system

Abstract

The system according to this embodiment aims to streamline the matching process between organizations and promote the creation of collaborative projects. [Solution] The system according to the embodiment comprises an analysis unit, a matching unit, and a generation unit. The analysis unit analyzes public information and internal documents of each organization. The matching unit automatically matches the most suitable partner based on the information analyzed by the analysis unit. The generation unit automatically generates a specific collaborative project proposal based on the partner matched by the matching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the matching process between organizations is not efficiently performed, and it takes time and labor to create a collaborative project.

[0005] The system according to the embodiment aims to improve the matching process between organizations and promote the creation of collaborative projects.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a matching unit, and a generation unit. The analysis unit analyzes publicly available information and internal documents of each organization. The matching unit automatically matches the most suitable partner based on the information analyzed by the analysis unit. The generation unit automatically generates a specific collaborative project proposal based on the partner matched by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the matching process between organizations and promote the creation of collaborative projects. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. [[ID=十六]]

[0018] [[ID=十七]] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that autonomously matches organizations such as companies, local governments, educational institutions, and NPOs, and creates collaborative projects that optimize the needs and resources of both parties. This AI agent system utilizes generative AI to analyze public information and internal documents of each organization to understand the needs, resources, and goals of both parties in detail. Next, it automatically matches the most suitable partner and automatically generates a concrete collaborative project proposal. This mechanism streamlines the matching process between organizations and promotes the creation of new value. For example, it uses generative AI to analyze public information and internal documents of each organization. In this process, it understands the needs, resources, and goals of each organization in detail. For example, by analyzing information on a company's CSR activities or information on a local government's regional revitalization project, it is possible to understand the specific needs and resources of each organization. Next, based on the information analyzed by the generative AI, it automatically matches the most suitable partner. For example, by matching a company's CSR activities with a local government's regional revitalization project, it is possible to create a collaborative project that optimizes the needs and resources of both parties. Furthermore, the generative AI automatically generates a concrete collaborative project proposal. For example, when a company and an educational institution jointly develop a STEM education program, the generative AI automatically generates a project proposal. This proposal includes the project's objectives, specific activities, necessary resources, and expected outcomes. This mechanism streamlines the matching process between organizations and promotes the creation of new value. For instance, it can reduce the time and cost it takes for companies to find suitable partners and projects. It can also increase opportunities for organizations such as local governments, educational institutions, and NPOs to collaborate with companies that have the necessary funding and expertise. This AI agent utilizes generative AI to analyze and match the needs and resources of organizations, supporting the planning and execution of collaborative projects. This enables efficient and effective partnerships, aiming to create a sustainable and prosperous society.This allows the AI ​​agent system to autonomously match organizations such as companies, local governments, educational institutions, and NPOs, creating collaborative projects that optimize the needs and resources of both parties.

[0029] The AI ​​agent system according to this embodiment comprises an analysis unit, a matching unit, and a generation unit. The analysis unit analyzes publicly available information and internal documents of each organization. For example, the analysis unit analyzes information on a company's CSR activities and information on a local government's regional revitalization project. The analysis unit uses generation AI to grasp the needs, resources, and goals of each organization in detail. For example, the analysis unit can analyze information on a company's CSR activities to grasp the company's specific needs and resources. The analysis unit can also analyze information on a local government's regional revitalization project to grasp the local government's specific needs and resources. The matching unit automatically matches the optimal partner based on the information analyzed by the analysis unit. For example, the matching unit matches a company's CSR activities with a local government's regional revitalization project. The matching unit uses generation AI to automatically select the optimal partner. For example, by matching a company's CSR activities with a local government's regional revitalization project, the matching unit can create a collaborative project that optimizes the needs and resources of both parties. The generation unit automatically generates a specific collaborative project proposal based on the partner matched by the matching unit. The generation unit generates a project proposal for, for example, a joint development of a STEM education program between a company and an educational institution. Using generation AI, the generation unit generates a project proposal that includes the project's objectives, specific activities, necessary resources, and expected outcomes. For example, by generating a project proposal for a joint development of a STEM education program between a company and an educational institution, the planning of collaborative projects is streamlined. As a result, the AI ​​agent system according to this embodiment can analyze publicly available information and internal documents of each organization, automatically match them with the most suitable partners, and automatically generate a specific collaborative project proposal.

[0030] The analysis department analyzes publicly available information and internal documents from various organizations. For example, it analyzes information on a company's CSR activities and a local government's regional revitalization project. Specifically, the analysis department uses generative AI to gain a detailed understanding of each organization's needs, resources, and goals. The generative AI utilizes natural language processing technology to analyze vast amounts of text data and extract important information. For example, when analyzing information on a company's CSR activities, it collects publicly available information such as the company's official website, press releases, and annual reports, and the generative AI analyzes this information to understand the company's specific needs and resources. Similarly, when analyzing information on a local government's regional revitalization project, it collects publicly available information such as the local government's official website, project reports, and local news, and the generative AI analyzes this information to understand the local government's specific needs and resources. Furthermore, the analysis department also analyzes internal documents from each organization. For example, it analyzes internal company documents such as project plans, budgets, and internal reports to gain a detailed understanding of the company's internal resources and goals. Internal documents from local governments, such as project plans, budgets, and feedback from local residents, are analyzed to gain a detailed understanding of the local government's internal resources and goals. This allows the analysis department to comprehensively analyze publicly available information and internal documents from each organization, enabling them to gain a detailed understanding of each organization's needs, resources, and goals.

[0031] The matching unit automatically matches the most suitable partner based on the information analyzed by the analysis unit. Specifically, the matching unit uses generative AI to automatically select the optimal partner. The generative AI executes an algorithm to select the best partner based on the needs, resources, and goals of each organization provided by the analysis unit. For example, when matching a company's CSR activities with a local government's regional revitalization project, it compares the objectives and resources of the company's CSR activities with those of the local government's regional revitalization project, and selects a partner whose needs and resources best match those of both parties. The generative AI learns from past matching data and success stories to build an optimal matching algorithm. This allows the matching unit to create collaborative projects that optimize the needs and resources of both companies and local governments. Furthermore, the matching unit can update the matching results in real time and select the best partner based on the latest information. For example, if new information is added to a company's CSR activities or a local government's regional revitalization project, the generative AI immediately analyzes this information and updates the matching results. This allows the matching unit to always select the best partner based on the latest information, increasing the success rate of collaborative projects.

