respond immediately. For example, it can analyze market fluctuations and competitor trends and propose countermeasures immediately. This makes it possible to monitor market trends in real time and respond immediately.

[0048] The sales strategy department can compare data from different markets and propose optimal strategies globally. For example, the sales strategy department can use generative AI to compare data from different markets and propose optimal sales strategies globally. For example, it can analyze demand and competitive trends in each market and propose optimal strategies. This makes it possible to propose sales strategies from a global perspective.

[0049] The sales strategy department can combine different sales channels and propose the optimal strategy. For example, the generation AI can combine different sales channels and propose the optimal sales strategy. For example, it can propose a strategy that combines online sales and offline sales. This makes it possible to propose the optimal sales strategy that combines different sales channels.

[0050] The self-learning unit can learn not only past negotiation data but also related industry news and trend information. For example, when the generative AI self-learns, it learns not only past negotiation data but also related industry news and trend information. For example, it learns the latest technological trends and market fluctuations to improve negotiation skills. This makes it possible to improve negotiation skills to a higher level by learning industry news and trend information in addition to past negotiation data.

[0051] The self-learning unit can collect new data in real time and instantly update its learning content. For example, during the self-learning process of the generative AI, the self-learning unit collects new data in real time and instantly updates its learning content. For example, it collects the latest negotiation data and market information and updates its learning content. In this way, by collecting new data in real time and instantly updating its learning content, it is possible to always negotiate based on the latest information.

[0052] The self-learning unit learns data from different industries and can improve negotiation skills in a multifaceted way. For example, when the generative AI self-learns, the self-learning unit learns data from different industries and improves negotiation skills in a multifaceted way. For example, it can learn negotiation data from the IT industry and the medical industry and improve negotiation skills. This makes it possible to improve negotiation skills in a multifaceted way by learning data from different industries.

[0053] The self-learning unit can combine different learning algorithms and select the optimal learning method. For example, during the self-learning process of the generative AI, the self-learning unit combines different learning algorithms and selects the optimal learning method. For example, it selects a learning method that combines deep learning and reinforcement learning. This makes it possible to select the optimal learning method by combining different learning algorithms.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The agent AI system can further include a risk assessment unit, which performs detailed analysis of project risks and proposes optimal risk management methods. For example, the generative AI can analyze the probability and impact of risks based on past project data and propose risk avoidance and mitigation measures. The risk assessment unit can also monitor risk occurrence in real time and propose immediate countermeasures. This allows for more effective project risk management.

[0056] The proxy AI system can further include a communication support unit, which provides support to facilitate smooth communication between the parties involved. For example, the generation AI can automatically collect, organize, and share the opinions and requests of the parties involved. It can also automatically generate minutes of meetings between the parties involved and highlight important points. This facilitates smooth communication between the parties involved and helps the project progress smoothly.

[0057] The proxy AI system can also be equipped with a legal support section, which provides support for legal issues related to the project. For example, generative AI can automate the creation and review of contracts to minimize legal risks. It can also quickly propose countermeasures when legal issues arise. This allows for effective management of legal risks in projects.

[0058] The agent AI system can also be equipped with a data visualization unit, which visually displays the project's progress and important data. For example, the generative AI can display the project's progress in graphs and charts, allowing stakeholders to understand it at a glance. It can also visually highlight risks and problems to encourage prompt action. This makes it easier to visually grasp the project's progress.

[0059] The agent AI system can further include a predictive analysis unit, which predicts future trends in the project and proposes optimal countermeasures. For example, the generative AI can predict the progress of the project and the probability of risk occurrence based on past data. It can also propose optimal countermeasures based on the prediction results. This makes it possible to predict future trends in the project and take countermeasures in advance.

