system

Abstract

The system according to the embodiment aims to quickly and efficiently conclude agreements with local governments. [Solution] A system according to an embodiment includes a template creation unit, a simulation unit, and a negotiation unit. The template creation unit creates a standard template. The simulation unit performs a simulation based on the template created by the template creation unit. The negotiation unit negotiates with a local government based on the simulation results obtained by the simulation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, concluding agreements with local governments required a lot of man-hours, making it difficult to conclude them quickly.

[0005] The system according to the embodiment aims to quickly and efficiently conclude agreements with local governments. [Means for solving the problem]

[0006] The system according to the embodiment includes a template creation unit, a simulation unit, and a negotiation unit. The template creation unit creates a standard template. The simulation unit performs a simulation based on the template created by the template creation unit. The negotiation unit negotiates with the local government based on the simulation results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and efficiently conclude agreements with local governments. [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 agreement conclusion support system according to an embodiment of the present invention packages agreements with local governments, thereby reducing the amount of work required and expediting the process. This agreement conclusion support system significantly reduces the time and effort required to create agreements by creating standard templates and customizing them for each local government. It also simulates the cost-effectiveness of agreements to maximize benefits. Based on the simulation results, negotiations with local governments are conducted and proposals are made to maximize revenue and contributions to the local government. For example, a standard agreement template is created. Next, the template is customized according to the characteristics of each local government. This reduces the time and effort required to create agreements. Next, a simulation is conducted. The simulation calculates the cost-effectiveness of the agreement and aims to conclude an agreement that maximizes benefits. The simulation results are updated in real time, providing the latest information. Furthermore, negotiations with local governments are conducted based on the simulation results. By providing specific numerical figures, it becomes easier for local governments to understand and agree. During negotiations, the system specifically demonstrates the increase in revenue and the contribution to the local government. Finally, a follow-up system is established after the agreement is concluded. The system monitors how the agreement is actually being implemented and makes improvement proposals as necessary. This maximizes the effectiveness of the agreement and strengthens trust with the local government. This allows the agreement conclusion support system to speed up the agreement conclusion and reduce the amount of work required through agreement creation, simulation, negotiation, and follow-up.

[0029] The agreement conclusion support system according to the embodiment includes a template creation unit, a simulation unit, and a negotiation unit. The template creation unit creates a standard template. For example, the template creation unit defines standard agreement formats and items and generates templates applicable to each local government. For example, the template creation unit can standardize the content of the agreement and quickly create templates. The simulation unit performs a simulation based on the template created by the template creation unit. For example, the simulation unit calculates the cost-effectiveness of the agreement and aims to conclude an agreement that maximizes benefits. For example, the simulation unit evaluates the profitability of the agreement and its contribution to the local government and proposes optimal agreement content. The negotiation unit negotiates with the local government based on the simulation results obtained by the simulation unit. For example, the negotiation unit presents specific numerical values ​​based on the simulation results to facilitate understanding and acceptance by the local government. For example, the negotiation unit presents specific figures for revenue increase and contribution to the local government to promote agreement conclusion. As a result, the agreement conclusion support system according to the embodiment can speed up agreement conclusion and reduce labor costs through agreement creation, simulation, and negotiation.

[0030] The agreement conclusion support system includes a customization unit that customizes templates according to the characteristics of each local government. The customization unit customizes templates according to the characteristics of each local government. The customization unit adjusts the content of the templates, for example, taking into account the laws, regulations, and characteristics of each local government. The customization unit optimizes templates based on the population, economic situation, past agreement history, etc. of each local government. For example, by customizing according to the characteristics of each local government, the customization unit can reduce the time and effort required to create agreements. This makes it possible to customize templates according to the characteristics of each local government.

[0031] The agreement conclusion support system includes an update unit that updates the simulation results in real time. The update unit updates the simulation results in real time. For example, the update unit updates the simulation results based on the latest information, and always provides the latest simulation results. For example, by updating the simulation results in real time, the update unit can provide the latest information when an agreement is concluded. For example, by updating the simulation results in real time, the update unit can speed up the agreement conclusion and reduce labor costs. In this way, by updating the simulation results in real time, the latest information can be provided.

[0032] The agreement conclusion support system includes a follow-up unit that performs follow-up after the agreement is concluded. The follow-up unit performs follow-up after the agreement is concluded. For example, the follow-up unit monitors how the agreement is actually being implemented and makes improvement proposals as necessary. For example, the follow-up unit performs follow-up to maximize the effects of the agreement and strengthen the relationship of trust with the local government. For example, by performing follow-up after the agreement is concluded, the follow-up unit can maximize the effects of the agreement and strengthen the relationship of trust with the local government. This makes it possible to perform follow-up after the agreement is concluded.

[0033] The customization unit can customize templates in cooperation with the template creation unit. The customization unit customizes templates in cooperation with the template creation unit. The customization unit, for example, customizes a standard template created by the template creation unit according to the characteristics of each local government. The customization unit, for example, can perform more effective customization by working with the template creation unit. The customization unit, for example, can customize a template in cooperation with the template creation unit, thereby reducing the time and effort required to create an agreement. This allows more effective customization by working with the template creation unit.

[0034] The update unit can update the simulation results in cooperation with the simulation unit. The update unit updates the simulation results in cooperation with the simulation unit. The update unit updates the simulation results, for example, based on the latest data provided by the simulation unit. For example, by working with the simulation unit, the update unit improves the accuracy of the simulation results. For example, by working with the simulation unit to update the simulation results, the update unit can provide the latest information when an agreement is concluded. As a result, by working with the simulation unit, the accuracy of the simulation results is improved.

