System

Abstract

An object of a system according to an embodiment is to efficiently create a plan of a solar power generation business and to customize the plan according to a user's request.SOLUTION: A system according to an embodiment includes a user input information collection unit, a data analysis unit, a plan generation unit, a customization unit, and an output unit. The user input information collection unit collects user input information. The data analyzing section analyzes the user input information collected by the user input information collecting section. The plan generation unit generates a plan of a solar power generation business based on the data analyzed by the data analysis unit. The customization unit customizes the plan generated by the plan generation unit according to a request of the user. The output unit outputs the plan customized by the customization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to efficiently create plans for solar power generation projects and customize them according to user needs.

[0005] The system according to the embodiment aims to efficiently create a plan for a solar power generation business and customize it according to the user's requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a user input information collection unit, a data analysis unit, a plan generation unit, a customization unit, and an output unit. The user input information collection unit collects user input information. The data analysis unit analyzes the user input information collected by the user input information collection unit. The plan generation unit generates a plan for a solar power generation business based on the data analyzed by the data analysis unit. The customization unit customizes the plan generated by the plan generation unit according to user requests. The output unit outputs the plan customized by the customization unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create a plan for a solar power generation business and customize it according to the user's requests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The solar power generation business plan creation system according to the embodiment of the present invention is a system in which a generation AI automatically creates a detailed plan for a solar power generation business based on information input by a user, thereby enabling the solar power generation business plan creation system to quickly and efficiently plan a solar power generation business.

[0029] A solar power generation business plan creation system according to an embodiment includes a user-input information collection unit, a data analysis unit, a plan generation unit, a customization unit, and an output unit. The user-input information collection unit collects user-input information. For example, a user inputs information such as the installation location of a power plant, power generation capacity, budget, and schedule. The data analysis unit analyzes the user-input information collected by the user-input information collection unit. For example, the generation AI analyzes climate data, sunshine hours, land characteristics, and the like of the installation location. The plan generation unit generates a solar power generation business plan based on the data analyzed by the data analysis unit. For example, the generation AI generates a plan including a power plant design, budget estimate, schedule, risk assessment, and the like. The customization unit customizes the plan generated by the plan generation unit according to user requests. For example, the generation AI proposes an optimal power generation plan within a specific budget or adjusts the schedule. The output unit outputs the plan customized by the customization unit. For example, the generation AI outputs the plan in PDF or Word format and provides it to the user. As a result, the solar power generation business plan creation system can automatically create a solar power generation business plan, customize it according to the user's requests, and output it.

[0030] The user-input information collection unit allows the generation AI to automatically suggest complementary information to the information entered by the user, reducing the user's input effort. For example, when a user enters the location of a power plant, the generation AI automatically suggests the local climate data and sunshine hours. For example, if a user enters "Tokyo," the generation AI displays Tokyo's annual sunshine hours and average temperature. When a user enters a power generation capacity, the generation AI suggests the optimal power generation capacity based on past data. For example, if a user enters "100kW," the generation AI suggests the type of panels and placement method appropriate for that capacity. When a user enters a budget, the generation AI suggests the optimal plan within that budget based on past project data. For example, if a user enters "5 million yen," the generation AI suggests the power plant design and equipment possible within that budget. This reduces the user's input effort.

[0031] The user input information collection unit allows the generation AI to present optimal input candidates based on the user's past input history. The user input information collection unit allows the generation AI to present input candidates for a new project based on, for example, the location and power generation capacity of a power plant previously entered by the user. For example, if the user previously entered "Osaka," the generation AI will present "Osaka" as a candidate the next time. The generation AI also presents input candidates for a new project based on the budget and schedule previously entered by the user. For example, if the user previously entered "6 months," the generation AI will present "6 months" as a candidate the next time. The generation AI also presents optimal input candidates based on data previously entered by the user. For example, if the user previously entered "200kW," the generation AI will present "200kW" as a candidate the next time. This allows the generation AI to present optimal input candidates based on the user's past input history.

[0032] The user input information collection unit can use voice input or image input to enable users to provide information more intuitively. The user input information collection unit, for example, allows users to input the location of the power plant by voice. For example, when a user voice-inputs "Tokyo," the generation AI converts the information into text and processes it. Also, when a user uploads an image of the power plant's installation location, the generation AI analyzes the characteristics of the location. For example, when a user uploads a photo of the installation location, the generation AI analyzes the location's topography and sunlight conditions. Also, the user can input the power generation capacity and budget by voice. For example, when a user voice-inputs "100kW," the generation AI converts the information into text and processes it. This allows users to provide information more intuitively.

