System

Abstract

A system is provided.SOLUTION: A system comprising: a data collection means; a data analysis means; a plan generation means; a funding means; a project execution means; and a best practices assessment means.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] Many poor countries and regions face serious social and economic challenges caused by poverty, and solving these challenges requires a multifaceted approach. Effective solutions also require accurate data collection and analysis, fundraising, and sustainable project implementation. However, there is currently a lack of systems for managing and implementing these processes in an integrated manner. Given this background, there is a need for a system that utilizes external funding without using capital, solves the fundamental challenges facing poor countries, and supports sustainable growth. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: The present invention is a system including a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, and a success case evaluation means.

[0006] In this system, the data collection means receives data from local user devices, the data analysis means analyzes the collected data using an AI algorithm, the plan generation means generates specific solutions based on the AI ​​analysis results, the fundraising means raises funds through crowdfunding and social impact investment funds, the project implementation means carries out specific projects on site using the raised funds, and the success case evaluation means evaluates the project results and records them in a database as success cases.

[0007] This will enable comprehensive solutions to the diverse challenges faced in poor regions and support the creation of sustainable economic activity.

[0008] "Data collection means" refers to the function of receiving data from local user terminals.

[0009] "Data analysis means" refers to the function of analyzing collected data using AI algorithms.

[0010] "Plan generation means" refers to the function that generates specific solutions based on the results of AI analysis.

[0011] "Funding methods" refers to the ability to raise funds through crowdfunding and social impact investment funds.

[0012] "Project implementation means" refers to the function of implementing specific projects locally using the funds raised.

[0013] "Means for evaluating success stories" refers to the function of evaluating the results of a project and recording them in a database as success stories. [Brief explanation of the drawings]

[0014] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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, a 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), and an APU (Accelerated Processing Unit).

[0018] 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.

[0019] 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.

[0020] 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), Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0026] 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention is an integrated system for solving various problems in impoverished regions and supporting the creation of sustainable economic activities. This system is composed of means for data collection, data analysis, plan generation, fundraising, project implementation, and evaluation of success cases.

[0036] Data collection methods

[0037] Users use their devices (smartphones or tablets) to input local situation data (e.g., soil data, climate data, crop growth status, etc.) through a dedicated app. This data is sent in real time via the Internet to a server, which then stores the data in a database.

[0038] Data Analysis Methods

[0039] The server passes the local data stored in the database to an AI algorithm for analysis. The results of the analysis identify and prioritize the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.). These analysis results are used in subsequent processes.

[0040] Plan Generation Method

[0041] The server automatically generates specific solutions based on the results of the AI ​​analysis. For example, it may suggest solutions such as using organic fertilizer or introducing a new irrigation system. This generated plan is then reviewed by experts and revised or supplemented as necessary.

[0042] Funding methods

[0043] The server automatically posts the finalized plan on crowdfunding sites and social impact investment fund platforms. Users (backers and investors) access these platforms, review the plan, and then donate or invest in support. The server manages the collected funds and distributes the necessary resources according to each phase of the project.

[0044] Project implementation method

[0045] The server uses the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment, etc.) to local experts and NGOs. The user (local project team) uses the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.).

[0046] Success case evaluation tools

[0047] The server periodically evaluates the project's results and records them in a database as success stories. This allows other impoverished areas facing similar challenges to implement projects based on these success stories. Users (project teams in other areas) can retrieve information from this database and apply it to projects in their own areas.

[0048] Specific examples

[0049] For example, consider the case of implementing an agricultural improvement project in a poor area.

[0050] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, and crop growth status into their terminals. This information is sent to the server and stored in a database.

[0051] 2. The server analyzes the stored data using AI algorithms to identify key problem factors, such as nutrient deficiencies in the soil or insufficient irrigation.

[0052] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer or introducing a new irrigation system. The generated plan is reviewed and revised by experts.

[0053] 4. The server posts the revised plan on the crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the posted plan.

[0054] 5. The server uses the funds raised to provide local farmers with organic fertilizer and arrange resources to install new irrigation systems. The users (local farmers) use these resources to improve agricultural efficiency.

[0055] 6. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[0056] In this way, this system can solve specific problems in poor areas and build sustainable socio-economic activities.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The device (a smartphone or tablet used by the user) uses a dedicated app to input local situation data, including soil condition, climate, and crop growth status. The device then transmits the data to a server via the internet along with a unique identifier.

[0060] Step 2:

[0061] The server stores the data received from the devices in a database that stores all data collected over time and is used for subsequent data analysis.

[0062] Step 3:

[0063] The server preprocesses the data stored in the database. Specifically, it removes incomplete data, corrects outliers, and prepares the data in a format suitable for analysis. The preprocessed data is then saved in the data store for analysis.

[0064] Step 4:

[0065] The server then passes the preprocessed data to an AI algorithm for data analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These results are used in the subsequent plan generation process.

[0066] Step 5:

[0067] The server automatically generates specific solutions based on the AI ​​analysis results, such as using organic fertilizer or introducing a new irrigation system, and displays the resulting plan on a dashboard for expert review.

[0068] Step 6:

[0069] The expert reviews the plan presented by the server through a dashboard and makes corrections or additions as necessary. The expert's feedback is sent to the server, and the final version of the plan is completed.

[0070] Step 7:

[0071] The server generates a page for posting the final plan on a crowdfunding site or social impact investment platform, including a summary of the plan, the funding goal, and the expected impact.

[0072] Step 8:

[0073] Users (backers and investors) access the crowdfunding site, check the posted plans, make donations or investments to support the project, and the funds are sent to the server.

[0074] Step 9:

[0075] The server manages the collected funds and distributes the necessary resources for each phase of the project to local experts and NGOs.

[0076] Step 10:

[0077] Users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.). Activity status is reported successively from the terminals to the server.

[0078] Step 11:

[0079] The server periodically evaluates the progress of the project and records success stories in a database. The evaluation is based on the project's progress, results, and effectiveness of problem-solving.

[0080] Step 12:

[0081] Users (project teams in other regions) access the database of successful cases to obtain information, which they can then apply to projects in their own regions, aiming to solve similar problems.

[0082] These are the specific steps of the program processing of this system, which will effectively solve various issues in poor areas and enable the creation of sustainable socio-economic activities.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] The purpose of this invention is to comprehensively solve various social and economic issues in poor regions and create sustainable economic activities. Conventional support systems often lack efficient data collection and analysis, appropriate plan creation, fundraising, and on-site implementation based on those plans, making it difficult to provide effective solutions.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project implementation means, a success story evaluation means, a user terminal that inputs local situation data and transmits it to the server in real time, a means by which the server analyzes the data stored in the database and identifies social issues using an AI algorithm, a means for generating specific solutions based on the analysis results and having them reviewed by experts, a means for posting the plan on a crowdfunding site or investment fund and raising funds, a means for providing the necessary resources based on the raised funds and carrying out project activities on site, and a means for evaluating the project results and recording them in the database as success stories. This makes it possible to efficiently collect data tailored to the local situation in impoverished areas, generate optimal solutions based on the analysis results, effectively raise funds and implement projects based on them, and share success stories.

[0088] The "data collection means" is a means for inputting situation data from a local user terminal and transmitting it to a server in real time.

[0089] "Data analysis means" refers to the means by which the server passes data stored in the database to an AI algorithm for analysis.

[0090] The "plan generation means" is a means for automatically generating specific solutions based on the analysis results and having them reviewed by experts.

[0091] A "fundraising method" is a means of raising funds from supporters and investors by posting a finalized plan on a crowdfunding site or a social impact investment fund platform.

[0092] "Project implementation means" refers to the means by which the funds raised are used to provide the necessary resources to local experts and NGOs to carry out specific project activities.

[0093] The "success case evaluation method" is a method for evaluating the results of a project and recording them in a database as success cases.

[0094] "Local situation data" refers to data showing the current state of poor areas, such as soil data, climate data, and crop growth conditions.

[0095] A "user terminal" is a device such as a smartphone or tablet that is used to input local situation data and send it to a server.

[0096] A "server" is a computer system that receives data sent from the field, stores it in a database, analyzes the data, and executes various processes.

[0097] A "database" is a system for storing and managing data received by a server.

[0098] An "AI algorithm" is an algorithm that allows a server to analyze data stored in a database, identify important social issues, and generate solutions.

[0099] A "crowdfunding site" is a platform for raising funds from people who support a specific project via the Internet.

[0100] A "social impact investment fund" is a fund that specializes in investing in projects that have social value.

[0101] "Project activities" are concrete solutions implemented on the ground, such as spreading fertilizer or installing irrigation systems.

[0102] The present invention is an integrated system for solving various issues in impoverished regions and creating sustainable economic activities. This system is composed of data collection means, data analysis means, plan generation means, fundraising means, project implementation means, and success case evaluation means. Specific embodiments of each means are described below.

[0103] Data collection methods

[0104] The user's device (smartphone or tablet) inputs local situation data (e.g., soil data, climate data, crop growth status, etc.) through a dedicated app. This data is sent in real time to a server via the Internet. The server stores this data in a database. Specifically, the device sends the data to the server via an HTTP request, and the server stores the data using a database management system such as MySQL or PostgreSQL.

[0105] Data Analysis Methods

[0106] The server then passes the on-site data stored in the database to an AI algorithm for analysis. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to input the data into an AI model and identify key fault factors, such as soil nutrient deficiencies or insufficient irrigation. The results of this analysis are then used in subsequent processes.

[0107] Plan Generation Method

[0108] The server automatically generates specific solutions based on the results of the AI ​​analysis. For example, it may propose solutions such as using organic fertilizer or installing a new irrigation system. These plans are then reviewed by experts and revised or supplemented as necessary. Specifically, solutions are generated using natural language processing and then sent to the expert review team via email or a web portal.

[0109] Funding methods

[0110] The server automatically posts the finalized plan on crowdfunding sites and social impact investment fund platforms. Users (backers and investors) access these platforms, review the plan, and then donate or invest to support the project. The server manages the collected funds and distributes the necessary resources according to each phase of the project. Specifically, the server uses the platform's API to post plan information and provide donation and investment management functions.

[0111] Project implementation method

[0112] The server uses the funds raised to arrange for the provision of necessary resources to experts and NGOs working on the ground. The users (local project teams) use the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation facilities, etc.). Specifically, the server uses the funds to coordinate with supply suppliers, deliver the appropriate supplies to the ground, and provide specific instructions to the local project teams.

[0113] Success case evaluation tools

[0114] The server periodically evaluates project results and records them in a database as success stories. This allows other impoverished regions facing similar challenges to implement projects based on these success stories. Users (project teams in other regions) can retrieve information from this database and apply it to projects in their own regions. Specifically, they input the results data into an AI evaluation model, and periodically report and update the database.

[0115] Specific examples

[0116] For example, consider an agricultural improvement project in a poor region. Users (local farmers or researchers) input information such as soil condition, climate data, and crop growth status from their devices. This information is sent to a server and stored in a database. The server then analyzes the stored data using an AI algorithm to identify key obstacles, such as soil nutrient deficiencies and insufficient irrigation. The server then generates specific solutions to address the identified obstacles, such as the use of organic fertilizer and the introduction of a new irrigation system. The generated plan is reviewed and revised by experts. The server then posts the revised plan on a crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the published plan. The server uses the raised funds to provide organic fertilizer to local farmers and arrange resources for the introduction of a new irrigation system. The users (local farmers) use these resources to improve agricultural efficiency. The server evaluates the project's results and records them in a database as success stories. Users (farmers in other regions or project teams) can then refer to the success stories and apply them to their own local projects.

[0117] Prompt Sentence Examples

[0118] For example, the following prompt sentence is input to the generative AI model:

[0119] "The local soil data is as follows: pH: 5.5, humidity: 30%, temperature: 28°C. Please suggest the best agricultural improvement measures for this situation."

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] The user launches the dedicated app and uses a device (smartphone or tablet) to input data on the local situation. The input data includes soil pH value, temperature, humidity, and crop growth status. The input data is sent to the server in real time by the device. Input: Soil pH value, temperature, humidity, crop growth status, etc. Output: Data sent to the server.

[0123] Step 2:

[0124] The server receives the data sent from the terminal and stores it in a database. At this time, the server stores the data using a database management system such as MySQL or PostgreSQL. Input: Data sent by the user through the terminal. Output: Data stored in the database.

[0125] Step 3:

[0126] The server passes the local data stored in the database to an AI algorithm for analysis. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to input the stored data into an AI model and identify important local issues. Analysis results include soil nutrient deficiencies and insufficient irrigation. Input: Data stored in the database. Output: Analysis results (e.g., soil nutrient deficiencies, insufficient irrigation, etc.).

[0127] Step 4:

[0128] The server automatically generates specific solutions based on the analysis results of the AI ​​model. For example, it generates proposals such as using organic fertilizer or introducing a new irrigation system. This generated plan is sent to experts via email or a web portal. Input: Analysis results. Output: Automatically generated solution plan.

[0129] Step 5:

[0130] The expert reviews the plan sent from the server and corrects and completes it as necessary. The corrected plan is sent back to the server. Input: Generated solution plan. Output: Reviewed and corrected plan.

[0131] Step 6:

[0132] The server automatically posts the finalized plan on the crowdfunding site or social impact investment fund platform. The server posts the plan information using the platform's API. Input: Reviewed and revised plan. Output: Posting on the crowdfunding site or investment fund.

[0133] Step 7:

[0134] Users (backers and investors) access crowdfunding sites and investment funds, check the posted plans, and then donate or invest to support the project. The server manages the funds and distributes resources according to each phase of the project. Input: Funds collected through crowdfunding sites and investment funds. Output: Distribution of required resources.

[0135] Step 8:

[0136] The server uses the raised funds to arrange for the provision of necessary resources to experts and NGOs working on the ground. The users (local project teams) use the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation facilities, etc.). Input: Funds raised. Output: Resources provided to the ground.

[0137] Step 9:

[0138] The server periodically evaluates the project's results and records them in a database as success stories. This results data is applied to similar projects in other poor areas. Users (project teams in other areas) retrieve the success stories from the database and apply them to projects in their own areas. Input: Project results data. Output: Success stories recorded in the database.

[0139] (Application example 1)

[0140] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] Traditional factory operations face many challenges, including excessive energy consumption and inefficient production processes. These challenges increase factory operating costs and contribute to a greater environmental impact. Furthermore, the process of proposing improvements and raising funds to resolve these issues is often done manually, requiring time and effort. Therefore, there is a need for a system that can collect and analyze data in real time and provide quick and effective improvement proposals.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0143] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, a means for collecting data through a factory sensor system and analyzing the data in real time, a means for automatically generating factory improvement proposals based on the analysis results, a means for raising funds in cooperation with a crowdfunding platform, and a means for implementing a factory improvement project using the raised funds. This makes it possible to quickly and effectively solve problems while improving the operational efficiency of the factory.

[0144] A "data collection means" is a device that has the function of acquiring data in real time from sensors installed within the factory and sending it to a server.

[0145] The "data analysis means" is a device that uses AI algorithms to analyze collected data and identify problems related to factory operations.

[0146] The "plan generation means" is a device that automatically generates improvement proposals for the factory based on the analysis results of the data analysis means.

[0147] The "fundraising instrument" is a device that automatically posts the generated improvement proposals on crowdfunding platforms and social impact investment funds, thereby raising funds.

