system

Abstract

The system according to this embodiment aims to efficiently facilitate proposals to local governments and the formulation of strategies. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a simulation unit, and a proposal unit. The collection unit collects literature such as policies or strategies, measures, and guidelines of each local government. The generation unit analyzes the literature collected by the collection unit and generates a virtual local government. The simulation unit simulates proposals or strategies for the virtual local government generated by the generation unit. The proposal unit proposes the most suitable approach based on the results obtained by the simulation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a great deal of labor and time to make proposals to local governments and formulate strategies, and it is difficult to respond efficiently.

[0005] The system according to the embodiment aims to efficiently make proposals to local governments and formulate strategies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a simulation unit, and a proposal unit. The collection unit collects literature such as policies or strategies, measures, and guidelines of each local government. The generation unit analyzes the literature collected by the collection unit and generates a virtual local government. The simulation unit simulates proposals or strategies for the virtual local government generated by the generation unit. The proposal unit proposes the most suitable approach based on the results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently make proposals to local governments and formulate strategies. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] [[ID=,21]] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system for streamlining local government approaches according to an embodiment of the present invention is a system that collects literature such as policies, strategies, measures, and guidelines of each local government, generates virtual local governments using generating AIs, and simulates SoftBank's proposals and strategies. This system significantly reduces the effort required for preliminary research, document preparation, and travel to local governments, making it possible to take action on local governments efficiently. For example, the system for streamlining local government approaches collects literature such as policies, strategies, measures, and guidelines of each local government. In this process, it collects information that is publicly available on the web. For example, it downloads literature such as policies, strategies, measures, and guidelines for each year from the official website of each local government. Next, it uploads the collected literature to generating AIs and generates virtual local governments. The generating AIs analyze the uploaded literature and learn the policies and needs of each local government. For example, it generates virtual local governments such as XX Prefecture, XX City, △△ Town, and □□ Village. SoftBank's proposals and strategies are input to the generated virtual local governments and a simulation is performed. The generating AIs simulate SoftBank's proposals and strategies on the virtual local governments and find the optimal approach method. For example, it simulates what kind of proposal would be effective for a particular local government. This system significantly reduces the effort involved in preliminary research, document preparation, and travel to local governments, enabling efficient action towards them. For example, it allows for the creation of optimal proposals for specific local governments from among more than 1,700. Thus, the system for streamlining local government approaches significantly reduces the effort involved in preliminary research, document preparation, and travel to local governments, enabling efficient action towards them.

[0029] The system for streamlining local government approaches according to this embodiment comprises a collection unit, a generation unit, a simulation unit, and a proposal unit. The collection unit collects documents such as policies, strategies, measures, and guidelines of each local government. The collection unit collects information that is publicly available on the web, for example. For example, it downloads documents such as policies, strategies, measures, and guidelines for each year from the official website of each local government. The collection unit can also use generation AI to determine the priority of the documents to be collected. For example, the collection unit analyzes the changes in past policies and strategies to determine the priority of the documents to be collected. The generation unit analyzes the collected documents and generates virtual local governments. The generation unit uses generation AI to analyze the collected documents and generate virtual local governments, for example. For example, the generation unit generates virtual local governments such as XX Prefecture, XX City, △△ Town, and □□ Village. The simulation unit simulates SoftBank's proposals and strategies for the generated virtual local governments. The simulation unit uses generation AI to simulate SoftBank's proposals and strategies for the virtual local governments, for example. For example, the simulation unit simulates what kind of proposal would be effective for a particular local government. The proposal department proposes the optimal approach based on the simulation results. For example, the proposal department uses generative AI to propose the optimal approach based on the simulation results. For example, the proposal department makes the optimal proposal for a specific local government. As a result, the local government approach efficiency system according to the embodiment significantly reduces the effort involved in preliminary surveys, document preparation, and travel to local governments, enabling efficient action to be taken with local governments.

[0030] The data collection unit can collect documents such as policies, strategies, measures, and guidelines of local governments that are publicly available on the web. For example, the data collection unit can download documents such as policies, strategies, measures, and guidelines for each year from the official websites of local governments. For example, the data collection unit can collect information from the official websites of the government and the homepages of local governments. The data collection unit can also use generative AI to determine the priority of the documents to be collected. For example, the data collection unit can analyze the changes in past policies and strategies to determine the priority of the documents to be collected. This allows for the efficient collection of information that is publicly available on the web. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI. For example, the data collection unit can input information that is publicly available on the web into generative AI and have the generative AI perform the information collection.

[0031] The generation unit can analyze collected literature and generate virtual municipalities. The generation unit can, for example, use a generation AI to analyze collected literature and generate virtual municipalities. For example, the generation unit can generate virtual municipalities such as XX Prefecture, XX City, △△ Town, and □□ Village. The generation unit uses a generation AI to analyze collected literature and learn the policies and needs of each municipality. For example, the generation unit has the generation AI analyze the literature and extract the policies and needs of each municipality. This allows the generation unit to generate virtual municipalities based on the collected literature. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input collected literature into a generation AI and have the generation AI perform the generation of virtual municipalities.