[0060] The agent AI system can further include an intercultural support section, which supports communication between parties with different cultural backgrounds. For example, the generative AI can learn business etiquette and communication styles from different cultures and provide appropriate advice to the parties involved. It can also suggest specific measures to prevent misunderstandings between different cultures. This enables smooth communication between parties with different cultural backgrounds.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The site selection department listens to the requests and conditions of the parties involved and proposes the most suitable site based on that. For example, the generation AI receives input prompts such as conditions such as a location with good transportation access or close to commercial facilities, and lists candidate sites based on those conditions. The generation AI also learns from past successes and failures to make more appropriate proposals. Step 2: The project planning department listens to the requests and conditions of stakeholders and creates an optimal plan based on them. For example, the generation AI receives input prompts such as a plan to maximize profits within a budget or an environmentally friendly plan, and creates a plan based on those inputs. The generation AI also learns from past project data and proposes more effective plans. Step 3: The Financial Procurement Department listens to the requests and conditions of the parties involved and proposes the optimal financial procurement method based on that. For example, the Generative AI receives input prompts such as conditions for low-interest loans and quick fundraising, and based on that, it lists financial institutions and investors. The Generative AI also learns from past financial procurement data to make more appropriate proposals. Step 4: The Construction Management Department listens to the requests and conditions of the parties involved and proposes the optimal management method based on that. For example, the Generative AI receives input prompts such as shortening construction time and reducing costs, and creates a construction schedule and cost management plan based on those inputs. The Generative AI also learns from past construction project data and proposes more effective management methods. Step 5: The sales strategy department listens to the requests and conditions of the parties involved and proposes the optimal sales strategy based on that. For example, the generation AI receives input prompts such as conditions for high prices and quick sales, and creates a sales plan based on those conditions. The generation AI also learns from past sales data and proposes more effective sales strategies. Step 6: The self-learning unit learns from past successes and failures and applies this knowledge to future negotiations. For example, the generation AI analyzes past negotiation data to learn what conditions led to success and what failures occurred. This is expected to improve the agent AI system's negotiation skills, as well as the speed and quality of deal closings.

[0063] (Example 2) The proxy AI system according to the embodiment of the present invention is a system that efficiently supports complex development projects and negotiation processes in the real estate industry. As a result, the proxy AI system can efficiently and effectively support complex development projects and negotiation processes in the real estate industry.

[0064] The proxy AI system according to the embodiment includes a site selection unit, a project planning unit, a financing unit, a construction management unit, a sales strategy unit, and a self-learning unit. The site selection unit listens to the requests and conditions of stakeholders and proposes the optimal site based on the information. For example, the generation AI receives input prompts such as conditions for a location with good transportation access or proximity to commercial facilities, and lists candidate sites based on the input prompts. The generation AI also learns from past successes and failures to make more appropriate proposals. The project planning unit listens to the requests and conditions of stakeholders and develops an optimal plan based on the input prompts. For example, the generation AI receives input prompts such as a plan to maximize profits within a budget or an environmentally friendly plan, and creates a plan based on the input prompts. The generation AI also learns from past project data to propose more effective plans. The financing unit listens to the requests and conditions of stakeholders and proposes the optimal financing method based on the input prompts. For example, the generation AI receives input prompts such as conditions for low-interest loans and quick fundraising, and lists financial institutions and investors based on the input prompts. The generation AI also learns from past financial procurement data to make more appropriate proposals. The construction management department listens to the requests and conditions of stakeholders and proposes optimal management methods based on them. For example, the generation AI receives input prompts such as shortening construction time and reducing costs, and creates construction schedules and cost management plans based on those. The generation AI also learns from past construction project data to propose more effective management methods. The sales strategy department listens to the requests and conditions of stakeholders and proposes optimal sales strategies based on those. For example, the generation AI receives input prompts such as selling at a high price or selling quickly, and creates sales plans based on those. The generation AI also learns from past sales data to propose more effective sales strategies. The self-learning department learns from past successes and failures and applies that knowledge to future negotiations. For example, the generation AI analyzes past negotiation data to learn what conditions led to success and what failures occurred. This is expected to improve the agent AI system's negotiation skills, as well as the speed and quality of deal completions.

[0065] The site selection unit can take into account not only geographical data, but also local social trends and future development plans. For example, the site selection unit uses generative AI to select the optimal site by considering local social trends and future development plans in addition to geographical data. For example, it can analyze local demographic trends and infrastructure development plans and suggest sites that are likely to increase in value in the future. This makes it possible to select sites that take into account local social trends and future development plans.

[0066] The site selection unit can collect the opinions of local residents in real time and reflect them. For example, the site selection unit uses a generation AI to collect the opinions of local residents in real time and reflect them in the site selection. For example, it analyzes online surveys and social media posts to select the optimal site based on the opinions of residents. This makes it possible to select a site that reflects the opinions of local residents.

[0067] The site selection unit can use the emotion estimation function to analyze the emotions of the people involved and select a site that will elicit the most positive response. The site selection unit, for example, uses the emotion estimation function to analyze the emotions of the people involved and selects a site that will elicit the most positive response. For example, it analyzes the facial expressions and voices of the people involved and selects a site that has strong positive emotions. This makes it possible to select a site that takes into account the emotions of the people involved.