[0148] The "project implementation means" is a device that uses the procured funds to arrange resources for implementing the factory improvement project and carries out specific improvement activities.

[0149] The "success case evaluation means" is a device that has the function of evaluating the results of a project and recording them in a database as success cases.

[0150] A "factory sensor system" is a group of sensors that collect data from various devices and processes within a factory.

[0151] A "means for real-time analysis" is a device that has the ability to instantly pass collected data to an AI algorithm for analysis.

[0152] The "means for automatic generation" is a device that has the function of automatically creating optimal improvement proposals based on the analysis results of an AI algorithm.

[0153] A "crowdfunding platform" is a service for raising funds online, a website where project details are published and funding is sought from supporters.

[0154] The "means for implementing improvement projects" refers to a device that has the function of arranging the necessary resources after fundraising and actually carrying out improvement activities in the factory.

[0155] The present invention is an integrated system for supporting efficiency in factory operations, which includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, a factory sensor system, a real-time analysis means, an automatic generation means, a crowdfunding platform, and a means for implementing an improvement project.

[0156] Hardware and Software Configuration

[0157] Hardware

[0158] Factory sensor system: A set of sensors used to collect data from each device and process in the factory. For example, temperature sensors, humidity sensors, vibration sensors, etc. are used.

[0159] Server: A central computer that handles data collection, analysis, plan generation, fundraising management, project execution, and success evaluation.

[0160] Smart glasses or terminals: Used for real-time monitoring and data entry on-site.

[0161] software

[0162] Python: A programming language used to build entire programs and process data.

[0163] Sci-kit Learn: A machine learning library for data analysis.

[0164] Requests: Used for API integration with crowdfunding platforms.

[0165] Generative AI model: An AI model that generates optimal plans from data analysis results.

[0166] Data processing and calculation

[0167] 1. Data Collection:

[0168] The server collects data in real time from the factory's sensor system. The sensors acquire various data (temperature, humidity, vibration, etc.) within the factory, and this data is sent to the server via smart glasses or terminals.

[0169] 2. Data Analysis:

[0170] The server analyzes the collected data using AI algorithms powered by Sci-kit Learn, which identifies key issues in factory operations (such as excessive energy consumption and production inefficiencies).

[0171] 3. Plan Generation:

[0172] Based on the analysis results, the server uses a generative AI model to automatically generate improvement suggestions, such as specific measures to reduce energy consumption or optimize production processes.

[0173] 4. Funding:

[0174] The server then posts the automatically generated plan on the crowdfunding platform, where investors and supporters can review the plan and provide funding.

[0175] 5. Project execution:

[0176] The server uses the funds raised to implement on-site factory improvement projects, specifically converting the funds into resources (such as new equipment and machinery) and supplying them to the factory.

[0177] 6. Success Case Evaluation:

[0178] The server evaluates the project results and records them in a database as success stories that can be used as references at other factories to solve similar problems.

[0179] Examples of concrete examples and prompts

[0180] Specific examples

[0181] If Factory A has a problem with high energy consumption, sensors collect real-time information on factory temperatures and machine operation, and send the data to a server. The server then analyzes the data using an AI algorithm to identify the causes of energy consumption. Based on the analysis results, improvement proposals are generated, such as replacing machines with more energy-efficient ones or introducing a new energy management system. These proposals are then posted on a crowdfunding platform, and once funds are raised, actual improvement activities are carried out on-site.

[0182] Prompt Sentence Examples

[0183] "Please suggest the best plan to reduce energy consumption in the factory. The sensor data is as follows: {sensor_data}"

[0184] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0185] Step 1:

[0186] Data collection

[0187] The server receives data in real time from sensors installed in the factory, including information on temperature, humidity, vibration, operating hours, etc. The input sensor data is sent to the server and stored in a database.

[0188] Step 2:

[0189] Data analysis

[0190] The server passes the collected data to an AI algorithm for analysis. Sci-kit Learn is used to apply machine learning models to identify problems in factory operations (e.g., excessive energy consumption, inefficient processes, etc.). The input is sensor data stored in a database, and the output is the identified problems as a result of the analysis.

[0191] Step 3:

[0192] Plan Generation

[0193] The server uses a generative AI model based on the results of data analysis to automatically generate improvement suggestions, including energy-saving measures, production process optimization, resource reallocation, etc. The input is the results of data analysis, and the output is detailed improvement suggestions.

[0194] Step 4:

[0195] Crowdfunding

[0196] The server automatically posts the generated improvement proposal on the crowdfunding platform. Specifically, it uses the Requests library to submit the plan in the appropriate format. The input is the generated improvement proposal, and the output is the project information posted on the crowdfunding platform.

[0197] Step 5:

[0198] Funding

[0199] Users (investors and supporters) review improvement proposals posted on the crowdfunding platform and provide funding. The server manages the collected funds and distributes resources according to the required phase. The input is funding information from the crowdfunding platform, and the output is the success of fundraising.

[0200] Step 6:

[0201] Project Execution

[0202] The server uses the raised funds to arrange resources to implement factory improvement projects. Resources can include new machinery, energy efficiency devices, improvement supplies, etc. The inputs are the successful fundraising and required resource information, and the outputs are the arranged resources and the implemented improvement projects.

[0203] Step 7:

[0204] Success Case Evaluation

[0205] The server evaluates the project results and records them in a database as success stories. It also provides the evaluation results to other factories as reference information. The input is the project execution result data, and the output is the evaluated success stories and their records.

[0206] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0207] This invention is an integrated system for solving social issues in impoverished areas and building sustainable economic activities. This system consists of a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a means for evaluating success cases, and an emotion engine that recognizes the user's emotions.

[0208] Data collection methods

[0209] The user's device (smartphone or tablet) inputs local situation data through a dedicated app. The input data includes soil condition, climate, crop growth status, and the user's emotions. The device then sends the data via the Internet to a server, which then stores the data in a database.

[0210] Data Analysis Methods

[0211] The server then passes the local data stored in the database to an AI algorithm for analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These analysis results are then used in the subsequent plan generation process.

[0212] Emotion Engine

[0213] The server uses an emotion engine to analyze data received from the local user terminal and recognize the user's emotion. The recognized emotion data is fed back to the data analysis means and plan generation means, which then generate and adjust plans that take the user's emotional state into consideration.

[0214] Plan Generation Method

[0215] The server automatically generates specific solutions based on the AI ​​analysis results and emotional data. For example, it may suggest solutions such as using organic fertilizer or installing a new irrigation system. The generated plan is displayed on a dashboard for expert review.

[0216] Funding methods

[0217] The server generates a page for posting the finalized plan on a crowdfunding site or social impact investment fund platform. This page includes an overview of the plan, the target funding amount, the expected impact, etc. Users (backers and investors) access the crowdfunding site, review the posted plan, and then make donations or investments to support the project.

[0218] Project implementation method

[0219] The server uses the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment, etc.) to local experts and NGOs. The user (local project team) uses the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.).

[0220] Success case evaluation tools

[0221] The server periodically evaluates the project's results and records them in a database as success stories. This allows other impoverished areas facing similar challenges to implement projects based on these success stories. Users (project teams in other areas) can retrieve information from this database and apply it to projects in their own areas.

[0222] Specific examples

[0223] For example, consider the case of implementing an agricultural improvement project in a poor area.

[0224] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, crop growth status, and emotional state into their terminals. This information is sent to the server and stored in a database.

[0225] 2. The server analyzes the stored data using AI algorithms and an emotion engine to identify key fault factors, such as soil nutrient deficiency, insufficient irrigation, and the user's emotional state.

[0226] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer, introducing a new irrigation system, or support measures that take the user's emotional state into account. The generated plans are then reviewed and revised by experts.

[0227] 4. The server posts the revised plan on the crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the posted plan.

[0228] 5. The server uses the funds raised to provide local farmers with organic fertilizer and arrange resources to install new irrigation systems. The users (local farmers) use these resources to improve the efficiency and quality of their farming.

[0229] 6. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[0230] In this way, by solving specific problems in poor areas through this system and taking into account the emotional state of the user, it is possible to build more effective and sustainable socio-economic activities.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] The device (a smartphone or tablet used by the user) inputs local situation data using a dedicated app. The input data includes soil condition, climate, crop growth status, and the user's emotional state. The device then transmits the data to a server via the Internet.

[0234] Step 2:

[0235] The server stores the data received from the devices in a database that stores all the data collected over time and stores the data for subsequent analysis and planning.

[0236] Step 3:

[0237] The server preprocesses the data stored in the database. Specifically, it removes incomplete data, corrects outliers, and prepares the data in a format suitable for analysis. The preprocessed data is then stored in the data store for analysis.

[0238] Step 4:

[0239] The server then passes the preprocessed data to an AI algorithm for data analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These results are used in the subsequent plan generation process.

[0240] Step 5:

[0241] The server analyzes the user's emotional data using an emotion engine. It detects the user's emotional state in real time based on their input and usage, and collects the data. The emotional data is then integrated with the data analysis results.

[0242] Step 6:

[0243] The server automatically generates specific solutions based on the data analysis results and emotional data. For example, these could include using organic fertilizers or installing a new irrigation system, as well as psychological support and training sessions that take the user's emotional state into account. The generated plans are then displayed on a dashboard for expert review.

[0244] Step 7:

[0245] The expert reviews the plan presented by the server through a dashboard and makes corrections or additions as necessary. The expert's feedback is sent to the server, and the final version of the plan is completed.

[0246] Step 8:

[0247] The server generates a page for posting the final plan on a crowdfunding site or social impact investment platform, including a summary of the plan, the funding goal, and the expected impact.

[0248] Step 9:

[0249] Users (backers and investors) access the crowdfunding site, check the posted plans, make donations or investments to support the project, and the funds are sent to the server.

[0250] Step 10:

[0251] The server manages the collected funds and distributes the necessary resources for each phase of the project, including fertilizer, irrigation equipment, educational materials, psychological support resources, etc. The funds are distributed to experts and NGOs working on the ground.

[0252] Step 11:

[0253] Users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, conducting psychological support sessions, etc.). Activity status is reported sequentially from the terminals to the server.

[0254] Step 12:

[0255] The server periodically evaluates the progress of the project and records it in a database as a success story. This evaluation is based on the project's progress, results, changes in user emotions, and the effectiveness of problem solving.

[0256] Step 13:

[0257] The server will publish a database of success stories and make them accessible to users (project teams in other regions). Success stories include specific solutions, implementation methods, results, and changes in user emotions.

[0258] Step 14:

[0259] Users (project teams in other regions) can obtain information from the database of success stories and apply it to projects in their own regions. Based on the information obtained, they can carry out projects aiming to solve similar problems.

[0260] These are the specific steps of the program processing of this system, which can effectively solve various problems in poor areas and, by taking into account the emotional state of the user, can build more effective and sustainable socio-economic activities.

[0261] Example 2

[0262] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0263] Building sustainable socioeconomic activities in impoverished areas requires a detailed understanding of specific local issues and the rapid and efficient provision of appropriate solutions based on that understanding. However, conventional methods lack an integrated process, from data collection and analysis to solution proposals, fundraising, implementation, and outcome evaluation, resulting in time-consuming information sharing and decision-making at each stage. Furthermore, they fail to take into account the emotional state of users, limiting the means for maximizing project effectiveness. Therefore, the present invention aims to provide an integrated system that solves these issues.

[0264] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data analysis means, an emotion recognition means, a plan generation means, a fundraising means, a project execution means, and a success case evaluation means. This makes it possible to quickly and efficiently identify specific local issues and provide appropriate solutions based on the analysis results and the user's emotional state. Furthermore, by managing a series of processes from fundraising to project execution and outcome evaluation in an integrated manner, information sharing and decision-making can be accelerated, effectively promoting the establishment of sustainable socioeconomic activities in impoverished areas.

[0265] The "data collection means" is a device or system for receiving data from information processing devices used by local users.

[0266] A "data analysis means" is a device or system for analyzing collected data using machine learning algorithms.

[0267] An "emotion recognizer" is a device or system for identifying a user's emotional state from collected data.

[0268] The "plan generation means" is a device or system for generating specific solutions based on the data analysis results and emotion data.

[0269] A "funding vehicle" is a device or system for generating information for posting a generated plan on a funding platform.

[0270] A "Project Execution Vehicle" is a device or system that utilizes raised funds to provide resources and execute a project.

[0271] A "success case evaluation tool" is a device or system for evaluating the results of a project and recording the results in a database.

[0272] This invention is an integrated system for solving social issues in impoverished areas and building sustainable economic activities. This system consists of data collection means, data analysis means, emotion recognition means, plan generation means, fundraising means, project implementation means, and success case evaluation means.

[0273] Data collection methods

[0274] Users input data about their local conditions through a dedicated app installed on their smartphone or tablet. The data includes soil pH, temperature, precipitation, crop growth status, and the user's emotional state. The device then transmits this data to a server in real time via an internet connection.

[0275] Data Analysis Methods

[0276] The server stores the received data in a database (e.g., MySQL, PostgreSQL), then passes the data to an AI algorithm (e.g., TensorFlow, PyTorch) for analysis. Specific analysis results include determining the nutrient status of the soil and assessing the risk of drought.

[0277] emotion recognition means

[0278] The server uses an emotion engine (e.g., OpenAI's GPT-3) to recognize the user's emotions from the received data. The emotion data (e.g., anxiety, hope, irritation) is stored in a database and subsequent processing is based on it.

[0279] Plan Generation Method

[0280] The server automatically generates specific solutions based on the AI ​​analysis results and emotion data. The generated plan suggests measures such as using organic fertilizer, installing a new irrigation system, and providing psychological support. The plan is displayed on a dashboard (e.g., PowerBI) and awaits expert review.

[0281] Funding methods

[0282] The server automatically generates a page for posting the approved plan on a crowdfunding site (e.g., Kickstarter) or a social impact investment fund platform, detailing the plan's outline, target funding amount, and expected social impact.

[0283] Project implementation method

[0284] The server uses the funds raised to arrange for the provision of necessary resources (e.g., fertilizer, irrigation equipment) to local experts and NGOs. The provision of resources includes coordination with logistics centers and local vendors. The users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment).

[0285] Success case evaluation tools

[0286] The server periodically evaluates the project's performance and records the results in a database. Evaluation criteria include profitability, sustainability, and social impact. Success stories can also be used as reference for projects in other regions.

[0287] Specific examples

[0288] For example, consider the case of implementing an agricultural improvement project in a poor area.

[0289] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, crop growth status, and emotional state into their devices. This information is sent to the server and stored in a database.

[0290] 2. The server analyzes the stored data using AI algorithms and an emotion engine to identify key fault factors, such as soil nutrient deficiency, insufficient irrigation, and the user's emotional state.

[0291] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer, introducing a new irrigation system, or support measures that take the user's emotional state into account. The generated plans are reviewed by experts and posted on a crowdfunding site.

[0292] 4. Users (backers and investors) access the crowdfunding site and provide funding.

[0293] 5. The server will use the funds raised to provide organic fertilizer to local farmers and arrange resources to install a new irrigation system.

[0294] 6. Users (local farmers) use these resources to improve the efficiency and quality of their farming.

[0295] 7. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[0296] Prompt Sentence Examples

[0297] Use the following data to generate a plan for improving agriculture in a poor area.

[0298] Soil condition: Highly acidic

[0299] Climate data: Short rainy season

[0300] Crop growth status: Poor growth

[0301] User Emotion: Anxiety

[0302] Please explain your specific solution and why.

