System

Abstract

An object of a system according to an embodiment is to analyze past bid item data and bid item data of each local government body and generate an optimal bidding strategy for a new bid item.SOLUTION: A system includes an in-house data collection part, an external data collection part, and a winner generation part. The in-house data collection part collects past bid item data. The external data collection unit collects bid item data of each local government body. The winner generation unit analyzes the data collected by the internal data collection unit and the external data collection unit, and generates an optimal bidding strategy for the new bid item.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] Conventional technology has the problem that it is difficult to effectively utilize data on past bidding projects and bidding projects from each local government to formulate optimal bidding strategies for new bidding projects.

[0005] The system according to the embodiment aims to analyze data on past bidding items and data on bidding items from each local government, and to generate an optimal bidding strategy for a new bidding item. [Means for solving the problem]

[0006] The system according to the embodiment includes an in-house data collection unit, an external data collection unit, and a winning strategy generation unit. The in-house data collection unit collects data on past bidding projects. The external data collection unit collects data on bidding projects from each local government. The winning strategy generation unit analyzes the data collected by the in-house data collection unit and the external data collection unit, and generates an optimal bidding strategy for a new bidding project. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data on past bidding items and data on bidding items from each local government, and generate an optimal bidding strategy for a new bidding item. [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 bidding support system according to the embodiment of the present invention is a system that generates winning strategies for new bidding projects by utilizing past bidding data and external data. This enables the bidding support system to develop efficient and effective bidding strategies.

[0029] The bidding support system according to the embodiment includes an internal data collection unit, an external data collection unit, and a winning strategy generation unit. The internal data collection unit collects data on past bidding projects, such as bid amounts, bidding conditions, and bidding results. The internal data collection unit can also record detailed records of problems that arise during the course of a project and their solutions, such as technical issues, schedule delays, and resource shortages. The external data collection unit collects data on bidding projects from each local government, such as bid announcements, bidding results, and bidding companies. The external data collection unit can also analyze the local government's economic situation and political background, such as budget status, policy changes, and economic indicators. The winning strategy generation unit analyzes the data collected by the internal data collection unit and the external data collection unit to generate an optimal bidding strategy for a new bidding project. For example, the winning strategy generation unit proposes optimal bid amounts and proposal content based on past success and failure patterns. The winning strategy generation unit can also use an emotion estimation function to evaluate the emotional impact of proposal content on local government officials and generate emotionally positive proposals. For example, the system analyzes the emotional response that the proposal content gives to the person in charge and generates a positive proposal. As a result, the bidding support system according to the embodiment can develop an efficient and effective bidding strategy. For example, by utilizing past data and external data, it is possible to gain an advantage over competitors and increase the success rate of bidding. In addition, by customizing the proposal content, it is possible to make optimal proposals that meet the needs of each local government. Furthermore, by learning from bidding results as feedback, it is possible to continuously improve the bidding strategy.

[0030] The internal data collection department records in detail data from past bidding projects as well as problems that arose during the course of a project and their solutions, and the generation AI analyzes this data to extract success patterns. For example, the internal data collection department records in detail problems and solutions in past bidding projects, and the generation AI analyzes this data to extract success patterns. For example, it analyzes what solutions were effective when a specific problem arose. The internal data collection department also records problems that arose during the course of a project and their solutions in real time, and the generation AI extracts success patterns based on this data. For example, it analyzes problem-solving approaches according to the progress of the project. The internal data collection department also collects data on problem solving in past bidding projects, and the generation AI analyzes this data to extract success patterns. For example, it analyzes what solutions were most effective when a specific problem arose. This makes it possible to analyze problems and their solutions and extract success patterns.

[0031] The internal data collection department can analyze the leadership style and team dynamics of each project to evaluate their impact on bid results. For example, the internal data collection department records the leadership style of each project in detail, and the generative AI analyzes this data to evaluate its impact on bid results. For example, it analyzes how leadership style affects project success. The internal data collection department also monitors team dynamics in real time, and the generative AI analyzes this data to evaluate its impact on bid results. For example, it analyzes how team cooperation and communication affect project success. The internal data collection department also records the leadership style and team dynamics of each project in detail, and the generative AI analyzes this data to evaluate its impact on bid results. For example, it analyzes how leadership style and team dynamics affect the quality of bid proposals and the success of presentations. This makes it possible to evaluate the impact of leadership style and team dynamics on bid results.

[0032] The internal data collection department also collects detailed data on the tools and technologies used during the progress of the project, and the generation AI can analyze this data to extract success patterns. The internal data collection department, for example, collects detailed data on the tools and technologies used during the progress of the project, and the generation AI can analyze this data to extract success patterns. For example, it analyzes how a particular tool or technology contributed to the success of the project. The internal data collection department also records detailed data on the tools and technologies used in each project, and the generation AI can extract success patterns based on this data. For example, it analyzes how a particular technology supported the progress of the project. The internal data collection department also collects data on the tools and technologies used during the progress of the project in real time, and the generation AI can analyze this data to extract success patterns. For example, it analyzes how a particular tool improved the efficiency of the project. This allows the data on the tools and technologies used to be analyzed and success patterns to be extracted.