[0035] The follow-up department can work with the negotiation department to support operations after the agreement is concluded. The follow-up department works with the negotiation department to support operations after the agreement is concluded. The follow-up department supports operations after the agreement is concluded, for example, based on information provided by the negotiation department. For example, by working with the negotiation department, the follow-up department can ensure smooth operations after the agreement is concluded. For example, by working with the negotiation department to support operations after the agreement is concluded, the follow-up department can maximize the effects of the agreement and strengthen the relationship of trust with the local government. In this way, by working with the negotiation department, operations after the agreement is concluded can be ensured smoothly.

[0036] The template creation unit can analyze past agreement data and automatically generate an optimal template. The template creation unit analyzes past agreement data and automatically generates an optimal template. The template creation unit, for example, analyzes past agreement data and automatically reflects frequently used clauses and wording in the template. The template creation unit, for example, extracts success stories from past agreement data and generates an optimal template based on them. The template creation unit, for example, automatically generates a template suitable for a specific local government based on past agreement data. This makes it possible to provide an optimal template based on past agreement data.

[0037] The template creation unit can automatically reflect the laws, regulations, and characteristics of each local government. The template creation unit automatically reflects the laws, regulations, and characteristics of each local government. For example, the template creation unit references the laws, regulations database of each local government and automatically adds the necessary legal requirements to the template. For example, the template creation unit adjusts the content of the template taking into account the characteristics of each local government (population, economic situation, etc.). For example, the template creation unit references the past agreement history of each local government and reflects specific requirements in the template. This makes it possible to provide templates that reflect the laws, regulations, and characteristics of each local government.

[0038] The template creation unit can optimize the template by referring to success stories of other municipalities. The template creation unit optimizes the template by referring to success stories of other municipalities. For example, the template creation unit refers to templates of agreements that have been successful in other municipalities and optimizes them for municipalities with similar conditions. For example, the template creation unit analyzes success stories of other municipalities and reflects common elements in the template. For example, the template creation unit creates an optimal template under specific conditions based on success stories of other municipalities. This makes it possible to provide an optimal template based on success stories of other municipalities.

[0039] The template creation unit can customize a template taking into account the user's past agreement history. The template creation unit customizes a template taking into account the user's past agreement history. For example, the template creation unit references the history of agreements previously concluded by the user and provides a template with similar conditions. For example, the template creation unit reflects frequently used clauses from the user's past agreement history in the template. For example, the template creation unit customizes a template that includes specific requirements based on the user's past agreement history. This makes it possible to provide an optimal template based on the user's past agreement history.

[0040] The simulation unit can improve the accuracy of the simulation by referring to past agreement data. The simulation unit improves the accuracy of the simulation by referring to past agreement data. The simulation unit, for example, adjusts simulation parameters based on past agreement data. The simulation unit, for example, extracts success cases from past agreement data and improves the accuracy of the simulation based on the success cases. The simulation unit, for example, analyzes past agreement data and improves the accuracy of the simulation under specific conditions. This makes it possible to improve the accuracy of the simulation based on past agreement data.

[0041] The simulation unit can perform a simulation taking into account the financial situation and characteristics of each local government. The simulation unit performs a simulation taking into account the financial situation and characteristics of each local government. The simulation unit, for example, refers to the financial data of each local government and adjusts the parameters of the simulation. The simulation unit, for example, refers to the characteristics of each local government (population, economic situation, etc.) and adjusts the content of the simulation. The simulation unit, for example, refers to the past financial history of each local government and performs a simulation under specific conditions. This makes it possible to perform a simulation taking into account the financial situation and characteristics of each local government.

[0042] The simulation unit can optimize the simulation by referring to success stories of other municipalities. The simulation unit optimizes the simulation by referring to success stories of other municipalities. For example, the simulation unit refers to the simulation results of agreements that have been successful in other municipalities and optimizes for municipalities with similar conditions. For example, the simulation unit analyzes success stories of other municipalities and reflects common elements in the simulation. For example, the simulation unit performs an optimal simulation under specific conditions based on success stories of other municipalities. This makes it possible to provide an optimal simulation based on success stories of other municipalities.

[0043] The simulation unit can customize the simulation taking into account the user's past agreement history. The simulation unit customizes the simulation taking into account the user's past agreement history. For example, the simulation unit references the history of agreements previously concluded by the user and provides a simulation with similar conditions. For example, the simulation unit reflects frequently used parameters from the user's past agreement history in the simulation. For example, the simulation unit customizes a simulation that includes specific requirements based on the user's past agreement history. This makes it possible to provide an optimal simulation based on the user's past agreement history.

[0044] The negotiation unit can propose an optimal negotiation strategy by referring to past negotiation data. The negotiation unit proposes an optimal negotiation strategy by referring to past negotiation data. The negotiation unit proposes an optimal negotiation strategy, for example, based on past negotiation data. The negotiation unit extracts success stories from past negotiation data, for example, and proposes a negotiation strategy based on the success stories. The negotiation unit analyzes past negotiation data, for example, and proposes an optimal negotiation strategy under specific conditions. This makes it possible to provide an optimal negotiation strategy based on past negotiation data.

[0045] The negotiation unit can conduct negotiations taking into account the characteristics and past negotiation history of each local government. The negotiation unit conducts negotiations taking into account the characteristics and past negotiation history of each local government. The negotiation unit adjusts the content of the negotiation taking into account, for example, the characteristics of each local government (population, economic situation, etc.). The negotiation unit, for example, refers to the past negotiation history of each local government and conducts negotiations that include specific requirements. The negotiation unit proposes an optimal negotiation strategy based on, for example, the characteristics and past negotiation history of each local government. This makes it possible to provide optimal negotiations based on the characteristics and past negotiation history of each local government.

[0046] The negotiation unit can optimize negotiations by referring to success stories of other municipalities. The negotiation unit optimizes negotiations by referring to success stories of other municipalities. For example, the negotiation unit refers to the results of negotiations of agreements that have been successful in other municipalities and optimizes for municipalities with similar conditions. For example, the negotiation unit analyzes success stories of other municipalities and reflects common elements in negotiations. For example, the negotiation unit conducts optimal negotiations under specific conditions based on success stories of other municipalities. This makes it possible to provide optimal negotiations based on success stories of other municipalities.