[0068] The site selection unit can automatically perform environmental impact assessments and make environmentally friendly site selections. For example, the generation AI can automatically perform environmental impact assessments and make environmentally friendly site selections. For example, it can analyze the land's ecosystem, water quality, air quality, etc. to select environmentally friendly sites. This makes it possible to select sites that are environmentally friendly.

[0069] The site selection unit can compare data from different cities and countries and select the most suitable site globally. For example, the generation AI can compare data from different cities and countries and select the most suitable site globally. For example, it can analyze the economic situation and infrastructure development status of each city and select the most suitable site. This makes it possible to select sites from a global perspective.

[0070] The project planning department can perform a detailed analysis of the factors that led to success and failure of past projects and create optimal plans. For example, the project planning department uses generative AI to perform a detailed analysis of past project data and identify the factors that led to success and failure. For example, it analyzes the commonalities between successful projects and the problems of unsuccessful projects and creates optimal plans. This makes it possible to create optimal plans based on past project data.

[0071] The project planning department can adjust the schedules and resources of the people involved in the project in real time. For example, the generative AI adjusts the schedules and resources of the people involved in the project in real time to create an optimal project plan. For example, it analyzes the free time and resource usage status of the people involved and proposes an efficient plan. This allows for efficient project planning by adjusting the schedules and resources of the people involved in the project in real time.

[0072] The project planning department can use the emotion estimation function to analyze the emotions of stakeholders and formulate a plan that will elicit the most positive response. The project planning department, for example, uses the emotion estimation function to analyze the emotions of stakeholders and formulate a project plan that will elicit the most positive response. For example, based on the emotion scores of stakeholders, a plan that will elicit the most positive responses is proposed. This makes it possible to formulate a project plan that takes stakeholders' emotions into consideration.

[0073] The project planning department can incorporate best practices from different industries. For example, generative AI can incorporate best practices from different industries to create optimal project plans. For example, it can propose plans that incorporate agile methods from the IT industry and lean production methods from the manufacturing industry. This makes it possible to create project plans that incorporate best practices from different industries.

[0074] The project planning department can simulate different scenarios and select the optimal plan. For example, the generation AI simulates different scenarios and selects the optimal project plan. For example, it simulates multiple budget scenarios and schedule scenarios and proposes the optimal plan. This makes it possible to select the optimal plan by simulating different scenarios.

[0075] The project planning department can use the emotion estimation function to monitor the emotions of stakeholders in real time and reflect them in the plan. The project planning department, for example, can use the emotion estimation function to monitor the emotions of stakeholders in real time and reflect them in the project plan. For example, based on the emotion scores of stakeholders, the department can propose a plan that receives a lot of positive responses. This makes it possible to create a project plan that reflects the emotions of stakeholders in real time.

[0076] The Financial Procurement Department can perform detailed analysis of past financial market trends and propose optimal financing methods. For example, the Financial Procurement Department uses generative AI to perform detailed analysis of past financial market data and propose optimal financing methods. For example, it analyzes past interest rate trends and investor trends to create a list of optimal borrowers and investors. This makes it possible to propose optimal financing methods based on past financial market data.

[0077] The Financial Procurement Department can collect the opinions of financial institutions and investors in real time and reflect them. For example, the Financial Procurement Department uses generative AI to collect the opinions of financial institutions and investors in real time and reflect them in the financial procurement process. For example, it analyzes online surveys and social media posts and proposes the optimal method of procurement based on investor opinions. This makes it possible to carry out financial procurement that reflects the opinions of financial institutions and investors.

[0078] The financing department can use the emotion estimation function to analyze investor emotions and make proposals that will elicit the most positive response. The financing department, for example, uses the emotion estimation function to analyze investor emotions and make financing proposals that will elicit the most positive response. For example, the department can analyze the investor's facial expressions and voice and make proposals that reflect strong positive emotions. This makes it possible to make financing proposals that take investor emotions into consideration.

[0079] The Financial Procurement Department can compare financial markets in different countries and propose optimal financing methods globally. For example, the Financial Procurement Department's AI can compare financial market data from different countries and propose optimal financing methods globally. For example, it can analyze interest rates and investor trends in each country and propose optimal financing methods. This makes it possible to raise funds from a global perspective.

[0080] The Financial Procurement Department can combine different financial products to propose the optimal portfolio. For example, the Financial Procurement Department uses a generative AI to combine different financial products to propose the optimal portfolio. For example, it proposes a portfolio that combines stocks, bonds, real estate investment trusts (REITs), etc. This makes it possible to propose the optimal portfolio that combines different financial products.