[0033] The internal data collection department can analyze the content and frequency of communications during the course of a project to evaluate their impact on bidding results. For example, the internal data collection department records in detail the content and frequency of communications during the course of a project, and the generation AI analyzes this data to evaluate their impact on bidding results. For example, it analyzes how the frequency of communication affects the success of a project. The internal data collection department also records in detail the content of communications during each project, and the generation AI uses this data to evaluate their impact on bidding results. For example, it analyzes how a particular communication style contributed to the success of a project. The internal data collection department also monitors in real time the frequency of communications during the course of a project, and the generation AI analyzes this data to evaluate their impact on bidding results. For example, it analyzes how the frequency of communication affects the quality of bidding proposals and the success of presentations. This makes it possible to evaluate the impact of the content and frequency of communication on bidding results.

[0034] The external data collection unit can analyze the economic situation and political background of each local government in addition to data on each local government's bidding project, and evaluate the impact this has on the bidding results. For example, the external data collection unit records each local government's economic situation and political background in detail, and the generation AI analyzes this data to evaluate the impact on the bidding results. For example, it analyzes how the economic situation affects the bid amount. The external data collection unit also records the local government's political background in detail, and the generation AI uses this data to evaluate the impact on the bidding results. For example, it analyzes how specific political factors affect the bidding results. The external data collection unit also monitors each local government's economic situation and political background in real time, and the generation AI analyzes this data to evaluate the impact on the bidding results. For example, it analyzes how the economic situation and political background affect the quality of the bidding proposal and the success of the presentation. This makes it possible to evaluate the impact of the economic situation and political background on the bidding results.

[0035] The external data collection unit performs detailed analysis of competitors' past bidding strategies and proposal content, and the generation AI can predict competitor trends based on this data. For example, the external data collection unit records competitors' past bidding strategies and proposal content in detail, and the generation AI analyzes this data to predict competitor trends. For example, it analyzes what strategies a specific competitor is adopting. The external data collection unit also records competitor proposal content in detail, and the generation AI predicts competitor trends based on this data. For example, it analyzes how specific proposal content contributes to a competitor's success. The external data collection unit also monitors competitors' past bidding strategies and proposal content in real time, and the generation AI analyzes this data to predict competitor trends. For example, it analyzes how a competitor's strategy affects the quality of the bidding proposal and the success of the presentation. This makes it possible to predict competitor trends.

[0036] The external data collection unit collects data on the successes and failures of each local government's past projects in addition to data on bidding projects, and the generation AI can analyze this data to extract success patterns. The external data collection unit, for example, collects data on the successes and failures of each local government's past projects, and the generation AI analyzes this data to extract success patterns. For example, it analyzes the factors that led to the success or failure of a particular project. The external data collection unit also records detailed data on the local government's past projects, and the generation AI extracts success patterns based on this data. For example, it analyzes the conditions under which a particular project succeeded or failed. The external data collection unit also collects data on each local government's past projects in real time, and the generation AI analyzes this data to extract success patterns. For example, it analyzes the factors that led to the success or failure of a particular project. This makes it possible to analyze data on the successes and failures of past projects and extract success patterns.

[0037] The external data collection unit collects data on each local government's bidding projects as well as feedback and opinions from local government residents, allowing the generation AI to analyze this data to understand the residents' needs. For example, the external data collection unit collects feedback and opinions from local government residents, and the generation AI analyzes this data to understand the residents' needs. For example, it analyzes how residents' opinions affect bidding results. The external data collection unit also collects feedback from local government residents in real time, and the generation AI uses this data to understand the residents' needs. For example, it analyzes how residents' opinions affect the quality of bidding proposals and the success of presentations. The external data collection unit also records the feedback and opinions of each local government residents in detail, and the generation AI analyzes this data to understand the residents' needs. For example, it analyzes how residents' opinions affect bidding results. This allows the generation AI to analyze residents' feedback and opinions to understand their needs.

[0038] The winning strategy generation unit analyzes past successful patterns as well as current market trends and technological trends, and can propose optimal bidding strategies based on this. For example, the winning strategy generation unit analyzes past successful patterns as well as current market trends and technological trends, and the generation AI proposes optimal bidding strategies based on this data. For example, it analyzes how the latest technological trends affect the quality of bid proposals. Furthermore, when generating winning strategies for new bidding projects, the winning strategy generation unit monitors current market trends and technological trends in real time, and the generation AI proposes optimal bidding strategies based on this data. For example, it analyzes how market trends affect the quality of bid proposals. Furthermore, the winning strategy generation unit records past successful patterns as well as current market trends and technological trends in detail, and the generation AI proposes optimal bidding strategies based on this data. For example, it analyzes how technological trends affect the quality of bid proposals. This allows the unit to analyze market trends and technological trends and propose optimal bidding strategies.

[0039] The winning strategy generation unit can perform detailed simulations of the proposal contents and evaluate how the proposal contents will actually be realized. For example, the winning strategy generation unit performs detailed simulations of the proposal contents for a new bidding project, and the generation AI evaluates how the proposal contents will actually be realized based on this data. For example, it analyzes whether the proposal contents are feasible. The winning strategy generation unit also performs detailed simulations of the proposal contents in real time, and the generation AI evaluates how the proposal contents will actually be realized based on this data. For example, it analyzes whether the proposal contents are feasible. The winning strategy generation unit also records detailed simulations of the proposal contents for a new bidding project, and the generation AI evaluates how the proposal contents will actually be realized based on this data. For example, it analyzes whether the proposal contents are feasible. This makes it possible to evaluate how the proposal contents will actually be realized.