[0303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0304] Step 1: Data collection

[0305] Users input local situation data, such as soil pH, temperature, precipitation, crop growth status, and the user's emotional state, through a dedicated app installed on a smartphone or tablet.

[0306] Input: Local condition data (e.g. soil pH, temperature, precipitation, crop condition, emotional state)

[0307] Output: Data stored on a smartphone or tablet

[0308] Step 2: Send data

[0309] The terminal transmits the input data to the server in real time via the Internet. During transmission, encrypted communication (e.g., SSL / TLS) is used to maintain data integrity.

[0310] Input: Data stored on a smartphone or tablet

[0311] Output: Data sent to the server

[0312] Step 3: Save Data

[0313] The server stores the received data in a database (e.g., MySQL, PostgreSQL). When storing the data, it also backs up the data to prevent data loss. It also monitors the capacity of the data storage.

[0314] Input: Received data

[0315] Output: Data stored in the database

[0316] Step 4: Data analysis

[0317] The server then passes the stored data to AI algorithms (e.g., TensorFlow, PyTorch) for analysis. The analysis includes determining the nutrient status of the soil and assessing the risk of drought. The most important social issues are identified and prioritized.

[0318] Input: Data stored in a database

[0319] Output: Analysis results (e.g., soil nutrient deficiency, insufficient irrigation)

[0320] Step 5: Emotion Recognition

[0321] The server uses an emotion engine (e.g., OpenAI's GPT-3) to recognize the user's emotion from the received data. The recognized emotion data is stored in a database and subsequent processing is based on it.

[0322] Input: Local situation data stored in a database

[0323] Output: Recognized emotion data

[0324] Step 6: Generate a plan

[0325] The server automatically generates specific solutions based on the AI ​​analysis results and emotional data. The generated plan suggests measures such as using organic fertilizer, installing a new irrigation system, and providing psychological support. The plan is displayed on a dashboard (e.g., PowerBI).

[0326] Input: Analysis results and emotion data

[0327] Output: Auto-generated solution (plan)

[0328] Step 7: Expert Review

[0329] The expert reviews the plan generated by the server via a dashboard, providing corrections or additional advice as necessary, and the reviewed plan moves on to the next step after receiving final approval.

[0330] Input: Auto-generated solution (plan)

[0331] Output: Reviewed and revised plan

[0332] Step 8: Generate a Fundraising Page

[0333] The server automatically generates a page for posting the approved plan on a crowdfunding site (e.g., Kickstarter) or a social impact investment fund platform, detailing the plan's outline, target funding amount, and expected social impact.

[0334] Input: Reviewed and revised plan

[0335] Output: Crowdfunding page

[0336] Step 9: Funding

[0337] Users (backers and investors) access crowdfunding sites, check the plans posted, and then donate or invest in support via credit card or digital payment systems (e.g., PayPal).

[0338] Input: Crowdfunding page

[0339] Output: Funds provided

[0340] Step 10: Resource Arrangements

[0341] Using the funds raised, the server will provide necessary resources (e.g., fertilizer, irrigation equipment) to local experts and NGOs, including liaising with logistics centers and local vendors.

[0342] Input: Funds Provided

[0343] Output: Arranged resources

[0344] Step 11: Project execution

[0345] Users (local project teams) utilize the provided resources to carry out specific project activities, such as spreading fertilizer, installing irrigation systems, and implementing educational programs.

[0346] Input: Arranged resource

[0347] Output: Executed projects

[0348] Step 12: Success Case Evaluation

[0349] The server periodically evaluates the project's performance and records the results in a database. Evaluation criteria include profitability, sustainability, and social impact. Success stories can also be used as reference for projects in other regions.

[0350] Input: Data of the executed project

[0351] Output: Database of evaluation results and success stories

[0352] (Application example 2)

[0353] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0354] Traditional operations in poor areas and brick-and-mortar stores require efficient data collection and analysis, solution generation, fundraising, and project implementation, but there is a lack of systems that handle these elements in an integrated manner. Furthermore, while it is important to reflect the emotions and specific needs of users in solving local issues, few systems have this functionality. This makes it difficult to generate specific solutions tailored to local conditions and to raise funds effectively.

[0355] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0356] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, an emotion recognition means, and a crowdfunding page generation means, which enable the integrated collection and analysis of data, the generation of specific solutions that take into account user emotions, effective support for fundraising, and the execution and evaluation of projects.

[0357] The "data collection means" is a device or software that collects local situation data from the user's terminal and transmits it to the server.

[0358] "Data analysis tools" are devices or software that analyze collected data using AI algorithms and identify important social issues.

[0359] The "plan generation means" is a device or software that automatically generates specific solutions based on the results of data analysis and emotion data.

[0360] A "fundraising vehicle" is a device or software used to post the generated plan on a crowdfunding site or investment platform and raise the necessary funds.

[0361] "Project Implementation Instrument" means equipment or software that provides resources to support the implementation of the Project on the ground, based on the funds raised.

[0362] A "success case evaluation tool" is a device or software that evaluates the results of a project and records them in a database as success cases.

[0363] An "emotion recognition means" is a device or software that analyzes the user's emotions and feeds the results back into data analysis and plan generation.

[0364] A "crowdfunding page generation means" is a device or software that automatically generates a page for a crowdfunding site based on the generated plan.

[0365] The present invention is an integrated system that supports the improvement of business management in poor areas and brick-and-mortar stores. The system includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, an emotion recognition means, and a crowdfunding page generation means.

[0366] Data collection methods

[0367] Users use devices (smartphones or tablets) to input local situation data and store operation data through a dedicated app. The input data includes sales data, customer feedback, inventory status, and the user's emotional state. The devices then send the data via the Internet to a server, which stores the data in a database.

[0368] Data Analysis Methods

[0369] The server passes the local data and store operation data stored in the database to an AI algorithm for analysis. As a result of the analysis, the most important issues (e.g., declining sales, declining customer satisfaction, etc.) are identified and prioritized. The results of this analysis are used in the subsequent plan generation process.

[0370] emotion recognition means

[0371] The server uses the emotion recognition means to analyze emotions from the user's input data. The analysis results are fed back to the data analysis means and the plan generation means, and a plan is generated or adjusted taking the user's emotional state into consideration.

[0372] Plan Generation Method

[0373] The server automatically generates specific solutions based on the AI ​​analysis results and emotion data, such as suggestions for improving marketing strategies or optimizing inventory management. The generated plans are then displayed on a dashboard for expert review.

[0374] Funding methods

[0375] The server automatically generates a page to post the finalized plan on a crowdfunding site or social impact investment fund platform. This page includes an overview of the plan, the target funding amount, and the expected effects. Users (backers and investors) access the crowdfunding site, review the posted plan, and then make donations or investments to support the project.

[0376] Project implementation method

[0377] The server uses the funds raised to provide the necessary resources (e.g., equipment, materials, etc.) to the local team. The users (local project teams and store operators) use the provided resources to carry out specific project activities (e.g., introducing new equipment, training employees, etc.).

[0378] Success case evaluation tools

[0379] The server periodically evaluates the project results and records them in a database as success stories. This allows other regions and stores facing similar challenges to use these success stories as references for their own projects. Users (project teams and store managers in other regions) can retrieve information from this database and apply it to projects in their own regions and stores.

[0380] Hardware and software used

[0381] Hardware: Server

[0382] Software: Python, Flask, scikit-learn, SQLAlchemy, emotion recognition library

[0383] Examples of prompt statements

[0384] For example, say a cafe owner is experiencing declining sales and customer satisfaction. The owner enters the following data into the app:

[0385] Sales data: January - 100,000 yen, February - 80,000 yen

[0386] Customer feedback: "The coffee is cold" and "The store is crowded"

[0387] Availability: Coffee beans - 50 bags, Milk - 20 bottles

[0388] Operator's emotional state: "I feel stressed"

[0389] Enter this as the prompt.

[0390] Prompt Sentence Examples

[0391] Sales data: January - 100,000 yen, February - 80,000 yen

[0392] Customer feedback: "The coffee is cold" and "The store is crowded"

[0393] Availability: Coffee beans - 50 bags, Milk - 20 bottles

[0394] Operator's emotional state: "I feel stressed"

[0395] Use this data to automatically generate specific plans to improve sales and customer satisfaction.

[0396] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0397] Step 1:

[0398] The terminal collects local situation data and store operation data entered by the user.

[0399] Inputs: Sales data, customer feedback, inventory status, emotional state

[0400] Specific operation: The user enters data using the app, and the entered data is sent to the server by the device.

[0401] Step 2:

[0402] The server stores the received data in a database.

[0403] Input: Data sent from the terminal

[0404] Output: Data stored in the database

[0405] Specific operation: The server automatically stores the received data in the database.

[0406] Step 3:

[0407] The server uses data analysis means to analyze the data stored in the database using AI algorithms.

[0408] Input: Data stored in the database

[0409] Output: Analysis results (identification and prioritization of important issues)

[0410] What happens: The server runs an AI algorithm (e.g., random forest) to identify issues like declining sales or poor customer satisfaction.

[0411] Step 4:

[0412] The server uses an emotion recognition means to analyze the user's emotions.

[0413] Input: User emotional state data

[0414] Output: Recognized emotion data

[0415] Specific operation: The server uses an emotion recognition library to analyze the emotional state and saves the results.

[0416] Step 5:

[0417] The server generates a specific solution using a plan generation means based on the analysis results and emotion data.

[0418] Input: Analysis results, emotion data

[0419] Output: Specific solutions (e.g., marketing strategies, inventory management optimization)

[0420] Specific operation: The server executes the automatic generation algorithm and generates an improvement plan.

[0421] Step 6:

[0422] The server generates a page for posting the generated plan on the crowdfunding site using a crowdfunding page generation means.

[0423] Input: Specific solution

[0424] Output: Crowdfunding page

[0425] Specific operation: The server automatically generates a page outlining the plan, the target funding amount, and the expected results.

[0426] Step 7:

[0427] Users (supporters and investors) access the crowdfunding site, check the posted plans, and make donations or investments to support the project.

[0428] Enter: Crowdfunding page

[0429] Output: Support and investment

[0430] Specific actions: The user reviews the plan and makes a decision to support or invest.

[0431] Step 8:

[0432] The server provides resources using the project execution means based on the raised funds.

[0433] Input: Funds raised

[0434] Output: Resources provided (e.g., equipment, materials)

[0435] Specific actions: The server arranges for the necessary resources and provides them to the local project team and store operators.

[0436] Step 9:

[0437] The server evaluates the results of the project using the success case evaluation means and records the results in a database.

[0438] Input: Project execution data

[0439] Output: Recorded data of success stories

[0440] Specific actions: The server evaluates the project's results and stores them in a database as success stories to help with similar projects.

[0441] Step 10:

[0442] Users (project teams and store operators in other regions) retrieve success stories from the database and apply them to projects in their own regions or stores.

[0443] Input: Success story data retrieved from the database

[0444] Output: Specific application of the project

[0445] Specific actions: Users refer to success stories and implement projects tailored to the characteristics of their local area.

[0446] 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.

[0447] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0448] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0449] [Second embodiment]

[0450] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0451] 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.

[0452] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0453] 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.

[0454] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0455] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0456] 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.

[0457] 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.

[0458] 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 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.

[0459] 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.

[0460] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0461] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0462] This invention is an integrated system for solving various problems in impoverished regions and supporting the creation of sustainable economic activities. This system is composed of means for data collection, data analysis, plan generation, fundraising, project implementation, and evaluation of success cases.

[0463] Data collection methods

[0464] Users use their devices (smartphones or tablets) to input local situation data (e.g., soil data, climate data, crop growth status, etc.) through a dedicated app. This data is sent in real time via the Internet to a server, which then stores the data in a database.

[0465] Data Analysis Methods

[0466] The server passes the local data stored in the database to an AI algorithm for analysis. The results of the analysis identify and prioritize the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.). These analysis results are used in subsequent processes.

[0467] Plan Generation Method

[0468] The server automatically generates specific solutions based on the results of the AI ​​analysis. For example, it may suggest solutions such as using organic fertilizer or introducing a new irrigation system. This generated plan is then reviewed by experts and revised or supplemented as necessary.

[0469] Funding methods

[0470] The server automatically posts the finalized plan on crowdfunding sites and social impact investment fund platforms. Users (backers and investors) access these platforms, review the plan, and then donate or invest in support. The server manages the collected funds and distributes the necessary resources according to each phase of the project.

[0471] Project implementation method

[0472] The server uses the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment, etc.) to local experts and NGOs. The user (local project team) uses the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.).

[0473] Success case evaluation tools

[0474] The server periodically evaluates the project's results and records them in a database as success stories. This allows other impoverished areas facing similar challenges to implement projects based on these success stories. Users (project teams in other areas) can retrieve information from this database and apply it to projects in their own areas.

[0475] Specific examples

[0476] For example, consider the case of implementing an agricultural improvement project in a poor area.

[0477] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, and crop growth status into their terminals. This information is sent to the server and stored in a database.

[0478] 2. The server analyzes the stored data using AI algorithms to identify key problem factors, such as nutrient deficiencies in the soil or insufficient irrigation.

[0479] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer or introducing a new irrigation system. The generated plan is reviewed and revised by experts.

[0480] 4. The server posts the revised plan on the crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the posted plan.

[0481] 5. The server uses the funds raised to provide local farmers with organic fertilizer and arrange resources to install new irrigation systems. The users (local farmers) use these resources to improve agricultural efficiency.

[0482] 6. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[0483] In this way, this system can solve specific problems in poor areas and build sustainable socio-economic activities.

[0484] The processing flow will be explained below.

[0485] Step 1:

[0486] The device (a smartphone or tablet used by the user) uses a dedicated app to input local situation data, including soil condition, climate, and crop growth status. The device then transmits the data to a server via the internet along with a unique identifier.

[0487] Step 2:

[0488] The server stores the data received from the devices in a database that stores all data collected over time and is used for subsequent data analysis.

[0489] Step 3:

[0490] The server preprocesses the data stored in the database. Specifically, it removes incomplete data, corrects outliers, and prepares the data in a format suitable for analysis. The preprocessed data is then saved in the data store for analysis.

[0491] Step 4:

[0492] The server then passes the preprocessed data to an AI algorithm for data analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These results are used in the subsequent plan generation process.

[0493] Step 5:

[0494] The server automatically generates specific solutions based on the AI ​​analysis results, such as using organic fertilizer or introducing a new irrigation system, and displays the resulting plan on a dashboard for expert review.

[0495] Step 6:

[0496] The expert reviews the plan presented by the server through a dashboard and makes corrections or additions as necessary. The expert's feedback is sent to the server, and the final version of the plan is completed.

[0497] Step 7:

[0498] The server generates a page for posting the final plan on a crowdfunding site or social impact investment platform, including a summary of the plan, the funding goal, and the expected impact.

[0499] Step 8:

[0500] Users (backers and investors) access the crowdfunding site, check the posted plans, make donations or investments to support the project, and the funds are sent to the server.

[0501] Step 9:

[0502] The server manages the collected funds and distributes the necessary resources for each phase of the project to local experts and NGOs.

[0503] Step 10:

[0504] Users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.). Activity status is reported successively from the terminals to the server.

[0505] Step 11:

[0506] The server periodically evaluates the progress of the project and records success stories in a database. The evaluation is based on the project's progress, results, and effectiveness of problem-solving.

