System

Abstract

An object of a system according to an embodiment is to efficiently match a problem of a company with a solution of a business operator.SOLUTION: A system includes a problem disclosure part, a solution application part, and a matching part. The issue publication unit publishes an issue of a company. The solution application unit receives a solution of a business operator. The matching unit matches a problem of a company with a solution of a business operator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently match corporate challenges with business solutions.

[0005] The system according to the embodiment aims to efficiently match corporate issues with business solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a problem disclosure unit, a solution application unit, and a matching unit. The problem disclosure unit discloses problems of companies. The solution application unit accepts solutions from businesses. The matching unit matches the problems of companies with the solutions from businesses. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match corporate issues with business solutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The reverse pitch platform according to an embodiment of the present invention is a system in which companies disclose their challenges, a generation AI analyzes the challenges, and businesses submit solutions. This enables companies to quickly find new technologies and innovative approaches within limited resources and time.

[0029] The reverse pitch platform according to the embodiment comprises a problem disclosure unit, a solution application unit, and a matching unit. The problem disclosure unit discloses a company's problem. For example, a company discloses a problem such as "I want to improve the efficiency of product quality control." The solution application unit accepts solutions from businesses. For example, a business proposes a "quality control system using AI." The matching unit matches the company's problem with the business's solution. For example, a generation AI analyzes the company's problem and the business's solution, evaluates their compatibility, and performs matching. In this way, the reverse pitch platform discloses a company's problem, accepts business solutions, and performs matching, thereby efficiently solving problems.

[0030] The problem disclosure section can analyze a company's issues, referencing similar past issues and their solutions, and automatically generate a more specific problem definition. For example, in the problem disclosure section, the generation AI analyzes a company's issues and searches a database for similar past issues and their solutions. For example, if there was a past issue of "improving the efficiency of product quality control," the generation AI references the solution and automatically generates a specific definition that can be applied to the current issue. The generation AI also analyzes a company's issues and automatically generates detailed requirements for the issue based on solutions to similar past issues. For example, it proposes specific methods and technologies for "improving the efficiency of quality control." The generation AI also analyzes a company's issues and references similar past issues and their solutions to clarify the background and purpose of the issue. For example, it automatically generates "specific steps and methods for improving the efficiency of product quality control." This makes it possible to automatically generate more specific problem definitions by referencing similar past issues and their solutions.

[0031] The Problem Disclosure Department automatically adds background information and related technology trends to a problem before it is made public, allowing for a deeper understanding of the problem. In the Problem Disclosure Department, for example, the generation AI analyzes a company's problem and automatically adds related technology trends and background information. For example, it adds "the latest technologies and market trends for improving the efficiency of product quality control" to the problem. The generation AI also analyzes a company's problem and automatically adds background information to the problem. For example, it includes "past research and case studies on improving the efficiency of quality control." The generation AI also analyzes a company's problem and automatically adds related technology trends. For example, it adds "the latest technologies and market trends for quality control using AI" to the problem. In this way, adding background information and related technology trends to the problem allows for a deeper understanding of the problem.

[0032] When a problem is made public, the problem disclosure unit allows the generation AI to automatically search for related patent information and academic papers, and provide them as reference material for solving the problem. For example, in the problem disclosure unit, the generation AI analyzes a company's problem and automatically searches for related patent information. For example, it adds "patented technology for improving the efficiency of product quality control" to the problem. The generation AI also analyzes a company's problem and automatically searches for related academic papers. For example, it adds "the latest research results on improving the efficiency of quality control" to the problem. The generation AI also analyzes a company's problem and automatically searches for related patent information and academic papers. For example, it adds "the latest technologies and research results for quality control using AI" to the problem. In this way, by providing related patent information and academic papers, it is possible to provide reference material for solving the problem.

[0033] In the problem disclosure section, when companies from different industries have a common problem, the generative AI can automatically group those companies and work together to solve the problem. For example, when the generative AI analyzes a company's problem, it automatically groups those companies if they have a common problem. For example, it groups companies that have the problem of "improving the efficiency of product quality control" and works together to find a solution. In addition, when the generative AI analyzes a company's problem and companies from different industries have a common problem, it automatically groups those companies. For example, it groups companies that have the problem of "improving the efficiency of quality control" and works together to find a solution. In addition, when the generative AI analyzes a company's problem and companies from different industries have a common problem, it automatically groups those companies. For example, it groups companies that have the problem of "improving the efficiency of quality control using AI" and works together to find a solution. This makes it possible to group companies from different industries and work together to solve the problem.

[0034] When analyzing a business's solution, the Solution Submission Department can evaluate the effectiveness of the proposal by referring to past success stories and failure stories. In the Solution Submission Department, for example, the generation AI analyzes the business's solution and searches a database for past success stories and failure stories. For example, for a proposal for a "quality control system using AI," the effectiveness of the proposal is evaluated by referring to past success stories and failure stories. The generation AI also analyzes the business's solution and evaluates the effectiveness of the proposal based on past success stories and failure stories. For example, for a proposal for "improving quality control efficiency," the effectiveness of the proposal is evaluated by referring to past cases. The generation AI also analyzes the business's solution and evaluates the effectiveness of the proposal by referring to past success stories and failure stories. For example, for a proposal for a "quality control system using AI," the effectiveness of the proposal is evaluated by referring to past cases. In this way, the effectiveness of the proposal can be evaluated by referring to past success stories and failure stories.