[0032] The simulation unit can simulate SoftBank's proposals and strategies against a virtual local government. For example, the simulation unit uses generative AI to simulate SoftBank's proposals and strategies against a virtual local government. For instance, the simulation unit can simulate which proposals would be effective for a specific local government. The simulation unit uses generative AI to simulate SoftBank's proposals and strategies against a virtual local government and find the optimal approach. For example, the simulation unit uses generative AI to analyze proposals and strategies and propose the optimal approach for the virtual local government. This allows SoftBank's proposals and strategies to be simulated against a virtual local government. Some or all of the above-described processes in the simulation unit may be performed using generative AI, or without it. For example, the simulation unit can input SoftBank's proposals and strategies into the generative AI and have the generative AI execute the simulation.

[0033] The proposal unit can propose the optimal approach based on the simulation results. For example, the proposal unit uses generative AI to propose the optimal approach based on the simulation results. For example, the proposal unit makes the optimal proposal for a specific local government. The proposal unit uses generative AI to analyze the simulation results and find the optimal approach. For example, the proposal unit has generative AI analyze the simulation results and propose the optimal approach. This allows the proposal unit to propose the optimal approach based on the simulation results. Some or all of the above processing in the proposal unit may be performed using generative AI, for example, or without using generative AI. For example, the proposal unit can input the simulation results into generative AI and have the generative AI execute the proposal of the optimal approach.

[0034] The collection unit can analyze the historical changes in each local government's policies and strategies and determine the priority of the literature to be collected. For example, the collection unit can prioritize collecting literature from local governments whose policies and strategies have changed frequently. It can also postpone collecting literature from local governments whose policies and strategies have been consistent. Furthermore, the collection unit can prioritize collecting literature from local governments whose policies and strategies have focused on specific themes. This allows for efficient determination of the priority of the literature to be collected by analyzing the historical changes in policies and strategies. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not. For example, the collection unit can input data on past policies and strategies into a generative AI and have the generative AI determine the priority of the literature.

[0035] The collection unit can filter literature based on the local government's current projects and areas of interest during the collection process. For example, the collection unit can prioritize collecting literature related to ongoing projects. It can also filter and collect literature related to areas of interest to the local government. Furthermore, the collection unit can obtain the latest project information from the local government's official website and social media and collect relevant literature. This allows for the efficient collection of highly relevant literature by filtering it based on current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input the local government's project information into a generative AI and have the generative AI perform the literature filtering.

[0036] The collection unit can prioritize the collection of highly relevant literature by considering the geographical location information of local governments when collecting literature. For example, the collection unit can prioritize the collection of literature from neighboring local governments based on the geographical location of a local government. It can also prioritize the collection of literature from local governments in the same region based on the geographical location of a local government. Furthermore, the collection unit can filter and collect highly relevant literature based on the geographical location of a local government. In this way, highly relevant literature can be efficiently collected by considering geographical location information. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the geographical location information of local governments into a generative AI and have the generative AI perform the literature collection.

[0037] The collection unit can analyze the social media activities of local governments and collect relevant literature when collecting documents. For example, the collection unit can identify current areas of interest from the social media activities of local governments and collect relevant literature. The collection unit can also obtain the latest project information from the social media activities of local governments and collect relevant literature. Furthermore, the collection unit can analyze the social media activities of local governments and prioritize the collection of important literature. This allows for the efficient collection of highly relevant literature by analyzing social media activities. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input the social media data of local governments into a generative AI and have the generative AI perform the literature collection.

[0038] The generation unit can adjust the level of detail of the generated virtual municipalities based on the importance of the collected literature. For example, the generation unit can generate detailed virtual municipalities based on highly important literature. It can also generate simplified virtual municipalities based on less important literature. Furthermore, the generation unit can adjust the level of detail of the generated virtual municipalities in stages according to the importance of the literature. This allows for efficient virtual municipality generation by adjusting the level of detail of the generated virtual municipalities based on the importance of the literature. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input literature importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated virtual municipalities.

[0039] The generation unit can apply different generation algorithms depending on the category of the local government when generating virtual local governments. For example, the generation unit can apply a generation algorithm specialized in urban planning to urban local governments. It can also apply a generation algorithm specialized in agricultural policy to rural local governments. Furthermore, it can apply a generation algorithm specialized in tourism strategy to tourist destination local governments. By applying a generation algorithm according to the category of the local government, efficient generation of virtual local governments becomes possible. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input local government category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0040] The generation unit can determine the generation priority based on the submission date of each municipality when generating virtual municipalities. For example, the generation unit can prioritize generating documents from municipalities with more recent submission dates. It can also postpone the generation of documents from municipalities with older submission dates. Furthermore, the generation unit can adjust the generation priority in stages based on the submission dates. This enables efficient generation of virtual municipalities by determining the generation priority based on the submission dates. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input submission date data into a generation AI and have the generation AI determine the generation priority.

[0041] The generation unit can adjust the generation order based on the relationships between municipalities when generating virtual municipalities. For example, the generation unit can prioritize generating literature for highly relevant municipalities. It can also postpone generating literature for less relevant municipalities. Furthermore, the generation unit can adjust the generation order in stages based on relationships. This allows for efficient virtual municipality generation by adjusting the generation order based on relationships. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relationship data between municipalities into a generation AI and have the generation AI perform the adjustment of the generation order.