[0047] The negotiation unit can customize the negotiation taking into account the user's past agreement history. The negotiation unit customizes the negotiation taking into account the user's past agreement history. For example, the negotiation unit refers to the history of agreements previously concluded by the user and provides negotiations with similar conditions. For example, the negotiation unit reflects frequently used parameters from the user's past agreement history in the negotiation. For example, the negotiation unit customizes the negotiation including specific requirements based on the user's past agreement history. This makes it possible to provide the optimal negotiation based on the user's past agreement history.

[0048] The customization unit can automatically reflect the laws, regulations, and characteristics of each local government. The customization unit automatically reflects the laws, regulations, and characteristics of each local government. For example, the customization unit references the laws, regulations database of each local government and automatically adds the legal requirements necessary for customization. For example, the customization unit adjusts the content of the customization taking into account the characteristics of each local government (population, economic situation, etc.). For example, the customization unit references the past agreement history of each local government and reflects specific requirements in the customization. This makes it possible to perform customization that reflects the laws, regulations, and characteristics of each local government.

[0049] The customization department can optimize the customization by referring to the success stories of other municipalities. The customization department optimizes the customization by referring to the success stories of other municipalities. For example, the customization department refers to the customization results of agreements that have been successful in other municipalities and optimizes for municipalities with similar conditions. For example, the customization department analyzes the success stories of other municipalities and reflects common elements in the customization. For example, the customization department performs optimal customization under specific conditions based on the success stories of other municipalities. This makes it possible to provide optimal customization based on the success stories of other municipalities.

[0050] The update unit can optimize the update algorithm by referring to past simulation data. The update unit optimizes the update algorithm by referring to past simulation data. The update unit adjusts parameters of the update algorithm based on, for example, past simulation data. The update unit extracts success cases from, for example, past simulation data, and optimizes the update algorithm based on the success cases. The update unit analyzes, for example, past simulation data, and optimizes the update algorithm under specific conditions. This makes it possible to optimize the update algorithm based on past simulation data.

[0051] The update unit can optimize the update by referring to success stories of other municipalities. The update unit optimizes the update by referring to success stories of other municipalities. For example, the update unit refers to the results of agreement updates that have been successful in other municipalities and optimizes for municipalities with similar conditions. For example, the update unit analyzes success stories of other municipalities and reflects common elements in the update. For example, the update unit performs the optimal update under specific conditions based on success stories of other municipalities. This makes it possible to provide the optimal update based on success stories of other municipalities.

[0052] The follow-up unit can propose an optimal follow-up method by referring to past agreement data. The follow-up unit proposes an optimal follow-up method by referring to past agreement data. The follow-up unit proposes an optimal follow-up method, for example, based on past agreement data. The follow-up unit extracts success stories from past agreement data, for example, and proposes a follow-up method based on the success stories. The follow-up unit analyzes past agreement data, for example, and proposes an optimal follow-up method under specific conditions. This makes it possible to provide an optimal follow-up method based on past agreement data.

[0053] The follow-up department can optimize the follow-up by referring to the success stories of other local governments. The follow-up department optimizes the follow-up by referring to the success stories of other local governments. For example, the follow-up department refers to the follow-up results of agreements that have been successful in other local governments and optimizes for local governments with similar conditions. For example, the follow-up department analyzes the success stories of other local governments and reflects common elements in the follow-up. For example, the follow-up department performs optimal follow-up under specific conditions based on the success stories of other local governments. This makes it possible to provide optimal follow-up based on the success stories of other local governments.

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

[0055] The template creation unit can analyze the user's past input history and automatically reflect frequently used items in the template. For example, clauses and phrases that many users have used in the past can be automatically added to the template. This allows the user to quickly create an optimal template based on their past input history. The template creation unit can also suggest the optimal template under specific conditions based on the user's past input history. This makes it possible to provide more effective templates by utilizing the user's past input history.

[0056] The renewal department can optimize the renewal by referring to the success stories of other municipalities. For example, it can refer to the successful renewal results of agreements in other municipalities and optimize for municipalities with similar conditions. It can also analyze the success stories of other municipalities and reflect common elements in the renewal. Furthermore, it can perform the optimal renewal under specific conditions based on the success stories of other municipalities. This makes it possible to provide the optimal renewal based on the success stories of other municipalities.

[0057] The template creation unit can optimize templates by referencing successful cases in other fields. For example, successful templates in other fields, such as business contracts and license agreements, can be referenced and applied to agreement conclusion. It can also analyze successful cases in other fields and reflect common elements in the template. Furthermore, it can create an optimal template under specific conditions based on successful cases in other fields. This makes it possible to provide an optimal template based on successful cases in other fields.

[0058] The negotiation unit can customize the negotiation taking into account the user's past negotiation history. For example, it can refer to the history of agreements concluded by the user in the past and provide negotiations with similar conditions. It can also reflect frequently used parameters from the user's past negotiation history in the negotiation. Furthermore, it can customize the negotiation including specific requirements based on the user's past negotiation history. This makes it possible to provide the optimal negotiation based on the user's past negotiation history.

[0059] The update unit can optimize the update algorithm by referring to past simulation data. For example, the parameters of the update algorithm can be adjusted based on the past simulation data. It can also extract successful cases from the past simulation data and optimize the update algorithm based on them. Furthermore, it can analyze the past simulation data and optimize the update algorithm under specific conditions. This makes it possible to provide an optimal update algorithm based on the past simulation data.