[0035] When evaluating solutions, the Solution Submission Department can automatically verify the technical details and implementability of the proposal. For example, in the Solution Submission Department, the generation AI analyzes the business's solution and automatically verifies the technical details of the proposal. For example, it analyzes the technical details of a "quality control system using AI" and evaluates its implementability. The generation AI also analyzes the business's solution and automatically verifies the technical details of the proposal. For example, it analyzes the technical details of a proposal regarding "improving quality control efficiency" and evaluates its implementability. The generation AI also analyzes the business's solution and automatically verifies the technical details of the proposal. For example, it analyzes the technical details of a "quality control system using AI" and evaluates its implementability. This makes it possible to automatically verify the technical details and implementability of the proposal.

[0036] When a solution is submitted, the generation AI automatically provides relevant market data and competitive information to the Solution Submission Department, enabling it to evaluate the competitiveness of the proposal. For example, in the Solution Submission Department, the generation AI analyzes a business operator's solution and automatically provides relevant market data. For example, for a proposal for a "quality control system using AI," it provides relevant market data and evaluates the competitiveness of the proposal. In addition, the generation AI analyzes a business operator's solution and automatically provides relevant competitive information. For example, for a proposal for "improving quality control efficiency," it provides relevant competitive information and evaluates the competitiveness of the proposal. In addition, the generation AI analyzes a business operator's solution and automatically provides relevant market data and competitive information. For example, for a proposal for a "quality control system using AI," it provides relevant market data and competitive information and evaluates the competitiveness of the proposal. In this way, the relevant market data and competitive information can be provided to evaluate the competitiveness of the proposal.

[0037] The Solution Submission Department allows the generation AI to automatically combine solutions submitted by different businesses to propose new hybrid solutions. For example, the generation AI analyzes solutions from different businesses and automatically combines them. For example, it combines a "quality control system using AI" with a "quality control system using IoT sensors" to propose a new hybrid solution. The generation AI also analyzes solutions from different businesses and automatically combines them. For example, it combines different proposals related to "improving quality control efficiency" to propose a new hybrid solution. The generation AI also analyzes solutions from different businesses and automatically combines them. For example, it combines a "quality control system using AI" with a "quality control system using big data analysis" to propose a new hybrid solution. This allows the solution submission department to combine solutions from different businesses and propose new hybrid solutions.

[0038] When matching a company's issues with a business's solutions, the matching unit can refer to past matching results and their outcomes to improve matching accuracy. For example, the generation AI analyzes a company's issues and a business's solutions and refers to past matching results and their outcomes. For example, for the issue of "improving the efficiency of product quality control," the matching accuracy is improved based on past successful matching cases. The generation AI also analyzes a company's issues and a business's solutions and refers to past matching results and their outcomes. For example, for the issue of "improving the efficiency of quality control," the matching accuracy is improved based on past successful matching cases. The generation AI also analyzes a company's issues and a business's solutions and refers to past matching results and their outcomes. For example, for a proposal for an "AI-based quality control system," the matching accuracy is improved based on past successful matching cases. In this way, by referring to past matching results and their outcomes, matching accuracy can be improved.

[0039] The matching unit can automatically perform a detailed technical evaluation and risk assessment of the proposal content before providing the matching results. In the matching unit, for example, the generation AI analyzes the company's issues and the business operator's solution, and automatically performs a detailed technical evaluation of the proposal content. For example, it analyzes the technical details of a "quality control system using AI" and performs a risk assessment. The generation AI also analyzes the company's issues and the business operator's solution, and automatically performs a detailed technical evaluation of the proposal content. For example, it analyzes the technical details of a proposal regarding "improving quality control efficiency" and performs a risk assessment. The generation AI also analyzes the company's issues and the business operator's solution, and automatically performs a detailed technical evaluation of the proposal content. For example, it analyzes the technical details of a "quality control system using AI" and performs a risk assessment. In this way, by automatically performing a detailed technical evaluation and risk assessment of the proposal content, the accuracy of matching can be improved.

[0040] The matching unit can match companies and businesses from different industries and regions, promoting cross-industry collaboration. For example, the generation AI analyzes a company's challenges and a business's solutions, and matches them with companies and businesses from different industries and regions. For example, for the challenge of "improving the efficiency of product quality control," it matches businesses from different industries, promoting cross-industry collaboration. The generation AI can also analyze a company's challenges and a business's solutions, and match them with companies and businesses from different industries and regions. For example, for the challenge of "improving the efficiency of quality control," it matches businesses from different industries, promoting cross-industry collaboration. The generation AI can also analyze a company's challenges and a business's solutions, and match them with companies and businesses from different industries and regions. For example, for a proposal for an "AI-based quality control system," it matches businesses from different industries, promoting cross-industry collaboration. This allows companies and businesses from different industries and regions to be matched, promoting cross-industry collaboration.