[0042] The simulation unit can improve the accuracy of the simulation by considering the interrelationships between SoftBank's proposals and strategies during the simulation. For example, the simulation unit can analyze the interrelationships between SoftBank's proposals and strategies to improve the accuracy of the simulation. The simulation unit can also obtain optimal simulation results by considering the interrelationships between the proposals and strategies. Furthermore, the simulation unit can adjust the level of detail of the simulation based on the interrelationships between the proposals and strategies. This improves the accuracy of the simulation by considering the interrelationships between the proposals and strategies. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input proposal and strategy data into a generative AI and have the generative AI perform the simulation accuracy improvement.

[0043] The simulation unit can perform simulations while considering the attribute information of the proposer or strategy submitter. For example, the simulation unit can perform simulations while considering the proposer or strategy submitter's area of ​​expertise. The simulation unit can also perform simulations while considering the proposer or strategy submitter's past performance. Furthermore, the simulation unit can adjust the level of detail of the simulation based on the proposer or strategy submitter's attribute information. This makes it possible to perform more accurate simulations by considering the submitter's attribute information. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the simulation unit can input the submitter's attribute information into a generative AI and have the generative AI execute the simulation.

[0044] The simulation unit can perform simulations while considering the geographical distribution of proposals and strategies. For example, the simulation unit can analyze the geographical distribution of proposals and strategies to improve the accuracy of the simulation. The simulation unit can also obtain optimal simulation results by considering the geographical distribution. Furthermore, the simulation unit can adjust the level of detail of the simulation based on the geographical distribution. This makes it possible to perform more accurate simulations by considering the geographical distribution. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the simulation unit can input geographical distribution data of proposals and strategies into a generative AI and have the generative AI execute the simulation.

[0045] The simulation unit can improve the accuracy of the simulation by referring to relevant literature on proposals and strategies during the simulation. For example, the simulation unit can analyze relevant literature on proposals and strategies to improve the accuracy of the simulation. The simulation unit can also obtain optimal simulation results by referring to relevant literature. Furthermore, the simulation unit can adjust the level of detail of the simulation based on the relevant literature. In this way, the accuracy of the simulation is improved by referring to relevant literature. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input data on relevant literature on proposals and strategies into a generative AI and have the generative AI perform the simulation accuracy improvement.

[0046] The proposal unit can adjust the level of detail of its proposals based on the importance of the simulation results. For example, the proposal unit can make detailed proposals based on high-importance simulation results. It can also make simplified proposals based on low-importance simulation results. Furthermore, the proposal unit can adjust the level of detail of its proposals in stages according to the importance of the simulation results. This allows for efficient proposals by adjusting the level of detail of the proposals based on the importance of the simulation results. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance data of the simulation results into a generative AI and have the generative AI perform the adjustment of the level of detail of the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the simulation results during the proposal process. For example, the proposal unit can apply an economic proposal algorithm to simulation results related to economic policy. It can also apply an environmental science proposal algorithm to simulation results related to environmental policy. Furthermore, it can apply an educational proposal algorithm to simulation results related to education policy. This allows for efficient proposals by applying proposal algorithms appropriate to the category. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category data of the simulation results into a generative AI and have the generative AI execute the application of the proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the submission timing of simulation results. For example, the proposal department can prioritize proposals based on more recent simulation results. It can also postpone proposals based on older simulation results. Furthermore, the proposal department can adjust the priority of proposals in stages based on the submission timing. This enables efficient proposals by determining the priority of proposals based on the submission timing. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the submission timing data of the simulation results into a generative AI and have the generative AI determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the simulation results. For example, the proposal unit can prioritize proposals based on highly relevant simulation results. It can also postpone proposals based on less relevant simulation results. Furthermore, the proposal unit can adjust the order of proposals in stages based on relevance. This allows for efficient proposals by adjusting the order of proposals based on relevance. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the relevance data of the simulation results into a generative AI and have the generative AI adjust the order of proposals.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The information collection department can collect not only information from the local government's official website but also related news articles and press releases. For example, the department can collect the latest news articles concerning the local government and grasp changes in the local government's policies and strategies in real time. In addition, the department can collect not only official announcements from the local government but also press releases from third parties, allowing it to analyze the local government's situation from a more multifaceted perspective. Furthermore, the department can collect documents that are not published on the local government's official website but can be obtained from other reliable sources. As a result, the department can collect more comprehensive and up-to-date information and gain a more accurate understanding of the local government's policies and strategies.

[0052] The generation unit can consider the historical background and cultural elements of a local government when generating a virtual local government. For example, the generation unit can generate a virtual local government that reflects the historical events and cultural characteristics of the local government. It can also generate a virtual local government that incorporates cultural elements such as traditional events and festivals of the local government. Furthermore, it can generate a virtual local government that reflects the historical buildings and landmarks of the local government. As a result, the generation unit can generate a virtual local government that takes into account the history and culture of the local government, enabling a more realistic simulation.

[0053] The simulation unit can provide the simulation results of proposals and strategies for a virtual local government in a visually easy-to-understand format. For example, the simulation unit can display the effects of proposals and strategies using graphs and charts. It can also display the simulation results on a map to visually demonstrate geographical impacts. Furthermore, the simulation unit can provide the simulation results in animation and interactive formats, enabling users to understand them intuitively. This allows the simulation unit to visually present the effects of proposals and strategies in an easily understandable way, deepening user comprehension.