[0032] The generation unit automatically generates specific collaborative project proposals based on partners matched by the matching unit. Specifically, the generation unit uses a generation AI to generate proposals that include project objectives, specific activities, necessary resources, and expected outcomes. The generation AI utilizes natural language generation technology to create detailed proposals based on information provided by the analysis unit and the matching unit. For example, when generating a proposal for a joint STEM education program development between a company and an educational institution, it details the company's CSR activities, resources, and the educational institution's needs and resources, clearly defining specific activities and expected outcomes. The generation AI learns from past success stories and best practices to generate the optimal proposal format and content. This allows the generation unit to automatically generate efficient and high-quality proposals, streamlining the planning of collaborative projects. Furthermore, the generation unit can update the content of proposals in real time, providing proposals based on the latest information. For example, if there are changes in the needs or resources of a company or educational institution, the generation AI immediately reflects this information and updates the proposal. This allows the generation unit to consistently provide high-quality project proposals based on the latest information, thereby increasing the success rate of collaborative projects.

[0033] The Understanding Department thoroughly understands the needs and resources of each organization. For example, it uses generative AI to analyze publicly available information and internal documents of each organization to understand their needs and resources. For instance, it can analyze information on a company's CSR activities to understand the company's specific needs and resources. It can also analyze information on a local government's regional revitalization project to understand the local government's specific needs and resources. By thoroughly understanding the needs and resources of each organization, more appropriate matching becomes possible.

[0034] The execution department executes the project based on the generated project proposal. The execution department executes the project based on the generated project proposal, for example, using generative AI. For example, the execution department executes the project based on a project proposal for a company and an educational institution to jointly develop a STEM education program. The execution department executes the project based on a project proposal that includes the project's objectives, specific activities, necessary resources, and expected outcomes, using generative AI. For example, by executing the project based on a project proposal for a company and an educational institution to jointly develop a STEM education program, the execution of collaborative projects becomes more efficient. This makes the execution of collaborative projects more efficient by executing the project based on the generated project proposal.

[0035] The analysis department analyzes information on companies' CSR activities and local government revitalization projects. For example, the analysis department analyzes information on companies' CSR activities to understand their specific needs and resources. The analysis department uses generative AI to analyze information on companies' CSR activities. For instance, the analysis department can analyze information on companies' CSR activities to understand their specific needs and resources. Furthermore, the analysis department can analyze information on local government revitalization projects to understand the specific needs and resources of those local governments. This allows for more appropriate matching by analyzing information on companies' CSR activities and local government revitalization projects.

[0036] The generation unit generates project proposals for companies and educational institutions to jointly develop STEM education programs. For example, the generation unit generates project proposals for companies and educational institutions to jointly develop STEM education programs. The generation unit uses generation AI to generate project proposals for companies and educational institutions to jointly develop STEM education programs. For example, by generating project proposals for companies and educational institutions to jointly develop STEM education programs, the planning of collaborative projects becomes more efficient. This leads to more efficient planning of collaborative projects by generating project proposals for companies and educational institutions to jointly develop STEM education programs.

[0037] The generation unit generates a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes. For example, the generation unit generates a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes. The generation unit uses generation AI to generate a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes. For example, by generating a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes, the planning of collaborative projects becomes more efficient. This allows for more efficient planning of collaborative projects by generating a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes.

[0038] The analysis unit improves analysis accuracy by referring to each organization's past project history during the analysis process. For example, the analysis unit uses generative AI to analyze each organization's past project history and extract commonalities from successful projects. The analysis unit can also use generative AI to refer to each organization's past project history and identify the causes of failed projects. The analysis unit can also use generative AI to analyze each organization's past project history and identify best practices that can be applied to current projects. This improves analysis accuracy by referring to each organization's past project history.

[0039] The analysis unit includes internal communication data from each organization in its analysis. For example, it uses generative AI to analyze internal emails from each organization and extract important information related to the project. The analysis unit can also use generative AI to analyze chat logs from each organization to understand the progress of the project. The analysis unit can also use generative AI to analyze meeting records from each organization to identify project issues and solutions. By including internal communication data from each organization in the analysis, the accuracy of the analysis is improved.

[0040] The analysis unit considers the geographical distribution of each organization during the analysis. For example, the analysis unit uses generative AI to consider the location of each organization and perform analysis that reflects the characteristics of each region. The analysis unit can also use generative AI to consider the geographical distribution of each organization and analyze cooperative relationships between regions. The analysis unit can also use generative AI to consider the geographical distribution of each organization and optimize the needs and resources of each region. By considering the geographical distribution of each organization during the analysis, a more appropriate analysis becomes possible.

[0041] The analysis unit includes each organization's social media activities in its analysis. For example, it uses generative AI to analyze each organization's social media posts and identify project-related trends. It can also use generative AI to analyze each organization's social media activities and assess the project's impact. Furthermore, it can use generative AI to analyze each organization's social media activities and identify project success factors. This improves analysis accuracy by including each organization's social media activities in the analysis.

[0042] The matching unit improves matching accuracy by considering the success rate of each organization's past collaborative projects during the matching process. For example, the matching unit uses generative AI to analyze the success rate of each organization's past collaborative projects and prioritizes matching organizations with high success rates. The matching unit can also use generative AI to consider the success rate of each organization's past collaborative projects and postpone matching organizations with low success rates. The matching unit can also use generative AI to analyze the success rate of each organization's past collaborative projects and match organizations with intermediate success rates. This improves matching accuracy by considering the success rate of each organization's past collaborative projects.

[0043] The matching unit considers the leadership style of each organization during the matching process. For example, it may use generative AI to analyze the leadership style of each organization and prioritize matching organizations with similar leadership styles. The matching unit can also use generative AI to consider the leadership style of each organization and postpone matching organizations with different leadership styles. The matching unit can also use generative AI to analyze the leadership style of each organization and match organizations with intermediate leadership styles to the middle position. By considering the leadership style of each organization during the matching process, more appropriate matching becomes possible.

[0044] The matching unit considers the cultural background of each organization during the matching process. For example, the matching unit uses generative AI to analyze the cultural background of each organization and prioritizes matching organizations with similar cultural backgrounds. The matching unit can also use generative AI to consider the cultural background of each organization and postpone matching organizations with different cultural backgrounds. The matching unit can also use generative AI to analyze the cultural background of each organization and match organizations with intermediate cultural backgrounds in the middle. By considering the cultural background of each organization during the matching process, more appropriate matching becomes possible.

[0045] The matching unit considers the industry characteristics of each organization during the matching process. For example, the matching unit uses generative AI to analyze the industry characteristics of each organization and prioritizes matching organizations with similar industry characteristics. The matching unit can also use generative AI to consider the industry characteristics of each organization and postpone matching organizations with different industry characteristics. The matching unit can also use generative AI to analyze the industry characteristics of each organization and match organizations with intermediate industry characteristics to the middle position. By considering the industry characteristics of each organization during the matching process, more appropriate matching becomes possible.