[0081] The financing department can use the emotion estimation function to monitor the emotions of financial institutions and investors in real time and reflect them in proposals. For example, the financing department can use the emotion estimation function to monitor the emotions of financial institutions and investors in real time and reflect them in financing proposals. For example, based on investor emotion scores, the department can make proposals that receive a lot of positive responses. This makes it possible to make financing proposals that reflect the emotions of financial institutions and investors in real time.

[0082] The construction management department can perform detailed analysis of data from past construction projects and propose optimal management methods. For example, the generation AI can perform detailed analysis of data from past construction projects and propose optimal management methods. For example, it can analyze commonalities between successful projects and problems in unsuccessful projects and propose optimal management methods. This makes it possible to propose optimal management methods based on past construction project data.

[0083] The construction management department can monitor the situation at the construction site in real time and take immediate action. For example, the generation AI can monitor the situation at the construction site in real time and take immediate action. For example, it can analyze the progress and problems at the site and propose immediate countermeasures. This makes it possible to monitor the situation at the construction site in real time and take immediate action.

[0084] The construction management department can use the emotion estimation function to analyze the emotions of construction site workers and propose a management method that will elicit the most positive response. For example, the construction management department can use the emotion estimation function to analyze the emotions of construction site workers and propose a management method that will elicit the most positive response. For example, the department can analyze the facial expressions and voices of workers and propose a management method that elicits stronger positive emotions. This makes it possible to propose a management method that takes into account the emotions of construction site workers.

[0085] The construction management department can incorporate different construction technologies and propose the optimal management method. For example, the generative AI can incorporate different construction technologies and propose the optimal management method. For example, it can propose management methods that utilize 3D printing technology or drone technology. This makes it possible to propose the optimal management method that incorporates different construction technologies.

[0086] The construction management department can compare data from different construction projects and select the optimal management method. For example, the generation AI compares data from different construction projects and selects the optimal management method. For example, it analyzes the commonalities between successful projects and the problems of unsuccessful projects and selects the optimal management method. This makes it possible to select the optimal management method by comparing data from different construction projects.

[0087] The construction management department can use the emotion estimation function to monitor the emotions of construction site workers in real time and reflect them in management methods. For example, the construction management department can use the emotion estimation function to monitor the emotions of construction site workers in real time and reflect them in management methods. For example, based on the emotion scores of workers, it can propose management methods that generate a lot of positive responses. This makes it possible to propose management methods that reflect the emotions of construction site workers in real time.

[0088] The sales strategy department can analyze past sales data in detail and propose the optimal strategy. For example, the sales strategy department uses generative AI to analyze past sales data in detail and propose the optimal sales strategy. For example, it analyzes the commonalities of successful sales strategies and the problems of unsuccessful strategies and proposes the optimal strategy. This makes it possible to propose the optimal sales strategy based on past sales data.

[0089] The sales strategy department can monitor market trends in real time and respond immediately. For example, the sales strategy department can use generative AI to monitor market trends in real time and respond immediately. For example, it can analyze market fluctuations and competitor trends and propose countermeasures immediately. This makes it possible to monitor market trends in real time and respond immediately.

[0090] The sales strategy department can use the emotion estimation function to analyze customer emotions and propose a strategy that will elicit the most positive response. For example, the sales strategy department can use the emotion estimation function to analyze customer emotions and propose a sales strategy that will elicit the most positive response. For example, the sales strategy department can analyze the customer's facial expressions and voice and propose a strategy that will elicit the most positive emotion. This makes it possible to propose a sales strategy that takes customer emotions into consideration.

[0091] The sales strategy department can compare data from different markets and propose optimal strategies globally. For example, the sales strategy department can use generative AI to compare data from different markets and propose optimal sales strategies globally. For example, it can analyze demand and competitive trends in each market and propose optimal strategies. This makes it possible to propose sales strategies from a global perspective.

[0092] The sales strategy department can combine different sales channels and propose the optimal strategy. For example, the generation AI can combine different sales channels and propose the optimal sales strategy. For example, it can propose a strategy that combines online sales and offline sales. This makes it possible to propose the optimal sales strategy that combines different sales channels.

[0093] The sales strategy department can use the emotion estimation function to monitor customer emotions in real time and reflect them in their strategies. The sales strategy department can, for example, use the emotion estimation function to monitor customer emotions in real time and reflect them in their sales strategies. For example, based on the customer's emotion score, a strategy that generates a lot of positive responses can be proposed. This makes it possible to propose sales strategies that reflect customer emotions in real time.