[0033] The user-input information collection unit can link with databases from different industries and automatically complete the information entered by the user. For example, when a user enters the location of a power plant, the generation AI automatically provides complementary information by linking with the local climate database. For example, if a user enters "Tokyo," the generation AI displays the annual sunshine hours and average temperature in Tokyo. When a user enters a power generation capacity, the generation AI links with a database of past projects to suggest the optimal power generation capacity. For example, if a user enters "100kW," the generation AI suggests the type of panels and placement method appropriate for that capacity. When a user enters a budget, the generation AI links with a market database to suggest the optimal plan within that budget. For example, if a user enters "5 million yen," the generation AI suggests the power plant design and equipment possible within that budget. This allows the unit to link with databases from different industries and automatically complete the information entered by the user.

[0034] The data analysis unit can add an anomaly detection function to the data to be analyzed and automatically correct outliers. For example, when the generation AI analyzes climate data for the installation location, the data analysis unit uses the anomaly detection function to automatically correct outliers. For example, if abnormally high temperature data is detected, the generation AI corrects the data. Furthermore, when the generation AI analyzes power generation capacity data, the data analysis unit uses the anomaly detection function to automatically correct outliers. For example, if abnormally low power generation capacity data is detected, the generation AI corrects the data. Furthermore, when the generation AI analyzes budget data, the data analysis unit uses the anomaly detection function to automatically correct outliers. For example, if abnormally high budget data is detected, the generation AI corrects the data. In this way, an anomaly detection function can be added to the data to be analyzed and outliers can be automatically corrected.

[0035] The data analysis unit can refer to past success stories and failure stories for the data being analyzed and propose the optimal plan. For example, when the generation AI analyzes climate data for the installation location, the data analysis unit refers to past success stories and failure stories to propose the optimal plan. For example, a proposal is made based on a power generation plan that was successful in the same area. Also, when the generation AI analyzes power generation capacity data, it refers to past success stories and failure stories to propose the optimal plan. For example, a proposal is made based on a successful project with the same power generation capacity. Also, when the generation AI analyzes budget data, it refers to past success stories and failure stories to propose the optimal plan. For example, a proposal is made based on a successful project with the same budget. In this way, it is possible to refer to past success stories and failure stories to propose the optimal plan.

[0036] The data analysis unit can integrate climate data and market data from different regions and generate a plan from a global perspective. In the data analysis unit, for example, the generation AI integrates climate data from different regions and generates an optimal power generation plan from a global perspective. For example, it compares climate data from multiple regions and proposes the optimal installation location. The generation AI also integrates market data from different regions and generates an optimal power generation plan from a global perspective. For example, it compares market data from multiple regions and proposes the optimal budget allocation. The generation AI also integrates climate data and market data from different regions and generates an optimal power generation plan from a global perspective. For example, it proposes the optimal schedule based on the climate data and market data. In this way, it is possible to integrate climate data and market data from different regions and generate a plan from a global perspective.

[0037] The data analysis unit can reflect market trends in real time in the data being analyzed and generate a plan based on the latest information. In the data analysis unit, for example, the generation AI analyzes market trends in real time and generates a power generation plan based on the latest information. For example, the budget is adjusted based on the latest market prices. The generation AI also analyzes market trends in real time and generates a power generation plan based on the latest information. For example, the design is adjusted based on the latest technological trends. The generation AI also analyzes market trends in real time and generates a power generation plan based on the latest information. For example, risk assessment is adjusted based on the latest policy trends. In this way, market trends can be reflected in real time and a plan can be generated based on the latest information.

[0038] The customization unit can learn the user's past customization history and make optimal customization suggestions. For example, the generation AI in the customization unit learns the user's past customization history and makes optimal customization suggestions the next time a plan is created. For example, the generation AI makes suggestions based on the budget allocation and schedule previously selected by the user. The generation AI also automatically adjusts the layout and content of the plan based on the user's past customization history. For example, it creates a new plan based on a layout previously preferred by the user. The generation AI also analyzes the user's past customization history and suggests optimal customization options. For example, it makes suggestions based on the risk assessment method previously selected by the user. In this way, the generation AI can learn the user's past customization history and make optimal customization suggestions.

[0039] The customization unit reflects user feedback in real time and can continuously improve the plan. In the customization unit, for example, the generation AI collects user feedback in real time and immediately improves the content of the plan. For example, if a user provides feedback such as "I would like to increase the budget," the generation AI recalculates the budget and updates the plan. The generation AI also continuously improves the layout and content of the plan based on user feedback. For example, if a user provides feedback such as "I would like to change the layout," the generation AI adjusts the layout. The generation AI also analyzes user feedback, identifies areas for improvement in the plan, and automatically corrects them. For example, if a user provides feedback such as "I would like to provide more detailed risk assessment," the generation AI will detail the risk assessment. This allows user feedback to be reflected in real time and the plan to be continuously improved.