[0041] The matching unit allows the generation AI to automatically visualize the matching results, allowing companies and businesses to intuitively understand. For example, the generation AI analyzes a company's challenges and a business's solutions, and automatically visualizes the matching results. For example, the matching results for the challenge of "improving the efficiency of product quality control" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. The generation AI also analyzes a company's challenges and a business's solutions, and automatically visualizes the matching results. For example, the matching results for the challenge of "improving the efficiency of quality control" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. The generation AI also analyzes a company's challenges and a business's solutions, and automatically visualizes the matching results. For example, the matching results for a proposal for an "AI-based quality control system" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. In this way, visualizing the matching results allows companies and businesses to intuitively understand.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Problem Disclosure Department can automatically add background information and related technological trends to a company's problem before it is made public, deepening understanding of the problem. For example, the generation AI analyzes a company's problem and automatically adds related technological trends and background information. For example, it adds "the latest technologies and market trends for improving the efficiency of product quality control" to the problem. The generation AI also analyzes a company's problem and automatically adds background information to the problem. For example, it includes "past research and case studies on improving the efficiency of quality control" in the problem. The generation AI also analyzes a company's problem and automatically adds related technological trends. For example, it adds "the latest technologies and market trends for quality control using AI" to the problem. In this way, adding background information and related technological trends to the problem allows for a deeper understanding of the problem.

[0044] When analyzing a business's solution, the Solution Submission Department can evaluate the effectiveness of the proposal by referring to past success stories and failure cases. For example, the generation AI analyzes the business's solution and searches a database for past success stories and failure cases. For example, for a proposal for a "quality control system using AI," the effectiveness of the proposal is evaluated by referring to past success stories and failure cases. The generation AI also analyzes the business's solution and evaluates the effectiveness of the proposal based on past success stories and failure cases. For example, for a proposal for "improving quality control efficiency," the effectiveness of the proposal is evaluated by referring to past cases. The generation AI also analyzes the business's solution and evaluates the effectiveness of the proposal by referring to past success stories and failure cases. For example, for a proposal for a "quality control system using AI," the effectiveness of the proposal is evaluated by referring to past cases. In this way, the effectiveness of the proposal can be evaluated by referring to past success stories and failure cases.

[0045] When a solution is submitted, the Solution Submission Department can have the generation AI automatically provide relevant market data and competitive information to evaluate the competitiveness of the proposal. For example, the generation AI analyzes a business operator's solution and automatically provides relevant market data. For example, for a proposal for an "AI-based quality control system," it provides relevant market data to evaluate the competitiveness of the proposal. The generation AI also analyzes a business operator's solution and automatically provides relevant competitive information. For example, for a proposal for "improving quality control efficiency," it provides relevant competitive information to evaluate the competitiveness of the proposal. The generation AI also analyzes a business operator's solution and automatically provides relevant market data and competitive information. For example, for a proposal for an "AI-based quality control system," it provides relevant market data and competitive information to evaluate the competitiveness of the proposal. This allows the provision of relevant market data and competitive information to evaluate the competitiveness of the proposal.

[0046] When matching a company's issues with a business's solutions, the matching unit can refer to past matching results and their outcomes to improve matching accuracy. For example, the generation AI analyzes a company's issues and a business's solutions, and refers to past matching results and their outcomes. For example, for the issue of "improving the efficiency of product quality control," matching accuracy is improved based on past successful matching cases. The generation AI also analyzes a company's issues and a business's solutions, and refers to past matching results and their outcomes. For example, for the issue of "improving the efficiency of quality control," matching accuracy is improved based on past successful matching cases. The generation AI also analyzes a company's issues and a business's solutions, and refers to past matching results and their outcomes. For example, for a proposal for an "AI-based quality control system," matching accuracy is improved based on past successful matching cases. In this way, matching accuracy can be improved by referring to past matching results and their outcomes.

[0047] The matching unit can match companies and businesses from different industries and regions, promoting cross-industry collaboration. For example, the generation AI analyzes a company's challenges and a business's solutions, and matches them with companies and businesses from different industries and regions. For example, for the challenge of "improving the efficiency of product quality control," it matches businesses from different industries, promoting cross-industry collaboration. The generation AI can also analyze a company's challenges and a business's solutions, and match them with companies and businesses from different industries and regions. For example, for the challenge of "improving the efficiency of quality control," it matches businesses from different industries, promoting cross-industry collaboration. The generation AI can also analyze a company's challenges and a business's solutions, and match them with companies and businesses from different industries and regions. For example, for a proposal for an "AI-based quality control system," it matches businesses from different industries, promoting cross-industry collaboration. This allows companies and businesses from different industries and regions to be matched, promoting cross-industry collaboration.

[0048] The matching unit allows the generation AI to automatically visualize the matching results, allowing companies and businesses to intuitively understand. For example, the generation AI analyzes a company's challenges and the business's solutions, and automatically visualizes the matching results. For example, the matching results for the challenge of "improving the efficiency of product quality control" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. The generation AI also analyzes a company's challenges and the business's solutions, and automatically visualizes the matching results. For example, the matching results for the challenge of "improving the efficiency of quality control" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. The generation AI also analyzes a company's challenges and the business's solutions, and automatically visualizes the matching results. For example, the matching results for a proposal for an "AI-based quality control system" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. In this way, visualizing the matching results allows companies and businesses to intuitively understand.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The problem disclosure department discloses the company's problem. For example, a company may disclose a problem such as "We want to improve the efficiency of product quality control." Step 2: The Solution Submission Department accepts solutions from businesses. For example, a business proposes a "quality control system using AI." Step 3: The matching unit matches the company's issues with the business's solutions. For example, the generation AI analyzes the company's issues and the business's solutions, evaluates their compatibility, and matches them.