[0046] The generation unit improves generation accuracy by referencing past successful project proposals during the generation process. For example, the generation unit uses generation AI to analyze past successful project proposals, extract success factors, and reflect them in new proposals. The generation unit can also use generation AI to refer to past successful project proposals and apply them to similar projects. The generation unit can also use generation AI to analyze past successful project proposals and incorporate best practices into new proposals. This improves generation accuracy by referencing past successful project proposals.

[0047] The generation unit, during generation, details the specific means of achieving each organization's goals. For example, the generation unit uses generation AI to analyze each organization's means of achieving its goals and includes specific steps in the proposal. The generation unit can also use generation AI to refer to each organization's means of achieving its goals and reflect feasible means in the proposal. The generation unit can also use generation AI to analyze each organization's means of achieving its goals and include the optimal means in the proposal. This enriches the content of the proposal by detailing each organization's specific means of achieving its goals.

[0048] The generation unit determines the priority of project proposals based on each organization's submission timing during the generation process. For example, the generation unit uses generation AI to analyze each organization's submission timing and prioritizes generating proposals with approaching deadlines. The generation unit can also use generation AI to consider each organization's submission timing and postpone proposals with later deadlines. The generation unit can also use generation AI to refer to each organization's submission timing and generate proposals with intermediate deadlines in the middle of the process. By determining the priority of project proposals based on each organization's submission timing, efficient proposal generation according to submission deadlines becomes possible.

[0049] The generation unit enriches the content of the project proposal by referring to relevant literature from each organization during the generation process. For example, the generation unit uses generation AI to analyze relevant literature from each organization and cite it in the proposal. The generation unit can also use generation AI to refer to relevant literature from each organization and reinforce the content of the proposal. The generation unit can also use generation AI to analyze relevant literature from each organization and apply it to the proposal. As a result, the content of the project proposal is enriched by referring to relevant literature from each organization.

[0050] The assessment unit improves its accuracy by referring to each organization's past project history during the assessment process. For example, the assessment unit uses generative AI to analyze each organization's past project history and extract commonalities from successful projects. The assessment unit can also use generative AI to refer to each organization's past project history and identify the causes of failed projects. The assessment unit can also use generative AI to analyze each organization's past project history and identify best practices that can be applied to current projects. This improves the accuracy of the assessment by referring to each organization's past project history.

[0051] The assessment unit, when assessing needs and resources, considers the geographical distribution of each organization. For example, it uses generative AI to consider the location of each organization and assess needs and resources that reflect the characteristics of each region. The assessment unit can also use generative AI to consider the geographical distribution of each organization and assess inter-regional cooperation. The assessment unit can also use generative AI to consider the geographical distribution of each organization and optimize the needs and resources for each region. This allows for a more accurate assessment by considering the geographical distribution of each organization when assessing needs and resources.

[0052] The execution unit improves execution accuracy by referring to each organization's past project execution history during execution. For example, the execution unit uses generative AI to analyze each organization's past project execution history and extract commonalities from successful projects. The execution unit can also use generative AI to refer to each organization's past project execution history and identify the causes of failed projects. The execution unit can also use generative AI to analyze each organization's past project execution history and identify best practices that can be applied to the current project. As a result, execution accuracy is improved by referring to each organization's past project execution history.

[0053] The execution unit executes projects while considering the geographical distribution of each organization. For example, the execution unit uses generative AI to consider the location of each organization and execute projects that reflect the characteristics of each region. The execution unit can also use generative AI to consider the geographical distribution of each organization and execute projects that strengthen cooperation between regions. The execution unit can also use generative AI to consider the geographical distribution of each organization and execute projects that optimize the needs and resources of each region. This allows for more appropriate execution by considering the geographical distribution of each organization when executing projects.

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

[0055] The analysis unit can consider the success rate of each organization's past projects when analyzing publicly available information and internal documents. For example, it can use generative AI to analyze the success rate of each organization's past projects and extract common characteristics of projects with high success rates. The analysis unit can also identify the causes of past project failures and take measures to avoid similar failures. Furthermore, the analysis unit can analyze the success factors of past projects and apply them to current projects to increase the likelihood of success. This allows the analysis unit to perform more accurate analyses by utilizing data from past projects.

[0056] The execution unit can carry out projects based on the generated project proposals while considering the geographical distribution of each organization. For example, the execution unit can use generation AI to consider the location of each organization and execute projects that reflect the characteristics of each region. It can also execute projects that strengthen cooperation between regions. Furthermore, it can execute projects that optimize the needs and resources of each region. As a result, the execution unit can execute projects more appropriately by considering the geographical distribution of each organization.

[0057] The analysis unit can include each organization's social media activities in its analysis of publicly available information and internal documents. For example, it can use generative AI to analyze each organization's social media posts and identify trends related to a project. It can also analyze social media activities to evaluate the impact of a project. Furthermore, it can analyze social media activities to identify factors that contribute to the success of a project. By including each organization's social media activities in its analysis, the analysis unit can improve the accuracy of its analysis.

[0058] The matching unit can analyze publicly available information and internal documents from each organization, taking into account each organization's leadership style when performing matching. For example, the matching unit can use generative AI to analyze each organization's leadership style and prioritize matching organizations with similar leadership styles. It can also postpone matching organizations with different leadership styles. Furthermore, it can match organizations with intermediate leadership styles to the middle position. In this way, the matching unit can perform more appropriate matching by considering each organization's leadership style.

[0059] The generation unit can improve its generation accuracy by referring to past successful project proposals from each organization when executing a project based on the generated proposals. For example, the generation unit can use generation AI to analyze past successful project proposals, extract success factors, and reflect them in new proposals. It can also refer to past successful project proposals and apply them to similar projects. Furthermore, it can analyze past successful project proposals and incorporate best practices into new proposals. In this way, the generation unit improves its generation accuracy by referring to past successful project proposals.

[0060] The matching unit can analyze publicly available information and internal documents from each organization, taking into account each organization's cultural background when performing matching. For example, the matching unit can use generative AI to analyze the cultural background of each organization and prioritize matching organizations with similar cultural backgrounds. It can also postpone matching organizations with different cultural backgrounds. Furthermore, it can match organizations with intermediate cultural backgrounds. In this way, the matching unit can perform more appropriate matching by considering the cultural background of each organization.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis department analyzes publicly available information and internal documents from each organization. For example, it analyzes information on companies' CSR activities and local government regional revitalization projects to gain a detailed understanding of each organization's needs, resources, and goals. Step 2: The matching unit automatically matches the most suitable partner based on the information analyzed by the analysis unit. For example, it matches a company's CSR activities with a local government's regional revitalization project, creating a collaborative project that optimizes the needs and resources of both parties. Step 3: The generation unit automatically generates a specific collaborative project proposal based on the partners matched by the matching unit. For example, it generates a project proposal for a company and an educational institution to jointly develop a STEM education program, including the project objectives, specific activities, necessary resources, and expected outcomes.