[0054] The proposal department can evaluate the feasibility of a proposal based on the simulation results. For example, the proposal department can evaluate the resources and costs required to implement the proposal and determine its feasibility. It can also evaluate the risks associated with implementing the proposal and propose risk management measures. Furthermore, the proposal department can evaluate the time and schedule required to implement the proposal and create an implementation plan. This allows the proposal department to comprehensively evaluate the feasibility of a proposal based on the simulation results and develop a concrete plan for implementation.

[0055] The information collection department can collect not only information from the local government's official website but also related news articles and press releases. For example, the department can collect the latest news articles concerning the local government and grasp changes in the local government's policies and strategies in real time. In addition, the department can collect not only official announcements from the local government but also press releases from third parties, allowing it to analyze the local government's situation from a more multifaceted perspective. Furthermore, the department can collect documents that are not published on the local government's official website but can be obtained from other reliable sources. As a result, the department can collect more comprehensive and up-to-date information and gain a more accurate understanding of the local government's policies and strategies.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection department collects documents such as policies, strategies, measures, and guidelines from each local government. The collection department collects information that is publicly available on the web, for example. Specifically, it downloads documents such as policies, strategies, measures, and guidelines for each year from each local government's official website. The collection department can also use generative AI to determine the priority of the documents to be collected. For example, it can analyze the changes in past policies and strategies to determine the priority of the documents to be collected. Step 2: The generation unit analyzes the collected literature and generates virtual municipalities. The generation unit uses, for example, a generation AI to analyze the collected literature and generate virtual municipalities. Specifically, it generates virtual municipalities such as XX Prefecture, XX City, △△ Town, and □□ Village. Step 3: The simulation unit simulates proposals and strategies for the generated virtual municipalities. The simulation unit uses, for example, a generating AI to simulate proposals and strategies for the virtual municipalities. Specifically, it simulates what kind of proposals would be effective for a particular municipality. Step 4: The proposal team proposes the optimal approach based on the simulation results. For example, the proposal team uses generative AI to propose the optimal approach based on the simulation results. Specifically, they make the optimal proposal for a particular local government.

[0058] (Example of form 2) The system for streamlining local government approaches according to an embodiment of the present invention is a system that collects literature such as policies, strategies, measures, and guidelines of each local government, generates virtual local governments using generating AIs, and simulates SoftBank's proposals and strategies. This system significantly reduces the effort required for preliminary research, document preparation, and travel to local governments, making it possible to take action on local governments efficiently. For example, the system for streamlining local government approaches collects literature such as policies, strategies, measures, and guidelines of each local government. In this process, it collects information that is publicly available on the web. For example, it downloads literature such as policies, strategies, measures, and guidelines for each year from the official website of each local government. Next, it uploads the collected literature to generating AIs and generates virtual local governments. The generating AIs analyze the uploaded literature and learn the policies and needs of each local government. For example, it generates virtual local governments such as XX Prefecture, XX City, △△ Town, and □□ Village. SoftBank's proposals and strategies are input to the generated virtual local governments and a simulation is performed. The generating AIs simulate SoftBank's proposals and strategies on the virtual local governments and find the optimal approach method. For example, it simulates what kind of proposal would be effective for a particular local government. This system significantly reduces the effort involved in preliminary research, document preparation, and travel to local governments, enabling efficient action towards them. For example, it allows for the creation of optimal proposals for specific local governments from among more than 1,700. Thus, the system for streamlining local government approaches significantly reduces the effort involved in preliminary research, document preparation, and travel to local governments, enabling efficient action towards them.

[0059] The system for streamlining local government approaches according to this embodiment comprises a collection unit, a generation unit, a simulation unit, and a proposal unit. The collection unit collects documents such as policies, strategies, measures, and guidelines of each local government. The collection unit collects information that is publicly available on the web, for example. For example, it downloads documents such as policies, strategies, measures, and guidelines for each year from the official website of each local government. The collection unit can also use generation AI to determine the priority of the documents to be collected. For example, the collection unit analyzes the changes in past policies and strategies to determine the priority of the documents to be collected. The generation unit analyzes the collected documents and generates virtual local governments. The generation unit uses generation AI to analyze the collected documents and generate virtual local governments, for example. For example, the generation unit generates virtual local governments such as XX Prefecture, XX City, △△ Town, and □□ Village. The simulation unit simulates SoftBank's proposals and strategies for the generated virtual local governments. The simulation unit uses generation AI to simulate SoftBank's proposals and strategies for the virtual local governments, for example. For example, the simulation unit simulates what kind of proposal would be effective for a particular local government. The proposal department proposes the optimal approach based on the simulation results. For example, the proposal department uses generative AI to propose the optimal approach based on the simulation results. For example, the proposal department makes the optimal proposal for a specific local government. As a result, the local government approach efficiency system according to the embodiment significantly reduces the effort involved in preliminary surveys, document preparation, and travel to local governments, enabling efficient action to be taken with local governments.