[0040] The customization department can incorporate best practices from different industries to increase the customization options that users can select from. For example, the generation AI incorporates best practices from different industries to increase the customization options that users can select from. For example, by incorporating best practices from the construction industry, it increases the design options for power plants. Furthermore, the generation AI incorporates best practices from different industries to increase the customization options that users can select from. For example, by incorporating best practices from the financial industry, it increases the budget management options. Furthermore, the generation AI incorporates best practices from different industries to increase the customization options that users can select from. For example, by incorporating best practices from the IT industry, it increases the project management options. In this way, best practices from different industries can be incorporated to increase the customization options that users can select from.

[0041] The customization unit can make optimal proposals according to the user's business model and strategy. In the customization unit, for example, the generation AI analyzes the user's business model and makes optimal customization proposals accordingly. For example, if the user adopts a leasing model, the generation AI proposes a power generation plan suitable for leasing. The generation AI also analyzes the user's business strategy and makes optimal customization proposals accordingly. For example, if the user adopts a growth strategy, the generation AI proposes a highly scalable power generation plan. The generation AI also analyzes the user's business model and strategy and makes optimal customization proposals accordingly. For example, if the user adopts a cost reduction strategy, the generation AI proposes a cost-efficient power generation plan. This makes it possible to make optimal proposals according to the user's business model and strategy.

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

[0043] The user-input information collection unit allows the generation AI to automatically suggest complementary information to the information entered by the user, reducing the user's input effort. For example, when a user enters the location of a power plant, the generation AI automatically suggests the local climate data and sunshine hours. For example, if a user enters "Tokyo," the generation AI displays Tokyo's annual sunshine hours and average temperature. Furthermore, when a user enters a power generation capacity, the generation AI suggests the optimal power generation capacity based on past data. For example, if a user enters "100kW," the generation AI suggests the type of panels and placement method appropriate for that capacity. Furthermore, when a user enters a budget, the generation AI suggests the optimal plan within that budget based on past project data. For example, if a user enters "5 million yen," the generation AI suggests the power plant design and equipment possible within that budget. This reduces the user's input effort.

[0044] The user input information collection unit allows the generation AI to present optimal input candidates based on the user's past input history. For example, the generation AI presents input candidates for a new project based on the power plant installation location and power generation capacity previously entered by the user. For example, if the user previously entered "Osaka," the generation AI will present "Osaka" as a candidate again the next time. The generation AI also presents input candidates for a new project based on the budget and schedule previously entered by the user. For example, if the user previously entered "6 months," the generation AI will present "6 months" as a candidate again the next time. The generation AI also presents optimal input candidates based on data previously entered by the user. For example, if the user previously entered "200kW," the generation AI will present "200kW" as a candidate again the next time. This allows the generation AI to present optimal input candidates based on the user's past input history.

[0045] The user input information collection unit can use voice input or image input to enable users to provide information more intuitively. For example, the unit allows users to input the location of the power plant by voice. For example, when a user voice-inputs "Tokyo," the generation AI converts the information into text and processes it. Also, when a user uploads an image of the power plant's installation location, the generation AI analyzes the characteristics of the location. For example, when a user uploads a photo of the installation location, the generation AI analyzes the location's topography and sunlight conditions. Also, the unit allows users to voice-input the power generation capacity and budget. For example, when a user voice-inputs "100kW," the generation AI converts the information into text and processes it. This allows users to provide information more intuitively.

[0046] The user-input information collection unit can link with databases from different industries to automatically complete the information entered by the user. For example, when a user enters the location of a power plant, the generation AI automatically provides complementary information by linking with the local climate database. For example, if a user enters "Tokyo," the generation AI displays the annual sunshine hours and average temperature in Tokyo. When a user enters a power generation capacity, the generation AI links with a database of past projects to suggest the optimal power generation capacity. For example, if a user enters "100kW," the generation AI suggests the type of panels and placement method appropriate for that capacity. When a user enters a budget, the generation AI links with a market database to suggest the optimal plan within that budget. For example, if a user enters "5 million yen," the generation AI suggests the power plant design and equipment possible within that budget. This allows the system to link with databases from different industries and automatically complete the information entered by the user.

[0047] The data analysis unit can add an anomaly detection function to the data to be analyzed and automatically correct outliers. For example, when the generation AI analyzes climate data for the installation location, it uses the anomaly detection function to automatically correct outliers. For example, if abnormally high temperature data is detected, the generation AI corrects that data. Also, when the generation AI analyzes power generation capacity data, it uses the anomaly detection function to automatically correct outliers. For example, if abnormally low power generation capacity data is detected, the generation AI corrects that data. Also, when the generation AI analyzes budget data, it uses the anomaly detection function to automatically correct outliers. For example, if abnormally high budget data is detected, the generation AI corrects that data. In this way, it is possible to add an anomaly detection function to the data to be analyzed and automatically correct outliers.