[0051] (Example 2) The reverse pitch platform according to an embodiment of the present invention is a system in which companies disclose their challenges, a generation AI analyzes the challenges, and businesses submit solutions. This enables companies to quickly find new technologies and innovative approaches within limited resources and time.

[0052] The reverse pitch platform according to the embodiment comprises a problem disclosure unit, a solution application unit, and a matching unit. The problem disclosure unit discloses a company's problem. For example, a company discloses a problem such as "I want to improve the efficiency of product quality control." The solution application unit accepts solutions from businesses. For example, a business proposes a "quality control system using AI." The matching unit matches the company's problem with the business's solution. For example, a generation AI analyzes the company's problem and the business's solution, evaluates their compatibility, and performs matching. In this way, the reverse pitch platform discloses a company's problem, accepts business solutions, and performs matching, thereby efficiently solving problems.

[0053] The problem disclosure section can analyze a company's issues, referencing similar past issues and their solutions, and automatically generate a more specific problem definition. For example, in the problem disclosure section, the generation AI analyzes a company's issues and searches a database for similar past issues and their solutions. For example, if there was a past issue of "improving the efficiency of product quality control," the generation AI references the solution and automatically generates a specific definition that can be applied to the current issue. The generation AI also analyzes a company's issues and automatically generates detailed requirements for the issue based on solutions to similar past issues. For example, it proposes specific methods and technologies for "improving the efficiency of quality control." The generation AI also analyzes a company's issues and references similar past issues and their solutions to clarify the background and purpose of the issue. For example, it automatically generates "specific steps and methods for improving the efficiency of product quality control." This makes it possible to automatically generate more specific problem definitions by referencing similar past issues and their solutions.

[0054] The Problem Disclosure Department automatically adds background information and related technology trends to a problem before it is made public, allowing for a deeper understanding of the problem. In the Problem Disclosure Department, for example, the generation AI analyzes a company's problem and automatically adds related technology trends and background information. For example, it adds "the latest technologies and market trends for improving the efficiency of product quality control" to the problem. The generation AI also analyzes a company's problem and automatically adds background information to the problem. For example, it includes "past research and case studies on improving the efficiency of quality control." The generation AI also analyzes a company's problem and automatically adds related technology trends. For example, it adds "the latest technologies and market trends for quality control using AI" to the problem. In this way, adding background information and related technology trends to the problem allows for a deeper understanding of the problem.

[0055] The issue disclosure unit can use the emotion estimation function to analyze internal and external reactions to a company's issues and evaluate the importance and urgency of the issue. For example, in the issue disclosure unit, the generation AI analyzes a company's issues and uses the emotion estimation function to collect internal and external reactions. For example, the unit analyzes employee and customer reactions to the issue of "improving the efficiency of product quality control" and evaluates the importance of the issue. The generation AI also analyzes a company's issues and uses the emotion estimation function to collect internal and external reactions. For example, the unit analyzes employee and customer emotions regarding "improving the efficiency of quality control" and evaluates the urgency of the issue. The generation AI also analyzes a company's issues and uses the emotion estimation function to collect internal and external reactions. For example, the unit analyzes employee and customer emotions regarding "specific methods for improving the efficiency of product quality control" and evaluates the importance and urgency of the issue. In this way, the emotion estimation function can be used to analyze internal and external reactions and evaluate the importance and urgency of the issue.

[0056] When a problem is made public, the problem disclosure unit allows the generation AI to automatically search for related patent information and academic papers, and provide them as reference material for solving the problem. For example, in the problem disclosure unit, the generation AI analyzes a company's problem and automatically searches for related patent information. For example, it adds "patented technology for improving the efficiency of product quality control" to the problem. The generation AI also analyzes a company's problem and automatically searches for related academic papers. For example, it adds "the latest research results on improving the efficiency of quality control" to the problem. The generation AI also analyzes a company's problem and automatically searches for related patent information and academic papers. For example, it adds "the latest technologies and research results for quality control using AI" to the problem. In this way, by providing related patent information and academic papers, it is possible to provide reference material for solving the problem.

[0057] In the problem disclosure section, when companies from different industries have a common problem, the generative AI can automatically group those companies and work together to solve the problem. For example, when the generative AI analyzes a company's problem, it automatically groups those companies if they have a common problem. For example, it groups companies that have the problem of "improving the efficiency of product quality control" and works together to find a solution. In addition, when the generative AI analyzes a company's problem and companies from different industries have a common problem, it automatically groups those companies. For example, it groups companies that have the problem of "improving the efficiency of quality control" and works together to find a solution. In addition, when the generative AI analyzes a company's problem and companies from different industries have a common problem, it automatically groups those companies. For example, it groups companies that have the problem of "improving the efficiency of quality control using AI" and works together to find a solution. This makes it possible to group companies from different industries and work together to solve the problem.