[0060] The data collection unit can collect documents such as policies, strategies, measures, and guidelines of local governments that are publicly available on the web. For example, the data collection unit can download documents such as policies, strategies, measures, and guidelines for each year from the official websites of local governments. For example, the data collection unit can collect information from the official websites of the government and the homepages of local governments. The data collection unit can also use generative AI to determine the priority of the documents to be collected. For example, the data collection unit can analyze the changes in past policies and strategies to determine the priority of the documents to be collected. This allows for the efficient collection of information that is publicly available on the web. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI. For example, the data collection unit can input information that is publicly available on the web into generative AI and have the generative AI perform the information collection.

[0061] The generation unit can analyze collected literature and generate virtual municipalities. The generation unit can, for example, use a generation AI to analyze collected literature and generate virtual municipalities. For example, the generation unit can generate virtual municipalities such as XX Prefecture, XX City, △△ Town, and □□ Village. The generation unit uses a generation AI to analyze collected literature and learn the policies and needs of each municipality. For example, the generation unit has the generation AI analyze the literature and extract the policies and needs of each municipality. This allows the generation unit to generate virtual municipalities based on the collected literature. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input collected literature into a generation AI and have the generation AI perform the generation of virtual municipalities.

[0062] The simulation unit can simulate SoftBank's proposals and strategies against a virtual local government. For example, the simulation unit uses generative AI to simulate SoftBank's proposals and strategies against a virtual local government. For instance, the simulation unit can simulate which proposals would be effective for a specific local government. The simulation unit uses generative AI to simulate SoftBank's proposals and strategies against a virtual local government and find the optimal approach. For example, the simulation unit uses generative AI to analyze proposals and strategies and propose the optimal approach for the virtual local government. This allows SoftBank's proposals and strategies to be simulated against a virtual local government. Some or all of the above-described processes in the simulation unit may be performed using generative AI, or without it. For example, the simulation unit can input SoftBank's proposals and strategies into the generative AI and have the generative AI execute the simulation.

[0063] The proposal unit can propose the optimal approach based on the simulation results. For example, the proposal unit uses generative AI to propose the optimal approach based on the simulation results. For example, the proposal unit makes the optimal proposal for a specific local government. The proposal unit uses generative AI to analyze the simulation results and find the optimal approach. For example, the proposal unit has generative AI analyze the simulation results and propose the optimal approach. This allows the proposal unit to propose the optimal approach based on the simulation results. Some or all of the above processing in the proposal unit may be performed using generative AI, for example, or without using generative AI. For example, the proposal unit can input the simulation results into generative AI and have the generative AI execute the proposal of the optimal approach.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of literature collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect literature when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can advance the collection timing to collect literature efficiently. Furthermore, if the user is in a hurry, the data collection unit can set the collection timing immediately to collect literature quickly. This allows for efficient literature collection by adjusting the timing of literature collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of literature collection.

[0065] The collection unit can analyze the historical changes in each local government's policies and strategies and determine the priority of the literature to be collected. For example, the collection unit can prioritize collecting literature from local governments whose policies and strategies have changed frequently. It can also postpone collecting literature from local governments whose policies and strategies have been consistent. Furthermore, the collection unit can prioritize collecting literature from local governments whose policies and strategies have focused on specific themes. This allows for efficient determination of the priority of the literature to be collected by analyzing the historical changes in policies and strategies. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not. For example, the collection unit can input data on past policies and strategies into a generative AI and have the generative AI determine the priority of the literature.

[0066] The collection unit can filter literature based on the local government's current projects and areas of interest during the collection process. For example, the collection unit can prioritize collecting literature related to ongoing projects. It can also filter and collect literature related to areas of interest to the local government. Furthermore, the collection unit can obtain the latest project information from the local government's official website and social media and collect relevant literature. This allows for the efficient collection of highly relevant literature by filtering it based on current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input the local government's project information into a generative AI and have the generative AI perform the literature filtering.

[0067] The collection unit can estimate the user's emotions and determine the priority of the literature to collect based on the estimated emotions. For example, if the user is stressed, the collection unit may postpone collecting less important literature. Conversely, if the user is relaxed, the collection unit may prioritize collecting more important literature. Furthermore, if the user is in a hurry, the collection unit may prioritize collecting the most important literature. This enables efficient literature collection by determining the priority of the literature to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using or without a generative AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the literature.

[0068] The collection unit can prioritize the collection of highly relevant literature by considering the geographical location information of local governments when collecting literature. For example, the collection unit can prioritize the collection of literature from neighboring local governments based on the geographical location of a local government. It can also prioritize the collection of literature from local governments in the same region based on the geographical location of a local government. Furthermore, the collection unit can filter and collect highly relevant literature based on the geographical location of a local government. In this way, highly relevant literature can be efficiently collected by considering geographical location information. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the geographical location information of local governments into a generative AI and have the generative AI perform the literature collection.

[0069] The collection unit can analyze the social media activities of local governments and collect relevant literature when collecting documents. For example, the collection unit can identify current areas of interest from the social media activities of local governments and collect relevant literature. The collection unit can also obtain the latest project information from the social media activities of local governments and collect relevant literature. Furthermore, the collection unit can analyze the social media activities of local governments and prioritize the collection of important literature. This allows for the efficient collection of highly relevant literature by analyzing social media activities. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input the social media data of local governments into a generative AI and have the generative AI perform the literature collection.