[0063] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that autonomously matches organizations such as companies, local governments, educational institutions, and NPOs, and creates collaborative projects that optimize the needs and resources of both parties. This AI agent system utilizes generative AI to analyze public information and internal documents of each organization to understand the needs, resources, and goals of both parties in detail. Next, it automatically matches the most suitable partner and automatically generates a concrete collaborative project proposal. This mechanism streamlines the matching process between organizations and promotes the creation of new value. For example, it uses generative AI to analyze public information and internal documents of each organization. In this process, it understands the needs, resources, and goals of each organization in detail. For example, by analyzing information on a company's CSR activities or information on a local government's regional revitalization project, it is possible to understand the specific needs and resources of each organization. Next, based on the information analyzed by the generative AI, it automatically matches the most suitable partner. For example, by matching a company's CSR activities with a local government's regional revitalization project, it is possible to create a collaborative project that optimizes the needs and resources of both parties. Furthermore, the generative AI automatically generates a concrete collaborative project proposal. For example, when a company and an educational institution jointly develop a STEM education program, the generative AI automatically generates a project proposal. This proposal includes the project's objectives, specific activities, necessary resources, and expected outcomes. This mechanism streamlines the matching process between organizations and promotes the creation of new value. For instance, it can reduce the time and cost it takes for companies to find suitable partners and projects. It can also increase opportunities for organizations such as local governments, educational institutions, and NPOs to collaborate with companies that have the necessary funding and expertise. This AI agent utilizes generative AI to analyze and match the needs and resources of organizations, supporting the planning and execution of collaborative projects. This enables efficient and effective partnerships, aiming to create a sustainable and prosperous society.This allows the AI ​​agent system to autonomously match organizations such as companies, local governments, educational institutions, and NPOs, creating collaborative projects that optimize the needs and resources of both parties.

[0064] The AI ​​agent system according to this embodiment comprises an analysis unit, a matching unit, and a generation unit. The analysis unit analyzes publicly available information and internal documents of each organization. For example, the analysis unit analyzes information on a company's CSR activities and information on a local government's regional revitalization project. The analysis unit uses generation AI to grasp the needs, resources, and goals of each organization in detail. For example, the analysis unit can analyze information on a company's CSR activities to grasp the company's specific needs and resources. The analysis unit can also analyze information on a local government's regional revitalization project to grasp the local government's specific needs and resources. The matching unit automatically matches the optimal partner based on the information analyzed by the analysis unit. For example, the matching unit matches a company's CSR activities with a local government's regional revitalization project. The matching unit uses generation AI to automatically select the optimal partner. For example, by matching a company's CSR activities with a local government's regional revitalization project, the matching unit can create a collaborative project that optimizes the needs and resources of both parties. The generation unit automatically generates a specific collaborative project proposal based on the partner matched by the matching unit. The generation unit generates a project proposal for, for example, a joint development of a STEM education program between a company and an educational institution. Using generation AI, the generation unit generates a project proposal that includes the project's objectives, specific activities, necessary resources, and expected outcomes. For example, by generating a project proposal for a joint development of a STEM education program between a company and an educational institution, the planning of collaborative projects is streamlined. As a result, the AI ​​agent system according to this embodiment can analyze publicly available information and internal documents of each organization, automatically match them with the most suitable partners, and automatically generate a specific collaborative project proposal.

[0065] The analysis department analyzes publicly available information and internal documents from various organizations. For example, it analyzes information on a company's CSR activities and a local government's regional revitalization project. Specifically, the analysis department uses generative AI to gain a detailed understanding of each organization's needs, resources, and goals. The generative AI utilizes natural language processing technology to analyze vast amounts of text data and extract important information. For example, when analyzing information on a company's CSR activities, it collects publicly available information such as the company's official website, press releases, and annual reports, and the generative AI analyzes this information to understand the company's specific needs and resources. Similarly, when analyzing information on a local government's regional revitalization project, it collects publicly available information such as the local government's official website, project reports, and local news, and the generative AI analyzes this information to understand the local government's specific needs and resources. Furthermore, the analysis department also analyzes internal documents from each organization. For example, it analyzes internal company documents such as project plans, budgets, and internal reports to gain a detailed understanding of the company's internal resources and goals. Internal documents from local governments, such as project plans, budgets, and feedback from local residents, are analyzed to gain a detailed understanding of the local government's internal resources and goals. This allows the analysis department to comprehensively analyze publicly available information and internal documents from each organization, enabling them to gain a detailed understanding of each organization's needs, resources, and goals.

[0066] The matching unit automatically matches the most suitable partner based on the information analyzed by the analysis unit. Specifically, the matching unit uses generative AI to automatically select the optimal partner. The generative AI executes an algorithm to select the best partner based on the needs, resources, and goals of each organization provided by the analysis unit. For example, when matching a company's CSR activities with a local government's regional revitalization project, it compares the objectives and resources of the company's CSR activities with those of the local government's regional revitalization project, and selects a partner whose needs and resources best match those of both parties. The generative AI learns from past matching data and success stories to build an optimal matching algorithm. This allows the matching unit to create collaborative projects that optimize the needs and resources of both companies and local governments. Furthermore, the matching unit can update the matching results in real time and select the best partner based on the latest information. For example, if new information is added to a company's CSR activities or a local government's regional revitalization project, the generative AI immediately analyzes this information and updates the matching results. This allows the matching unit to always select the best partner based on the latest information, increasing the success rate of collaborative projects.

[0067] The generation unit automatically generates specific collaborative project proposals based on partners matched by the matching unit. Specifically, the generation unit uses a generation AI to generate proposals that include project objectives, specific activities, necessary resources, and expected outcomes. The generation AI utilizes natural language generation technology to create detailed proposals based on information provided by the analysis unit and the matching unit. For example, when generating a proposal for a joint STEM education program development between a company and an educational institution, it details the company's CSR activities, resources, and the educational institution's needs and resources, clearly defining specific activities and expected outcomes. The generation AI learns from past success stories and best practices to generate the optimal proposal format and content. This allows the generation unit to automatically generate efficient and high-quality proposals, streamlining the planning of collaborative projects. Furthermore, the generation unit can update the content of proposals in real time, providing proposals based on the latest information. For example, if there are changes in the needs or resources of a company or educational institution, the generation AI immediately reflects this information and updates the proposal. This allows the generation unit to consistently provide high-quality project proposals based on the latest information, thereby increasing the success rate of collaborative projects.