[0060] The Follow-up Department can optimize follow-up by referring to the success stories of other municipalities. For example, it can refer to the follow-up results of successful agreements in other municipalities and optimize for municipalities with similar conditions. It can also analyze the success stories of other municipalities and reflect common elements in follow-up. Furthermore, it can provide optimal follow-up under specific conditions based on the success stories of other municipalities. This allows it to provide optimal follow-up based on the success stories of other municipalities.

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

[0062] Step 1: The template creation department creates standard templates. For example, it defines standard formats and items for agreements and generates templates that can be applied to each local government. This standardizes the content of agreements and enables templates to be created quickly. Step 2: The Simulation Department conducts simulations based on the templates created by the Template Creation Department. For example, it calculates the cost-effectiveness of the agreement and aims to conclude an agreement that maximizes benefits. It also evaluates the profitability of the agreement and its contribution to the local government, and proposes optimal agreement content. Step 3: The negotiation department negotiates with the local government based on the simulation results obtained by the simulation department. For example, specific figures based on the simulation results can be presented to help the local government understand and agree. This will concretely demonstrate the increase in revenue and the degree of contribution to the local government, facilitating the conclusion of an agreement.

[0063] (Example 2) The agreement conclusion support system according to an embodiment of the present invention packages agreements with local governments, thereby reducing the amount of work required and expediting the process. This agreement conclusion support system significantly reduces the time and effort required to create agreements by creating standard templates and customizing them for each local government. It also simulates the cost-effectiveness of agreements to maximize benefits. Based on the simulation results, negotiations with local governments are conducted and proposals are made to maximize revenue and contributions to the local government. For example, a standard agreement template is created. Next, the template is customized according to the characteristics of each local government. This reduces the time and effort required to create agreements. Next, a simulation is conducted. The simulation calculates the cost-effectiveness of the agreement and aims to conclude an agreement that maximizes benefits. The simulation results are updated in real time, providing the latest information. Furthermore, negotiations with local governments are conducted based on the simulation results. By providing specific numerical figures, it becomes easier for local governments to understand and agree. During negotiations, the system specifically demonstrates the increase in revenue and the contribution to the local government. Finally, a follow-up system is established after the agreement is concluded. The system monitors how the agreement is actually being implemented and makes improvement proposals as necessary. This maximizes the effectiveness of the agreement and strengthens trust with the local government. This allows the agreement conclusion support system to speed up the agreement conclusion and reduce the amount of work required through agreement creation, simulation, negotiation, and follow-up.

[0064] The agreement conclusion support system according to the embodiment includes a template creation unit, a simulation unit, and a negotiation unit. The template creation unit creates a standard template. For example, the template creation unit defines standard agreement formats and items and generates templates applicable to each local government. For example, the template creation unit can standardize the content of the agreement and quickly create templates. The simulation unit performs a simulation based on the template created by the template creation unit. For example, the simulation unit calculates the cost-effectiveness of the agreement and aims to conclude an agreement that maximizes benefits. For example, the simulation unit evaluates the profitability of the agreement and its contribution to the local government and proposes optimal agreement content. The negotiation unit negotiates with the local government based on the simulation results obtained by the simulation unit. For example, the negotiation unit presents specific numerical values ​​based on the simulation results to facilitate understanding and acceptance by the local government. For example, the negotiation unit presents specific figures for revenue increase and contribution to the local government to promote agreement conclusion. As a result, the agreement conclusion support system according to the embodiment can speed up agreement conclusion and reduce labor costs through agreement creation, simulation, and negotiation.

[0065] The agreement conclusion support system includes a customization unit that customizes templates according to the characteristics of each local government. The customization unit customizes templates according to the characteristics of each local government. The customization unit adjusts the content of the templates, for example, taking into account the laws, regulations, and characteristics of each local government. The customization unit optimizes templates based on the population, economic situation, past agreement history, etc. of each local government. For example, by customizing according to the characteristics of each local government, the customization unit can reduce the time and effort required to create agreements. This makes it possible to customize templates according to the characteristics of each local government.

[0066] The agreement conclusion support system includes an update unit that updates the simulation results in real time. The update unit updates the simulation results in real time. For example, the update unit updates the simulation results based on the latest information, and always provides the latest simulation results. For example, by updating the simulation results in real time, the update unit can provide the latest information when an agreement is concluded. For example, by updating the simulation results in real time, the update unit can speed up the agreement conclusion and reduce labor costs. In this way, by updating the simulation results in real time, the latest information can be provided.

[0067] The agreement conclusion support system includes a follow-up unit that performs follow-up after the agreement is concluded. The follow-up unit performs follow-up after the agreement is concluded. For example, the follow-up unit monitors how the agreement is actually being implemented and makes improvement proposals as necessary. For example, the follow-up unit performs follow-up to maximize the effects of the agreement and strengthen the relationship of trust with the local government. For example, by performing follow-up after the agreement is concluded, the follow-up unit can maximize the effects of the agreement and strengthen the relationship of trust with the local government. This makes it possible to perform follow-up after the agreement is concluded.

[0068] The customization unit can customize templates in cooperation with the template creation unit. The customization unit customizes templates in cooperation with the template creation unit. The customization unit, for example, customizes a standard template created by the template creation unit according to the characteristics of each local government. The customization unit, for example, can perform more effective customization by working with the template creation unit. The customization unit, for example, can customize a template in cooperation with the template creation unit, thereby reducing the time and effort required to create an agreement. This allows more effective customization by working with the template creation unit.

[0069] The update unit can update the simulation results in cooperation with the simulation unit. The update unit updates the simulation results in cooperation with the simulation unit. The update unit updates the simulation results, for example, based on the latest data provided by the simulation unit. For example, by working with the simulation unit, the update unit improves the accuracy of the simulation results. For example, by working with the simulation unit to update the simulation results, the update unit can provide the latest information when an agreement is concluded. As a result, by working with the simulation unit, the accuracy of the simulation results is improved.