[0094] The self-learning unit can learn not only past negotiation data but also related industry news and trend information. For example, when the generative AI self-learns, it learns not only past negotiation data but also related industry news and trend information. For example, it learns the latest technological trends and market fluctuations to improve negotiation skills. This makes it possible to improve negotiation skills to a higher level by learning industry news and trend information in addition to past negotiation data.

[0095] The self-learning unit can collect new data in real time and instantly update its learning content. For example, during the self-learning process of the generative AI, the self-learning unit collects new data in real time and instantly updates its learning content. For example, it collects the latest negotiation data and market information and updates its learning content. In this way, by collecting new data in real time and instantly updating its learning content, it is possible to always negotiate based on the latest information.

[0096] The self-learning unit can use the emotion estimation function to analyze the emotions of the parties in past negotiations and learn how to elicit the most positive response. For example, the self-learning unit uses the emotion estimation function to analyze the emotions of the parties in past negotiations and learn how to elicit the most positive response. For example, based on the emotion scores of the parties, it learns a negotiation method that produces the most positive responses. In this way, by analyzing the emotions of the parties in past negotiations and learning how to elicit the most positive response, it is possible to improve negotiation skills.

[0097] The self-learning unit learns data from different industries and can improve negotiation skills in a multifaceted way. For example, when the generative AI self-learns, the self-learning unit learns data from different industries and improves negotiation skills in a multifaceted way. For example, it can learn negotiation data from the IT industry and the medical industry and improve negotiation skills. This makes it possible to improve negotiation skills in a multifaceted way by learning data from different industries.

[0098] The self-learning unit can combine different learning algorithms and select the optimal learning method. For example, during the self-learning process of the generative AI, the self-learning unit combines different learning algorithms and selects the optimal learning method. For example, it selects a learning method that combines deep learning and reinforcement learning. This makes it possible to select the optimal learning method by combining different learning algorithms.

[0099] The self-learning unit can use the emotion estimation function to monitor the emotions of the parties in past negotiations in real time and reflect them in the learning content. The self-learning unit, for example, uses the emotion estimation function to monitor the emotions of the parties in past negotiations in real time and reflect them in the learning content. For example, based on the emotion scores of the parties, the self-learning unit learns negotiation methods that generate a high number of positive responses. In this way, by monitoring the emotions of the parties in past negotiations in real time and reflecting them in the learning content, it is possible to improve negotiation skills more effectively.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The agent AI system can further include a risk assessment unit, which performs detailed analysis of project risks and proposes optimal risk management methods. For example, the generative AI can analyze the probability and impact of risks based on past project data and propose risk avoidance and mitigation measures. The risk assessment unit can also monitor risk occurrence in real time and propose immediate countermeasures. This allows for more effective project risk management.

[0102] The proxy AI system can further include a communication support unit, which provides support to facilitate smooth communication between the parties involved. For example, the generation AI can automatically collect, organize, and share the opinions and requests of the parties involved. It can also automatically generate minutes of meetings between the parties involved and highlight important points. This facilitates smooth communication between the parties involved and helps the project progress smoothly.

[0103] The proxy AI system can also be equipped with a legal support section, which provides support for legal issues related to the project. For example, generative AI can automate the creation and review of contracts to minimize legal risks. It can also quickly propose countermeasures when legal issues arise. This allows for effective management of legal risks in projects.

[0104] The proxy AI system can also use its emotion estimation function to monitor the stress levels of those involved and suggest appropriate countermeasures. For example, the generative AI can analyze the facial expressions and voice of those involved and, if the stress level is high, suggest relaxation methods or breaks. It can also identify the causes of stress and suggest specific countermeasures. This allows for effective stress management for those involved.

[0105] The agent AI system can also use its emotion estimation function to make suggestions to improve the motivation of stakeholders. For example, the generative AI can analyze stakeholders' emotions and, if their motivation is declining, suggest encouraging messages or incentives. It can also propose specific action plans to improve motivation. This helps maintain the motivation of stakeholders and contribute to the success of the project.

[0106] The agent AI system can further use its emotion estimation function to provide customized feedback based on the emotions of the stakeholders. For example, the generative AI can analyze the emotions of stakeholders and provide positive feedback or constructive advice. It can also adjust the content of the feedback according to changes in emotions. This allows for effective feedback that takes into account the emotions of stakeholders.