[0068] The Understanding Department thoroughly understands the needs and resources of each organization. For example, it uses generative AI to analyze publicly available information and internal documents of each organization to understand their needs and resources. For instance, it can analyze information on a company's CSR activities to understand the company's specific needs and resources. It can also analyze information on a local government's regional revitalization project to understand the local government's specific needs and resources. By thoroughly understanding the needs and resources of each organization, more appropriate matching becomes possible.

[0069] The execution department executes the project based on the generated project proposal. The execution department executes the project based on the generated project proposal, for example, using generative AI. For example, the execution department executes the project based on a project proposal for a company and an educational institution to jointly develop a STEM education program. The execution department executes the project based on a project proposal that includes the project's objectives, specific activities, necessary resources, and expected outcomes, using generative AI. For example, by executing the project based on a project proposal for a company and an educational institution to jointly develop a STEM education program, the execution of collaborative projects becomes more efficient. This makes the execution of collaborative projects more efficient by executing the project based on the generated project proposal.

[0070] The analysis department analyzes information on companies' CSR activities and local government revitalization projects. For example, the analysis department analyzes information on companies' CSR activities to understand their specific needs and resources. The analysis department uses generative AI to analyze information on companies' CSR activities. For instance, the analysis department can analyze information on companies' CSR activities to understand their specific needs and resources. Furthermore, the analysis department can analyze information on local government revitalization projects to understand the specific needs and resources of those local governments. This allows for more appropriate matching by analyzing information on companies' CSR activities and local government revitalization projects.

[0071] The generation unit generates project proposals for companies and educational institutions to jointly develop STEM education programs. For example, the generation unit generates project proposals for companies and educational institutions to jointly develop STEM education programs. The generation unit uses generation AI to generate project proposals for companies and educational institutions to jointly develop STEM education programs. For example, by generating project proposals for companies and educational institutions to jointly develop STEM education programs, the planning of collaborative projects becomes more efficient. This leads to more efficient planning of collaborative projects by generating project proposals for companies and educational institutions to jointly develop STEM education programs.

[0072] The generation unit generates a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes. For example, the generation unit generates a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes. The generation unit uses generation AI to generate a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes. For example, by generating a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes, the planning of collaborative projects becomes more efficient. This allows for more efficient planning of collaborative projects by generating a project proposal that includes the project objectives, specific activities, necessary resources, and expected outcomes.

[0073] The analysis unit estimates the sentiment of each organization and adjusts the analysis priority based on the estimated sentiment. For example, the analysis unit can use generative AI to estimate sentiment from publicly available information of each organization and prioritize the analysis of organizations with positive sentiment. The analysis unit can also use generative AI to estimate sentiment from internal documents of each organization and postpone the analysis of organizations with negative sentiment. The analysis unit can also use generative AI to estimate sentiment from each organization's social media activity and analyze organizations with neutral sentiment in the middle. By adjusting the analysis priority based on the sentiment of each organization, more appropriate analysis becomes possible. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The analysis unit improves analysis accuracy by referring to each organization's past project history during the analysis process. For example, the analysis unit uses generative AI to analyze each organization's past project history and extract commonalities from successful projects. The analysis unit can also use generative AI to refer to each organization's past project history and identify the causes of failed projects. The analysis unit can also use generative AI to analyze each organization's past project history and identify best practices that can be applied to current projects. This improves analysis accuracy by referring to each organization's past project history.

[0075] The analysis unit includes internal communication data from each organization in its analysis. For example, it uses generative AI to analyze internal emails from each organization and extract important information related to the project. The analysis unit can also use generative AI to analyze chat logs from each organization to understand the progress of the project. The analysis unit can also use generative AI to analyze meeting records from each organization to identify project issues and solutions. By including internal communication data from each organization in the analysis, the accuracy of the analysis is improved.

[0076] The analysis unit estimates the emotions of each organization and adjusts the display method of the analysis results based on the estimated emotions. For example, the analysis unit can use generative AI to estimate the emotions of each organization and display detailed analysis results for organizations with positive emotions. The analysis unit can also use generative AI to estimate the emotions of each organization and display concise analysis results for organizations with negative emotions. The analysis unit can also use generative AI to estimate the emotions of each organization and display standard analysis results for organizations with neutral emotions. This allows for the display of more appropriate analysis results by adjusting the display method of the analysis results based on the emotions of each organization. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The analysis unit considers the geographical distribution of each organization during the analysis. For example, the analysis unit uses generative AI to consider the location of each organization and perform analysis that reflects the characteristics of each region. The analysis unit can also use generative AI to consider the geographical distribution of each organization and analyze cooperative relationships between regions. The analysis unit can also use generative AI to consider the geographical distribution of each organization and optimize the needs and resources of each region. By considering the geographical distribution of each organization during the analysis, a more appropriate analysis becomes possible.

[0078] The analysis unit includes each organization's social media activities in its analysis. For example, it uses generative AI to analyze each organization's social media posts and identify project-related trends. It can also use generative AI to analyze each organization's social media activities and assess the project's impact. Furthermore, it can use generative AI to analyze each organization's social media activities and identify project success factors. This improves analysis accuracy by including each organization's social media activities in the analysis.

[0079] The matching unit estimates the sentiment of each organization and adjusts the matching criteria based on the estimated sentiment. For example, the matching unit can use generative AI to estimate the sentiment of each organization and prioritize matching organizations with positive sentiment. The matching unit can also use generative AI to estimate the sentiment of each organization and postpone matching organizations with negative sentiment. The matching unit can also use generative AI to estimate the sentiment of each organization and match organizations with neutral sentiment in the middle. This allows for more appropriate matching by adjusting the matching criteria based on the sentiment of each organization. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The matching unit improves matching accuracy by considering the success rate of each organization's past collaborative projects during the matching process. For example, the matching unit uses generative AI to analyze the success rate of each organization's past collaborative projects and prioritizes matching organizations with high success rates. The matching unit can also use generative AI to consider the success rate of each organization's past collaborative projects and postpone matching organizations with low success rates. The matching unit can also use generative AI to analyze the success rate of each organization's past collaborative projects and match organizations with intermediate success rates. This improves matching accuracy by considering the success rate of each organization's past collaborative projects.