[0070] The follow-up department can work with the negotiation department to support operations after the agreement is concluded. The follow-up department works with the negotiation department to support operations after the agreement is concluded. The follow-up department supports operations after the agreement is concluded, for example, based on information provided by the negotiation department. For example, by working with the negotiation department, the follow-up department can ensure smooth operations after the agreement is concluded. For example, by working with the negotiation department to support operations after the agreement is concluded, the follow-up department can maximize the effects of the agreement and strengthen the relationship of trust with the local government. In this way, by working with the negotiation department, operations after the agreement is concluded can be ensured smoothly.

[0071] The template creation unit can estimate the user's emotion and adjust the template expression method based on the estimated user's emotion. The template creation unit estimates the user's emotion and adjusts the template expression method based on the estimated user's emotion. For example, when the user is feeling stressed, the template creation unit provides a simple and intuitive template and minimizes input steps. For example, when the user is relaxed, the template creation unit provides detailed input options and suggests a customizable template. For example, when the user is in a hurry, the template creation unit prioritizes voice input and enables quick template creation. This makes it possible to provide a template expression method according to the user's emotion.

[0072] The template creation unit can analyze past agreement data and automatically generate an optimal template. The template creation unit analyzes past agreement data and automatically generates an optimal template. The template creation unit, for example, analyzes past agreement data and automatically reflects frequently used clauses and wording in the template. The template creation unit, for example, extracts success stories from past agreement data and generates an optimal template based on them. The template creation unit, for example, automatically generates a template suitable for a specific local government based on past agreement data. This makes it possible to provide an optimal template based on past agreement data.

[0073] The template creation unit can automatically reflect the laws, regulations, and characteristics of each local government. The template creation unit automatically reflects the laws, regulations, and characteristics of each local government. For example, the template creation unit references the laws, regulations database of each local government and automatically adds the necessary legal requirements to the template. For example, the template creation unit adjusts the content of the template taking into account the characteristics of each local government (population, economic situation, etc.). For example, the template creation unit references the past agreement history of each local government and reflects specific requirements in the template. This makes it possible to provide templates that reflect the laws, regulations, and characteristics of each local government.

[0074] The template creation unit can estimate the user's emotion and determine the priority of templates based on the estimated user's emotion. The template creation unit estimates the user's emotion and determines the priority of templates based on the estimated user's emotion. For example, when the user is feeling stressed, the template creation unit provides a template that prioritizes displaying simple and important items. For example, when the user is relaxed, the template creation unit provides a template that prioritizes displaying a template including detailed items. For example, when the user is in a hurry, the template creation unit provides a template that displays only the most important items. This makes it possible to provide a priority of templates according to the user's emotion.

[0075] The template creation unit can optimize the template by referring to success stories of other municipalities. The template creation unit optimizes the template by referring to success stories of other municipalities. For example, the template creation unit refers to templates of agreements that have been successful in other municipalities and optimizes them for municipalities with similar conditions. For example, the template creation unit analyzes success stories of other municipalities and reflects common elements in the template. For example, the template creation unit creates an optimal template under specific conditions based on success stories of other municipalities. This makes it possible to provide an optimal template based on success stories of other municipalities.

[0076] The template creation unit can customize a template taking into account the user's past agreement history. The template creation unit customizes a template taking into account the user's past agreement history. For example, the template creation unit references the history of agreements previously concluded by the user and provides a template with similar conditions. For example, the template creation unit reflects frequently used clauses from the user's past agreement history in the template. For example, the template creation unit customizes a template that includes specific requirements based on the user's past agreement history. This makes it possible to provide an optimal template based on the user's past agreement history.

[0077] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user's emotions. The simulation unit estimates the user's emotions and adjusts the display method of the simulation results based on the estimated user's emotions. For example, when the user is nervous, the simulation unit provides a simple, highly visible display method. For example, when the user is relaxed, the simulation unit provides a display method including detailed information. For example, when the user is in a hurry, the simulation unit provides a display method that focuses on the main points. In this way, it is possible to provide a display method of the simulation results according to the user's emotions.

[0078] The simulation unit can improve the accuracy of the simulation by referring to past agreement data. The simulation unit improves the accuracy of the simulation by referring to past agreement data. The simulation unit, for example, adjusts simulation parameters based on past agreement data. The simulation unit, for example, extracts success cases from past agreement data and improves the accuracy of the simulation based on the success cases. The simulation unit, for example, analyzes past agreement data and improves the accuracy of the simulation under specific conditions. This makes it possible to improve the accuracy of the simulation based on past agreement data.

[0079] The simulation unit can perform a simulation taking into account the financial situation and characteristics of each local government. The simulation unit performs a simulation taking into account the financial situation and characteristics of each local government. The simulation unit, for example, refers to the financial data of each local government and adjusts the parameters of the simulation. The simulation unit, for example, refers to the characteristics of each local government (population, economic situation, etc.) and adjusts the content of the simulation. The simulation unit, for example, refers to the past financial history of each local government and performs a simulation under specific conditions. This makes it possible to perform a simulation taking into account the financial situation and characteristics of each local government.

[0080] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated user's emotions. The simulation unit estimates the user's emotions and determines the priority of simulations based on the estimated user's emotions. For example, if the user is feeling stressed, the simulation unit prioritizes simulations of simple and important items. For example, if the user is relaxed, the simulation unit prioritizes simulations including detailed items. For example, if the user is in a hurry, the simulation unit simulates only the most important items. This makes it possible to provide a priority of simulations according to the user's emotions.

[0081] The simulation unit can optimize the simulation by referring to success stories of other municipalities. The simulation unit optimizes the simulation by referring to success stories of other municipalities. For example, the simulation unit refers to the simulation results of agreements that have been successful in other municipalities and optimizes for municipalities with similar conditions. For example, the simulation unit analyzes success stories of other municipalities and reflects common elements in the simulation. For example, the simulation unit performs an optimal simulation under specific conditions based on success stories of other municipalities. This makes it possible to provide an optimal simulation based on success stories of other municipalities.