[0058] The issue disclosure unit can use the emotion estimation function to analyze the emotions of the company's employees and customers when issues are disclosed and determine the priority of the issues. In the issue disclosure unit, for example, the generation AI analyzes the company's issues and uses the emotion estimation function to analyze the emotions of the employees and customers. For example, it analyzes the emotions of employees and customers regarding the issue of "wanting to improve the efficiency of product quality control" and determines the priority of the issues. The generation AI also analyzes the company's issues and uses the emotion estimation function to analyze the emotions of employees and customers. For example, it analyzes the emotions of employees and customers regarding "improving the efficiency of quality control" and determines the priority of the issues. The generation AI also analyzes the company's issues and uses the emotion estimation function to analyze the emotions of employees and customers. For example, it analyzes the emotions of employees and customers regarding "specific methods for improving the efficiency of product quality control" and determines the priority of the issues. In this way, it is possible to analyze the emotions of employees and customers and determine the priority of the issues.

[0059] When analyzing a business's solution, the Solution Submission Department can evaluate the effectiveness of the proposal by referring to past success stories and failure stories. In the Solution Submission Department, for example, the generation AI analyzes the business's solution and searches a database for past success stories and failure stories. For example, for a proposal for a "quality control system using AI," the effectiveness of the proposal is evaluated by referring to past success stories and failure stories. The generation AI also analyzes the business's solution and evaluates the effectiveness of the proposal based on past success stories and failure stories. For example, for a proposal for "improving quality control efficiency," the effectiveness of the proposal is evaluated by referring to past cases. The generation AI also analyzes the business's solution and evaluates the effectiveness of the proposal by referring to past success stories and failure stories. For example, for a proposal for a "quality control system using AI," the effectiveness of the proposal is evaluated by referring to past cases. In this way, the effectiveness of the proposal can be evaluated by referring to past success stories and failure stories.

[0060] When evaluating solutions, the Solution Submission Department can automatically verify the technical details and implementability of the proposal. For example, in the Solution Submission Department, the generation AI analyzes the business's solution and automatically verifies the technical details of the proposal. For example, it analyzes the technical details of a "quality control system using AI" and evaluates its implementability. The generation AI also analyzes the business's solution and automatically verifies the technical details of the proposal. For example, it analyzes the technical details of a proposal regarding "improving quality control efficiency" and evaluates its implementability. The generation AI also analyzes the business's solution and automatically verifies the technical details of the proposal. For example, it analyzes the technical details of a "quality control system using AI" and evaluates its implementability. This makes it possible to automatically verify the technical details and implementability of the proposal.

[0061] The solution application department can use the emotion estimation function to analyze the company's initial response to the business's proposal and provide feedback on improvements to the proposal. In the solution application department, for example, the generation AI analyzes the business's proposal and uses the emotion estimation function to collect the company's initial response. For example, the solution application department analyzes the company's initial response to a proposal for an "AI-based quality control system" and provides feedback on improvements to the proposal. The generation AI also analyzes the business's proposal and uses the emotion estimation function to collect the company's initial response. For example, the solution application department analyzes the company's initial response to a proposal for "improving quality control efficiency" and provides feedback on improvements to the proposal. The generation AI also analyzes the business's proposal and uses the emotion estimation function to collect the company's initial response. For example, the solution application department analyzes the company's initial response to a proposal for an "AI-based quality control system" and provides feedback on improvements to the proposal. In this way, the solution application department can analyze the company's initial response and provide feedback on improvements to the proposal.

[0062] When a solution is submitted, the generation AI automatically provides relevant market data and competitive information to the Solution Submission Department, enabling it to evaluate the competitiveness of the proposal. For example, in the Solution Submission Department, the generation AI analyzes a business operator's solution and automatically provides relevant market data. For example, for a proposal for a "quality control system using AI," it provides relevant market data and evaluates the competitiveness of the proposal. In addition, the generation AI analyzes a business operator's solution and automatically provides relevant competitive information. For example, for a proposal for "improving quality control efficiency," it provides relevant competitive information and evaluates the competitiveness of the proposal. In addition, the generation AI analyzes a business operator's solution and automatically provides relevant market data and competitive information. For example, for a proposal for a "quality control system using AI," it provides relevant market data and competitive information and evaluates the competitiveness of the proposal. In this way, the relevant market data and competitive information can be provided to evaluate the competitiveness of the proposal.

[0063] The Solution Submission Department allows the generation AI to automatically combine solutions submitted by different businesses to propose new hybrid solutions. For example, the generation AI analyzes solutions from different businesses and automatically combines them. For example, it combines a "quality control system using AI" with a "quality control system using IoT sensors" to propose a new hybrid solution. The generation AI also analyzes solutions from different businesses and automatically combines them. For example, it combines different proposals related to "improving quality control efficiency" to propose a new hybrid solution. The generation AI also analyzes solutions from different businesses and automatically combines them. For example, it combines a "quality control system using AI" with a "quality control system using big data analysis" to propose a new hybrid solution. This allows the solution submission department to combine solutions from different businesses and propose new hybrid solutions.