[0507] Step 12:

[0508] Users (project teams in other regions) access the database of successful cases to obtain information, which they can then apply to projects in their own regions, aiming to solve similar problems.

[0509] These are the specific steps of the program processing of this system, which will effectively solve various issues in poor areas and enable the creation of sustainable socio-economic activities.

[0510] Example 1

[0511] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0512] The purpose of this invention is to comprehensively solve various social and economic issues in poor regions and create sustainable economic activities. Conventional support systems often lack efficient data collection and analysis, appropriate plan creation, fundraising, and on-site implementation based on those plans, making it difficult to provide effective solutions.

[0513] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0514] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project implementation means, a success story evaluation means, a user terminal that inputs local situation data and transmits it to the server in real time, a means by which the server analyzes the data stored in the database and identifies social issues using an AI algorithm, a means for generating specific solutions based on the analysis results and having them reviewed by experts, a means for posting the plan on a crowdfunding site or investment fund and raising funds, a means for providing the necessary resources based on the raised funds and carrying out project activities on site, and a means for evaluating the project results and recording them in the database as success stories. This makes it possible to efficiently collect data tailored to the local situation in impoverished areas, generate optimal solutions based on the analysis results, effectively raise funds and implement projects based on them, and share success stories.

[0515] The "data collection means" is a means for inputting situation data from a local user terminal and transmitting it to a server in real time.

[0516] "Data analysis means" refers to the means by which the server passes data stored in the database to an AI algorithm for analysis.

[0517] The "plan generation means" is a means for automatically generating specific solutions based on the analysis results and having them reviewed by experts.

[0518] A "fundraising method" is a means of raising funds from supporters and investors by posting a finalized plan on a crowdfunding site or a social impact investment fund platform.

[0519] "Project implementation means" refers to the means by which the funds raised are used to provide the necessary resources to local experts and NGOs to carry out specific project activities.

[0520] The "success case evaluation method" is a method for evaluating the results of a project and recording them in a database as success cases.

[0521] "Local situation data" refers to data showing the current state of poor areas, such as soil data, climate data, and crop growth conditions.

[0522] A "user terminal" is a device such as a smartphone or tablet that is used to input local situation data and send it to a server.

[0523] A "server" is a computer system that receives data sent from the field, stores it in a database, analyzes the data, and executes various processes.

[0524] A "database" is a system for storing and managing data received by a server.

[0525] An "AI algorithm" is an algorithm that allows a server to analyze data stored in a database, identify important social issues, and generate solutions.

[0526] A "crowdfunding site" is a platform for raising funds from people who support a specific project via the Internet.

[0527] A "social impact investment fund" is a fund that specializes in investing in projects that have social value.

[0528] "Project activities" are concrete solutions implemented on the ground, such as spreading fertilizer or installing irrigation systems.

[0529] The present invention is an integrated system for solving various issues in impoverished regions and creating sustainable economic activities. This system is composed of data collection means, data analysis means, plan generation means, fundraising means, project implementation means, and success case evaluation means. Specific embodiments of each means are described below.

[0530] Data collection methods

[0531] The user's device (smartphone or tablet) inputs local situation data (e.g., soil data, climate data, crop growth status, etc.) through a dedicated app. This data is sent in real time to a server via the Internet. The server stores this data in a database. Specifically, the device sends the data to the server via an HTTP request, and the server stores the data using a database management system such as MySQL or PostgreSQL.

[0532] Data Analysis Methods

[0533] The server then passes the on-site data stored in the database to an AI algorithm for analysis. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to input the data into an AI model and identify key fault factors, such as soil nutrient deficiencies or insufficient irrigation. The results of this analysis are then used in subsequent processes.

[0534] Plan Generation Method

[0535] The server automatically generates specific solutions based on the results of the AI ​​analysis. For example, it may propose solutions such as using organic fertilizer or installing a new irrigation system. These plans are then reviewed by experts and revised or supplemented as necessary. Specifically, solutions are generated using natural language processing and then sent to the expert review team via email or a web portal.

[0536] Funding methods

[0537] The server automatically posts the finalized plan on crowdfunding sites and social impact investment fund platforms. Users (backers and investors) access these platforms, review the plan, and then donate or invest to support the project. The server manages the collected funds and distributes the necessary resources according to each phase of the project. Specifically, the server uses the platform's API to post plan information and provide donation and investment management functions.

[0538] Project implementation method

[0539] The server uses the funds raised to arrange for the provision of necessary resources to experts and NGOs working on the ground. The users (local project teams) use the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation facilities, etc.). Specifically, the server uses the funds to coordinate with supply suppliers, deliver the appropriate supplies to the ground, and provide specific instructions to the local project teams.

[0540] Success case evaluation tools

[0541] The server periodically evaluates project results and records them in a database as success stories. This allows other impoverished regions facing similar challenges to implement projects based on these success stories. Users (project teams in other regions) can retrieve information from this database and apply it to projects in their own regions. Specifically, they input the results data into an AI evaluation model, and periodically report and update the database.

[0542] Specific examples

[0543] For example, consider an agricultural improvement project in a poor region. Users (local farmers or researchers) input information such as soil condition, climate data, and crop growth status from their devices. This information is sent to a server and stored in a database. The server then analyzes the stored data using an AI algorithm to identify key obstacles, such as soil nutrient deficiencies and insufficient irrigation. The server then generates specific solutions to address the identified obstacles, such as the use of organic fertilizer and the introduction of a new irrigation system. The generated plan is reviewed and revised by experts. The server then posts the revised plan on a crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the published plan. The server uses the raised funds to provide organic fertilizer to local farmers and arrange resources for the introduction of a new irrigation system. The users (local farmers) use these resources to improve agricultural efficiency. The server evaluates the project's results and records them in a database as success stories. Users (farmers in other regions or project teams) can then refer to the success stories and apply them to their own local projects.

[0544] Prompt Sentence Examples

[0545] For example, the following prompt sentence is input to the generative AI model:

[0546] "The local soil data is as follows: pH: 5.5, humidity: 30%, temperature: 28°C. Please suggest the best agricultural improvement measures for this situation."

[0547] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0548] Step 1:

[0549] The user launches the dedicated app and uses a device (smartphone or tablet) to input data on the local situation. The input data includes soil pH value, temperature, humidity, and crop growth status. The input data is sent to the server in real time by the device. Input: Soil pH value, temperature, humidity, crop growth status, etc. Output: Data sent to the server.

[0550] Step 2:

[0551] The server receives the data sent from the terminal and stores it in a database. At this time, the server stores the data using a database management system such as MySQL or PostgreSQL. Input: Data sent by the user through the terminal. Output: Data stored in the database.

[0552] Step 3:

[0553] The server passes the local data stored in the database to an AI algorithm for analysis. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to input the stored data into an AI model and identify important local issues. Analysis results include soil nutrient deficiencies and insufficient irrigation. Input: Data stored in the database. Output: Analysis results (e.g., soil nutrient deficiencies, insufficient irrigation, etc.).

[0554] Step 4:

[0555] The server automatically generates specific solutions based on the analysis results of the AI ​​model. For example, it generates proposals such as using organic fertilizer or introducing a new irrigation system. This generated plan is sent to experts via email or a web portal. Input: Analysis results. Output: Automatically generated solution plan.

[0556] Step 5:

[0557] The expert reviews the plan sent from the server and corrects and completes it as necessary. The corrected plan is sent back to the server. Input: Generated solution plan. Output: Reviewed and corrected plan.

[0558] Step 6:

[0559] The server automatically posts the finalized plan on the crowdfunding site or social impact investment fund platform. The server posts the plan information using the platform's API. Input: Reviewed and revised plan. Output: Posting on the crowdfunding site or investment fund.

[0560] Step 7:

[0561] Users (backers and investors) access crowdfunding sites and investment funds, check the posted plans, and then donate or invest to support the project. The server manages the funds and distributes resources according to each phase of the project. Input: Funds collected through crowdfunding sites and investment funds. Output: Distribution of required resources.

[0562] Step 8:

[0563] The server uses the raised funds to arrange for the provision of necessary resources to experts and NGOs working on the ground. The users (local project teams) use the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation facilities, etc.). Input: Funds raised. Output: Resources provided to the ground.

[0564] Step 9:

[0565] The server periodically evaluates the project's results and records them in a database as success stories. This results data is applied to similar projects in other poor areas. Users (project teams in other areas) retrieve the success stories from the database and apply them to projects in their own areas. Input: Project results data. Output: Success stories recorded in the database.

[0566] (Application example 1)

[0567] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0568] Traditional factory operations face many challenges, including excessive energy consumption and inefficient production processes. These challenges increase factory operating costs and contribute to a greater environmental impact. Furthermore, the process of proposing improvements and raising funds to resolve these issues is often done manually, requiring time and effort. Therefore, there is a need for a system that can collect and analyze data in real time and provide quick and effective improvement proposals.

[0569] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0570] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, a means for collecting data through a factory sensor system and analyzing the data in real time, a means for automatically generating factory improvement proposals based on the analysis results, a means for raising funds in cooperation with a crowdfunding platform, and a means for implementing a factory improvement project using the raised funds. This makes it possible to quickly and effectively solve problems while improving the operational efficiency of the factory.

[0571] A "data collection means" is a device that has the function of acquiring data in real time from sensors installed within the factory and sending it to a server.

[0572] The "data analysis means" is a device that uses AI algorithms to analyze collected data and identify problems related to factory operations.

[0573] The "plan generation means" is a device that automatically generates improvement proposals for the factory based on the analysis results of the data analysis means.

[0574] The "fundraising instrument" is a device that automatically posts the generated improvement proposals on crowdfunding platforms and social impact investment funds, thereby raising funds.

[0575] The "project implementation means" is a device that uses the procured funds to arrange resources for implementing the factory improvement project and carries out specific improvement activities.

[0576] The "success case evaluation means" is a device that has the function of evaluating the results of a project and recording them in a database as success cases.

[0577] A "factory sensor system" is a group of sensors that collect data from various devices and processes within a factory.

[0578] A "means for real-time analysis" is a device that has the ability to instantly pass collected data to an AI algorithm for analysis.

[0579] The "means for automatic generation" is a device that has the function of automatically creating optimal improvement proposals based on the analysis results of an AI algorithm.

[0580] A "crowdfunding platform" is a service for raising funds online, a website where project details are published and funding is sought from supporters.

[0581] The "means for implementing improvement projects" refers to a device that has the function of arranging the necessary resources after fundraising and actually carrying out improvement activities in the factory.

[0582] The present invention is an integrated system for supporting efficiency in factory operations, which includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, a factory sensor system, a real-time analysis means, an automatic generation means, a crowdfunding platform, and a means for implementing an improvement project.

[0583] Hardware and Software Configuration

[0584] Hardware

[0585] Factory sensor system: A set of sensors used to collect data from each device and process in the factory. For example, temperature sensors, humidity sensors, vibration sensors, etc. are used.

[0586] Server: A central computer that handles data collection, analysis, plan generation, fundraising management, project execution, and success evaluation.

[0587] Smart glasses or terminals: Used for real-time monitoring and data entry on-site.

[0588] software

[0589] Python: A programming language used to build entire programs and process data.

[0590] Sci-kit Learn: A machine learning library for data analysis.

[0591] Requests: Used for API integration with crowdfunding platforms.

[0592] Generative AI model: An AI model that generates optimal plans from data analysis results.

[0593] Data processing and calculation

[0594] 1. Data Collection:

[0595] The server collects data in real time from the factory's sensor system. The sensors acquire various data (temperature, humidity, vibration, etc.) within the factory, and this data is sent to the server via smart glasses or terminals.

[0596] 2. Data Analysis:

[0597] The server analyzes the collected data using AI algorithms powered by Sci-kit Learn, which identifies key issues in factory operations (such as excessive energy consumption and production inefficiencies).

[0598] 3. Plan Generation:

[0599] Based on the analysis results, the server uses a generative AI model to automatically generate improvement suggestions, such as specific measures to reduce energy consumption or optimize production processes.

[0600] 4. Funding:

[0601] The server then posts the automatically generated plan on the crowdfunding platform, where investors and supporters can review the plan and provide funding.

[0602] 5. Project execution:

[0603] The server uses the funds raised to implement on-site factory improvement projects, specifically converting the funds into resources (such as new equipment and machinery) and supplying them to the factory.

[0604] 6. Success Case Evaluation:

[0605] The server evaluates the project results and records them in a database as success stories that can be used as references at other factories to solve similar problems.

[0606] Examples of concrete examples and prompts

[0607] Specific examples

[0608] If Factory A has a problem with high energy consumption, sensors collect real-time information on factory temperatures and machine operation, and send the data to a server. The server then analyzes the data using an AI algorithm to identify the causes of energy consumption. Based on the analysis results, improvement proposals are generated, such as replacing machines with more energy-efficient ones or introducing a new energy management system. These proposals are then posted on a crowdfunding platform, and once funds are raised, actual improvement activities are carried out on-site.

[0609] Prompt Sentence Examples

[0610] "Please suggest the best plan to reduce energy consumption in the factory. The sensor data is as follows: {sensor_data}"

[0611] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0612] Step 1:

[0613] Data collection

[0614] The server receives data in real time from sensors installed in the factory, including information on temperature, humidity, vibration, operating hours, etc. The input sensor data is sent to the server and stored in a database.

[0615] Step 2:

[0616] Data analysis

[0617] The server passes the collected data to an AI algorithm for analysis. Sci-kit Learn is used to apply machine learning models to identify problems in factory operations (e.g., excessive energy consumption, inefficient processes, etc.). The input is sensor data stored in a database, and the output is the identified problems as a result of the analysis.

[0618] Step 3:

[0619] Plan Generation

[0620] The server uses a generative AI model based on the results of data analysis to automatically generate improvement suggestions, including energy-saving measures, production process optimization, resource reallocation, etc. The input is the results of data analysis, and the output is detailed improvement suggestions.

[0621] Step 4:

[0622] Crowdfunding

[0623] The server automatically posts the generated improvement proposal on the crowdfunding platform. Specifically, it uses the Requests library to submit the plan in the appropriate format. The input is the generated improvement proposal, and the output is the project information posted on the crowdfunding platform.

[0624] Step 5:

[0625] Funding

[0626] Users (investors and supporters) review improvement proposals posted on the crowdfunding platform and provide funding. The server manages the collected funds and distributes resources according to the required phase. The input is funding information from the crowdfunding platform, and the output is the success of fundraising.

[0627] Step 6:

[0628] Project Execution

[0629] The server uses the raised funds to arrange resources to implement factory improvement projects. Resources can include new machinery, energy efficiency devices, improvement supplies, etc. The inputs are the successful fundraising and required resource information, and the outputs are the arranged resources and the implemented improvement projects.

[0630] Step 7:

[0631] Success Case Evaluation

[0632] The server evaluates the project results and records them in a database as success stories. It also provides the evaluation results to other factories as reference information. The input is the project execution result data, and the output is the evaluated success stories and their records.

[0633] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0634] This invention is an integrated system for solving social issues in impoverished areas and building sustainable economic activities. This system consists of a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a means for evaluating success cases, and an emotion engine that recognizes the user's emotions.

[0635] Data collection methods

[0636] The user's device (smartphone or tablet) inputs local situation data through a dedicated app. The input data includes soil condition, climate, crop growth status, and the user's emotions. The device then sends the data via the Internet to a server, which then stores the data in a database.