[0040] The winning strategy generation unit can refer to successful cases from different industries and fields and apply them to bidding strategies. For example, the winning strategy generation unit collects successful cases from different industries and fields, and the generation AI uses this data to propose the optimal bidding strategy for new bidding projects. For example, it analyzes how successful cases from other industries affect the quality of bidding proposals. In addition, when generating winning strategies for new bidding projects, the winning strategy generation unit monitors successful cases from different industries and fields in real time, and the generation AI uses this data to propose the optimal bidding strategy. For example, it analyzes how successful cases from other industries affect the quality of bidding proposals. In addition, the winning strategy generation unit records successful cases from different industries and fields in detail, and the generation AI uses this data to propose the optimal bidding strategy for new bidding projects. For example, it analyzes how successful cases from other industries affect the quality of bidding proposals. This allows successful cases from different industries and fields to be referred to and applied to bidding strategies.

[0041] The winning scenario generation unit can divide proposal contents into multiple scenarios and evaluate them, and select the most promising scenario. For example, the winning scenario generation unit divides proposal contents for a new bidding project into multiple scenarios and evaluates them, and the generation AI selects the most promising scenario based on this data. For example, it analyzes the feasibility and risks of each scenario. The winning scenario generation unit also divides proposal contents into multiple scenarios and evaluates them in real time, and the generation AI selects the most promising scenario based on this data. For example, it analyzes the feasibility and risks of each scenario. The winning scenario generation unit also divides proposal contents for a new bidding project into multiple scenarios and records them in detail, and the generation AI selects the most promising scenario based on this data. For example, it analyzes the feasibility and risks of each scenario. This makes it possible to divide proposal contents into multiple scenarios and evaluate them, and select the most promising scenario.

[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 bidding support system may further include a visualization unit that generates a visual presentation of the proposal. The visualization unit can, for example, display the proposal in visual formats such as graphs, charts, and infographics. This makes the proposal easier to understand and more persuasive to local government officials. The visualization unit can also visually display the results of simulations of the proposal. For example, it can use 3D models or animations to show scenarios in which the proposal is implemented. The visualization unit can also visually compare proposals. For example, it can compare multiple proposals side by side and visually show the advantages and disadvantages of each. This makes the proposal more persuasive to local government officials through visual presentations of the proposal.

[0044] The bidding support system may further include a risk assessment unit that performs risk assessment of the proposal content. The risk assessment unit can, for example, analyze risks hidden in the proposal content and evaluate the impact and probability of occurrence of those risks. This allows the risks of the proposal content to be identified in advance and appropriate risk management measures to be taken. The risk assessment unit can also evaluate the possibility of similar risks occurring based on risk data from past bidding projects. For example, it analyzes how specific risks occurred in past projects and how they were dealt with. Furthermore, the risk assessment unit can visually display the risks of the proposal content. For example, it can use a risk matrix or heat map to visually show the impact and probability of occurrence of risks. This allows the risks of the proposal content to be assessed in advance and appropriate risk management measures to be taken.

[0045] The bidding support system may further include a cost evaluation unit that evaluates the costs of the proposal. The cost evaluation unit may, for example, perform a detailed analysis of the costs of the proposal and evaluate the breakdown and total amount of the costs. This allows the costs of the proposal to be grasped in advance and appropriate cost management measures to be taken. The cost evaluation unit may also evaluate the possibility of similar costs being incurred based on cost data from past bidding projects. For example, it may analyze how specific costs were incurred and managed in past projects. Furthermore, the cost evaluation unit may visually display the costs of the proposal. For example, it may visually display the breakdown and total amount of costs using a cost breakdown or cost comparison chart. This allows the costs of the proposal to be evaluated in advance and appropriate cost management measures to be taken.

[0046] The bidding support system may further include an environmental impact assessment unit that performs an environmental impact assessment of the proposal. The environmental impact assessment unit may, for example, analyze the environmental impact of the proposal and assess the degree of impact. This allows the environmental impact of the proposal to be understood in advance and appropriate environmental protection measures to be taken. The environmental impact assessment unit may also assess the possibility of similar impacts occurring based on environmental impact data from past bidding cases. For example, it may analyze how specific environmental impacts occurred in past cases and how they were addressed. Furthermore, the environmental impact assessment unit may visually display the environmental impact of the proposal. For example, it may use an environmental impact matrix or heat map to visually show the degree of impact and probability of occurrence of environmental impacts. This allows the environmental impact of the proposal to be assessed in advance and appropriate environmental protection measures to be taken.

[0047] The bidding support system may further include a legal evaluation unit that performs a legal evaluation of the proposal content. The legal evaluation unit may, for example, analyze whether the proposal content is legally compliant and evaluate its degree of compliance. This allows the legal compliance of the proposal content to be determined in advance and appropriate legal measures to be taken. The legal evaluation unit may also evaluate the possibility of similar legal issues occurring based on legal data from past bidding cases. For example, it may analyze how a specific legal issue occurred in past cases and how it was addressed. Furthermore, the legal evaluation unit may visually display the legal compliance of the proposal content. For example, it may use a legal compliance matrix or heat map to visually show the degree of legal compliance and the probability of occurrence. This allows the legal compliance of the proposal content to be evaluated in advance and appropriate legal measures to be taken.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The internal data collection department collects data on past bidding projects. For example, they collect data on bid amounts, bidding conditions, and bidding results. They can also record detailed records of problems that occurred during the project and their solutions. For example, they collect data on technical issues, schedule delays, and resource shortages. Step 2: The external data collection department collects data on bidding projects for each local government. For example, it collects data on bidding announcements, bidding results, and participating companies. It can also analyze the local government's economic situation and political background. For example, it collects data on budget status, policy changes, economic indicators, etc. Step 3: The winning strategy generation unit analyzes the data collected by the internal data collection unit and external data collection unit to generate the optimal bidding strategy for new bidding projects. For example, it proposes the optimal bid amount and proposal content based on past patterns of success and failure. It can also use the emotion estimation function to evaluate the emotional impact that the proposal content will have on local government officials and generate emotionally positive proposals.