[0048] The data analysis unit can refer to past successes and failures for the data being analyzed and propose the optimal plan. For example, when the generation AI analyzes climate data for the installation location, it refers to past successes and failures to propose the optimal plan. For example, it makes a proposal based on a power generation plan that was successful in the same area. When the generation AI analyzes power generation capacity data, it refers to past successes and failures to propose the optimal plan. For example, it makes a proposal based on a successful project with the same power generation capacity. When the generation AI analyzes budget data, it refers to past successes and failures to propose the optimal plan. For example, it makes a proposal based on a successful project with the same budget. In this way, it is possible to refer to past successes and failures to propose the optimal plan.

[0049] The data analysis unit can integrate climate data and market data from different regions to generate plans from a global perspective. For example, the generation AI can integrate climate data from different regions to generate an optimal power generation plan from a global perspective. For example, it can compare climate data from multiple regions to propose the optimal installation location. The generation AI can also integrate market data from different regions to generate an optimal power generation plan from a global perspective. For example, it can compare market data from multiple regions to propose the optimal budget allocation. The generation AI can also integrate climate data and market data from different regions to generate an optimal power generation plan from a global perspective. For example, it can propose the optimal schedule based on climate data and market data. This makes it possible to integrate climate data and market data from different regions to generate a plan from a global perspective.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The user input information collection unit collects user input information. For example, the user inputs information such as the installation location of the power plant, power generation capacity, budget, schedule, etc. Step 2: The data analysis unit analyzes the user-input information collected by the user-input information collection unit. For example, the generation AI analyzes the climate data, sunlight hours, land characteristics, etc. of the installation location. Step 3: The plan generation unit generates a plan for the solar power generation project based on the data analyzed by the data analysis unit. For example, the generation AI generates a plan that includes the power plant's blueprint, budget estimate, schedule, risk assessment, etc. Step 4: The customization unit customizes the plan generated by the plan generation unit according to the user's requests. For example, the generation AI may propose an optimal power generation plan within a specific budget or adjust the schedule. Step 5: The output unit outputs the plan customized by the customization unit. For example, the generation AI outputs the plan in PDF or Word format and provides it to the user.

[0052] (Example 2) The solar power generation business plan creation system according to the embodiment of the present invention is a system in which a generation AI automatically creates a detailed plan for a solar power generation business based on information input by a user, thereby enabling the solar power generation business plan creation system to quickly and efficiently plan a solar power generation business.

[0053] A solar power generation business plan creation system according to an embodiment includes a user-input information collection unit, a data analysis unit, a plan generation unit, a customization unit, and an output unit. The user-input information collection unit collects user-input information. For example, a user inputs information such as the installation location of a power plant, power generation capacity, budget, and schedule. The data analysis unit analyzes the user-input information collected by the user-input information collection unit. For example, the generation AI analyzes climate data, sunshine hours, land characteristics, and the like of the installation location. The plan generation unit generates a solar power generation business plan based on the data analyzed by the data analysis unit. For example, the generation AI generates a plan including a power plant design, budget estimate, schedule, risk assessment, and the like. The customization unit customizes the plan generated by the plan generation unit according to user requests. For example, the generation AI proposes an optimal power generation plan within a specific budget or adjusts the schedule. The output unit outputs the plan customized by the customization unit. For example, the generation AI outputs the plan in PDF or Word format and provides it to the user. As a result, the solar power generation business plan creation system can automatically create a solar power generation business plan, customize it according to the user's requests, and output it.

[0054] The user-input information collection unit allows the generation AI to automatically suggest complementary information to the information entered by the user, reducing the user's input effort. For example, when a user enters the location of a power plant, the generation AI automatically suggests the local climate data and sunshine hours. For example, if a user enters "Tokyo," the generation AI displays Tokyo's annual sunshine hours and average temperature. When a user enters a power generation capacity, the generation AI suggests the optimal power generation capacity based on past data. For example, if a user enters "100kW," the generation AI suggests the type of panels and placement method appropriate for that capacity. When a user enters a budget, the generation AI suggests the optimal plan within that budget based on past project data. For example, if a user enters "5 million yen," the generation AI suggests the power plant design and equipment possible within that budget. This reduces the user's input effort.

[0055] The user input information collection unit allows the generation AI to present optimal input candidates based on the user's past input history. The user input information collection unit allows the generation AI to present input candidates for a new project based on, for example, the location and power generation capacity of a power plant previously entered by the user. For example, if the user previously entered "Osaka," the generation AI will present "Osaka" as a candidate the next time. The generation AI also presents input candidates for a new project based on the budget and schedule previously entered by the user. For example, if the user previously entered "6 months," the generation AI will present "6 months" as a candidate the next time. The generation AI also presents optimal input candidates based on data previously entered by the user. For example, if the user previously entered "200kW," the generation AI will present "200kW" as a candidate the next time. This allows the generation AI to present optimal input candidates based on the user's past input history.