[0107] The proxy AI system can also use its emotion estimation function to support the progress of meetings based on the emotions of the participants. For example, the generative AI can analyze the emotions of participants in real time during a meeting and, if emotions become heated, make suggestions to calm them down. It can also adjust the progress of the meeting according to changes in emotions. This makes it possible to smoothly progress meetings by taking into account the emotions of the participants.

[0108] The agent AI system can also be equipped with a data visualization unit, which visually displays the project's progress and important data. For example, the generative AI can display the project's progress in graphs and charts, allowing stakeholders to understand it at a glance. It can also visually highlight risks and problems to encourage prompt action. This makes it easier to visually grasp the project's progress.

[0109] The agent AI system can further include a predictive analysis unit, which predicts future trends in the project and proposes optimal countermeasures. For example, the generative AI can predict the progress of the project and the probability of risk occurrence based on past data. It can also propose optimal countermeasures based on the prediction results. This makes it possible to predict future trends in the project and take countermeasures in advance.

[0110] The agent AI system can further include an intercultural support section, which supports communication between parties with different cultural backgrounds. For example, the generative AI can learn business etiquette and communication styles from different cultures and provide appropriate advice to the parties involved. It can also suggest specific measures to prevent misunderstandings between different cultures. This enables smooth communication between parties with different cultural backgrounds.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The site selection department listens to the requests and conditions of the parties involved and proposes the most suitable site based on that. For example, the generation AI receives input prompts such as conditions such as a location with good transportation access or close to commercial facilities, and lists candidate sites based on those conditions. The generation AI also learns from past successes and failures to make more appropriate proposals. Step 2: The project planning department listens to the requests and conditions of stakeholders and creates an optimal plan based on them. For example, the generation AI receives input prompts such as a plan to maximize profits within a budget or an environmentally friendly plan, and creates a plan based on those inputs. The generation AI also learns from past project data and proposes more effective plans. Step 3: The Financial Procurement Department listens to the requests and conditions of the parties involved and proposes the optimal financial procurement method based on that. For example, the Generative AI receives input prompts such as conditions for low-interest loans and quick fundraising, and based on that, it lists financial institutions and investors. The Generative AI also learns from past financial procurement data to make more appropriate proposals. Step 4: The Construction Management Department listens to the requests and conditions of the parties involved and proposes the optimal management method based on that. For example, the Generative AI receives input prompts such as shortening construction time and reducing costs, and creates a construction schedule and cost management plan based on those inputs. The Generative AI also learns from past construction project data and proposes more effective management methods. Step 5: The sales strategy department listens to the requests and conditions of the parties involved and proposes the optimal sales strategy based on that. For example, the generation AI receives input prompts such as conditions for high prices and quick sales, and creates a sales plan based on those conditions. The generation AI also learns from past sales data and proposes more effective sales strategies. Step 6: The self-learning unit learns from past successes and failures and applies this knowledge to future negotiations. For example, the generation AI analyzes past negotiation data to learn what conditions led to success and what failures occurred. This is expected to improve the agent AI system's negotiation skills, as well as the speed and quality of deal closings.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0157] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, Site Selection Department; Project Planning Department and Financial Procurement Department and Construction Management Department and Sales Strategy Department and a self-learning unit; The site selection unit We listen to the requests and conditions of the parties involved, and then propose the most suitable site based on that. The project planning department We listen to the requests and conditions of the parties involved, and then create the optimal plan based on that. The Financial Procurement Department We listen to the requests and conditions of the parties involved and propose the most appropriate financing method based on that. The construction management department: We listen to the requests and conditions of the parties involved and propose the optimal management method based on that. The sales strategy department We listen to the requests and conditions of the parties involved, and based on that, we propose the optimal sales strategy. The self-learning unit Learn from past successes and failures and apply them to your next negotiation A system characterized by:

2. The site selection unit Automate environmental impact assessments and make environmentally friendly choices 2. The system of claim 1.

3. The project planning department Adopt best practices from different industries 2. The system of claim 1.

4. The Financial Procurement Department We will analyze the past trends in the financial markets in detail and propose the most suitable method of fund procurement.

2. The system of claim 1.

5. The construction management department: Conduct detailed analysis of data from past construction projects and propose optimal management methods 2. The system of claim 1.

6. The sales strategy department Analyze the past sales data in detail and propose the optimal strategy 2. The system of claim 1.

7. The self-learning unit Learn not only the past negotiation data but also relevant industry news and trend information 2. The system of claim 1.

8. The site selection unit Analyze the sentiment of these stakeholders and select the site that will elicit the most positive response 2. The system of claim 1.

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