[0050] (Example 2) The bidding support system according to the embodiment of the present invention is a system that generates winning strategies for new bidding projects by utilizing past bidding data and external data. This enables the bidding support system to develop efficient and effective bidding strategies.

[0051] The bidding support system according to the embodiment includes an internal data collection unit, an external data collection unit, and a winning strategy generation unit. The internal data collection unit collects data on past bidding projects, such as bid amounts, bidding conditions, and bidding results. The internal data collection unit can also record detailed records of problems that arise during the course of a project and their solutions, such as technical issues, schedule delays, and resource shortages. The external data collection unit collects data on bidding projects from each local government, such as bid announcements, bidding results, and bidding companies. The external data collection unit can also analyze the local government's economic situation and political background, such as budget status, policy changes, and economic indicators. The winning strategy generation unit analyzes the data collected by the internal data collection unit and the external data collection unit to generate an optimal bidding strategy for a new bidding project. For example, the winning strategy generation unit proposes optimal bid amounts and proposal content based on past success and failure patterns. The winning strategy generation unit can also use an emotion estimation function to evaluate the emotional impact of proposal content on local government officials and generate emotionally positive proposals. For example, the system analyzes the emotional response that the proposal content gives to the person in charge and generates a positive proposal. As a result, the bidding support system according to the embodiment can develop an efficient and effective bidding strategy. For example, by utilizing past data and external data, it is possible to gain an advantage over competitors and increase the success rate of bidding. In addition, by customizing the proposal content, it is possible to make optimal proposals that meet the needs of each local government. Furthermore, by learning from bidding results as feedback, it is possible to continuously improve the bidding strategy.

[0052] The internal data collection department can use the emotion estimation function to analyze the emotional factors of project members and evaluate the impact of emotional factors on bidding results. For example, the internal data collection department monitors the emotional state of each project member in real time and records emotional fluctuations that occur during the progress of the project. For example, it analyzes the members' stress levels and motivation depending on the progress of the project. The internal data collection department also uses the emotion estimation function to quantify the emotional responses of project members and extract emotional patterns related to the success or failure of the project. For example, it analyzes that successful projects tend to have more positive emotions and unsuccessful projects tend to have more negative emotions. The internal data collection department also analyzes emotional factors that occur during the progress of the project and evaluates their impact on bidding results. For example, it analyzes how the emotional state of project members affects the quality of the bidding proposal and the success of the presentation. This makes it possible to evaluate the impact of emotional factors on bidding results.

[0053] The internal data collection department records in detail data from past bidding projects as well as problems that arose during the course of a project and their solutions, and the generation AI analyzes this data to extract success patterns. For example, the internal data collection department records in detail problems and solutions in past bidding projects, and the generation AI analyzes this data to extract success patterns. For example, it analyzes what solutions were effective when a specific problem arose. The internal data collection department also records problems that arose during the course of a project and their solutions in real time, and the generation AI extracts success patterns based on this data. For example, it analyzes problem-solving approaches according to the progress of the project. The internal data collection department also collects data on problem solving in past bidding projects, and the generation AI analyzes this data to extract success patterns. For example, it analyzes what solutions were most effective when a specific problem arose. This makes it possible to analyze problems and their solutions and extract success patterns.

[0054] The internal data collection department can analyze the leadership style and team dynamics of each project to evaluate their impact on bid results. For example, the internal data collection department records the leadership style of each project in detail, and the generative AI analyzes this data to evaluate its impact on bid results. For example, it analyzes how leadership style affects project success. The internal data collection department also monitors team dynamics in real time, and the generative AI analyzes this data to evaluate its impact on bid results. For example, it analyzes how team cooperation and communication affect project success. The internal data collection department also records the leadership style and team dynamics of each project in detail, and the generative AI analyzes this data to evaluate its impact on bid results. For example, it analyzes how leadership style and team dynamics affect the quality of bid proposals and the success of presentations. This makes it possible to evaluate the impact of leadership style and team dynamics on bid results.

[0055] The internal data collection department also collects detailed data on the tools and technologies used during the progress of the project, and the generation AI can analyze this data to extract success patterns. The internal data collection department, for example, collects detailed data on the tools and technologies used during the progress of the project, and the generation AI can analyze this data to extract success patterns. For example, it analyzes how a particular tool or technology contributed to the success of the project. The internal data collection department also records detailed data on the tools and technologies used in each project, and the generation AI can extract success patterns based on this data. For example, it analyzes how a particular technology supported the progress of the project. The internal data collection department also collects data on the tools and technologies used during the progress of the project in real time, and the generation AI can analyze this data to extract success patterns. For example, it analyzes how a particular tool improved the efficiency of the project. This allows the data on the tools and technologies used to be analyzed and success patterns to be extracted.