[0056] The user input information collection unit can use the emotion estimation function to analyze the user's emotions when entering text and provide an interface for reducing stress. For example, when the user is entering text, the generation AI analyzes their facial expressions and voice, and provides a relaxing interface if they are feeling stressed. For example, if the user is tense, the generation AI plays relaxing music. Also, when the user is entering text, the generation AI analyzes the emotion score and provides advice for reducing stress. For example, if the user is irritated, the generation AI suggests, "Take a deep breath." Also, when the user is entering text, the generation AI changes the color and design of the interface based on the emotion data. For example, if the user is feeling stressed, the generation AI changes the interface to a calming color. This reduces stress when the user is entering text.

[0057] The user input information collection unit can use voice input or image input to enable users to provide information more intuitively. The user input information collection unit, for example, allows users to input the location of the power plant by voice. For example, when a user voice-inputs "Tokyo," the generation AI converts the information into text and processes it. Also, when a user uploads an image of the power plant's installation location, the generation AI analyzes the characteristics of the location. For example, when a user uploads a photo of the installation location, the generation AI analyzes the location's topography and sunlight conditions. Also, the user can input the power generation capacity and budget by voice. For example, when a user voice-inputs "100kW," the generation AI converts the information into text and processes it. This allows users to provide information more intuitively.

[0058] The user-input information collection unit can link with databases from different industries and automatically complete the information entered by the user. For example, when a user enters the location of a power plant, the generation AI automatically provides complementary information by linking with the local climate database. For example, if a user enters "Tokyo," the generation AI displays the annual sunshine hours and average temperature in Tokyo. When a user enters a power generation capacity, the generation AI links with a database of past projects to suggest the optimal power generation capacity. For example, if a user enters "100kW," the generation AI suggests the type of panels and placement method appropriate for that capacity. When a user enters a budget, the generation AI links with a market database to suggest the optimal plan within that budget. For example, if a user enters "5 million yen," the generation AI suggests the power plant design and equipment possible within that budget. This allows the unit to link with databases from different industries and automatically complete the information entered by the user.

[0059] The user input information collection unit can use the emotion estimation function to analyze the emotions of the user when they are typing in real time and provide positive feedback. For example, when the user is typing, the generation AI analyzes their facial expressions and voice and provides feedback to elicit positive emotions. For example, if the user is typing with a smile, the generation AI will provide feedback such as "Great!". Also, when the user is typing, the generation AI analyzes the emotion score and provides positive feedback. For example, if the user is enjoying typing, the generation AI will provide feedback such as "Good luck!". Also, when the user is typing, the generation AI provides positive feedback based on the emotion data. For example, if the user is concentrating on typing, the generation AI will provide feedback such as "Very good!". In this way, the emotions of the user when they are typing can be analyzed in real time and positive feedback can be provided.

[0060] The data analysis unit can add an anomaly detection function to the data to be analyzed and automatically correct outliers. For example, when the generation AI analyzes climate data for the installation location, the data analysis unit uses the anomaly detection function to automatically correct outliers. For example, if abnormally high temperature data is detected, the generation AI corrects the data. Furthermore, when the generation AI analyzes power generation capacity data, the data analysis unit uses the anomaly detection function to automatically correct outliers. For example, if abnormally low power generation capacity data is detected, the generation AI corrects the data. Furthermore, when the generation AI analyzes budget data, the data analysis unit uses the anomaly detection function to automatically correct outliers. For example, if abnormally high budget data is detected, the generation AI corrects the data. In this way, an anomaly detection function can be added to the data to be analyzed and outliers can be automatically corrected.

[0061] The data analysis unit can refer to past success stories and failure stories for the data being analyzed and propose the optimal plan. For example, when the generation AI analyzes climate data for the installation location, the data analysis unit refers to past success stories and failure stories to propose the optimal plan. For example, a proposal is made based on a power generation plan that was successful in the same area. Also, when the generation AI analyzes power generation capacity data, it refers to past success stories and failure stories to propose the optimal plan. For example, a proposal is made based on a successful project with the same power generation capacity. Also, when the generation AI analyzes budget data, it refers to past success stories and failure stories to propose the optimal plan. For example, a proposal is made based on a successful project with the same budget. In this way, it is possible to refer to past success stories and failure stories to propose the optimal plan.

[0062] The data analysis unit can integrate climate data and market data from different regions and generate a plan from a global perspective. In the data analysis unit, for example, the generation AI integrates climate data from different regions and generates an optimal power generation plan from a global perspective. For example, it compares climate data from multiple regions and proposes the optimal installation location. The generation AI also integrates market data from different regions and generates an optimal power generation plan from a global perspective. For example, it compares market data from multiple regions and proposes the optimal budget allocation. The generation AI also integrates climate data and market data from different regions and generates an optimal power generation plan from a global perspective. For example, it proposes the optimal schedule based on the climate data and market data. In this way, it is possible to integrate climate data and market data from different regions and generate a plan from a global perspective.