[0082] The simulation unit can customize the simulation taking into account the user's past agreement history. The simulation unit customizes the simulation taking into account the user's past agreement history. For example, the simulation unit references the history of agreements previously concluded by the user and provides a simulation with similar conditions. For example, the simulation unit reflects frequently used parameters from the user's past agreement history in the simulation. For example, the simulation unit customizes a simulation that includes specific requirements based on the user's past agreement history. This makes it possible to provide an optimal simulation based on the user's past agreement history.

[0083] The negotiation unit can estimate the user's emotions and adjust the way the negotiation proceeds based on the estimated user's emotions. The negotiation unit estimates the user's emotions and adjusts the way the negotiation proceeds based on the estimated user's emotions. For example, when the user is nervous, the negotiation unit provides a simple, highly visible display method. For example, when the user is relaxed, the negotiation unit provides a display method including detailed information. For example, when the user is in a hurry, the negotiation unit provides a display method that focuses on the main points. This makes it possible to provide a way to proceed with the negotiation according to the user's emotions.

[0084] The negotiation unit can propose an optimal negotiation strategy by referring to past negotiation data. The negotiation unit proposes an optimal negotiation strategy by referring to past negotiation data. The negotiation unit proposes an optimal negotiation strategy, for example, based on past negotiation data. The negotiation unit extracts success stories from past negotiation data, for example, and proposes a negotiation strategy based on the success stories. The negotiation unit analyzes past negotiation data, for example, and proposes an optimal negotiation strategy under specific conditions. This makes it possible to provide an optimal negotiation strategy based on past negotiation data.

[0085] The negotiation unit can conduct negotiations taking into account the characteristics and past negotiation history of each local government. The negotiation unit conducts negotiations taking into account the characteristics and past negotiation history of each local government. The negotiation unit adjusts the content of the negotiation taking into account, for example, the characteristics of each local government (population, economic situation, etc.). The negotiation unit, for example, refers to the past negotiation history of each local government and conducts negotiations that include specific requirements. The negotiation unit proposes an optimal negotiation strategy based on, for example, the characteristics and past negotiation history of each local government. This makes it possible to provide optimal negotiations based on the characteristics and past negotiation history of each local government.

[0086] The negotiation unit can estimate the user's emotions and determine the priority of negotiations based on the estimated user's emotions. The negotiation unit estimates the user's emotions and determines the priority of negotiations based on the estimated user's emotions. For example, when the user is feeling stressed, the negotiation unit prioritizes negotiations on simple and important items. For example, when the user is relaxed, the negotiation unit prioritizes negotiations including detailed items. For example, when the user is in a hurry, the negotiation unit negotiates only the most important items. This makes it possible to provide negotiation priorities according to the user's emotions.

[0087] The negotiation unit can optimize negotiations by referring to success stories of other municipalities. The negotiation unit optimizes negotiations by referring to success stories of other municipalities. For example, the negotiation unit refers to the results of negotiations of agreements that have been successful in other municipalities and optimizes for municipalities with similar conditions. For example, the negotiation unit analyzes success stories of other municipalities and reflects common elements in negotiations. For example, the negotiation unit conducts optimal negotiations under specific conditions based on success stories of other municipalities. This makes it possible to provide optimal negotiations based on success stories of other municipalities.

[0088] The negotiation unit can customize the negotiation taking into account the user's past agreement history. The negotiation unit customizes the negotiation taking into account the user's past agreement history. For example, the negotiation unit refers to the history of agreements previously concluded by the user and provides negotiations with similar conditions. For example, the negotiation unit reflects frequently used parameters from the user's past agreement history in the negotiation. For example, the negotiation unit customizes the negotiation including specific requirements based on the user's past agreement history. This makes it possible to provide the optimal negotiation based on the user's past agreement history.

[0089] The customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. The customization unit estimates the user's emotion and adjusts the customization method based on the estimated user's emotion. For example, when the user is nervous, the customization unit provides a simple and highly visible customization method. For example, when the user is relaxed, the customization unit provides a customization method including detailed information. For example, when the user is in a hurry, the customization unit provides a customization method that focuses on the main points. This makes it possible to provide a customization method according to the user's emotion.

[0090] The customization unit can automatically reflect the laws, regulations, and characteristics of each local government. The customization unit automatically reflects the laws, regulations, and characteristics of each local government. For example, the customization unit references the laws, regulations database of each local government and automatically adds the legal requirements necessary for customization. For example, the customization unit adjusts the content of the customization taking into account the characteristics of each local government (population, economic situation, etc.). For example, the customization unit references the past agreement history of each local government and reflects specific requirements in the customization. This makes it possible to perform customization that reflects the laws, regulations, and characteristics of each local government.

[0091] The customization unit can estimate the user's emotions and determine the priorities of customization based on the estimated user's emotions. The customization unit estimates the user's emotions and determines the priorities of customization based on the estimated user's emotions. For example, when the user is feeling stressed, the customization unit prioritizes customization of simple and important items. For example, when the user is relaxed, the customization unit prioritizes customization including detailed items. For example, when the user is in a hurry, the customization unit customizes only the most important items. This makes it possible to provide priorities of customization according to the user's emotions.

[0092] The customization department can optimize the customization by referring to the success stories of other municipalities. The customization department optimizes the customization by referring to the success stories of other municipalities. For example, the customization department refers to the customization results of agreements that have been successful in other municipalities and optimizes for municipalities with similar conditions. For example, the customization department analyzes the success stories of other municipalities and reflects common elements in the customization. For example, the customization department performs optimal customization under specific conditions based on the success stories of other municipalities. This makes it possible to provide optimal customization based on the success stories of other municipalities.