[0070] The generation unit can estimate the user's emotions and adjust the method of generating the virtual municipality based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed virtual municipality. If the user is in a hurry, the generation unit can also generate a simplified virtual municipality. Furthermore, if the user is excited, the generation unit can generate a visually appealing virtual municipality. This allows for efficient virtual municipality generation by adjusting the method of generating the virtual municipality according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the method of generating the virtual municipality.

[0071] The generation unit can adjust the level of detail of the generated virtual municipalities based on the importance of the collected literature. For example, the generation unit can generate detailed virtual municipalities based on highly important literature. It can also generate simplified virtual municipalities based on less important literature. Furthermore, the generation unit can adjust the level of detail of the generated virtual municipalities in stages according to the importance of the literature. This allows for efficient virtual municipality generation by adjusting the level of detail of the generated virtual municipalities based on the importance of the literature. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input literature importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated virtual municipalities.

[0072] The generation unit can apply different generation algorithms depending on the category of the local government when generating virtual local governments. For example, the generation unit can apply a generation algorithm specialized in urban planning to urban local governments. It can also apply a generation algorithm specialized in agricultural policy to rural local governments. Furthermore, it can apply a generation algorithm specialized in tourism strategy to tourist destination local governments. By applying a generation algorithm according to the category of the local government, efficient generation of virtual local governments becomes possible. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input local government category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0073] The generation unit can estimate the user's emotions and adjust the generation order of virtual municipalities based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate the most important municipalities first. If the user is in a hurry, the generation unit can also generate the most important municipalities first. Furthermore, if the user is excited, the generation unit can prioritize generating visually appealing municipalities. This allows for efficient virtual municipality generation by adjusting the generation order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the generation order of virtual municipalities.

[0074] The generation unit can determine the generation priority based on the submission date of each municipality when generating virtual municipalities. For example, the generation unit can prioritize generating documents from municipalities with more recent submission dates. It can also postpone the generation of documents from municipalities with older submission dates. Furthermore, the generation unit can adjust the generation priority in stages based on the submission dates. This enables efficient generation of virtual municipalities by determining the generation priority based on the submission dates. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input submission date data into a generation AI and have the generation AI determine the generation priority.

[0075] The generation unit can adjust the generation order based on the relationships between municipalities when generating virtual municipalities. For example, the generation unit can prioritize generating literature for highly relevant municipalities. It can also postpone generating literature for less relevant municipalities. Furthermore, the generation unit can adjust the generation order in stages based on relationships. This allows for efficient virtual municipality generation by adjusting the generation order based on relationships. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relationship data between municipalities into a generation AI and have the generation AI perform the adjustment of the generation order.

[0076] The simulation unit can estimate the user's emotions and adjust the simulation criteria based on the estimated emotions. For example, if the user is relaxed, the simulation unit can perform a detailed simulation. If the user is in a hurry, the simulation unit can perform a simplified simulation. Furthermore, if the user is excited, the simulation unit can perform a visually appealing simulation. This allows for efficient simulation by adjusting the simulation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI adjust the simulation criteria.

[0077] The simulation unit can improve the accuracy of the simulation by considering the interrelationships between SoftBank's proposals and strategies during the simulation. For example, the simulation unit can analyze the interrelationships between SoftBank's proposals and strategies to improve the accuracy of the simulation. The simulation unit can also obtain optimal simulation results by considering the interrelationships between the proposals and strategies. Furthermore, the simulation unit can adjust the level of detail of the simulation based on the interrelationships between the proposals and strategies. This improves the accuracy of the simulation by considering the interrelationships between the proposals and strategies. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input proposal and strategy data into a generative AI and have the generative AI perform the simulation accuracy improvement.

[0078] The simulation unit can perform simulations while considering the attribute information of the proposer or strategy submitter. For example, the simulation unit can perform simulations while considering the proposer or strategy submitter's area of ​​expertise. The simulation unit can also perform simulations while considering the proposer or strategy submitter's past performance. Furthermore, the simulation unit can adjust the level of detail of the simulation based on the proposer or strategy submitter's attribute information. This makes it possible to perform more accurate simulations by considering the submitter's attribute information. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the simulation unit can input the submitter's attribute information into a generative AI and have the generative AI execute the simulation.

[0079] The simulation unit can estimate the user's emotions and adjust the order in which the simulation results are displayed based on the estimated emotions. For example, if the user is relaxed, the simulation unit can display detailed simulation results first. It can also display the most important simulation results first if the user is in a hurry. Furthermore, if the user is excited, the simulation unit can display visually appealing simulation results first. This allows for efficient information provision by adjusting the display order of simulation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the simulation unit may be performed using, for example, a generative AI, or not. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the simulation results.

[0080] The simulation unit can perform simulations while considering the geographical distribution of proposals and strategies. For example, the simulation unit can analyze the geographical distribution of proposals and strategies to improve the accuracy of the simulation. The simulation unit can also obtain optimal simulation results by considering the geographical distribution. Furthermore, the simulation unit can adjust the level of detail of the simulation based on the geographical distribution. This makes it possible to perform more accurate simulations by considering the geographical distribution. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the simulation unit can input geographical distribution data of proposals and strategies into a generative AI and have the generative AI execute the simulation.