[0063] The data analysis unit can reflect market trends in real time in the data being analyzed and generate a plan based on the latest information. In the data analysis unit, for example, the generation AI analyzes market trends in real time and generates a power generation plan based on the latest information. For example, the budget is adjusted based on the latest market prices. The generation AI also analyzes market trends in real time and generates a power generation plan based on the latest information. For example, the design is adjusted based on the latest technological trends. The generation AI also analyzes market trends in real time and generates a power generation plan based on the latest information. For example, risk assessment is adjusted based on the latest policy trends. In this way, market trends can be reflected in real time and a plan can be generated based on the latest information.

[0064] The data analysis unit uses the emotion estimation function to prioritize analyzing data that the user is most interested in, and can generate a plan that meets the user's needs. In the data analysis unit, for example, the generation AI uses the emotion estimation function to prioritize analyzing data that the user is most interested in. For example, if the user is very interested in the installation location, that data is analyzed preferentially. In addition, the generation AI uses the emotion estimation function to prioritize analyzing data that the user is most interested in. For example, if the user is very interested in the budget, that data is analyzed preferentially. In addition, the generation AI uses the emotion estimation function to prioritize analyzing data that the user is most interested in. For example, if the user is very interested in power generation capacity, that data is analyzed preferentially. In this way, the data that the user is most interested in is analyzed preferentially, and a plan that meets the user's needs can be generated.

[0065] The customization unit can learn the user's past customization history and make optimal customization suggestions. For example, the generation AI in the customization unit learns the user's past customization history and makes optimal customization suggestions the next time a plan is created. For example, the generation AI makes suggestions based on the budget allocation and schedule previously selected by the user. The generation AI also automatically adjusts the layout and content of the plan based on the user's past customization history. For example, it creates a new plan based on a layout previously preferred by the user. The generation AI also analyzes the user's past customization history and suggests optimal customization options. For example, it makes suggestions based on the risk assessment method previously selected by the user. In this way, the generation AI can learn the user's past customization history and make optimal customization suggestions.

[0066] The customization unit reflects user feedback in real time and can continuously improve the plan. In the customization unit, for example, the generation AI collects user feedback in real time and immediately improves the content of the plan. For example, if a user provides feedback such as "I would like to increase the budget," the generation AI recalculates the budget and updates the plan. The generation AI also continuously improves the layout and content of the plan based on user feedback. For example, if a user provides feedback such as "I would like to change the layout," the generation AI adjusts the layout. The generation AI also analyzes user feedback, identifies areas for improvement in the plan, and automatically corrects them. For example, if a user provides feedback such as "I would like to provide more detailed risk assessment," the generation AI will detail the risk assessment. This allows user feedback to be reflected in real time and the plan to be continuously improved.

[0067] The customization unit uses the emotion estimation function to make customization suggestions based on the user's emotions, thereby improving user satisfaction. In the customization unit, for example, the generation AI uses the emotion estimation function to make customization suggestions based on the user's emotions. For example, if the user is feeling anxious, a suggestion to reduce risk is made. The generation AI also uses the emotion estimation function to make customization suggestions based on the user's emotions. For example, if the user is excited, a challenging plan is proposed. The generation AI also uses the emotion estimation function to make customization suggestions based on the user's emotions. For example, if the user is relaxed, a conservative plan is proposed. In this way, customization suggestions based on the user's emotions can be made, thereby improving user satisfaction.

[0068] The customization department can incorporate best practices from different industries to increase the customization options that users can select from. For example, the generation AI incorporates best practices from different industries to increase the customization options that users can select from. For example, by incorporating best practices from the construction industry, it increases the design options for power plants. Furthermore, the generation AI incorporates best practices from different industries to increase the customization options that users can select from. For example, by incorporating best practices from the financial industry, it increases the budget management options. Furthermore, the generation AI incorporates best practices from different industries to increase the customization options that users can select from. For example, by incorporating best practices from the IT industry, it increases the project management options. In this way, best practices from different industries can be incorporated to increase the customization options that users can select from.

[0069] The customization unit can make optimal proposals according to the user's business model and strategy. In the customization unit, for example, the generation AI analyzes the user's business model and makes optimal customization proposals accordingly. For example, if the user adopts a leasing model, the generation AI proposes a power generation plan suitable for leasing. The generation AI also analyzes the user's business strategy and makes optimal customization proposals accordingly. For example, if the user adopts a growth strategy, the generation AI proposes a highly scalable power generation plan. The generation AI also analyzes the user's business model and strategy and makes optimal customization proposals accordingly. For example, if the user adopts a cost reduction strategy, the generation AI proposes a cost-efficient power generation plan. This makes it possible to make optimal proposals according to the user's business model and strategy.