[0064] The solution application department can use the emotion estimation function to predict market reaction to the business operator's proposal and evaluate the market suitability of the proposal. In the solution application department, for example, the generation AI analyzes the business operator's proposal and uses the emotion estimation function to predict market reaction. For example, the generation AI analyzes the market's emotional reaction to a proposal for an "AI-based quality control system" and evaluates the proposal's market suitability. In addition, the generation AI analyzes the business operator's proposal and uses the emotion estimation function to predict market reaction. For example, the generation AI analyzes the market's emotional reaction to a proposal for "improving quality control efficiency" and evaluates the proposal's market suitability. In addition, the generation AI analyzes the business operator's proposal and uses the emotion estimation function to predict market reaction. For example, the generation AI analyzes the market's emotional reaction to a proposal for an "AI-based quality control system" and evaluates the proposal's market suitability. In this way, market reaction can be predicted and the proposal's market suitability can be evaluated.

[0065] When matching a company's issues with a business's solutions, the matching unit can refer to past matching results and their outcomes to improve matching accuracy. For example, the generation AI analyzes a company's issues and a business's solutions and refers to past matching results and their outcomes. For example, for the issue of "improving the efficiency of product quality control," the matching accuracy is improved based on past successful matching cases. The generation AI also analyzes a company's issues and a business's solutions and refers to past matching results and their outcomes. For example, for the issue of "improving the efficiency of quality control," the matching accuracy is improved based on past successful matching cases. The generation AI also analyzes a company's issues and a business's solutions and refers to past matching results and their outcomes. For example, for a proposal for an "AI-based quality control system," the matching accuracy is improved based on past successful matching cases. In this way, by referring to past matching results and their outcomes, matching accuracy can be improved.

[0066] The matching unit can automatically perform a detailed technical evaluation and risk assessment of the proposal content before providing the matching results. In the matching unit, for example, the generation AI analyzes the company's issues and the business operator's solution, and automatically performs a detailed technical evaluation of the proposal content. For example, it analyzes the technical details of a "quality control system using AI" and performs a risk assessment. The generation AI also analyzes the company's issues and the business operator's solution, and automatically performs a detailed technical evaluation of the proposal content. For example, it analyzes the technical details of a proposal regarding "improving quality control efficiency" and performs a risk assessment. The generation AI also analyzes the company's issues and the business operator's solution, and automatically performs a detailed technical evaluation of the proposal content. For example, it analyzes the technical details of a "quality control system using AI" and performs a risk assessment. In this way, by automatically performing a detailed technical evaluation and risk assessment of the proposal content, the accuracy of matching can be improved.

[0067] The matching unit can use the emotion estimation function to analyze the compatibility between companies and businesses and prioritize matches that are easy to empathize with emotionally. For example, the generation AI in the matching unit analyzes a company's challenges and a business's solutions, and then uses the emotion estimation function to analyze the compatibility between the company and business. For example, for the challenge of "improving the efficiency of product quality control," the unit analyzes the emotional compatibility between the company and business and prioritizes matches that are easy to empathize with. The generation AI also analyzes a company's challenges and a business's solutions, and then uses the emotion estimation function to analyze the compatibility between the company and business. For example, for the challenge of "improving the efficiency of quality control," the unit analyzes the emotional compatibility between the company and business and prioritizes matches that are easy to empathize with. The generation AI also analyzes a company's challenges and a business's solutions, and then uses the emotion estimation function to analyze the compatibility between the company and business. For example, for a proposal for an "AI-based quality control system," the unit analyzes the emotional compatibility between the company and business and prioritizes matches that are easy to empathize with emotionally. This allows for the smooth construction of cooperative relationships by analyzing the compatibility between companies and businesses and prioritizing matches that are easy to empathize with emotionally.

[0068] The matching unit can match companies and businesses from different industries and regions, promoting cross-industry collaboration. For example, the generation AI analyzes a company's challenges and a business's solutions, and matches them with companies and businesses from different industries and regions. For example, for the challenge of "improving the efficiency of product quality control," it matches businesses from different industries, promoting cross-industry collaboration. The generation AI can also analyze a company's challenges and a business's solutions, and match them with companies and businesses from different industries and regions. For example, for the challenge of "improving the efficiency of quality control," it matches businesses from different industries, promoting cross-industry collaboration. The generation AI can also analyze a company's challenges and a business's solutions, and match them with companies and businesses from different industries and regions. For example, for a proposal for an "AI-based quality control system," it matches businesses from different industries, promoting cross-industry collaboration. This allows companies and businesses from different industries and regions to be matched, promoting cross-industry collaboration.

[0069] The matching unit allows the generation AI to automatically visualize the matching results, allowing companies and businesses to intuitively understand. For example, the generation AI analyzes a company's challenges and a business's solutions, and automatically visualizes the matching results. For example, the matching results for the challenge of "improving the efficiency of product quality control" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. The generation AI also analyzes a company's challenges and a business's solutions, and automatically visualizes the matching results. For example, the matching results for the challenge of "improving the efficiency of quality control" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. The generation AI also analyzes a company's challenges and a business's solutions, and automatically visualizes the matching results. For example, the matching results for a proposal for an "AI-based quality control system" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. In this way, visualizing the matching results allows companies and businesses to intuitively understand.