[0081] The matching unit considers the leadership style of each organization during the matching process. For example, it may use generative AI to analyze the leadership style of each organization and prioritize matching organizations with similar leadership styles. The matching unit can also use generative AI to consider the leadership style of each organization and postpone matching organizations with different leadership styles. The matching unit can also use generative AI to analyze the leadership style of each organization and match organizations with intermediate leadership styles to the middle position. By considering the leadership style of each organization during the matching process, more appropriate matching becomes possible.

[0082] The matching unit estimates the sentiment of each organization and adjusts the display order of the matching results based on the estimated sentiment. For example, the matching unit can use generative AI to estimate the sentiment of each organization and display organizations with positive sentiment at the top. The matching unit can also use generative AI to estimate the sentiment of each organization and display organizations with negative sentiment at the bottom. The matching unit can also use generative AI to estimate the sentiment of each organization and display organizations with neutral sentiment in the middle. By adjusting the display order of the matching results based on the sentiment of each organization, it becomes possible to display more appropriate matching results. Sentiment estimation is achieved using a sentiment estimation function, for example, using a sentiment engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The matching unit considers the cultural background of each organization during the matching process. For example, the matching unit uses generative AI to analyze the cultural background of each organization and prioritizes matching organizations with similar cultural backgrounds. The matching unit can also use generative AI to consider the cultural background of each organization and postpone matching organizations with different cultural backgrounds. The matching unit can also use generative AI to analyze the cultural background of each organization and match organizations with intermediate cultural backgrounds in the middle. By considering the cultural background of each organization during the matching process, more appropriate matching becomes possible.

[0084] The matching unit considers the industry characteristics of each organization during the matching process. For example, the matching unit uses generative AI to analyze the industry characteristics of each organization and prioritizes matching organizations with similar industry characteristics. The matching unit can also use generative AI to consider the industry characteristics of each organization and postpone matching organizations with different industry characteristics. The matching unit can also use generative AI to analyze the industry characteristics of each organization and match organizations with intermediate industry characteristics to the middle position. By considering the industry characteristics of each organization during the matching process, more appropriate matching becomes possible.

[0085] The generation unit estimates the sentiment of each organization and adjusts the expression of the proposal based on the estimated sentiment. For example, the generation unit can use a generation AI to estimate the sentiment of each organization and use detailed expression for organizations with positive sentiment. The generation unit can also use a generation AI to estimate the sentiment of each organization and use concise expression for organizations with negative sentiment. The generation unit can also use a generation AI to estimate the sentiment of each organization and use standard expression for organizations with neutral sentiment. This allows for the generation of more appropriate proposals by adjusting the expression of the proposal based on the sentiment of each organization. Sentiment estimation is achieved using a sentiment estimation function, such as an sentiment engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The generation unit improves generation accuracy by referencing past successful project proposals during the generation process. For example, the generation unit uses generation AI to analyze past successful project proposals, extract success factors, and reflect them in new proposals. The generation unit can also use generation AI to refer to past successful project proposals and apply them to similar projects. The generation unit can also use generation AI to analyze past successful project proposals and incorporate best practices into new proposals. This improves generation accuracy by referencing past successful project proposals.

[0087] The generation unit, during generation, details the specific means of achieving each organization's goals. For example, the generation unit uses generation AI to analyze each organization's means of achieving its goals and includes specific steps in the proposal. The generation unit can also use generation AI to refer to each organization's means of achieving its goals and reflect feasible means in the proposal. The generation unit can also use generation AI to analyze each organization's means of achieving its goals and include the optimal means in the proposal. This enriches the content of the proposal by detailing each organization's specific means of achieving its goals.

[0088] The generation unit estimates the sentiment of each organization and adjusts the length of the proposal based on the estimated sentiment. For example, the generation unit can use a generation AI to estimate the sentiment of each organization and generate a detailed proposal for organizations with positive sentiment. The generation unit can also use a generation AI to estimate the sentiment of each organization and generate a concise proposal for organizations with negative sentiment. The generation unit can also use a generation AI to estimate the sentiment of each organization and generate a standard proposal for organizations with neutral sentiment. This allows for the generation of more appropriate proposals by adjusting the length of the proposal based on the sentiment of each organization. Sentiment estimation is achieved using a sentiment estimation function, such as an sentiment engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The generation unit determines the priority of project proposals based on each organization's submission timing during the generation process. For example, the generation unit uses generation AI to analyze each organization's submission timing and prioritizes generating proposals with approaching deadlines. The generation unit can also use generation AI to consider each organization's submission timing and postpone proposals with later deadlines. The generation unit can also use generation AI to refer to each organization's submission timing and generate proposals with intermediate deadlines in the middle of the process. By determining the priority of project proposals based on each organization's submission timing, efficient proposal generation according to submission deadlines becomes possible.

[0090] The generation unit enriches the content of the project proposal by referring to relevant literature from each organization during the generation process. For example, the generation unit uses generation AI to analyze relevant literature from each organization and cite it in the proposal. The generation unit can also use generation AI to refer to relevant literature from each organization and reinforce the content of the proposal. The generation unit can also use generation AI to analyze relevant literature from each organization and apply it to the proposal. As a result, the content of the project proposal is enriched by referring to relevant literature from each organization.

[0091] The understanding unit estimates the emotions of each organization and adjusts the method of understanding needs and resources based on the estimated emotions. For example, the understanding unit can use generative AI to estimate the emotions of each organization and understand detailed needs and resources for organizations with positive emotions. The understanding unit can also use generative AI to estimate the emotions of each organization and understand concise needs and resources for organizations with negative emotions. The understanding unit can also use generative AI to estimate the emotions of each organization and understand standard needs and resources for organizations with neutral emotions. This allows for more appropriate understanding by adjusting the method of understanding needs and resources based on the emotions of each organization. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The assessment unit improves its accuracy by referring to each organization's past project history during the assessment process. For example, the assessment unit uses generative AI to analyze each organization's past project history and extract commonalities from successful projects. The assessment unit can also use generative AI to refer to each organization's past project history and identify the causes of failed projects. The assessment unit can also use generative AI to analyze each organization's past project history and identify best practices that can be applied to current projects. This improves the accuracy of the assessment by referring to each organization's past project history.

[0093] The understanding unit estimates the emotions of each organization and determines the priority of needs and resources based on the estimated emotions. For example, the understanding unit can use generative AI to estimate the emotions of each organization and prioritize the needs and resources of organizations with positive emotions. The understanding unit can also use generative AI to estimate the emotions of each organization and postpone the needs and resources of organizations with negative emotions. The understanding unit can also use generative AI to estimate the emotions of each organization and give intermediate priority to the needs and resources of organizations with neutral emotions. This allows for more appropriate prioritization by determining the priority of needs and resources based on the emotions of each organization. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The assessment unit, when assessing needs and resources, considers the geographical distribution of each organization. For example, it uses generative AI to consider the location of each organization and assess needs and resources that reflect the characteristics of each region. The assessment unit can also use generative AI to consider the geographical distribution of each organization and assess inter-regional cooperation. The assessment unit can also use generative AI to consider the geographical distribution of each organization and optimize the needs and resources for each region. This allows for a more accurate assessment by considering the geographical distribution of each organization when assessing needs and resources.