[0637] Data Analysis Methods

[0638] The server then passes the local data stored in the database to an AI algorithm for analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These analysis results are then used in the subsequent plan generation process.

[0639] Emotion Engine

[0640] The server uses an emotion engine to analyze data received from the local user terminal and recognize the user's emotion. The recognized emotion data is fed back to the data analysis means and plan generation means, which then generate and adjust plans that take the user's emotional state into consideration.

[0641] Plan Generation Method

[0642] The server automatically generates specific solutions based on the AI ​​analysis results and emotional data. For example, it may suggest solutions such as using organic fertilizer or installing a new irrigation system. The generated plan is displayed on a dashboard for expert review.

[0643] Funding methods

[0644] The server generates a page for posting the finalized plan on a crowdfunding site or social impact investment fund platform. This page includes an overview of the plan, the target funding amount, the expected impact, etc. Users (backers and investors) access the crowdfunding site, review the posted plan, and then make donations or investments to support the project.

[0645] Project implementation method

[0646] The server uses the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment, etc.) to local experts and NGOs. The user (local project team) uses the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.).

[0647] Success case evaluation tools

[0648] The server periodically evaluates the project's results and records them in a database as success stories. This allows other impoverished areas facing similar challenges to implement projects based on these success stories. Users (project teams in other areas) can retrieve information from this database and apply it to projects in their own areas.

[0649] Specific examples

[0650] For example, consider the case of implementing an agricultural improvement project in a poor area.

[0651] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, crop growth status, and emotional state into their terminals. This information is sent to the server and stored in a database.

[0652] 2. The server analyzes the stored data using AI algorithms and an emotion engine to identify key fault factors, such as soil nutrient deficiency, insufficient irrigation, and the user's emotional state.

[0653] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer, introducing a new irrigation system, or support measures that take the user's emotional state into account. The generated plans are then reviewed and revised by experts.

[0654] 4. The server posts the revised plan on the crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the posted plan.

[0655] 5. The server uses the funds raised to provide local farmers with organic fertilizer and arrange resources to install new irrigation systems. The users (local farmers) use these resources to improve the efficiency and quality of their farming.

[0656] 6. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[0657] In this way, by solving specific problems in poor areas through this system and taking into account the emotional state of the user, it is possible to build more effective and sustainable socio-economic activities.

[0658] The processing flow will be explained below.

[0659] Step 1:

[0660] The device (a smartphone or tablet used by the user) inputs local situation data using a dedicated app. The input data includes soil condition, climate, crop growth status, and the user's emotional state. The device then transmits the data to a server via the Internet.

[0661] Step 2:

[0662] The server stores the data received from the devices in a database that stores all the data collected over time and stores the data for subsequent analysis and planning.

[0663] Step 3:

[0664] The server preprocesses the data stored in the database. Specifically, it removes incomplete data, corrects outliers, and prepares the data in a format suitable for analysis. The preprocessed data is then stored in the data store for analysis.

[0665] Step 4:

[0666] The server then passes the preprocessed data to an AI algorithm for data analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These results are used in the subsequent plan generation process.

[0667] Step 5:

[0668] The server analyzes the user's emotional data using an emotion engine. It detects the user's emotional state in real time based on their input and usage, and collects the data. The emotional data is then integrated with the data analysis results.

[0669] Step 6:

[0670] The server automatically generates specific solutions based on the data analysis results and emotional data. For example, these could include using organic fertilizers or installing a new irrigation system, as well as psychological support and training sessions that take the user's emotional state into account. The generated plans are then displayed on a dashboard for expert review.

[0671] Step 7:

[0672] The expert reviews the plan presented by the server through a dashboard and makes corrections or additions as necessary. The expert's feedback is sent to the server, and the final version of the plan is completed.

[0673] Step 8:

[0674] The server generates a page for posting the final plan on a crowdfunding site or social impact investment platform, including a summary of the plan, the funding goal, and the expected impact.

[0675] Step 9:

[0676] Users (backers and investors) access the crowdfunding site, check the posted plans, make donations or investments to support the project, and the funds are sent to the server.

[0677] Step 10:

[0678] The server manages the collected funds and distributes the necessary resources for each phase of the project, including fertilizer, irrigation equipment, educational materials, psychological support resources, etc. The funds are distributed to experts and NGOs working on the ground.

[0679] Step 11:

[0680] Users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, conducting psychological support sessions, etc.). Activity status is reported sequentially from the terminals to the server.

[0681] Step 12:

[0682] The server periodically evaluates the progress of the project and records it in a database as a success story. This evaluation is based on the project's progress, results, changes in user emotions, and the effectiveness of problem solving.

[0683] Step 13:

[0684] The server will publish a database of success stories and make them accessible to users (project teams in other regions). Success stories include specific solutions, implementation methods, results, and changes in user emotions.

[0685] Step 14:

[0686] Users (project teams in other regions) can obtain information from the database of success stories and apply it to projects in their own regions. Based on the information obtained, they can carry out projects aiming to solve similar problems.

[0687] These are the specific steps of the program processing of this system, which can effectively solve various problems in poor areas and, by taking into account the emotional state of the user, can build more effective and sustainable socio-economic activities.

[0688] Example 2

[0689] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0690] Building sustainable socioeconomic activities in impoverished areas requires a detailed understanding of specific local issues and the rapid and efficient provision of appropriate solutions based on that understanding. However, conventional methods lack an integrated process, from data collection and analysis to solution proposals, fundraising, implementation, and outcome evaluation, resulting in time-consuming information sharing and decision-making at each stage. Furthermore, they fail to take into account the emotional state of users, limiting the means for maximizing project effectiveness. Therefore, the present invention aims to provide an integrated system that solves these issues.

[0691] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data analysis means, an emotion recognition means, a plan generation means, a fundraising means, a project execution means, and a success case evaluation means. This makes it possible to quickly and efficiently identify specific local issues and provide appropriate solutions based on the analysis results and the user's emotional state. Furthermore, by managing a series of processes from fundraising to project execution and outcome evaluation in an integrated manner, information sharing and decision-making can be accelerated, effectively promoting the establishment of sustainable socioeconomic activities in impoverished areas.

[0692] The "data collection means" is a device or system for receiving data from information processing devices used by local users.

[0693] A "data analysis means" is a device or system for analyzing collected data using machine learning algorithms.

[0694] An "emotion recognizer" is a device or system for identifying a user's emotional state from collected data.

[0695] The "plan generation means" is a device or system for generating specific solutions based on the data analysis results and emotion data.

[0696] A "funding vehicle" is a device or system for generating information for posting a generated plan on a funding platform.

[0697] A "Project Execution Vehicle" is a device or system that utilizes raised funds to provide resources and execute a project.

[0698] A "success case evaluation tool" is a device or system for evaluating the results of a project and recording the results in a database.

[0699] This invention is an integrated system for solving social issues in impoverished areas and building sustainable economic activities. This system consists of data collection means, data analysis means, emotion recognition means, plan generation means, fundraising means, project implementation means, and success case evaluation means.

[0700] Data collection methods

[0701] Users input data about their local conditions through a dedicated app installed on their smartphone or tablet. The data includes soil pH, temperature, precipitation, crop growth status, and the user's emotional state. The device then transmits this data to a server in real time via an internet connection.

[0702] Data Analysis Methods

[0703] The server stores the received data in a database (e.g., MySQL, PostgreSQL), then passes the data to an AI algorithm (e.g., TensorFlow, PyTorch) for analysis. Specific analysis results include determining the nutrient status of the soil and assessing the risk of drought.

[0704] emotion recognition means

[0705] The server uses an emotion engine (e.g., OpenAI's GPT-3) to recognize the user's emotions from the received data. The emotion data (e.g., anxiety, hope, irritation) is stored in a database and subsequent processing is based on it.

[0706] Plan Generation Method

[0707] The server automatically generates specific solutions based on the AI ​​analysis results and emotion data. The generated plan suggests measures such as using organic fertilizer, installing a new irrigation system, and providing psychological support. The plan is displayed on a dashboard (e.g., PowerBI) and awaits expert review.

[0708] Funding methods

[0709] The server automatically generates a page for posting the approved plan on a crowdfunding site (e.g., Kickstarter) or a social impact investment fund platform, detailing the plan's outline, target funding amount, and expected social impact.

[0710] Project implementation method

[0711] The server uses the funds raised to arrange for the provision of necessary resources (e.g., fertilizer, irrigation equipment) to local experts and NGOs. The provision of resources includes coordination with logistics centers and local vendors. The users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment).

[0712] Success case evaluation tools

[0713] The server periodically evaluates the project's performance and records the results in a database. Evaluation criteria include profitability, sustainability, and social impact. Success stories can also be used as reference for projects in other regions.

[0714] Specific examples

[0715] For example, consider the case of implementing an agricultural improvement project in a poor area.

[0716] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, crop growth status, and emotional state into their devices. This information is sent to the server and stored in a database.

[0717] 2. The server analyzes the stored data using AI algorithms and an emotion engine to identify key fault factors, such as soil nutrient deficiency, insufficient irrigation, and the user's emotional state.

[0718] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer, introducing a new irrigation system, or support measures that take the user's emotional state into account. The generated plans are reviewed by experts and posted on a crowdfunding site.

[0719] 4. Users (backers and investors) access the crowdfunding site and provide funding.

[0720] 5. The server will use the funds raised to provide organic fertilizer to local farmers and arrange resources to install a new irrigation system.

[0721] 6. Users (local farmers) use these resources to improve the efficiency and quality of their farming.

[0722] 7. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[0723] Prompt Sentence Examples

[0724] Use the following data to generate a plan for improving agriculture in a poor area.

[0725] Soil condition: Highly acidic

[0726] Climate data: Short rainy season

[0727] Crop growth status: Poor growth

[0728] User Emotion: Anxiety

[0729] Please explain your specific solution and why.

[0730] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0731] Step 1: Data collection

[0732] Users input local situation data, such as soil pH, temperature, precipitation, crop growth status, and the user's emotional state, through a dedicated app installed on a smartphone or tablet.

[0733] Input: Local condition data (e.g. soil pH, temperature, precipitation, crop condition, emotional state)

[0734] Output: Data stored on a smartphone or tablet

[0735] Step 2: Send data

[0736] The terminal transmits the input data to the server in real time via the Internet. During transmission, encrypted communication (e.g., SSL / TLS) is used to maintain data integrity.

[0737] Input: Data stored on a smartphone or tablet

[0738] Output: Data sent to the server

[0739] Step 3: Save Data

[0740] The server stores the received data in a database (e.g., MySQL, PostgreSQL). When storing the data, it also backs up the data to prevent data loss. It also monitors the capacity of the data storage.

[0741] Input: Received data

[0742] Output: Data stored in the database

[0743] Step 4: Data analysis

[0744] The server then passes the stored data to AI algorithms (e.g., TensorFlow, PyTorch) for analysis. The analysis includes determining the nutrient status of the soil and assessing the risk of drought. The most important social issues are identified and prioritized.

[0745] Input: Data stored in a database

[0746] Output: Analysis results (e.g., soil nutrient deficiency, insufficient irrigation)

[0747] Step 5: Emotion Recognition

[0748] The server uses an emotion engine (e.g., OpenAI's GPT-3) to recognize the user's emotion from the received data. The recognized emotion data is stored in a database and subsequent processing is based on it.

[0749] Input: Local situation data stored in a database

[0750] Output: Recognized emotion data

[0751] Step 6: Generate a plan

[0752] The server automatically generates specific solutions based on the AI ​​analysis results and emotional data. The generated plan suggests measures such as using organic fertilizer, installing a new irrigation system, and providing psychological support. The plan is displayed on a dashboard (e.g., PowerBI).

[0753] Input: Analysis results and emotion data

[0754] Output: Auto-generated solution (plan)

[0755] Step 7: Expert Review

[0756] The expert reviews the plan generated by the server via a dashboard, providing corrections or additional advice as necessary, and the reviewed plan moves on to the next step after receiving final approval.

[0757] Input: Auto-generated solution (plan)

[0758] Output: Reviewed and revised plan

[0759] Step 8: Generate a Fundraising Page

[0760] The server automatically generates a page for posting the approved plan on a crowdfunding site (e.g., Kickstarter) or a social impact investment fund platform, detailing the plan's outline, target funding amount, and expected social impact.

[0761] Input: Reviewed and revised plan

[0762] Output: Crowdfunding page

[0763] Step 9: Funding

[0764] Users (backers and investors) access crowdfunding sites, check the plans posted, and then donate or invest in support via credit card or digital payment systems (e.g., PayPal).

[0765] Input: Crowdfunding page

[0766] Output: Funds provided

[0767] Step 10: Resource Arrangements

[0768] Using the funds raised, the server will provide necessary resources (e.g., fertilizer, irrigation equipment) to local experts and NGOs, including liaising with logistics centers and local vendors.

[0769] Input: Funds Provided

[0770] Output: Arranged resources

[0771] Step 11: Project execution

[0772] Users (local project teams) utilize the provided resources to carry out specific project activities, such as spreading fertilizer, installing irrigation systems, and implementing educational programs.

[0773] Input: Arranged resource

[0774] Output: Executed projects

[0775] Step 12: Success Case Evaluation

[0776] The server periodically evaluates the project's performance and records the results in a database. Evaluation criteria include profitability, sustainability, and social impact. Success stories can also be used as reference for projects in other regions.

[0777] Input: Data of the executed project

[0778] Output: Database of evaluation results and success stories

[0779] (Application example 2)

[0780] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0781] Traditional operations in poor areas and brick-and-mortar stores require efficient data collection and analysis, solution generation, fundraising, and project implementation, but there is a lack of systems that handle these elements in an integrated manner. Furthermore, while it is important to reflect the emotions and specific needs of users in solving local issues, few systems have this functionality. This makes it difficult to generate specific solutions tailored to local conditions and to raise funds effectively.

[0782] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0783] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, an emotion recognition means, and a crowdfunding page generation means, which enable the integrated collection and analysis of data, the generation of specific solutions that take into account user emotions, effective support for fundraising, and the execution and evaluation of projects.

[0784] The "data collection means" is a device or software that collects local situation data from the user's terminal and transmits it to the server.

[0785] "Data analysis tools" are devices or software that analyze collected data using AI algorithms and identify important social issues.

[0786] The "plan generation means" is a device or software that automatically generates specific solutions based on the results of data analysis and emotion data.

[0787] A "fundraising vehicle" is a device or software used to post the generated plan on a crowdfunding site or investment platform and raise the necessary funds.

[0788] "Project Implementation Instrument" means equipment or software that provides resources to support the implementation of the Project on the ground, based on the funds raised.

[0789] A "success case evaluation tool" is a device or software that evaluates the results of a project and records them in a database as success cases.

[0790] An "emotion recognition means" is a device or software that analyzes the user's emotions and feeds the results back into data analysis and plan generation.

[0791] A "crowdfunding page generation means" is a device or software that automatically generates a page for a crowdfunding site based on the generated plan.

[0792] The present invention is an integrated system that supports the improvement of business management in poor areas and brick-and-mortar stores. The system includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, an emotion recognition means, and a crowdfunding page generation means.

[0793] Data collection methods

[0794] Users use devices (smartphones or tablets) to input local situation data and store operation data through a dedicated app. The input data includes sales data, customer feedback, inventory status, and the user's emotional state. The devices then send the data via the Internet to a server, which stores the data in a database.