[0070] The matching unit uses the emotion estimation function to support communication between companies and businesses after matching, allowing for the building of a smooth cooperative relationship. For example, the generation AI analyzes a company's challenges and a business's solutions, and uses the emotion estimation function to support communication after matching. For example, after matching for the challenge of "improving the efficiency of product quality control," the generation AI analyzes the emotional compatibility between the company and business, and uses the emotion estimation function to support communication after matching. For example, after matching for the challenge of "improving the efficiency of quality control," the generation AI analyzes the emotional compatibility between the company and business, and uses the emotion estimation function to support communication after matching. For example, after matching for the challenge of "improving the efficiency of quality control," the generation AI analyzes the company's challenges and a business's solutions, and uses the emotion estimation function to support communication after matching. For example, after matching for a proposal for an "AI-based quality control system," the generation AI analyzes the emotional compatibility between the company and business, and uses the emotion estimation function to support communication after matching.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The Problem Disclosure Department can automatically add background information and related technological trends to a company's problem before it is made public, deepening understanding of the problem. For example, the generation AI analyzes a company's problem and automatically adds related technological trends and background information. For example, it adds "the latest technologies and market trends for improving the efficiency of product quality control" to the problem. The generation AI also analyzes a company's problem and automatically adds background information to the problem. For example, it includes "past research and case studies on improving the efficiency of quality control" in the problem. The generation AI also analyzes a company's problem and automatically adds related technological trends. For example, it adds "the latest technologies and market trends for quality control using AI" to the problem. In this way, adding background information and related technological trends to the problem allows for a deeper understanding of the problem.

[0073] The issue disclosure department can use the emotion estimation function to analyze internal and external reactions to a company's issues and evaluate the importance and urgency of the issue. For example, the generation AI analyzes a company's issues and uses the emotion estimation function to collect internal and external reactions. For example, it analyzes employee and customer reactions to the issue of "improving the efficiency of product quality control" and evaluates the importance of the issue. The generation AI also analyzes a company's issues and uses the emotion estimation function to collect internal and external reactions. For example, it analyzes employee and customer emotions regarding "improving the efficiency of quality control" and evaluates the urgency of the issue. The generation AI also analyzes a company's issues and uses the emotion estimation function to collect internal and external reactions. For example, it analyzes employee and customer emotions regarding "specific methods for improving the efficiency of product quality control" and evaluates the importance and urgency of the issue. In this way, the emotion estimation function can be used to analyze internal and external reactions and evaluate the importance and urgency of the issue.

[0074] When analyzing a business's solution, the Solution Submission Department can evaluate the effectiveness of the proposal by referring to past success stories and failure cases. For example, the generation AI analyzes the business's solution and searches a database for past success stories and failure cases. For example, for a proposal for a "quality control system using AI," the effectiveness of the proposal is evaluated by referring to past success stories and failure cases. The generation AI also analyzes the business's solution and evaluates the effectiveness of the proposal based on past success stories and failure cases. For example, for a proposal for "improving quality control efficiency," the effectiveness of the proposal is evaluated by referring to past cases. The generation AI also analyzes the business's solution and evaluates the effectiveness of the proposal by referring to past success stories and failure cases. For example, for a proposal for a "quality control system using AI," the effectiveness of the proposal is evaluated by referring to past cases. In this way, the effectiveness of the proposal can be evaluated by referring to past success stories and failure cases.

[0075] The Solution Application Department can use the emotion estimation function to analyze companies' initial reactions to the business's proposal and provide feedback on improvements to the proposal. For example, the generation AI analyzes the business's proposal and uses the emotion estimation function to collect companies' initial reactions. For example, the department analyzes companies' initial reactions to a proposal for an "AI-based quality control system" and provides feedback on improvements to the proposal. The generation AI also analyzes the business's proposal and uses the emotion estimation function to collect companies' initial reactions. For example, the department analyzes companies' initial reactions to a proposal for "improving quality control efficiency" and provides feedback on improvements to the proposal. The generation AI also analyzes the business's proposal and uses the emotion estimation function to collect companies' initial reactions. For example, the department analyzes companies' initial reactions to a proposal for an "AI-based quality control system" and provides feedback on improvements to the proposal. This makes it possible to analyze companies' initial reactions and provide feedback on improvements to the proposal.

[0076] When a solution is submitted, the Solution Submission Department can have the generation AI automatically provide relevant market data and competitive information to evaluate the competitiveness of the proposal. For example, the generation AI analyzes a business operator's solution and automatically provides relevant market data. For example, for a proposal for an "AI-based quality control system," it provides relevant market data to evaluate the competitiveness of the proposal. The generation AI also analyzes a business operator's solution and automatically provides relevant competitive information. For example, for a proposal for "improving quality control efficiency," it provides relevant competitive information to evaluate the competitiveness of the proposal. The generation AI also analyzes a business operator's solution and automatically provides relevant market data and competitive information. For example, for a proposal for an "AI-based quality control system," it provides relevant market data and competitive information to evaluate the competitiveness of the proposal. This allows the provision of relevant market data and competitive information to evaluate the competitiveness of the proposal.

[0077] When matching a company's issues with a business's solutions, the matching unit can refer to past matching results and their outcomes to improve matching accuracy. For example, the generation AI analyzes a company's issues and a business's solutions, and refers to past matching results and their outcomes. For example, for the issue of "improving the efficiency of product quality control," matching accuracy is improved based on past successful matching cases. The generation AI also analyzes a company's issues and a business's solutions, and refers to past matching results and their outcomes. For example, for the issue of "improving the efficiency of quality control," matching accuracy is improved based on past successful matching cases. The generation AI also analyzes a company's issues and a business's solutions, and refers to past matching results and their outcomes. For example, for a proposal for an "AI-based quality control system," matching accuracy is improved based on past successful matching cases. In this way, matching accuracy can be improved by referring to past matching results and their outcomes.