[0095] The execution unit estimates the sentiment of each organization and adjusts the project execution method based on the estimated sentiment. For example, the execution unit can use generative AI to estimate the sentiment of each organization and propose an aggressive execution method to organizations with positive sentiment. The execution unit can also use generative AI to estimate the sentiment of each organization and propose a cautious execution method to organizations with negative sentiment. The execution unit can also use generative AI to estimate the sentiment of each organization and propose a standard execution method to organizations with neutral sentiment. This allows for more appropriate execution by adjusting the project execution method based on the sentiment of each organization. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The execution unit improves execution accuracy by referring to each organization's past project execution history during execution. For example, the execution unit uses generative AI to analyze each organization's past project execution history and extract commonalities from successful projects. The execution unit can also use generative AI to refer to each organization's past project execution history and identify the causes of failed projects. The execution unit can also use generative AI to analyze each organization's past project execution history and identify best practices that can be applied to the current project. As a result, execution accuracy is improved by referring to each organization's past project execution history.

[0097] The execution unit estimates the sentiment of each organization and determines the priority of project execution based on the estimated sentiment. For example, the execution unit can use generative AI to estimate the sentiment of each organization and prioritize projects of organizations with positive sentiment. The execution unit can also use generative AI to estimate the sentiment of each organization and postpone projects of organizations with negative sentiment. The execution unit can also use generative AI to estimate the sentiment of each organization and prioritize projects of organizations with neutral sentiment in the middle. This allows for more appropriate prioritization by determining the priority of project execution based on the sentiment of each organization. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The execution unit executes projects while considering the geographical distribution of each organization. For example, the execution unit uses generative AI to consider the location of each organization and execute projects that reflect the characteristics of each region. The execution unit can also use generative AI to consider the geographical distribution of each organization and execute projects that strengthen cooperation between regions. The execution unit can also use generative AI to consider the geographical distribution of each organization and execute projects that optimize the needs and resources of each region. This allows for more appropriate execution by considering the geographical distribution of each organization when executing projects.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The analysis unit can consider the success rate of each organization's past projects when analyzing publicly available information and internal documents. For example, it can use generative AI to analyze the success rate of each organization's past projects and extract common characteristics of projects with high success rates. The analysis unit can also identify the causes of past project failures and take measures to avoid similar failures. Furthermore, the analysis unit can analyze the success factors of past projects and apply them to current projects to increase the likelihood of success. This allows the analysis unit to perform more accurate analyses by utilizing data from past projects.

[0101] The understanding unit can estimate the sentiment of each organization when gaining a detailed understanding of their needs and resources, and adjust its understanding method based on the estimated sentiment. For example, the understanding unit can use generative AI to estimate the sentiment of each organization, and can grasp detailed needs and resources for organizations with positive sentiment. It can also grasp concise needs and resources for organizations with negative sentiment. Furthermore, it can grasp standard needs and resources for organizations with neutral sentiment. As a result, the understanding unit can adjust its needs and resource assessment method based on the sentiment of each organization, enabling more accurate understanding.

[0102] The execution unit can carry out projects based on the generated project proposals while considering the geographical distribution of each organization. For example, the execution unit can use generation AI to consider the location of each organization and execute projects that reflect the characteristics of each region. It can also execute projects that strengthen cooperation between regions. Furthermore, it can execute projects that optimize the needs and resources of each region. As a result, the execution unit can execute projects more appropriately by considering the geographical distribution of each organization.

[0103] The analysis unit can include each organization's social media activities in its analysis of publicly available information and internal documents. For example, it can use generative AI to analyze each organization's social media posts and identify trends related to a project. It can also analyze social media activities to evaluate the impact of a project. Furthermore, it can analyze social media activities to identify factors that contribute to the success of a project. By including each organization's social media activities in its analysis, the analysis unit can improve the accuracy of its analysis.

[0104] The generation unit can estimate the sentiment of each organization when executing a project based on the generated proposals, and adjust the expression of the proposals based on the estimated sentiments. For example, the generation unit can use generation AI to estimate the sentiments of each organization and use detailed expressions for organizations with positive sentiments. It can also use concise expressions for organizations with negative sentiments. Furthermore, it can use standard expressions for organizations with neutral sentiments. In this way, the generation unit can adjust the expression of the proposals based on the sentiments of each organization, thereby generating more appropriate proposals.

[0105] The matching unit can analyze publicly available information and internal documents from each organization, taking into account each organization's leadership style when performing matching. For example, the matching unit can use generative AI to analyze each organization's leadership style and prioritize matching organizations with similar leadership styles. It can also postpone matching organizations with different leadership styles. Furthermore, it can match organizations with intermediate leadership styles to the middle position. In this way, the matching unit can perform more appropriate matching by considering each organization's leadership style.

[0106] The analysis unit can estimate the sentiment of each organization when analyzing publicly available information and internal documents, and adjust the analysis priority based on the estimated sentiment. For example, the analysis unit can use generative AI to estimate sentiment from each organization's publicly available information and prioritize the analysis of organizations with positive sentiment. It can also postpone the analysis of organizations with negative sentiment. Furthermore, it can analyze organizations with neutral sentiment in the middle. In this way, the analysis unit can perform more appropriate analysis by adjusting the analysis priority based on the sentiment of each organization.

[0107] The generation unit can improve its generation accuracy by referring to past successful project proposals from each organization when executing a project based on the generated proposals. For example, the generation unit can use generation AI to analyze past successful project proposals, extract success factors, and reflect them in new proposals. It can also refer to past successful project proposals and apply them to similar projects. Furthermore, it can analyze past successful project proposals and incorporate best practices into new proposals. In this way, the generation unit improves its generation accuracy by referring to past successful project proposals.

[0108] The matching unit can analyze publicly available information and internal documents from each organization, taking into account each organization's cultural background when performing matching. For example, the matching unit can use generative AI to analyze the cultural background of each organization and prioritize matching organizations with similar cultural backgrounds. It can also postpone matching organizations with different cultural backgrounds. Furthermore, it can match organizations with intermediate cultural backgrounds. In this way, the matching unit can perform more appropriate matching by considering the cultural background of each organization.