[0056] The internal data collection department can analyze the content and frequency of communications during the course of a project to evaluate their impact on bidding results. For example, the internal data collection department records in detail the content and frequency of communications during the course of a project, and the generation AI analyzes this data to evaluate their impact on bidding results. For example, it analyzes how the frequency of communication affects the success of a project. The internal data collection department also records in detail the content of communications during each project, and the generation AI uses this data to evaluate their impact on bidding results. For example, it analyzes how a particular communication style contributed to the success of a project. The internal data collection department also monitors in real time the frequency of communications during the course of a project, and the generation AI analyzes this data to evaluate their impact on bidding results. For example, it analyzes how the frequency of communication affects the quality of bidding proposals and the success of presentations. This makes it possible to evaluate the impact of the content and frequency of communication on bidding results.

[0057] The internal data collection department can use the emotion estimation function to monitor the emotional responses of project members in real time and provide appropriate feedback according to the progress of the project. For example, the internal data collection department can use the emotion estimation function to monitor the emotional responses of project members in real time and provide appropriate feedback according to the progress of the project. For example, if a member's stress level is high, the department can suggest ways for them to relax. The internal data collection department can also monitor the emotional responses of members as the project progresses, and the generation AI can provide appropriate feedback based on this data. For example, it can send an encouraging message to a member whose motivation is declining. The internal data collection department can also use the emotion estimation function to analyze the emotional responses of project members in real time and provide appropriate feedback according to the progress of the project. For example, it can adjust task allocation according to the member's emotional state. This makes it possible to monitor the emotional responses of project members and provide appropriate feedback.

[0058] The external data collection unit can analyze the economic situation and political background of each local government in addition to data on each local government's bidding project, and evaluate the impact this has on the bidding results. For example, the external data collection unit records each local government's economic situation and political background in detail, and the generation AI analyzes this data to evaluate the impact on the bidding results. For example, it analyzes how the economic situation affects the bid amount. The external data collection unit also records the local government's political background in detail, and the generation AI uses this data to evaluate the impact on the bidding results. For example, it analyzes how specific political factors affect the bidding results. The external data collection unit also monitors each local government's economic situation and political background in real time, and the generation AI analyzes this data to evaluate the impact on the bidding results. For example, it analyzes how the economic situation and political background affect the quality of the bidding proposal and the success of the presentation. This makes it possible to evaluate the impact of the economic situation and political background on the bidding results.

[0059] The external data collection unit performs detailed analysis of competitors' past bidding strategies and proposal content, and the generation AI can predict competitor trends based on this data. For example, the external data collection unit records competitors' past bidding strategies and proposal content in detail, and the generation AI analyzes this data to predict competitor trends. For example, it analyzes what strategies a specific competitor is adopting. The external data collection unit also records competitor proposal content in detail, and the generation AI predicts competitor trends based on this data. For example, it analyzes how specific proposal content contributes to a competitor's success. The external data collection unit also monitors competitors' past bidding strategies and proposal content in real time, and the generation AI analyzes this data to predict competitor trends. For example, it analyzes how a competitor's strategy affects the quality of the bidding proposal and the success of the presentation. This makes it possible to predict competitor trends.

[0060] The external data collection unit uses data on each local government's bidding projects as well as the local government's residents' emotion estimation function to analyze residents' needs and expectations and evaluate their impact on bidding results. For example, the external data collection unit collects residents' emotion estimation data for each local government, and the generation AI analyzes this data to evaluate residents' needs and expectations. For example, it analyzes how residents' emotional states affect bidding results. The external data collection unit also monitors residents' emotion estimation data in real time, and the generation AI evaluates residents' needs and expectations based on this data. For example, it analyzes how residents' emotional states affect the quality of bidding proposals and the success of presentations. The external data collection unit also records residents' emotion estimation data in detail for each local government, and the generation AI analyzes this data to evaluate residents' needs and expectations. For example, it analyzes how residents' emotional states affect bidding results. This makes it possible to evaluate the impact of residents' needs and expectations on bidding results.

[0061] The external data collection unit collects data on the successes and failures of each local government's past projects in addition to data on bidding projects, and the generation AI can analyze this data to extract success patterns. The external data collection unit, for example, collects data on the successes and failures of each local government's past projects, and the generation AI analyzes this data to extract success patterns. For example, it analyzes the factors that led to the success or failure of a particular project. The external data collection unit also records detailed data on the local government's past projects, and the generation AI extracts success patterns based on this data. For example, it analyzes the conditions under which a particular project succeeded or failed. The external data collection unit also collects data on each local government's past projects in real time, and the generation AI analyzes this data to extract success patterns. For example, it analyzes the factors that led to the success or failure of a particular project. This makes it possible to analyze data on the successes and failures of past projects and extract success patterns.

[0062] The external data collection unit collects data on each local government's bidding projects as well as feedback and opinions from local government residents, allowing the generation AI to analyze this data to understand the residents' needs. For example, the external data collection unit collects feedback and opinions from local government residents, and the generation AI analyzes this data to understand the residents' needs. For example, it analyzes how residents' opinions affect bidding results. The external data collection unit also collects feedback from local government residents in real time, and the generation AI uses this data to understand the residents' needs. For example, it analyzes how residents' opinions affect the quality of bidding proposals and the success of presentations. The external data collection unit also records the feedback and opinions of each local government residents in detail, and the generation AI analyzes this data to understand the residents' needs. For example, it analyzes how residents' opinions affect bidding results. This allows the generation AI to analyze residents' feedback and opinions to understand their needs.