[0795] Data Analysis Methods

[0796] The server passes the local data and store operation data stored in the database to an AI algorithm for analysis. As a result of the analysis, the most important issues (e.g., declining sales, declining customer satisfaction, etc.) are identified and prioritized. The results of this analysis are used in the subsequent plan generation process.

[0797] emotion recognition means

[0798] The server uses the emotion recognition means to analyze emotions from the user's input data. The analysis results are fed back to the data analysis means and the plan generation means, and a plan is generated or adjusted taking the user's emotional state into consideration.

[0799] Plan Generation Method

[0800] The server automatically generates specific solutions based on the AI ​​analysis results and emotion data, such as suggestions for improving marketing strategies or optimizing inventory management. The generated plans are then displayed on a dashboard for expert review.

[0801] Funding methods

[0802] The server automatically generates a page to post the finalized plan on a crowdfunding site or social impact investment fund platform. This page includes an overview of the plan, the target funding amount, and the expected effects. Users (backers and investors) access the crowdfunding site, review the posted plan, and then make donations or investments to support the project.

[0803] Project implementation method

[0804] The server uses the funds raised to provide the necessary resources (e.g., equipment, materials, etc.) to the local team. The users (local project teams and store operators) use the provided resources to carry out specific project activities (e.g., introducing new equipment, training employees, etc.).

[0805] Success case evaluation tools

[0806] The server periodically evaluates the project results and records them in a database as success stories. This allows other regions and stores facing similar challenges to use these success stories as references for their own projects. Users (project teams and store managers in other regions) can retrieve information from this database and apply it to projects in their own regions and stores.

[0807] Hardware and software used

[0808] Hardware: Server

[0809] Software: Python, Flask, scikit-learn, SQLAlchemy, emotion recognition library

[0810] Examples of prompt statements

[0811] For example, say a cafe owner is experiencing declining sales and customer satisfaction. The owner enters the following data into the app:

[0812] Sales data: January - 100,000 yen, February - 80,000 yen

[0813] Customer feedback: "The coffee is cold" and "The store is crowded"

[0814] Availability: Coffee beans - 50 bags, Milk - 20 bottles

[0815] Operator's emotional state: "I feel stressed"

[0816] Enter this as the prompt.

[0817] Prompt Sentence Examples

[0818] Sales data: January - 100,000 yen, February - 80,000 yen

[0819] Customer feedback: "The coffee is cold" and "The store is crowded"

[0820] Availability: Coffee beans - 50 bags, Milk - 20 bottles

[0821] Operator's emotional state: "I feel stressed"

[0822] Use this data to automatically generate specific plans to improve sales and customer satisfaction.

[0823] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0824] Step 1:

[0825] The terminal collects local situation data and store operation data entered by the user.

[0826] Inputs: Sales data, customer feedback, inventory status, emotional state

[0827] Specific operation: The user enters data using the app, and the entered data is sent to the server by the device.

[0828] Step 2:

[0829] The server stores the received data in a database.

[0830] Input: Data sent from the terminal

[0831] Output: Data stored in the database

[0832] Specific operation: The server automatically stores the received data in the database.

[0833] Step 3:

[0834] The server uses data analysis means to analyze the data stored in the database using AI algorithms.

[0835] Input: Data stored in the database

[0836] Output: Analysis results (identification and prioritization of important issues)

[0837] What happens: The server runs an AI algorithm (e.g., random forest) to identify issues like declining sales or poor customer satisfaction.

[0838] Step 4:

[0839] The server uses an emotion recognition means to analyze the user's emotions.

[0840] Input: User emotional state data

[0841] Output: Recognized emotion data

[0842] Specific operation: The server uses an emotion recognition library to analyze the emotional state and saves the results.

[0843] Step 5:

[0844] The server generates a specific solution using a plan generation means based on the analysis results and emotion data.

[0845] Input: Analysis results, emotion data

[0846] Output: Specific solutions (e.g., marketing strategies, inventory management optimization)

[0847] Specific operation: The server executes the automatic generation algorithm and generates an improvement plan.

[0848] Step 6:

[0849] The server generates a page for posting the generated plan on the crowdfunding site using a crowdfunding page generation means.

[0850] Input: Specific solution

[0851] Output: Crowdfunding page

[0852] Specific operation: The server automatically generates a page outlining the plan, the target funding amount, and the expected results.

[0853] Step 7:

[0854] Users (supporters and investors) access the crowdfunding site, check the posted plans, and make donations or investments to support the project.

[0855] Enter: Crowdfunding page

[0856] Output: Support and investment

[0857] Specific actions: The user reviews the plan and makes a decision to support or invest.

[0858] Step 8:

[0859] The server provides resources using the project execution means based on the raised funds.

[0860] Input: Funds raised

[0861] Output: Resources provided (e.g., equipment, materials)

[0862] Specific actions: The server arranges for the necessary resources and provides them to the local project team and store operators.

[0863] Step 9:

[0864] The server evaluates the results of the project using the success case evaluation means and records the results in a database.

[0865] Input: Project execution data

[0866] Output: Recorded data of success stories

[0867] Specific actions: The server evaluates the project's results and stores them in a database as success stories to help with similar projects.

[0868] Step 10:

[0869] Users (project teams and store operators in other regions) retrieve success stories from the database and apply them to projects in their own regions or stores.

[0870] Input: Success story data retrieved from the database

[0871] Output: Specific application of the project

[0872] Specific actions: Users refer to success stories and implement projects tailored to the characteristics of their local area.

[0873] 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.

[0874] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0875] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0876] [Third embodiment]

[0877] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0878] 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.

[0879] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0880] 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.

[0881] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0882] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0883] 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.

[0884] 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.

[0885] 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 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.

[0886] 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.

[0887] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0888] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0889] This invention is an integrated system for solving various problems in impoverished regions and supporting the creation of sustainable economic activities. This system is composed of means for data collection, data analysis, plan generation, fundraising, project implementation, and evaluation of success cases.

[0890] Data collection methods

[0891] Users use their devices (smartphones or tablets) to input local situation data (e.g., soil data, climate data, crop growth status, etc.) through a dedicated app. This data is sent in real time via the Internet to a server, which then stores the data in a database.

[0892] Data Analysis Methods

[0893] The server passes the local data stored in the database to an AI algorithm for analysis. The results of the analysis identify and prioritize the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.). These analysis results are used in subsequent processes.

[0894] Plan Generation Method

[0895] The server automatically generates specific solutions based on the results of the AI ​​analysis. For example, it may suggest solutions such as using organic fertilizer or introducing a new irrigation system. This generated plan is then reviewed by experts and revised or supplemented as necessary.

[0896] Funding methods

[0897] The server automatically posts the finalized plan on crowdfunding sites and social impact investment fund platforms. Users (backers and investors) access these platforms, review the plan, and then donate or invest in support. The server manages the collected funds and distributes the necessary resources according to each phase of the project.

[0898] Project implementation method

[0899] The server uses the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment, etc.) to local experts and NGOs. The user (local project team) uses the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.).

[0900] Success case evaluation tools

[0901] The server periodically evaluates the project's results and records them in a database as success stories. This allows other impoverished areas facing similar challenges to implement projects based on these success stories. Users (project teams in other areas) can retrieve information from this database and apply it to projects in their own areas.

[0902] Specific examples

[0903] For example, consider the case of implementing an agricultural improvement project in a poor area.

[0904] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, and crop growth status into their terminals. This information is sent to the server and stored in a database.

[0905] 2. The server analyzes the stored data using AI algorithms to identify key problem factors, such as nutrient deficiencies in the soil or insufficient irrigation.

[0906] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer or introducing a new irrigation system. The generated plan is reviewed and revised by experts.

[0907] 4. The server posts the revised plan on the crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the posted plan.

[0908] 5. The server uses the funds raised to provide local farmers with organic fertilizer and arrange resources to install new irrigation systems. The users (local farmers) use these resources to improve agricultural efficiency.

[0909] 6. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[0910] In this way, this system can solve specific problems in poor areas and build sustainable socio-economic activities.

[0911] The processing flow will be explained below.

[0912] Step 1:

[0913] The device (a smartphone or tablet used by the user) uses a dedicated app to input local situation data, including soil condition, climate, and crop growth status. The device then transmits the data to a server via the internet along with a unique identifier.

[0914] Step 2:

[0915] The server stores the data received from the devices in a database that stores all data collected over time and is used for subsequent data analysis.

[0916] Step 3:

[0917] The server preprocesses the data stored in the database. Specifically, it removes incomplete data, corrects outliers, and prepares the data in a format suitable for analysis. The preprocessed data is then saved in the data store for analysis.

[0918] Step 4:

[0919] The server then passes the preprocessed data to an AI algorithm for data analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These results are used in the subsequent plan generation process.

[0920] Step 5:

[0921] The server automatically generates specific solutions based on the AI ​​analysis results, such as using organic fertilizer or introducing a new irrigation system, and displays the resulting plan on a dashboard for expert review.

[0922] Step 6:

[0923] The expert reviews the plan presented by the server through a dashboard and makes corrections or additions as necessary. The expert's feedback is sent to the server, and the final version of the plan is completed.

[0924] Step 7:

[0925] The server generates a page for posting the final plan on a crowdfunding site or social impact investment platform, including a summary of the plan, the funding goal, and the expected impact.

[0926] Step 8:

[0927] Users (backers and investors) access the crowdfunding site, check the posted plans, make donations or investments to support the project, and the funds are sent to the server.

[0928] Step 9:

[0929] The server manages the collected funds and distributes the necessary resources for each phase of the project to local experts and NGOs.

[0930] Step 10:

[0931] Users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.). Activity status is reported successively from the terminals to the server.

[0932] Step 11:

[0933] The server periodically evaluates the progress of the project and records success stories in a database. The evaluation is based on the project's progress, results, and effectiveness of problem-solving.

[0934] Step 12:

[0935] Users (project teams in other regions) access the database of successful cases to obtain information, which they can then apply to projects in their own regions, aiming to solve similar problems.

[0936] These are the specific steps of the program processing of this system, which will effectively solve various issues in poor areas and enable the creation of sustainable socio-economic activities.

[0937] Example 1

[0938] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0939] The purpose of this invention is to comprehensively solve various social and economic issues in poor regions and create sustainable economic activities. Conventional support systems often lack efficient data collection and analysis, appropriate plan creation, fundraising, and on-site implementation based on those plans, making it difficult to provide effective solutions.

[0940] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0941] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project implementation means, a success story evaluation means, a user terminal that inputs local situation data and transmits it to the server in real time, a means by which the server analyzes the data stored in the database and identifies social issues using an AI algorithm, a means for generating specific solutions based on the analysis results and having them reviewed by experts, a means for posting the plan on a crowdfunding site or investment fund and raising funds, a means for providing the necessary resources based on the raised funds and carrying out project activities on site, and a means for evaluating the project results and recording them in the database as success stories. This makes it possible to efficiently collect data tailored to the local situation in impoverished areas, generate optimal solutions based on the analysis results, effectively raise funds and implement projects based on them, and share success stories.

[0942] The "data collection means" is a means for inputting situation data from a local user terminal and transmitting it to a server in real time.

[0943] "Data analysis means" refers to the means by which the server passes data stored in the database to an AI algorithm for analysis.

[0944] The "plan generation means" is a means for automatically generating specific solutions based on the analysis results and having them reviewed by experts.

[0945] A "fundraising method" is a means of raising funds from supporters and investors by posting a finalized plan on a crowdfunding site or a social impact investment fund platform.

[0946] "Project implementation means" refers to the means by which the funds raised are used to provide the necessary resources to local experts and NGOs to carry out specific project activities.

[0947] The "success case evaluation method" is a method for evaluating the results of a project and recording them in a database as success cases.

[0948] "Local situation data" refers to data showing the current state of poor areas, such as soil data, climate data, and crop growth conditions.

[0949] A "user terminal" is a device such as a smartphone or tablet that is used to input local situation data and send it to a server.

[0950] A "server" is a computer system that receives data sent from the field, stores it in a database, analyzes the data, and executes various processes.

[0951] A "database" is a system for storing and managing data received by a server.

[0952] An "AI algorithm" is an algorithm that allows a server to analyze data stored in a database, identify important social issues, and generate solutions.

[0953] A "crowdfunding site" is a platform for raising funds from people who support a specific project via the Internet.

[0954] A "social impact investment fund" is a fund that specializes in investing in projects that have social value.

[0955] "Project activities" are concrete solutions implemented on the ground, such as spreading fertilizer or installing irrigation systems.

[0956] The present invention is an integrated system for solving various issues in impoverished regions and creating sustainable economic activities. This system is composed of data collection means, data analysis means, plan generation means, fundraising means, project implementation means, and success case evaluation means. Specific embodiments of each means are described below.

[0957] Data collection methods

[0958] The user's device (smartphone or tablet) inputs local situation data (e.g., soil data, climate data, crop growth status, etc.) through a dedicated app. This data is sent in real time to a server via the Internet. The server stores this data in a database. Specifically, the device sends the data to the server via an HTTP request, and the server stores the data using a database management system such as MySQL or PostgreSQL.

[0959] Data Analysis Methods

[0960] The server then passes the on-site data stored in the database to an AI algorithm for analysis. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to input the data into an AI model and identify key fault factors, such as soil nutrient deficiencies or insufficient irrigation. The results of this analysis are then used in subsequent processes.

[0961] Plan Generation Method

[0962] The server automatically generates specific solutions based on the results of the AI ​​analysis. For example, it may propose solutions such as using organic fertilizer or installing a new irrigation system. These plans are then reviewed by experts and revised or supplemented as necessary. Specifically, solutions are generated using natural language processing and then sent to the expert review team via email or a web portal.

[0963] Funding methods

[0964] The server automatically posts the finalized plan on crowdfunding sites and social impact investment fund platforms. Users (backers and investors) access these platforms, review the plan, and then donate or invest to support the project. The server manages the collected funds and distributes the necessary resources according to each phase of the project. Specifically, the server uses the platform's API to post plan information and provide donation and investment management functions.

[0965] Project implementation method

[0966] The server uses the funds raised to arrange for the provision of necessary resources to experts and NGOs working on the ground. The users (local project teams) use the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation facilities, etc.). Specifically, the server uses the funds to coordinate with supply suppliers, deliver the appropriate supplies to the ground, and provide specific instructions to the local project teams.

[0967] Success case evaluation tools

[0968] The server periodically evaluates project results and records them in a database as success stories. This allows other impoverished regions facing similar challenges to implement projects based on these success stories. Users (project teams in other regions) can retrieve information from this database and apply it to projects in their own regions. Specifically, they input the results data into an AI evaluation model, and periodically report and update the database.

[0969] Specific examples

[0970] For example, consider an agricultural improvement project in a poor region. Users (local farmers or researchers) input information such as soil condition, climate data, and crop growth status from their devices. This information is sent to a server and stored in a database. The server then analyzes the stored data using an AI algorithm to identify key obstacles, such as soil nutrient deficiencies and insufficient irrigation. The server then generates specific solutions to address the identified obstacles, such as the use of organic fertilizer and the introduction of a new irrigation system. The generated plan is reviewed and revised by experts. The server then posts the revised plan on a crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the published plan. The server uses the raised funds to provide organic fertilizer to local farmers and arrange resources for the introduction of a new irrigation system. The users (local farmers) use these resources to improve agricultural efficiency. The server evaluates the project's results and records them in a database as success stories. Users (farmers in other regions or project teams) can then refer to the success stories and apply them to their own local projects.