[0081] The simulation unit can improve the accuracy of the simulation by referring to relevant literature on proposals and strategies during the simulation. For example, the simulation unit can analyze relevant literature on proposals and strategies to improve the accuracy of the simulation. The simulation unit can also obtain optimal simulation results by referring to relevant literature. Furthermore, the simulation unit can adjust the level of detail of the simulation based on the relevant literature. In this way, the accuracy of the simulation is improved by referring to relevant literature. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input data on relevant literature on proposals and strategies into a generative AI and have the generative AI perform the simulation accuracy improvement.

[0082] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually appealing suggestions. This allows for efficient suggestions by adjusting the presentation of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0083] The proposal unit can adjust the level of detail of its proposals based on the importance of the simulation results. For example, the proposal unit can make detailed proposals based on high-importance simulation results. It can also make simplified proposals based on low-importance simulation results. Furthermore, the proposal unit can adjust the level of detail of its proposals in stages according to the importance of the simulation results. This allows for efficient proposals by adjusting the level of detail of the proposals based on the importance of the simulation results. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance data of the simulation results into a generative AI and have the generative AI perform the adjustment of the level of detail of the proposals.

[0084] The proposal unit can apply different proposal algorithms depending on the category of the simulation results during the proposal process. For example, the proposal unit can apply an economic proposal algorithm to simulation results related to economic policy. It can also apply an environmental science proposal algorithm to simulation results related to environmental policy. Furthermore, it can apply an educational proposal algorithm to simulation results related to education policy. This allows for efficient proposals by applying proposal algorithms appropriate to the category. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category data of the simulation results into a generative AI and have the generative AI execute the application of the proposal algorithm.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. This allows for efficient suggestions by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0086] The proposal department can determine the priority of proposals based on the submission timing of simulation results. For example, the proposal department can prioritize proposals based on more recent simulation results. It can also postpone proposals based on older simulation results. Furthermore, the proposal department can adjust the priority of proposals in stages based on the submission timing. This enables efficient proposals by determining the priority of proposals based on the submission timing. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the submission timing data of the simulation results into a generative AI and have the generative AI determine the priority of proposals.

[0087] The proposal unit can adjust the order of proposals based on the relevance of the simulation results. For example, the proposal unit can prioritize proposals based on highly relevant simulation results. It can also postpone proposals based on less relevant simulation results. Furthermore, the proposal unit can adjust the order of proposals in stages based on relevance. This allows for efficient proposals by adjusting the order of proposals based on relevance. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the relevance data of the simulation results into a generative AI and have the generative AI adjust the order of proposals. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, generation unit, simulation unit, and proposal unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects publicly available information on the Web by the control unit 46A of the smart device 14 and determines the priority of the literature to be collected by the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the collected literature by the specific processing unit 290 of the data processing unit 12 and generates a virtual local government. The simulation unit simulates SoftBank's proposals and strategies for the virtual local government by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal approach based on the simulation results by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, generation unit, simulation unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects publicly available information on the Web by the control unit 46A of the smart glasses 214 and determines the priority of the literature to be collected by the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the collected literature by the specific processing unit 290 of the data processing unit 12 and generates a virtual municipality. The simulation unit simulates SoftBank's proposals and strategies for the virtual municipality by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal approach based on the simulation results by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, generation unit, simulation unit, and proposal unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects publicly available information on the Web by the control unit 46A of the headset terminal 314 and determines the priority of the literature to be collected by the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the collected literature by the specific processing unit 290 of the data processing unit 12 and generates a virtual local government. The simulation unit simulates SoftBank's proposals and strategies for the virtual local government by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal approach based on the simulation results by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, generation unit, simulation unit, and proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects publicly available information on the Web by the control unit 46A of the robot 414 and determines the priority of the literature to be collected by the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the collected literature by the specific processing unit 290 of the data processing unit 12 and generates a virtual municipality. The simulation unit simulates SoftBank's proposals and strategies for the virtual municipality by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal approach based on the simulation results by the specific processing unit 290 of the data processing unit 12.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The information collection department can collect not only information from the local government's official website but also related news articles and press releases. For example, the department can collect the latest news articles concerning the local government and grasp changes in the local government's policies and strategies in real time. In addition, the department can collect not only official announcements from the local government but also press releases from third parties, allowing it to analyze the local government's situation from a more multifaceted perspective. Furthermore, the department can collect documents that are not published on the local government's official website but can be obtained from other reliable sources. As a result, the department can collect more comprehensive and up-to-date information and gain a more accurate understanding of the local government's policies and strategies.

[0090] The generation unit can consider the historical background and cultural elements of a local government when generating a virtual local government. For example, the generation unit can generate a virtual local government that reflects the historical events and cultural characteristics of the local government. It can also generate a virtual local government that incorporates cultural elements such as traditional events and festivals of the local government. Furthermore, it can generate a virtual local government that reflects the historical buildings and landmarks of the local government. As a result, the generation unit can generate a virtual local government that takes into account the history and culture of the local government, enabling a more realistic simulation.

[0091] The simulation unit can provide the simulation results of proposals and strategies for a virtual local government in a visually easy-to-understand format. For example, the simulation unit can display the effects of proposals and strategies using graphs and charts. It can also display the simulation results on a map to visually demonstrate geographical impacts. Furthermore, the simulation unit can provide the simulation results in animation and interactive formats, enabling users to understand them intuitively. This allows the simulation unit to visually present the effects of proposals and strategies in an easily understandable way, deepening user comprehension.