[0063] The external data collection unit uses the emotion estimation function to monitor residents' emotional reactions to each local government's bid in real time, and based on this, can propose an optimal bidding strategy. For example, the external data collection unit uses the emotion estimation function to monitor residents' emotional reactions to each local government's bid in real time, and the generation AI proposes an optimal bidding strategy based on this data. For example, it analyzes how residents' emotional state affects the quality of the bid proposal and the success of the presentation. The external data collection unit also collects residents' emotional reactions to each local government's bid in real time, and the generation AI proposes an optimal bidding strategy based on this data. For example, it analyzes how residents' emotional state affects the bidding results. The external data collection unit also uses the emotion estimation function to record residents' emotional reactions to each local government's bid in detail, and the generation AI proposes an optimal bidding strategy based on this data. For example, it analyzes how residents' emotional state affects the quality of the bid proposal and the success of the presentation. This makes it possible to monitor residents' emotional reactions and propose an optimal bidding strategy.

[0064] The winning strategy generation unit uses the emotion estimation function to evaluate the emotional impact of the proposal content on local government officials and can generate emotionally positive proposals. For example, the winning strategy generation unit uses the emotion estimation function to evaluate the emotional impact of the proposal content for a new bidding project on local government officials, and the generation AI generates emotionally positive proposals based on this data. For example, the winning strategy generation unit analyzes the emotional reaction that the proposal content has on the officials. The winning strategy generation unit also monitors the proposal content for a new bidding project in real time and uses the emotion estimation function to evaluate the emotional reaction of the officials. For example, the winning strategy generation unit analyzes the emotional impact that the proposal content has on the officials and generates positive proposals. The winning strategy generation unit also uses the emotion estimation function to record in detail the emotional impact that the proposal content for a new bidding project has on local government officials, and the generation AI generates emotionally positive proposals based on this data. For example, the winning strategy generation unit analyzes the emotional reaction that the proposal content has on the officials. This allows the emotional impact of the proposal content on the officials to be evaluated and positive proposals to be generated.

[0065] The winning strategy generation unit analyzes past successful patterns as well as current market trends and technological trends, and can propose optimal bidding strategies based on this. For example, the winning strategy generation unit analyzes past successful patterns as well as current market trends and technological trends, and the generation AI proposes optimal bidding strategies based on this data. For example, it analyzes how the latest technological trends affect the quality of bid proposals. Furthermore, when generating winning strategies for new bidding projects, the winning strategy generation unit monitors current market trends and technological trends in real time, and the generation AI proposes optimal bidding strategies based on this data. For example, it analyzes how market trends affect the quality of bid proposals. Furthermore, the winning strategy generation unit records past successful patterns as well as current market trends and technological trends in detail, and the generation AI proposes optimal bidding strategies based on this data. For example, it analyzes how technological trends affect the quality of bid proposals. This allows the unit to analyze market trends and technological trends and propose optimal bidding strategies.

[0066] The winning strategy generation unit can perform detailed simulations of the proposal contents and evaluate how the proposal contents will actually be realized. For example, the winning strategy generation unit performs detailed simulations of the proposal contents for a new bidding project, and the generation AI evaluates how the proposal contents will actually be realized based on this data. For example, it analyzes whether the proposal contents are feasible. The winning strategy generation unit also performs detailed simulations of the proposal contents in real time, and the generation AI evaluates how the proposal contents will actually be realized based on this data. For example, it analyzes whether the proposal contents are feasible. The winning strategy generation unit also records detailed simulations of the proposal contents for a new bidding project, and the generation AI evaluates how the proposal contents will actually be realized based on this data. For example, it analyzes whether the proposal contents are feasible. This makes it possible to evaluate how the proposal contents will actually be realized.

[0067] The winning strategy generation unit can refer to successful cases from different industries and fields and apply them to bidding strategies. For example, the winning strategy generation unit collects successful cases from different industries and fields, and the generation AI uses this data to propose the optimal bidding strategy for new bidding projects. For example, it analyzes how successful cases from other industries affect the quality of bidding proposals. In addition, when generating winning strategies for new bidding projects, the winning strategy generation unit monitors successful cases from different industries and fields in real time, and the generation AI uses this data to propose the optimal bidding strategy. For example, it analyzes how successful cases from other industries affect the quality of bidding proposals. In addition, the winning strategy generation unit records successful cases from different industries and fields in detail, and the generation AI uses this data to propose the optimal bidding strategy for new bidding projects. For example, it analyzes how successful cases from other industries affect the quality of bidding proposals. This allows successful cases from different industries and fields to be referred to and applied to bidding strategies.

[0068] The winning scenario generation unit can divide proposal contents into multiple scenarios and evaluate them, and select the most promising scenario. For example, the winning scenario generation unit divides proposal contents for a new bidding project into multiple scenarios and evaluates them, and the generation AI selects the most promising scenario based on this data. For example, it analyzes the feasibility and risks of each scenario. The winning scenario generation unit also divides proposal contents into multiple scenarios and evaluates them in real time, and the generation AI selects the most promising scenario based on this data. For example, it analyzes the feasibility and risks of each scenario. The winning scenario generation unit also divides proposal contents for a new bidding project into multiple scenarios and records them in detail, and the generation AI selects the most promising scenario based on this data. For example, it analyzes the feasibility and risks of each scenario. This makes it possible to divide proposal contents into multiple scenarios and evaluate them, and select the most promising scenario.