[0971] Prompt Sentence Examples

[0972] For example, the following prompt sentence is input to the generative AI model:

[0973] "The local soil data is as follows: pH: 5.5, humidity: 30%, temperature: 28°C. Please suggest the best agricultural improvement measures for this situation."

[0974] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0975] Step 1:

[0976] The user launches the dedicated app and uses a device (smartphone or tablet) to input data on the local situation. The input data includes soil pH value, temperature, humidity, and crop growth status. The input data is sent to the server in real time by the device. Input: Soil pH value, temperature, humidity, crop growth status, etc. Output: Data sent to the server.

[0977] Step 2:

[0978] The server receives the data sent from the terminal and stores it in a database. At this time, the server stores the data using a database management system such as MySQL or PostgreSQL. Input: Data sent by the user through the terminal. Output: Data stored in the database.

[0979] Step 3:

[0980] The server passes the local data stored in the database to an AI algorithm for analysis. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to input the stored data into an AI model and identify important local issues. Analysis results include soil nutrient deficiencies and insufficient irrigation. Input: Data stored in the database. Output: Analysis results (e.g., soil nutrient deficiencies, insufficient irrigation, etc.).

[0981] Step 4:

[0982] The server automatically generates specific solutions based on the analysis results of the AI ​​model. For example, it generates proposals such as using organic fertilizer or introducing a new irrigation system. This generated plan is sent to experts via email or a web portal. Input: Analysis results. Output: Automatically generated solution plan.

[0983] Step 5:

[0984] The expert reviews the plan sent from the server and corrects and completes it as necessary. The corrected plan is sent back to the server. Input: Generated solution plan. Output: Reviewed and corrected plan.

[0985] Step 6:

[0986] The server automatically posts the finalized plan on the crowdfunding site or social impact investment fund platform. The server posts the plan information using the platform's API. Input: Reviewed and revised plan. Output: Posting on the crowdfunding site or investment fund.

[0987] Step 7:

[0988] Users (backers and investors) access crowdfunding sites and investment funds, check the posted plans, and then donate or invest to support the project. The server manages the funds and distributes resources according to each phase of the project. Input: Funds collected through crowdfunding sites and investment funds. Output: Distribution of required resources.

[0989] Step 8:

[0990] The server uses the raised funds to arrange for the provision of necessary resources to experts and NGOs working on the ground. The users (local project teams) use the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation facilities, etc.). Input: Funds raised. Output: Resources provided to the ground.

[0991] Step 9:

[0992] The server periodically evaluates the project's results and records them in a database as success stories. This results data is applied to similar projects in other poor areas. Users (project teams in other areas) retrieve the success stories from the database and apply them to projects in their own areas. Input: Project results data. Output: Success stories recorded in the database.

[0993] (Application example 1)

[0994] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0995] Traditional factory operations face many challenges, including excessive energy consumption and inefficient production processes. These challenges increase factory operating costs and contribute to a greater environmental impact. Furthermore, the process of proposing improvements and raising funds to resolve these issues is often done manually, requiring time and effort. Therefore, there is a need for a system that can collect and analyze data in real time and provide quick and effective improvement proposals.

[0996] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0997] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, a means for collecting data through a factory sensor system and analyzing the data in real time, a means for automatically generating factory improvement proposals based on the analysis results, a means for raising funds in cooperation with a crowdfunding platform, and a means for implementing a factory improvement project using the raised funds. This makes it possible to quickly and effectively solve problems while improving the operational efficiency of the factory.

[0998] A "data collection means" is a device that has the function of acquiring data in real time from sensors installed within the factory and sending it to a server.

[0999] The "data analysis means" is a device that uses AI algorithms to analyze collected data and identify problems related to factory operations.

[1000] The "plan generation means" is a device that automatically generates improvement proposals for the factory based on the analysis results of the data analysis means.

[1001] The "fundraising instrument" is a device that automatically posts the generated improvement proposals on crowdfunding platforms and social impact investment funds, thereby raising funds.

[1002] The "project implementation means" is a device that uses the procured funds to arrange resources for implementing the factory improvement project and carries out specific improvement activities.

[1003] The "success case evaluation means" is a device that has the function of evaluating the results of a project and recording them in a database as success cases.

[1004] A "factory sensor system" is a group of sensors that collect data from various devices and processes within a factory.

[1005] A "means for real-time analysis" is a device that has the ability to instantly pass collected data to an AI algorithm for analysis.

[1006] The "means for automatic generation" is a device that has the function of automatically creating optimal improvement proposals based on the analysis results of an AI algorithm.

[1007] A "crowdfunding platform" is a service for raising funds online, a website where project details are published and funding is sought from backers.

[1008] The "means for implementing improvement projects" refers to a device that has the function of arranging the necessary resources after fundraising and actually carrying out improvement activities in the factory.

[1009] The present invention is an integrated system for supporting efficiency in factory operations, which includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, a factory sensor system, a real-time analysis means, an automatic generation means, a crowdfunding platform, and a means for implementing an improvement project.

[1010] Hardware and Software Configuration

[1011] Hardware

[1012] Factory sensor system: A set of sensors used to collect data from each device and process in the factory. For example, temperature sensors, humidity sensors, vibration sensors, etc. are used.

[1013] Server: A central computer that handles data collection, analysis, plan generation, fundraising management, project execution, and success evaluation.

[1014] Smart glasses or terminals: Used for real-time monitoring and data entry on-site.

[1015] software

[1016] Python: A programming language used to build entire programs and process data.

[1017] Sci-kit Learn: A machine learning library for data analysis.

[1018] Requests: Used for API integration with crowdfunding platforms.

[1019] Generative AI model: An AI model that generates optimal plans from data analysis results.

[1020] Data processing and calculation

[1021] 1. Data Collection:

[1022] The server collects data in real time from the factory's sensor system. The sensors acquire various data (temperature, humidity, vibration, etc.) within the factory, and this data is sent to the server via smart glasses or terminals.

[1023] 2. Data Analysis:

[1024] The server analyzes the collected data using AI algorithms powered by Sci-kit Learn, which identifies key issues in factory operations (such as excessive energy consumption and production inefficiencies).

[1025] 3. Plan Generation:

[1026] Based on the analysis results, the server uses a generative AI model to automatically generate improvement suggestions, such as specific measures to reduce energy consumption or optimize production processes.

[1027] 4. Funding:

[1028] The server then posts the automatically generated plan on the crowdfunding platform, where investors and supporters can review the plan and provide funding.

[1029] 5. Project execution:

[1030] The server uses the raised funds to implement on-site factory improvement projects, specifically converting the funds into resources (such as new equipment and machinery) and supplying them to the factory.

[1031] 6. Success Case Evaluation:

[1032] The server evaluates the project results and records them in a database as success stories that can be used as references at other factories to solve similar problems.

[1033] Examples of concrete examples and prompts

[1034] Specific examples

[1035] If Factory A has a problem with high energy consumption, sensors collect real-time information on the factory's temperature and machine operation, and send the data to a server. The server then analyzes the data using an AI algorithm to identify the cause of the energy consumption. Based on the analysis results, improvement proposals are generated, such as replacing machines with more energy-efficient ones or introducing a new energy management system. These proposals are then posted on a crowdfunding platform, and once funds are raised, actual improvement activities are carried out on-site.

[1036] Prompt Sentence Examples

[1037] "Please suggest the best plan to reduce energy consumption in the factory. The sensor data is as follows: {sensor_data}"

[1038] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1039] Step 1:

[1040] Data collection

[1041] The server receives data in real time from sensors installed in the factory, including information on temperature, humidity, vibration, operating hours, etc. The input sensor data is sent to the server and stored in a database.

[1042] Step 2:

[1043] Data analysis

[1044] The server passes the collected data to an AI algorithm for analysis. Sci-kit Learn is used to apply machine learning models to identify problems in factory operations (e.g., excessive energy consumption, inefficient processes, etc.). The input is sensor data stored in a database, and the output is the identified problems as a result of the analysis.

[1045] Step 3:

[1046] Plan Generation

[1047] The server uses a generative AI model based on the results of data analysis to automatically generate improvement suggestions, including energy-saving measures, production process optimization, resource reallocation, etc. The input is the results of data analysis, and the output is detailed improvement suggestions.

[1048] Step 4:

[1049] Crowdfunding

[1050] The server automatically posts the generated improvement proposal on the crowdfunding platform. Specifically, it uses the Requests library to submit the plan in the appropriate format. The input is the generated improvement proposal, and the output is the project information posted on the crowdfunding platform.

[1051] Step 5:

[1052] Funding

[1053] Users (investors and supporters) review improvement proposals posted on the crowdfunding platform and provide funding. The server manages the collected funds and distributes resources according to the required phase. The input is funding information from the crowdfunding platform, and the output is the success of fundraising.

[1054] Step 6:

[1055] Project Execution

[1056] The server uses the raised funds to arrange resources to implement factory improvement projects. Resources can include new machinery, energy efficiency devices, improvement supplies, etc. The inputs are the successful fundraising and required resource information, and the outputs are the arranged resources and the implemented improvement projects.

[1057] Step 7:

[1058] Success Case Evaluation

[1059] The server evaluates the project results and records them in a database as success stories. It also provides the evaluation results to other factories as reference information. The input is the project execution result data, and the output is the evaluated success stories and their records.

[1060] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1061] This invention is an integrated system for solving social issues in impoverished areas and building sustainable economic activities. This system consists of a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a means for evaluating success cases, and an emotion engine that recognizes the user's emotions.

[1062] Data collection methods

[1063] The user's device (smartphone or tablet) inputs local situation data through a dedicated app. The input data includes soil condition, climate, crop growth status, and the user's emotions. The device then sends the data via the Internet to a server, which then stores the data in a database.

[1064] Data Analysis Methods

[1065] The server then passes the local data stored in the database to an AI algorithm for analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These analysis results are then used in the subsequent plan generation process.

[1066] Emotion Engine

[1067] The server uses an emotion engine to analyze data received from the local user terminal and recognize the user's emotion. The recognized emotion data is fed back to the data analysis means and plan generation means, which then generate and adjust plans that take the user's emotional state into consideration.

[1068] Plan Generation Method

[1069] The server automatically generates specific solutions based on the AI ​​analysis results and emotional data. For example, it may suggest solutions such as using organic fertilizer or installing a new irrigation system. The generated plan is displayed on a dashboard for expert review.

[1070] Funding methods

[1071] The server generates a page for posting the finalized plan on a crowdfunding site or social impact investment fund platform. This page includes an overview of the plan, the target funding amount, the expected impact, etc. Users (backers and investors) access the crowdfunding site, review the posted plan, and then make donations or investments to support the project.

[1072] Project implementation method

[1073] The server uses the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment, etc.) to local experts and NGOs. The user (local project team) uses the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.).

[1074] Success case evaluation tools

[1075] The server periodically evaluates the project's results and records them in a database as success stories. This allows other impoverished areas facing similar challenges to implement projects based on these success stories. Users (project teams in other areas) can retrieve information from this database and apply it to projects in their own areas.

[1076] Specific examples

[1077] For example, consider the case of implementing an agricultural improvement project in a poor area.

[1078] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, crop growth status, and emotional state into their terminals. This information is sent to the server and stored in a database.

[1079] 2. The server analyzes the stored data using AI algorithms and an emotion engine to identify key fault factors, such as soil nutrient deficiency, insufficient irrigation, and the user's emotional state.

[1080] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer, introducing a new irrigation system, or support measures that take the user's emotional state into account. The generated plans are then reviewed and revised by experts.

[1081] 4. The server posts the revised plan on the crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the posted plan.

[1082] 5. The server uses the funds raised to provide local farmers with organic fertilizer and arrange resources to install new irrigation systems. The users (local farmers) use these resources to improve the efficiency and quality of their farming.

[1083] 6. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[1084] In this way, by solving specific problems in poor areas through this system and taking into account the emotional state of the user, it is possible to build more effective and sustainable socio-economic activities.

[1085] The processing flow will be explained below.

[1086] Step 1:

[1087] The device (a smartphone or tablet used by the user) inputs local situation data using a dedicated app. The input data includes soil condition, climate, crop growth status, and the user's emotional state. The device then transmits the data to a server via the Internet.

[1088] Step 2:

[1089] The server stores the data received from the devices in a database that stores all the data collected over time and stores the data for subsequent analysis and planning.

[1090] Step 3:

[1091] The server preprocesses the data stored in the database. Specifically, it removes incomplete data, corrects outliers, and prepares the data in a format suitable for analysis. The preprocessed data is then stored in the data store for analysis.

[1092] Step 4:

[1093] The server then passes the preprocessed data to an AI algorithm for data analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These results are used in the subsequent plan generation process.

[1094] Step 5:

[1095] The server uses an emotion engine to analyze the user's emotional data. It detects the user's emotional state in real time based on their input and usage, and collects the data. The emotional data is then integrated with the data analysis results.

[1096] Step 6:

[1097] The server automatically generates specific solutions based on the data analysis results and emotional data. For example, these could include using organic fertilizers or installing a new irrigation system, as well as psychological support and training sessions that take the user's emotional state into account. The generated plans are then displayed on a dashboard for expert review.

[1098] Step 7:

[1099] The expert reviews the plan presented by the server through a dashboard and makes corrections or additions as necessary. The expert's feedback is sent to the server, and the final version of the plan is completed.

[1100] Step 8:

[1101] The server generates a page for posting the final plan on a crowdfunding site or social impact investment platform, including a summary of the plan, the funding goal, and the expected impact.

[1102] Step 9:

[1103] Users (backers and investors) access the crowdfunding site, check the posted plans, make donations or investments to support the project, and the funds are sent to the server.

[1104] Step 10:

[1105] The server manages the collected funds and distributes the necessary resources for each phase of the project, including fertilizer, irrigation equipment, educational materials, psychological support resources, etc. The funds are distributed to experts and NGOs working on the ground.

[1106] Step 11:

[1107] Users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, conducting psychological support sessions, etc.). Activity status is reported sequentially from the terminals to the server.

[1108] Step 12:

[1109] The server periodically evaluates the progress of the project and records it in a database as a success story. This evaluation is based on the project's progress, results, changes in user emotions, and the effectiveness of problem solving.

[1110] Step 13:

[1111] The server will publish a database of success stories and make them accessible to users (project teams in other regions). Success stories include specific solutions, implementation methods, results, and changes in user emotions.

[1112] Step 14:

[1113] Users (project teams in other regions) can obtain information from the database of success stories and apply it to projects in their own regions. Based on the information obtained, they can carry out projects aiming to solve similar problems.

[1114] These are the specific steps of the program processing of this system, which can effectively solve various problems in poor areas and, by taking into account the emotional state of the user, can build more effective and sustainable socio-economic activities.

[1115] Example 2

[1116] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1117] Building sustainable socioeconomic activities in impoverished areas requires a detailed understanding of specific local issues and the rapid and efficient provision of appropriate solutions based on that understanding. However, conventional methods lack an integrated process, from data collection and analysis to solution proposals, fundraising, implementation, and outcome evaluation, resulting in time-consuming information sharing and decision-making at each stage. Furthermore, they fail to take into account the emotional state of users, limiting the means for maximizing project effectiveness. Therefore, the present invention aims to provide an integrated system that solves these issues.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data analysis means, an emotion recognition means, a plan generation means, a fundraising means, a project execution means, and a success case evaluation means. This makes it possible to quickly and efficiently identify specific local issues and provide appropriate solutions based on the analysis results and the user's emotional state. Furthermore, by managing a series of processes from fundraising to project execution and outcome evaluation in an integrated manner, information sharing and decision-making can be accelerated, effectively promoting the establishment of sustainable socioeconomic activities in impoverished areas.