[0078] The matching unit uses the emotion estimation function to analyze the compatibility between companies and businesses, prioritizing matches that are easy to empathize with emotionally. For example, the generation AI analyzes a company's challenges and a business's solutions, and then uses the emotion estimation function to analyze the compatibility between the company and business. For example, for the challenge of "improving the efficiency of product quality control," the emotional compatibility between the company and business is analyzed and a match that is easy to empathize with is prioritized. The generation AI also analyzes a company's challenges and a business's solutions, and then uses the emotion estimation function to analyze the compatibility between the company and business. For example, for the challenge of "improving the efficiency of quality control," the emotional compatibility between the company and business is analyzed and a match that is easy to empathize with is prioritized. The generation AI also analyzes a company's challenges and a business's solutions, and then uses the emotion estimation function to analyze the compatibility between the company and business. For example, for a proposal for an "AI-based quality control system," the emotional compatibility between the company and business is analyzed and a match that is easy to empathize with is prioritized. This allows for the smooth construction of cooperative relationships by analyzing the compatibility between companies and businesses and prioritizing matches that are easy to empathize with emotionally.

[0079] The matching unit can match companies and businesses from different industries and regions, promoting cross-industry collaboration. For example, the generation AI analyzes a company's challenges and a business's solutions, and matches them with companies and businesses from different industries and regions. For example, for the challenge of "improving the efficiency of product quality control," it matches businesses from different industries, promoting cross-industry collaboration. The generation AI can also analyze a company's challenges and a business's solutions, and match them with companies and businesses from different industries and regions. For example, for the challenge of "improving the efficiency of quality control," it matches businesses from different industries, promoting cross-industry collaboration. The generation AI can also analyze a company's challenges and a business's solutions, and match them with companies and businesses from different industries and regions. For example, for a proposal for an "AI-based quality control system," it matches businesses from different industries, promoting cross-industry collaboration. This allows companies and businesses from different industries and regions to be matched, promoting cross-industry collaboration.

[0080] The matching unit allows the generation AI to automatically visualize the matching results, allowing companies and businesses to intuitively understand. For example, the generation AI analyzes a company's challenges and the business's solutions, and automatically visualizes the matching results. For example, the matching results for the challenge of "improving the efficiency of product quality control" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. The generation AI also analyzes a company's challenges and the business's solutions, and automatically visualizes the matching results. For example, the matching results for the challenge of "improving the efficiency of quality control" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. The generation AI also analyzes a company's challenges and the business's solutions, and automatically visualizes the matching results. For example, the matching results for a proposal for an "AI-based quality control system" can be displayed in graphs and charts, allowing companies and businesses to intuitively understand. In this way, visualizing the matching results allows companies and businesses to intuitively understand.

[0081] The matching unit uses the emotion estimation function to support communication between companies and businesses after matching, allowing for the creation of a smooth cooperative relationship. For example, the generation AI analyzes a company's challenges and the business's solutions, and uses the emotion estimation function to support communication after matching. For example, after matching for the challenge of "improving the efficiency of product quality control," the emotional compatibility between the company and business is analyzed, leading to the creation of a smooth cooperative relationship. The generation AI also analyzes a company's challenges and the business's solutions, and uses the emotion estimation function to support communication after matching. For example, after matching for the challenge of "improving the efficiency of quality control," the emotional compatibility between the company and business is analyzed, leading to the creation of a smooth cooperative relationship. The generation AI also analyzes a company's challenges and the business's solutions, and uses the emotion estimation function to support communication after matching. For example, after matching for a proposal for an "AI-based quality control system," the emotional compatibility between the company and business is analyzed, leading to the creation of a smooth cooperative relationship. This supports communication between companies and businesses after matching, allowing for the creation of a smooth cooperative relationship.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The problem disclosure department discloses the company's problem. For example, a company may disclose a problem such as "We want to improve the efficiency of product quality control." Step 2: The Solution Submission Department accepts solutions from businesses. For example, a business proposes a "quality control system using AI." Step 3: The matching unit matches the company's issues with the business's solutions. For example, the generation AI analyzes the company's issues and the business's solutions, evaluates their compatibility, and matches them.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Issues Disclosure Department, which discloses the issues faced by companies, A solution application department that accepts solutions from businesses; It also has a matching department that matches corporate issues with business solutions. A system characterized by:

2. The assignment disclosure unit: Analyze the company's issues, refer to past similar issues and their solutions, and automatically generate a more specific problem definition.

2. The system of claim 1.

3. The assignment disclosure unit: Automatically add background information and related technology trends to deepen understanding of the issue before publishing it.

2. The system of claim 1.

4. The assignment disclosure unit: Analyze internal and external responses to the company's issues and evaluate the importance and urgency of the issues.

2. The system of claim 1.

5. The assignment disclosure unit: When the problem is published, the generation AI automatically searches for relevant patent information and academic papers and provides them as reference material for solving the problem.

2. The system of claim 1.

Images