[0070] The customization unit can use the emotion estimation function to suggest customization options that will most satisfy the user in real time. In the customization unit, for example, the generation AI uses the emotion estimation function to suggest customization options that will most satisfy the user in real time. For example, if the user is happy, suggestions are made to maintain that emotion. The generation AI also uses the emotion estimation function to suggest customization options that will most satisfy the user in real time. For example, if the user is feeling anxious, suggestions are made to ease that emotion. The generation AI also uses the emotion estimation function to suggest customization options that will most satisfy the user in real time. For example, if the user is excited, suggestions are made to make use of that emotion. In this way, customization options that will most satisfy the user can be suggested in real time.

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

[0072] The user-input information collection unit allows the generation AI to automatically suggest complementary information to the information entered by the user, reducing the user's input effort. For example, when a user enters the location of a power plant, the generation AI automatically suggests the local climate data and sunshine hours. For example, if a user enters "Tokyo," the generation AI displays Tokyo's annual sunshine hours and average temperature. Furthermore, when a user enters a power generation capacity, the generation AI suggests the optimal power generation capacity based on past data. For example, if a user enters "100kW," the generation AI suggests the type of panels and placement method appropriate for that capacity. Furthermore, when a user enters a budget, the generation AI suggests the optimal plan within that budget based on past project data. For example, if a user enters "5 million yen," the generation AI suggests the power plant design and equipment possible within that budget. This reduces the user's input effort.

[0073] The user input information collection unit allows the generation AI to present optimal input candidates based on the user's past input history. For example, the generation AI presents input candidates for a new project based on the power plant installation location and power generation capacity previously entered by the user. For example, if the user previously entered "Osaka," the generation AI will present "Osaka" as a candidate again the next time. The generation AI also presents input candidates for a new project based on the budget and schedule previously entered by the user. For example, if the user previously entered "6 months," the generation AI will present "6 months" as a candidate again the next time. The generation AI also presents optimal input candidates based on data previously entered by the user. For example, if the user previously entered "200kW," the generation AI will present "200kW" as a candidate again the next time. This allows the generation AI to present optimal input candidates based on the user's past input history.

[0074] The user input information collection unit can use the emotion estimation function to analyze the user's emotions when entering text and provide an interface for reducing stress. For example, when the user is entering text, the generation AI analyzes their facial expressions and voice, and provides a relaxing interface if they are feeling stressed. For example, if the user is tense, the generation AI plays relaxing music. Also, when the user is entering text, the generation AI analyzes the emotion score and provides advice for reducing stress. For example, if the user is irritated, the generation AI suggests, "Take a deep breath." Also, when the user is entering text, the generation AI changes the color and design of the interface based on the emotion data. For example, if the user is feeling stressed, the generation AI changes the interface to a calming color. This reduces stress when the user is entering text.

[0075] The user input information collection unit can use voice input or image input to enable users to provide information more intuitively. For example, the unit allows users to input the location of the power plant by voice. For example, when a user voice-inputs "Tokyo," the generation AI converts the information into text and processes it. Also, when a user uploads an image of the power plant's installation location, the generation AI analyzes the characteristics of the location. For example, when a user uploads a photo of the installation location, the generation AI analyzes the location's topography and sunlight conditions. Also, the unit allows users to voice-input the power generation capacity and budget. For example, when a user voice-inputs "100kW," the generation AI converts the information into text and processes it. This allows users to provide information more intuitively.

[0076] The user-input information collection unit can link with databases from different industries to automatically complete the information entered by the user. For example, when a user enters the location of a power plant, the generation AI automatically provides complementary information by linking with the local climate database. For example, if a user enters "Tokyo," the generation AI displays the annual sunshine hours and average temperature in Tokyo. When a user enters a power generation capacity, the generation AI links with a database of past projects to suggest the optimal power generation capacity. For example, if a user enters "100kW," the generation AI suggests the type of panels and placement method appropriate for that capacity. When a user enters a budget, the generation AI links with a market database to suggest the optimal plan within that budget. For example, if a user enters "5 million yen," the generation AI suggests the power plant design and equipment possible within that budget. This allows the system to link with databases from different industries and automatically complete the information entered by the user.

[0077] The user input information collection unit can use the emotion estimation function to analyze the emotions of the user when they are typing in real time and provide positive feedback. For example, when the user is typing, the generation AI analyzes their facial expressions and voice and provides feedback to elicit positive emotions. For example, if the user is typing with a smile, the generation AI will provide feedback such as "Great!". Also, when the user is typing, the generation AI analyzes the emotion score and provides positive feedback. For example, if the user is enjoying typing, the generation AI will provide feedback such as "Good luck!". Also, when the user is typing, the generation AI provides positive feedback based on the emotion data. For example, if the user is concentrating on typing, the generation AI will provide feedback such as "Very good!". This makes it possible to analyze the emotions of the user when they are typing in real time and provide positive feedback.