[0093] The update unit can estimate the user's emotion and adjust the update frequency based on the estimated user's emotion. The update unit estimates the user's emotion and adjusts the update frequency based on the estimated user's emotion. For example, when the user is feeling stressed, the update unit reduces the update frequency and provides stable information. For example, when the user is relaxed, the update unit increases the update frequency and provides the latest information. For example, when the user is in a hurry, the update unit prioritizes updating only important information. This makes it possible to provide an update frequency that corresponds to the user's emotion.

[0094] The update unit can optimize the update algorithm by referring to past simulation data. The update unit optimizes the update algorithm by referring to past simulation data. The update unit adjusts parameters of the update algorithm based on, for example, past simulation data. The update unit extracts success cases from, for example, past simulation data, and optimizes the update algorithm based on the success cases. The update unit analyzes, for example, past simulation data, and optimizes the update algorithm under specific conditions. This makes it possible to optimize the update algorithm based on past simulation data.

[0095] The update unit can estimate the user's emotion and determine the priority of updates based on the estimated user's emotion. The update unit estimates the user's emotion and determines the priority of updates based on the estimated user's emotion. For example, when the user is feeling stressed, the update unit prioritizes updating simple and important items. For example, when the user is relaxed, the update unit prioritizes updating including detailed items. For example, when the user is in a hurry, the update unit updates only the most important items. This makes it possible to provide update priorities according to the user's emotion.

[0096] The update unit can optimize the update by referring to success stories of other municipalities. The update unit optimizes the update by referring to success stories of other municipalities. For example, the update unit refers to the results of agreement updates that have been successful in other municipalities and optimizes for municipalities with similar conditions. For example, the update unit analyzes success stories of other municipalities and reflects common elements in the update. For example, the update unit performs the optimal update under specific conditions based on success stories of other municipalities. This makes it possible to provide the optimal update based on success stories of other municipalities.

[0097] The follow-up unit can estimate the user's emotions and adjust the follow-up method based on the estimated user's emotions. The follow-up unit estimates the user's emotions and adjusts the follow-up method based on the estimated user's emotions. For example, when the user is nervous, the follow-up unit provides a simple and highly visible follow-up method. For example, when the user is relaxed, the follow-up unit provides a follow-up method including detailed information. For example, when the user is in a hurry, the follow-up unit provides a follow-up method that focuses on the main points. In this way, a follow-up method according to the user's emotions can be provided.

[0098] The follow-up unit can propose an optimal follow-up method by referring to past agreement data. The follow-up unit proposes an optimal follow-up method by referring to past agreement data. The follow-up unit proposes an optimal follow-up method, for example, based on past agreement data. The follow-up unit extracts success stories from past agreement data, for example, and proposes a follow-up method based on the success stories. The follow-up unit analyzes past agreement data, for example, and proposes an optimal follow-up method under specific conditions. This makes it possible to provide an optimal follow-up method based on past agreement data.

[0099] The follow-up unit can estimate the user's emotions and determine the priority of follow-ups based on the estimated user's emotions. The follow-up unit estimates the user's emotions and determines the priority of follow-ups based on the estimated user's emotions. For example, when the user is feeling stressed, the follow-up unit prioritizes follow-ups on simple and important items. For example, when the user is relaxed, the follow-up unit prioritizes follow-ups including detailed items. For example, when the user is in a hurry, the follow-up unit follows up on only the most important items. This makes it possible to provide follow-up priorities according to the user's emotions.

[0100] The follow-up department can optimize the follow-up by referring to the success stories of other local governments. The follow-up department optimizes the follow-up by referring to the success stories of other local governments. For example, the follow-up department refers to the follow-up results of agreements that have been successful in other local governments and optimizes for local governments with similar conditions. For example, the follow-up department analyzes the success stories of other local governments and reflects common elements in the follow-up. For example, the follow-up department performs optimal follow-up under specific conditions based on the success stories of other local governments. This makes it possible to provide optimal follow-up based on the success stories of other local governments. === Hard Collateral 1-1 === Each of the multiple elements, including the template creation unit, simulation unit, negotiation unit, customization unit, update unit, and follow-up unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the template creation unit is realized by the control unit 46A of the smart device 14 and creates a standard template. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the cost-effectiveness of an agreement. The negotiation unit is realized by the specific processing unit 290 of the data processing device 12 and negotiates with the local government based on the simulation results. The customization unit is realized by the control unit 46A of the smart device 14 and customizes the template according to the characteristics of each local government. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the simulation results in real time. The follow-up unit is realized by the control unit 46A of the smart device 14 and performs follow-up after the agreement is concluded. === Hard Collateral 1-2 === Each of the multiple elements, including the template creation unit, simulation unit, negotiation unit, customization unit, update unit, and follow-up unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the template creation unit is realized by the control unit 46A of the smart glasses 214 and creates a standard template. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the cost-effectiveness of an agreement. The negotiation unit is realized by the specific processing unit 290 of the data processing device 12 and negotiates with the local government based on the simulation results. The customization unit is realized by the control unit 46A of the smart glasses 214 and customizes the template according to the characteristics of each local government. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the simulation results in real time. The follow-up unit is realized by the control unit 46A of the smart glasses 214 and performs follow-up after the agreement is concluded. === Hard Collateral 1-3 === Each of the multiple elements, including the template creation unit, simulation unit, negotiation unit, customization unit, update unit, and follow-up unit, described above, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the template creation unit is realized by the control unit 46A of the headset type terminal 314 and creates a standard template. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the cost-effectiveness of an agreement. The negotiation unit is realized by the specific processing unit 290 of the data processing device 12 and negotiates with the local government based on the simulation results. The customization unit is realized by the control unit 46A of the headset type terminal 314 and customizes the template according to the characteristics of each local government. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the simulation results in real time. The follow-up unit is realized by the control unit 46A of the headset type terminal 314 and performs follow-up after the agreement is concluded. === Hard Collateral 1-4 === Each of the multiple elements, including the template creation unit, simulation unit, negotiation unit, customization unit, update unit, and follow-up unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the template creation unit is realized by the control unit 46A of the robot 414 and creates a standard template. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the cost-effectiveness of an agreement. The negotiation unit is realized by the specific processing unit 290 of the data processing device 12 and negotiates with the local government based on the simulation results. The customization unit is realized by the control unit 46A of the robot 414 and customizes the template according to the characteristics of each local government. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the simulation results in real time. The follow-up unit is realized by the control unit 46A of the robot 414 and performs follow-up after the agreement is concluded.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The template creation unit can analyze the user's past input history and automatically reflect frequently used items in the template. For example, clauses and phrases that many users have used in the past can be automatically added to the template. This allows the user to quickly create an optimal template based on their past input history. The template creation unit can also suggest the optimal template under specific conditions based on the user's past input history. This makes it possible to provide more effective templates by utilizing the user's past input history.