[0069] The winning strategy generation unit uses the emotion estimation function to monitor in real time the emotional impact that the proposal content has on local government officials, and can adjust the proposal content as appropriate. For example, the winning strategy generation unit uses the emotion estimation function to monitor in real time the emotional impact that the proposal content for a new bidding project has on local government officials, and the generation AI adjusts the proposal content as appropriate based on this data. For example, it analyzes the emotional reaction that the proposal content has on the officials. The winning strategy generation unit also monitors the proposal content in real time and evaluates the officials' emotional reaction using the emotion estimation function. For example, it analyzes the emotional impact that the proposal content has on the officials, and generates a positive proposal. The winning strategy generation unit also uses the emotion estimation function to record in detail the emotional impact that the proposal content for a new bidding project has on local government officials, and the generation AI adjusts the proposal content as appropriate based on this data. For example, it analyzes the emotional reaction that the proposal content has on the officials. This allows the emotional impact that the proposal content has on the officials to be monitored in real time and adjusted as appropriate.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The bidding support system may further include a visualization unit that generates a visual presentation of the proposal. The visualization unit can, for example, display the proposal in visual formats such as graphs, charts, and infographics. This makes the proposal easier to understand and more persuasive to local government officials. The visualization unit can also visually display the results of simulations of the proposal. For example, it can use 3D models or animations to show scenarios in which the proposal is implemented. The visualization unit can also visually compare proposals. For example, it can compare multiple proposals side by side and visually show the advantages and disadvantages of each. This makes the proposal more persuasive to local government officials through visual presentations of the proposal.

[0072] The bidding support system may further include a risk assessment unit that performs risk assessment of the proposal content. The risk assessment unit can, for example, analyze risks hidden in the proposal content and evaluate the impact and probability of occurrence of those risks. This allows the risks of the proposal content to be identified in advance and appropriate risk management measures to be taken. The risk assessment unit can also evaluate the possibility of similar risks occurring based on risk data from past bidding projects. For example, it analyzes how specific risks occurred in past projects and how they were dealt with. Furthermore, the risk assessment unit can visually display the risks of the proposal content. For example, it can use a risk matrix or heat map to visually show the impact and probability of occurrence of risks. This allows the risks of the proposal content to be assessed in advance and appropriate risk management measures to be taken.

[0073] The bidding support system may further include a cost evaluation unit that evaluates the costs of the proposal. The cost evaluation unit may, for example, perform a detailed analysis of the costs of the proposal and evaluate the breakdown and total amount of the costs. This allows the costs of the proposal to be grasped in advance and appropriate cost management measures to be taken. The cost evaluation unit may also evaluate the possibility of similar costs being incurred based on cost data from past bidding projects. For example, it may analyze how specific costs were incurred and managed in past projects. Furthermore, the cost evaluation unit may visually display the costs of the proposal. For example, it may visually display the breakdown and total amount of costs using a cost breakdown or cost comparison chart. This allows the costs of the proposal to be evaluated in advance and appropriate cost management measures to be taken.

[0074] The bidding support system may further include an environmental impact assessment unit that performs an environmental impact assessment of the proposal. The environmental impact assessment unit may, for example, analyze the environmental impact of the proposal and assess the degree of impact. This allows the environmental impact of the proposal to be understood in advance and appropriate environmental protection measures to be taken. The environmental impact assessment unit may also assess the possibility of similar impacts occurring based on environmental impact data from past bidding cases. For example, it may analyze how specific environmental impacts occurred in past cases and how they were addressed. Furthermore, the environmental impact assessment unit may visually display the environmental impact of the proposal. For example, it may use an environmental impact matrix or heat map to visually show the degree of impact and probability of occurrence of environmental impacts. This allows the environmental impact of the proposal to be assessed in advance and appropriate environmental protection measures to be taken.

[0075] The bidding support system may further include a legal evaluation unit that performs a legal evaluation of the proposal content. The legal evaluation unit may, for example, analyze whether the proposal content is legally compliant and evaluate its degree of compliance. This allows the legal compliance of the proposal content to be determined in advance and appropriate legal measures to be taken. The legal evaluation unit may also evaluate the possibility of similar legal issues occurring based on legal data from past bidding cases. For example, it may analyze how a specific legal issue occurred in past cases and how it was addressed. Furthermore, the legal evaluation unit may visually display the legal compliance of the proposal content. For example, it may use a legal compliance matrix or heat map to visually show the degree of legal compliance and the probability of occurrence. This allows the legal compliance of the proposal content to be evaluated in advance and appropriate legal measures to be taken.

[0076] The bidding support system may further include an emotion evaluation unit that evaluates the emotional impact of the proposal content. The emotion evaluation unit may, for example, analyze the emotional impact of the proposal content on local government officials and evaluate the degree of impact. This allows the emotional impact of the proposal content on officials to be understood in advance and appropriate emotional countermeasures to be taken. The emotion evaluation unit may also evaluate the possibility of similar emotional impacts occurring based on emotional data from past bidding cases. For example, it may analyze how specific proposal content was received in past cases. Furthermore, the emotion evaluation unit may visually display the emotional impact of the proposal content. For example, it may use an emotion matrix or heat map to visually show the degree of impact and the probability of occurrence of emotional impact. This allows the emotional impact of the proposal content to be evaluated in advance and appropriate emotional countermeasures to be taken.