[0078] The data analysis unit can add an anomaly detection function to the data to be analyzed and automatically correct outliers. For example, when the generation AI analyzes climate data for the installation location, it uses the anomaly detection function to automatically correct outliers. For example, if abnormally high temperature data is detected, the generation AI corrects that data. Also, when the generation AI analyzes power generation capacity data, it uses the anomaly detection function to automatically correct outliers. For example, if abnormally low power generation capacity data is detected, the generation AI corrects that data. Also, when the generation AI analyzes budget data, it uses the anomaly detection function to automatically correct outliers. For example, if abnormally high budget data is detected, the generation AI corrects that data. In this way, it is possible to add an anomaly detection function to the data to be analyzed and automatically correct outliers.

[0079] The data analysis unit can refer to past successes and failures for the data being analyzed and propose the optimal plan. For example, when the generation AI analyzes climate data for the installation location, it refers to past successes and failures to propose the optimal plan. For example, it makes a proposal based on a power generation plan that was successful in the same area. When the generation AI analyzes power generation capacity data, it refers to past successes and failures to propose the optimal plan. For example, it makes a proposal based on a successful project with the same power generation capacity. When the generation AI analyzes budget data, it refers to past successes and failures to propose the optimal plan. For example, it makes a proposal based on a successful project with the same budget. In this way, it is possible to refer to past successes and failures to propose the optimal plan.

[0080] The data analysis unit can integrate climate data and market data from different regions to generate plans from a global perspective. For example, the generation AI can integrate climate data from different regions to generate an optimal power generation plan from a global perspective. For example, it can compare climate data from multiple regions to propose the optimal installation location. The generation AI can also integrate market data from different regions to generate an optimal power generation plan from a global perspective. For example, it can compare market data from multiple regions to propose the optimal budget allocation. The generation AI can also integrate climate data and market data from different regions to generate an optimal power generation plan from a global perspective. For example, it can propose the optimal schedule based on climate data and market data. This makes it possible to integrate climate data and market data from different regions to generate a plan from a global perspective.

[0081] The data analysis unit uses the emotion estimation function to prioritize analyzing the data in which the user is most interested, and can generate a plan that meets the user's needs. For example, the generation AI uses the emotion estimation function to prioritize analyzing the data in which the user is most interested. For example, if the user is very interested in the installation location, that data is analyzed first. The generation AI also uses the emotion estimation function to prioritize analyzing the data in which the user is most interested. For example, if the user is very interested in the budget, that data is analyzed first. The generation AI also uses the emotion estimation function to prioritize analyzing the data in which the user is most interested. For example, if the user is very interested in the power generation capacity, that data is analyzed first. This makes it possible to prioritize analyzing the data in which the user is most interested and generate a plan that meets the user's needs.

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

[0083] Step 1: The user input information collection unit collects user input information. For example, the user inputs information such as the installation location of the power plant, power generation capacity, budget, schedule, etc. Step 2: The data analysis unit analyzes the user-input information collected by the user-input information collection unit. For example, the generation AI analyzes the climate data, sunlight hours, land characteristics, etc. of the installation location. Step 3: The plan generation unit generates a plan for the solar power generation project based on the data analyzed by the data analysis unit. For example, the generation AI generates a plan that includes the power plant's blueprint, budget estimate, schedule, risk assessment, etc. Step 4: The customization unit customizes the plan generated by the plan generation unit according to the user's requests. For example, the generation AI may propose an optimal power generation plan within a specific budget or adjust the schedule. Step 5: The output unit outputs the plan customized by the customization unit. For example, the generation AI outputs the plan in PDF or Word format and provides it to the user.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

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

Claims

1. a user input information collection unit that collects user input information; a data analysis unit that analyzes the user input information collected by the user input information collection unit; a plan generation unit that generates a plan for a solar power generation business based on the data analyzed by the data analysis unit; a customization unit that customizes the plan generated by the plan generation unit according to a user's request; an output unit that outputs the plan customized by the customization unit; A system characterized by:

2. The user input information collection unit The generation AI automatically suggests complementary information for the information entered by the user, reducing the effort required for input by the user.

2. The system of claim 1.

3. The user input information collection unit The AI ​​then presents optimal input candidates based on the user's past input history.

2. The system of claim 1.

4. The user input information collection unit Analyzing the user's emotions when inputting information and providing an interface for reducing stress 2. The system of claim 1.

5. The user input information collection unit Using voice or image input, the user can provide information more intuitively.

2. The system of claim 1.

6. The user input information collection unit Link with databases from different industries to automatically complete the information entered by the user.

2. The system of claim 1.

7. The user input information collection unit Analyze the user's emotions in real time as they type and provide positive feedback 2. The system of claim 1.

8. The data analysis unit Add an anomaly detection function to the data being analyzed and automatically correct outliers.

2. The system of claim 1.

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