[0103] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive customization method is provided, minimizing input steps. Alternatively, if the user is relaxed, detailed customization options are provided, allowing the user to freely adjust. Furthermore, if the user is in a hurry, voice input is prioritized, allowing for quick customization. This makes it possible to provide an optimal customization method according to the user's emotions.

[0104] The renewal department can optimize the renewal by referring to the success stories of other municipalities. For example, it can refer to the successful renewal results of agreements in other municipalities and optimize for municipalities with similar conditions. It can also analyze the success stories of other municipalities and reflect common elements in the renewal. Furthermore, it can perform the optimal renewal under specific conditions based on the success stories of other municipalities. This makes it possible to provide the optimal renewal based on the success stories of other municipalities.

[0105] The follow-up unit can estimate the user's emotions and adjust the follow-up method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible follow-up method is provided. If the user is relaxed, a follow-up method including detailed information is provided. Furthermore, if the user is in a hurry, a follow-up method that focuses on the main points is provided. This makes it possible to provide the optimal follow-up method according to the user's emotions.

[0106] The template creation unit can optimize templates by referencing successful cases in other fields. For example, successful templates in other fields, such as business contracts and license agreements, can be referenced and applied to agreement conclusion. It can also analyze successful cases in other fields and reflect common elements in the template. Furthermore, it can create an optimal template under specific conditions based on successful cases in other fields. This makes it possible to provide an optimal template based on successful cases in other fields.

[0107] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. This makes it possible to provide an optimal display method of the simulation results according to the user's emotions.

[0108] The negotiation unit can customize the negotiation taking into account the user's past negotiation history. For example, it can refer to the history of agreements concluded by the user in the past and provide negotiations with similar conditions. It can also reflect frequently used parameters from the user's past negotiation history in the negotiation. Furthermore, it can customize the negotiation including specific requirements based on the user's past negotiation history. This makes it possible to provide the optimal negotiation based on the user's past negotiation history.

[0109] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. For example, if the user is feeling stressed, customization of simple and important items is prioritized. If the user is relaxed, customization including detailed items is prioritized. Furthermore, if the user is in a hurry, customization of only the most important items is prioritized. This makes it possible to provide optimal customization priorities according to the user's emotions.

[0110] The update unit can optimize the update algorithm by referring to past simulation data. For example, the parameters of the update algorithm can be adjusted based on the past simulation data. It can also extract successful cases from the past simulation data and optimize the update algorithm based on them. Furthermore, it can analyze the past simulation data and optimize the update algorithm under specific conditions. This makes it possible to provide an optimal update algorithm based on the past simulation data.

[0111] The Follow-up Department can optimize follow-up by referring to the success stories of other municipalities. For example, it can refer to the follow-up results of successful agreements in other municipalities and optimize for municipalities with similar conditions. It can also analyze the success stories of other municipalities and reflect common elements in follow-up. Furthermore, it can provide optimal follow-up under specific conditions based on the success stories of other municipalities. This allows it to provide optimal follow-up based on the success stories of other municipalities.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The template creation department creates standard templates. For example, it defines standard formats and items for agreements and generates templates that can be applied to each local government. This standardizes the content of agreements and enables templates to be created quickly. Step 2: The Simulation Department conducts simulations based on the templates created by the Template Creation Department. For example, it calculates the cost-effectiveness of the agreement and aims to conclude an agreement that maximizes benefits. It also evaluates the profitability of the agreement and its contribution to the local government, and proposes optimal agreement content. Step 3: The negotiation department negotiates with the local government based on the simulation results obtained by the simulation department. For example, specific figures based on the simulation results can be presented to help the local government understand and agree. This will concretely demonstrate the increase in revenue and the degree of contribution to the local government, facilitating the conclusion of an agreement.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

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

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

[0128] In the 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.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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.

[0143] 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.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a template creation unit that creates standard templates; a simulation unit that performs a simulation based on the template created by the template creation unit; a negotiation unit that negotiates with the local government based on the simulation results obtained by the simulation unit; Equipped with A system characterized by:

2. Customization department to customize according to the characteristics of each local government 2. The system of claim 1.

3. Equipped with an update unit that updates simulation results in real time 2. The system of claim 1.

4. Establish a follow-up department to follow up after the agreement is concluded 2. The system of claim 1.

5. The customization unit Customize templates in cooperation with the template creation unit 3. The system of claim 2.

6. The update unit Updates the simulation results in cooperation with the simulation unit 4. The system of claim 3.

7. The follow-up unit Cooperate with the negotiation department to support operations after the agreement is concluded 5. The system of claim 4.

8. The template creation unit Estimate the user's emotions and adjust the template's expression based on the estimated user emotions.

2. The system of claim 1.

9. The template creation unit Analyze past agreement data and automatically generate optimal templates 2. The system of claim 1.

Images