[0077] The bidding support system may further include an emotion monitoring unit that monitors emotional responses to the proposal content in real time. The emotion monitoring unit, for example, may monitor the emotional responses of local government officials to the proposal content in real time and adjust the proposal content appropriately based on the data. This allows the emotional impact of the proposal content on the officials to be understood in real time and appropriate emotional countermeasures to be taken. The emotion monitoring unit may also evaluate the possibility of similar emotional responses based on emotional data from past bidding cases. For example, it may analyze how specific proposal content was received in past cases. Furthermore, the emotion monitoring unit may visually display the emotional responses to the proposal content. For example, it may use an emotion matrix or heat map to visually show the impact and probability of occurrence of emotional responses. This allows the emotional responses to the proposal content to be monitored in real time and appropriate emotional countermeasures to be taken.

[0078] The bidding support system may further include an emotion prediction unit that predicts the emotional impact of the proposal content. The emotion prediction unit, for example, predicts the emotional impact of the proposal content on local government officials and can appropriately adjust the proposal content based on the prediction results. This allows the emotional impact of the proposal content on officials to be predicted in advance and appropriate emotional countermeasures to be taken. The emotion prediction unit may also predict the possibility of similar emotional impacts occurring based on emotional data from past bidding cases. For example, it may analyze how specific proposal content was received in past cases. Furthermore, the emotion prediction unit may visually display the emotional impact of the proposal content. For example, it may use an emotion matrix or heat map to visually show the impact level and probability of occurrence of emotional impacts. This allows the emotional impact of the proposal content to be predicted in advance and appropriate emotional countermeasures to be taken.

[0079] The bidding support system may further include an emotion simulation unit that simulates the emotional impact of the proposal content. The emotion simulation unit, for example, may simulate the emotional impact of the proposal content on local government officials and adjust the proposal content appropriately based on the simulation results. This allows the emotional impact of the proposal content on officials to be simulated in advance and appropriate emotional countermeasures to be taken. The emotion simulation unit may also simulate the possibility of similar emotional impacts occurring based on emotional data from past bidding cases. For example, it may analyze how specific proposal content was received in past cases. Furthermore, the emotion simulation unit may visually display the emotional impact of the proposal content. For example, it may use an emotion matrix or heat map to visually show the impact level and probability of occurrence of emotional impacts. This allows the emotional impact of the proposal content to be simulated in advance and appropriate emotional countermeasures to be taken.

[0080] The bidding support system may further include a sentiment analysis unit that analyzes the emotional impact of the proposal content. The sentiment analysis unit may, for example, perform a detailed analysis of the emotional impact of the proposal content on local government officials and adjust the proposal content appropriately based on the analysis results. This allows the emotional impact of the proposal content on officials to be analyzed in advance and appropriate emotional countermeasures to be taken. The sentiment analysis unit may also analyze the possibility of similar emotional impacts occurring based on emotional data from past bidding cases. For example, it may analyze how specific proposal content was received in past cases. Furthermore, the sentiment analysis unit may visually display the emotional impact of the proposal content. For example, it may use an emotional matrix or heat map to visually show the impact level and probability of occurrence of emotional impacts. This allows the emotional impact of the proposal content to be analyzed in advance and appropriate emotional countermeasures to be taken.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The internal data collection department collects data on past bidding projects. For example, they collect data on bid amounts, bidding conditions, and bidding results. They can also record detailed records of problems that occurred during the project and their solutions. For example, they collect data on technical issues, schedule delays, and resource shortages. Step 2: The external data collection department collects data on bidding projects for each local government. For example, it collects data on bidding announcements, bidding results, and participating companies. It can also analyze the local government's economic situation and political background. For example, it collects data on budget status, policy changes, economic indicators, etc. Step 3: The winning strategy generation unit analyzes the data collected by the internal data collection unit and external data collection unit to generate the optimal bidding strategy for new bidding projects. For example, it proposes the optimal bid amount and proposal content based on past patterns of success and failure. It can also use the emotion estimation function to evaluate the emotional impact that the proposal content will have on local government officials and generate emotionally positive proposals.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

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

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0119] The 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.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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, in order to avoid confusion and to 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.

[0149] 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]

[0150] 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. An internal data collection department that collects data on past bidding projects, An external data collection department that collects bidding data from each local government; a winning strategy generation unit that analyzes the data collected by the in-house data collection unit and the external data collection unit and generates an optimal bidding strategy for a new bidding item. A system characterized by:

2. The in-house data collection unit: Analyze each project member's emotional factors regarding the success or failure of the project and evaluate the impact of these emotional factors on the bidding results.

2. The system of claim 1.

3. The in-house data collection unit: In addition to data on past bidding projects, detailed records of problems that occurred during the project and their solutions are kept, and the generative AI analyzes this data to extract patterns of success.

2. The system of claim 1.

4. The in-house data collection unit: Analyze the leadership style and team dynamics of each project and evaluate their impact on the bid outcome 2. The system of claim 1.

5. The in-house data collection unit: Detailed data on the tools and technologies used during the project will also be collected, and the generative AI will analyze this data to extract patterns of success.

2. The system of claim 1.

6. The in-house data collection unit: Analyze the content and frequency of communications made during the project and evaluate the impact of said communications on the bidding results 2. The system of claim 1.

7. The in-house data collection unit: Monitor project members' emotional reactions in real time and provide appropriate feedback according to the project's progress 2. The system of claim 1.

8. The external data collection unit In addition to data on the bidding projects of each local government, we analyze the economic and political background of the said local government and evaluate the impact of this on the bidding results.

2. The system of claim 1.

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