[0109] The execution unit, when executing a project based on the generated project proposal, can estimate the sentiments of each organization and adjust the project execution method based on those estimated sentiments. For example, the execution unit can use generative AI to estimate the sentiments of each organization and propose an aggressive execution method to organizations with positive sentiments. It can also propose a cautious execution method to organizations with negative sentiments. Furthermore, it can propose a standard execution method to organizations with neutral sentiments. This allows the execution unit to adjust the project execution method based on the sentiments of each organization, enabling more appropriate execution.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The analysis department analyzes publicly available information and internal documents from each organization. For example, it analyzes information on companies' CSR activities and local government regional revitalization projects to gain a detailed understanding of each organization's needs, resources, and goals. Step 2: The matching unit automatically matches the most suitable partner based on the information analyzed by the analysis unit. For example, it matches a company's CSR activities with a local government's regional revitalization project, creating a collaborative project that optimizes the needs and resources of both parties. Step 3: The generation unit automatically generates a specific collaborative project proposal based on the partners matched by the matching unit. For example, it generates a project proposal for a company and an educational institution to jointly develop a STEM education program, including the project objectives, specific activities, necessary resources, and expected outcomes.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the analysis unit, matching unit, generation unit, understanding unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes information on a company's CSR activities and information on a local government's regional revitalization project. The matching unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically matches the optimal partner. The generation unit is implemented by the control unit 46A of the smart device 14 and automatically generates a project proposal for a specific collaborative project. The understanding unit is implemented by the specific processing unit 290 of the data processing device 12 and grasps the needs and resources of each organization in detail. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the project based on the generated project proposal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the analysis unit, matching unit, generation unit, understanding unit, and execution unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes information on a company's CSR activities and information on a local government's regional revitalization project. The matching unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically matches the optimal partner. The generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates a project proposal for a specific collaborative project. The understanding unit is implemented by the specific processing unit 290 of the data processing device 12 and grasps the needs and resources of each organization in detail. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the project based on the generated project proposal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the analysis unit, matching unit, generation unit, information gathering unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes information on a company's CSR activities and information on a local government's regional revitalization project. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically matches the optimal partner. The generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates a project proposal for a specific collaborative project. The information gathering unit is implemented by the specific processing unit 290 of the data processing unit 12 and grasps the needs and resources of each organization in detail. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the project based on the generated project proposal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the analysis unit, matching unit, generation unit, understanding unit, and execution unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes information on a company's CSR activities and information on a local government's regional revitalization project. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically matches the optimal partner. The generation unit is implemented by the control unit 46A of the robot 414 and automatically generates a project proposal for a specific collaborative project. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12 and grasps the needs and resources of each organization in detail. The execution unit is implemented by the control unit 46A of the robot 414 and executes the project based on the generated project proposal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The analysis department analyzes publicly available information and internal documents from each organization, A matching unit that automatically matches the optimal partner based on the information analyzed by the aforementioned analysis unit, A generation unit that automatically generates a specific collaborative project proposal based on the partners matched by the aforementioned matching unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with a unit that provides detailed information on the needs and resources of each organization. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an execution unit that carries out the project based on the generated project proposal. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We analyze information on corporate social responsibility (CSR) activities and local government regional revitalization projects. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate a project proposal for companies and educational institutions to jointly develop STEM education programs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate a project proposal that includes the project objectives, specific activities, required resources, and expected outcomes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate the sentiment of each organization and adjust the analysis priorities based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the past project history of each organization. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During the analysis, include internal communication data from each organization in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the emotions of each organization and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, the geographical distribution of each organization will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, include each organization's social media activity in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is We estimate the sentiment of each organization and adjust the matching criteria based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is To improve matching accuracy, we consider the success rate of past collaborative projects for each organization. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is During the matching process, we take into account the leadership style of each organization. The system described in Appendix 1, characterized by the features described herein. (Note 16) The matching unit is The system estimates the sentiment of each organization and adjusts the display order of the matching results based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is When matching organizations, the cultural background of each organization is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is During the matching process, the industry characteristics of each organization will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the sentiments of each organization and adjust the wording of the project proposal based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, we improve generation accuracy by referencing past successful project proposals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating the report, describe in detail the specific means by which each organization will achieve its goals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is Estimate the sentiments of each organization and adjust the length of the project proposal based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of project proposals is determined based on the submission timing of each organization. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During the generation process, the content of the project proposal will be enriched by referring to relevant literature from each organization. The system described in Appendix 1, characterized by the features described herein. (Note 25) The gripping part is, We estimate the sentiments of each organization and adjust how we understand their needs and resources based on those estimated sentiments. The system described in Appendix 2, characterized by the features described herein. (Note 26) The gripping part is, When assessing the situation, we improve the accuracy of the assessment by referring to the past project history of each organization. The system described in Appendix 2, characterized by the features described herein. (Note 27) The gripping part is, Estimate the sentiment of each organization and prioritize needs and resources based on those estimated sentiments. The system described in Appendix 2, characterized by the features described herein. (Note 28) The gripping part is, When assessing needs and resources, consider the geographical distribution of each organization. The system described in Appendix 2, characterized by the features described herein. (Note 29) The execution unit is, Estimate the sentiment of each organization and adjust the project execution method based on the estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The execution unit is, During execution, refer to each organization's past project execution history to improve execution accuracy. The system described in Appendix 3, characterized by the features described herein. (Note 31) The execution unit is, Estimate the sentiment of each organization and determine project execution priorities based on those estimated sentiments. The system described in Appendix 3, characterized by the features described herein. (Note 32) The execution unit is, During execution, the project will be run while taking into account the geographical distribution of each organization. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes publicly available information and internal documents from each organization, A matching unit that automatically matches the optimal partner based on the information analyzed by the aforementioned analysis unit, A generation unit that automatically generates a specific collaborative project proposal based on the partners matched by the aforementioned matching unit, Equipped with A system characterized by the following features.

2. It is equipped with a unit that provides detailed information on the needs and resources of each organization. The system according to feature 1.

3. It includes an execution unit that carries out the project based on the generated project proposal. The system according to feature 1.

4. The aforementioned analysis unit, We analyze information on corporate social responsibility (CSR) activities and local government regional revitalization projects. The system according to feature 1.

5. The generating unit is Generate a project proposal for companies and educational institutions to jointly develop STEM education programs. The system according to feature 1.

6. The generating unit is Generate a project proposal that includes the project objectives, specific activities, required resources, and expected outcomes. The system according to feature 1.

7. The aforementioned analysis unit, We estimate the sentiment of each organization and adjust the analysis priorities based on the estimated sentiment. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the past project history of each organization. The system according to feature 1.

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