[0092] The proposal department can evaluate the feasibility of a proposal based on the simulation results. For example, the proposal department can evaluate the resources and costs required to implement the proposal and determine its feasibility. It can also evaluate the risks associated with implementing the proposal and propose risk management measures. Furthermore, the proposal department can evaluate the time and schedule required to implement the proposal and create an implementation plan. This allows the proposal department to comprehensively evaluate the feasibility of a proposal based on the simulation results and develop a concrete plan for implementation.

[0093] The data collection unit can estimate the user's emotions and adjust the types of literature it collects based on those estimates. For example, if the user is stressed, the unit will prioritize collecting concise and to-the-point literature. If the user is relaxed, the unit may collect detailed and comprehensive literature. Furthermore, if the user is excited, the unit may collect visually appealing literature. This allows the unit to adjust the types of literature collected according to the user's emotions, enabling it to provide information tailored to the user's needs.

[0094] The generation unit can estimate the user's emotions and adjust the virtual municipality generation process based on those emotions. For example, if the user is relaxed, the generation unit will generate a detailed virtual municipality. If the user is in a hurry, the generation unit can also generate a simplified virtual municipality. Furthermore, if the user is excited, the generation unit can generate a visually appealing virtual municipality. In this way, the generation unit can adjust the virtual municipality generation process according to the user's emotions, enabling efficient virtual municipality generation.

[0095] The simulation unit can estimate the user's emotions and adjust the level of detail of the simulation based on those emotions. For example, if the user is relaxed, the simulation unit will perform a detailed simulation. If the user is in a hurry, the simulation unit can perform a simplified simulation. Furthermore, if the user is excited, it can perform a visually appealing simulation. In this way, the simulation unit can adjust the level of detail of the simulation according to the user's emotions, enabling efficient simulation.

[0096] The suggestion function can estimate the user's emotions and adjust the priority of suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will prioritize detailed suggestions. If the user is in a hurry, the suggestion function can also prioritize the most important suggestions. Furthermore, if the user is excited, the suggestion function can prioritize visually appealing suggestions. This allows the suggestion function to adjust the priority of suggestions according to the user's emotions, enabling more efficient suggestions.

[0097] The suggestion function can estimate the user's emotions and adjust the format of the suggestion based on those emotions. For example, if the user is relaxed, the suggestion function will present the suggestion in a detailed report format. If the user is in a hurry, the suggestion function can present the suggestion in a concise summary format. Furthermore, if the user is excited, the suggestion function can present the suggestion in a visually engaging presentation format. This allows the suggestion function to adjust the format of the suggestion according to the user's emotions, enabling more efficient suggestions.

[0098] The information collection department can collect not only information from the local government's official website but also related news articles and press releases. For example, the department can collect the latest news articles concerning the local government and grasp changes in the local government's policies and strategies in real time. In addition, the department can collect not only official announcements from the local government but also press releases from third parties, allowing it to analyze the local government's situation from a more multifaceted perspective. Furthermore, the department can collect documents that are not published on the local government's official website but can be obtained from other reliable sources. As a result, the department can collect more comprehensive and up-to-date information and gain a more accurate understanding of the local government's policies and strategies.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection department collects documents such as policies, strategies, measures, and guidelines from each local government. The collection department collects information that is publicly available on the web, for example. Specifically, it downloads documents such as policies, strategies, measures, and guidelines for each year from each local government's official website. The collection department can also use generative AI to determine the priority of the documents to be collected. For example, it can analyze the changes in past policies and strategies to determine the priority of the documents to be collected. Step 2: The generation unit analyzes the collected literature and generates virtual municipalities. The generation unit uses, for example, a generation AI to analyze the collected literature and generate virtual municipalities. Specifically, it generates virtual municipalities such as XX Prefecture, XX City, △△ Town, and □□ Village. Step 3: The simulation unit simulates proposals and strategies for the generated virtual municipalities. The simulation unit uses, for example, a generating AI to simulate proposals and strategies for the virtual municipalities. Specifically, it simulates what kind of proposals would be effective for a particular municipality. Step 4: The proposal team proposes the optimal approach based on the simulation results. For example, the proposal team uses generative AI to propose the optimal approach based on the simulation results. Specifically, they make the optimal proposal for a particular local government.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects literature on policies, strategies, measures, guidelines, etc., from each local government. A generation unit analyzes the literature collected by the aforementioned collection unit and generates a virtual local government, A simulation unit that simulates proposals or strategies for the virtual local government generated by the generation unit, Based on the results obtained by the simulation unit, the proposal unit proposes the most suitable approach method. Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect documents such as policies, strategies, measures, and guidelines from local governments that are publicly available on the web. The system according to feature 1.

3. The generating unit is Analyze the collected literature and generate a virtual local government. The system according to feature 1.

4. The aforementioned simulation unit, This simulates SoftBank's proposals and strategies for a hypothetical local government. The system according to feature 1.

5. The aforementioned proposal section is, We propose the optimal approach based on the simulation results. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of literature collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is We will analyze the historical changes in each local government's policies and strategies and determine the priority of the literature to collect. The system according to feature 1.

8. The aforementioned collection unit is When collecting literature, filter it based on the local government's current projects and areas of interest. The system according to feature 1.

Images