[1119] The "data collection means" is a device or system for receiving data from information processing devices used by local users.

[1120] A "data analysis means" is a device or system for analyzing collected data using machine learning algorithms.

[1121] An "emotion recognizer" is a device or system for identifying a user's emotional state from collected data.

[1122] The "plan generation means" is a device or system for generating specific solutions based on the data analysis results and emotion data.

[1123] A "funding vehicle" is a device or system for generating information for posting a generated plan on a funding platform.

[1124] A "Project Execution Vehicle" is a device or system that utilizes raised funds to provide resources and execute a project.

[1125] A "success case evaluation tool" is a device or system for evaluating the results of a project and recording the results in a database.

[1126] This invention is an integrated system for solving social issues in impoverished areas and building sustainable economic activities. This system consists of data collection means, data analysis means, emotion recognition means, plan generation means, fundraising means, project implementation means, and success case evaluation means.

[1127] Data collection methods

[1128] Users input data about their local conditions through a dedicated app installed on their smartphone or tablet. The data includes soil pH, temperature, precipitation, crop growth status, and the user's emotional state. The device then transmits this data to a server in real time via an internet connection.

[1129] Data Analysis Methods

[1130] The server stores the received data in a database (e.g., MySQL, PostgreSQL), then passes the data to an AI algorithm (e.g., TensorFlow, PyTorch) for analysis. Specific analysis results include determining the nutrient status of the soil and assessing the risk of drought.

[1131] emotion recognition means

[1132] The server uses an emotion engine (e.g., OpenAI's GPT-3) to recognize the user's emotions from the received data. The emotion data (e.g., anxiety, hope, irritation) is stored in a database and subsequent processing is based on it.

[1133] Plan Generation Method

[1134] The server automatically generates specific solutions based on the AI ​​analysis results and emotion data. The generated plan suggests measures such as using organic fertilizer, installing a new irrigation system, and providing psychological support. The plan is displayed on a dashboard (e.g., PowerBI) and awaits expert review.

[1135] Funding methods

[1136] The server automatically generates a page for posting the approved plan on a crowdfunding site (e.g., Kickstarter) or a social impact investment fund platform, detailing the plan's outline, target funding amount, and expected social impact.

[1137] Project implementation method

[1138] The server uses the funds raised to arrange for the provision of necessary resources (e.g., fertilizer, irrigation equipment) to local experts and NGOs. The provision of resources includes coordination with logistics centers and local vendors. The users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment).

[1139] Success case evaluation tools

[1140] The server periodically evaluates the project's performance and records the results in a database. Evaluation criteria include profitability, sustainability, and social impact. Success stories can also be used as reference for projects in other regions.

[1141] Specific examples

[1142] For example, consider the case of implementing an agricultural improvement project in a poor area.

[1143] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, crop growth status, and emotional state into their devices. This information is sent to the server and stored in a database.

[1144] 2. The server analyzes the stored data using AI algorithms and an emotion engine to identify key fault factors, such as soil nutrient deficiency, insufficient irrigation, and the user's emotional state.

[1145] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer, introducing a new irrigation system, or support measures that take the user's emotional state into account. The generated plans are reviewed by experts and posted on a crowdfunding site.

[1146] 4. Users (backers and investors) access the crowdfunding site and provide funding.

[1147] 5. The server will use the funds raised to provide organic fertilizer to local farmers and arrange resources to install a new irrigation system.

[1148] 6. Users (local farmers) use these resources to improve the efficiency and quality of their farming.

[1149] 7. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[1150] Prompt Sentence Examples

[1151] Use the following data to generate a plan for improving agriculture in a poor area.

[1152] Soil condition: Highly acidic

[1153] Climate data: Short rainy season

[1154] Crop growth status: Poor growth

[1155] User Emotion: Anxiety

[1156] Please explain your specific solution and why.

[1157] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1158] Step 1: Data collection

[1159] Users input local situation data, such as soil pH, temperature, precipitation, crop growth status, and the user's emotional state, through a dedicated app installed on a smartphone or tablet.

[1160] Input: Local condition data (e.g. soil pH, temperature, precipitation, crop condition, emotional state)

[1161] Output: Data stored on a smartphone or tablet

[1162] Step 2: Send data

[1163] The terminal sends the input data to the server in real time via the Internet. During transmission, encrypted communication (e.g., SSL / TLS) is used to maintain data integrity.

[1164] Input: Data stored on a smartphone or tablet

[1165] Output: Data sent to the server

[1166] Step 3: Save data

[1167] The server stores the received data in a database (e.g., MySQL, PostgreSQL). When storing the data, it also backs up the data to prevent data loss. It also monitors the data storage capacity.

[1168] Input: Received data

[1169] Output: Data stored in the database

[1170] Step 4: Data analysis

[1171] The server then passes the stored data to AI algorithms (e.g., TensorFlow, PyTorch) for analysis. The analysis includes determining the nutrient status of the soil and assessing the risk of drought. The most important social issues are identified and prioritized.

[1172] Input: Data stored in a database

[1173] Output: Analysis results (e.g., soil nutrient deficiency, insufficient irrigation)

[1174] Step 5: Emotion Recognition

[1175] The server uses an emotion engine (e.g., OpenAI's GPT-3) to recognize the user's emotion from the received data. The recognized emotion data is stored in a database and subsequent processing is based on it.

[1176] Input: Local situation data stored in a database

[1177] Output: Recognized emotion data

[1178] Step 6: Generate a plan

[1179] The server automatically generates specific solutions based on the AI ​​analysis results and emotional data. The generated plan suggests measures such as using organic fertilizer, installing a new irrigation system, and providing psychological support. The plan is displayed on a dashboard (e.g., PowerBI).

[1180] Input: Analysis results and emotion data

[1181] Output: Auto-generated solution (plan)

[1182] Step 7: Expert Review

[1183] The expert reviews the plan generated by the server via a dashboard, providing corrections or additional advice as necessary, and the reviewed plan moves on to the next step after receiving final approval.

[1184] Input: Auto-generated solution (plan)

[1185] Output: Reviewed and revised plan

[1186] Step 8: Generate your fundraising page

[1187] The server automatically generates a page for posting the approved plan on a crowdfunding site (e.g., Kickstarter) or a social impact investment fund platform, detailing the plan's outline, target funding amount, and expected social impact.

[1188] Input: Reviewed and revised plan

[1189] Output: Crowdfunding page

[1190] Step 9: Funding

[1191] Users (backers and investors) access crowdfunding sites, check the plans posted, and then donate or invest in support via credit card or digital payment systems (e.g., PayPal).

[1192] Input: Crowdfunding page

[1193] Output: Funds provided

[1194] Step 10: Resource Arrangements

[1195] The server will use the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment) to local experts and NGOs, including liaising with logistics centers and local vendors.

[1196] Input: Funds Provided

[1197] Output: Arranged resources

[1198] Step 11: Project execution

[1199] Users (local project teams) utilize the provided resources to carry out specific project activities, such as spreading fertilizer, installing irrigation systems, and implementing educational programs.

[1200] Input: Arranged resource

[1201] Output: Executed projects

[1202] Step 12: Success Case Evaluation

[1203] The server periodically evaluates the project's performance and records the results in a database. Evaluation criteria include profitability, sustainability, and social impact. Success stories can also be used as reference for projects in other regions.

[1204] Input: Data of the executed project

[1205] Output: Database of evaluation results and success stories

[1206] (Application example 2)

[1207] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1208] Traditional operations in poor areas and brick-and-mortar stores require efficient data collection and analysis, solution generation, fundraising, and project implementation, but there is a lack of systems that handle these elements in an integrated manner. Furthermore, while it is important to reflect the emotions and specific needs of users in solving local issues, few systems have this functionality. This makes it difficult to generate specific solutions tailored to local conditions and to raise funds effectively.

[1209] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1210] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, an emotion recognition means, and a crowdfunding page generation means, which enable the integrated collection and analysis of data, the generation of specific solutions that take into account user emotions, effective support for fundraising, and the execution and evaluation of projects.

[1211] The "data collection means" is a device or software that collects local situation data from the user's terminal and transmits it to the server.

[1212] "Data analysis tools" are devices or software that analyze collected data using AI algorithms and identify important social issues.

[1213] The "plan generation means" is a device or software that automatically generates specific solutions based on the results of data analysis and emotion data.

[1214] A "fundraising vehicle" is a device or software used to post the generated plan on a crowdfunding site or investment platform and raise the necessary funds.

[1215] "Project Implementation Instrument" means equipment or software that provides resources to support the implementation of the Project on the ground, based on the funds raised.

[1216] A "success case evaluation tool" is a device or software that evaluates the results of a project and records them in a database as success cases.

[1217] An "emotion recognition means" is a device or software that analyzes the user's emotions and feeds the results back into data analysis and plan generation.

[1218] A "crowdfunding page generation means" is a device or software that automatically generates a page for a crowdfunding site based on the generated plan.

[1219] The present invention is an integrated system that supports the improvement of business management in poor areas and brick-and-mortar stores. The system includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, an emotion recognition means, and a crowdfunding page generation means.

[1220] Data collection methods

[1221] Users use devices (smartphones or tablets) to input local situation data and store operation data through a dedicated app. The input data includes sales data, customer feedback, inventory status, and the user's emotional state. The devices then send the data via the Internet to a server, which stores the data in a database.

[1222] Data Analysis Methods

[1223] The server passes the local data and store operation data stored in the database to an AI algorithm for analysis. As a result of the analysis, the most important issues (e.g., declining sales, declining customer satisfaction, etc.) are identified and prioritized. The results of this analysis are used in the subsequent plan generation process.

[1224] emotion recognition means

[1225] The server uses the emotion recognition means to analyze emotions from the user's input data. The analysis results are fed back to the data analysis means and the plan generation means, and a plan is generated or adjusted taking the user's emotional state into consideration.

[1226] Plan Generation Method

[1227] The server automatically generates specific solutions based on the AI ​​analysis results and emotion data, such as suggestions for improving marketing strategies or optimizing inventory management. The generated plans are then displayed on a dashboard for expert review.

[1228] Funding methods

[1229] The server automatically generates a page to post the finalized plan on a crowdfunding site or social impact investment fund platform. This page includes an overview of the plan, the target funding amount, and the expected effects. Users (backers and investors) access the crowdfunding site, review the posted plan, and then make donations or investments to support the project.

[1230] Project implementation method

[1231] The server uses the funds raised to provide the necessary resources (e.g., equipment, materials, etc.) to the local team. The users (local project teams and store operators) use the provided resources to carry out specific project activities (e.g., introducing new equipment, training employees, etc.).

[1232] Success case evaluation tools

[1233] The server periodically evaluates the project results and records them in a database as success stories. This allows other regions and stores facing similar challenges to use these success stories as references for their own projects. Users (project teams and store managers in other regions) can retrieve information from this database and apply it to projects in their own regions and stores.

[1234] Hardware and software used

[1235] Hardware: Server

[1236] Software: Python, Flask, scikit-learn, SQLAlchemy, emotion recognition library

[1237] Examples of prompt statements

[1238] For example, say a cafe owner is experiencing declining sales and customer satisfaction. The owner enters the following data into the app:

[1239] Sales data: January - 100,000 yen, February - 80,000 yen

[1240] Customer feedback: "The coffee is cold" and "The store is crowded"

[1241] Availability: Coffee beans - 50 bags, Milk - 20 bottles

[1242] Operator's emotional state: "I feel stressed"

[1243] Enter this as the prompt.

[1244] Prompt Sentence Examples

[1245] Sales data: January - 100,000 yen, February - 80,000 yen

[1246] Customer feedback: "The coffee is cold" and "The store is crowded"

[1247] Availability: Coffee beans - 50 bags, Milk - 20 bottles

[1248] Operator's emotional state: "I feel stressed"

[1249] Use this data to automatically generate specific plans to improve sales and customer satisfaction.

[1250] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1251] Step 1:

[1252] The terminal collects local situation data and store operation data entered by the user.

[1253] Inputs: Sales data, customer feedback, inventory status, emotional state

[1254] Specific operation: The user enters data using the app, and the entered data is sent to the server by the device.

[1255] Step 2:

[1256] The server stores the received data in a database.

[1257] Input: Data sent from the terminal

[1258] Output: Data stored in the database

[1259] Specific operation: The server automatically stores the received data in the database.

[1260] Step 3:

[1261] The server uses data analysis means to analyze the data stored in the database using AI algorithms.

[1262] Input: Data stored in the database

[1263] Output: Analysis results (identification and prioritization of important issues)

[1264] What happens: The server runs an AI algorithm (e.g., random forest) to identify issues like declining sales or poor customer satisfaction.

[1265] Step 4:

[1266] The server uses an emotion recognition means to analyze the user's emotions.

[1267] Input: User emotional state data

[1268] Output: Recognized emotion data

[1269] Specific operation: The server uses an emotion recognition library to analyze the emotional state and saves the results.

[1270] Step 5:

[1271] The server generates a specific solution using a plan generation means based on the analysis results and emotion data.

[1272] Input: Analysis results, emotion data

[1273] Output: Specific solutions (e.g., marketing strategies, inventory management optimization)

[1274] Specific operation: The server executes the automatic generation algorithm and generates an improvement plan.

[1275] Step 6:

[1276] The server generates a page for posting the generated plan on the crowdfunding site using a crowdfunding page generation means.

[1277] Input: Specific solution

[1278] Output: Crowdfunding page

[1279] Specific operation: The server automatically generates a page outlining the plan, the target funding amount, and the expected results.

[1280] Step 7:

[1281] Users (supporters and investors) access the crowdfunding site, check the posted plans, and make donations or investments to support the project.

[1282] Enter: Crowdfunding page

[1283] Output: Support and investment

[1284] Specific actions: The user reviews the plan and makes a decision to support or invest.

[1285] Step 8:

[1286] The server provides resources using the project execution means based on the raised funds.

[1287] Input: Funds raised

[1288] Output: Resources provided (e.g., equipment, materials)

[1289] Specific actions: The server arranges for the necessary resources and provides them to the local project team and store operators.

[1290] Step 9:

[1291] The server evaluates the results of the project using the success case evaluation means and records the results in a database.

[1292] Input: Project execution data

[1293] Output: Recorded data of success stories

[1294] Specific actions: The server evaluates the project's results and stores them in a database as success stories to help with similar projects.

[1295] Step 10:

[1296] Users (project teams and store operators in other regions) retrieve success stories from the database and apply them to projects in their own regions or stores.

[1297] Input: Success story data retrieved from the database

[1298] Output: Specific application of the project

[1299] Specific actions: Users refer to success stories and implement projects tailored to the characteristics of their local area.

[1300] 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.

[1301] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1302] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1303] [Fourth embodiment]

[1304] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1305] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1306] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1307] 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.

[1308] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1309] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1310] 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.

[1311] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1312] 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.

[1313] 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 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.

[1314] 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.

[1315] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1316] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1317] This invention is an integrated system for solving various problems in impoverished regions and supporting the creation of sustainable economic activities. This system is composed of means for data collection, data analysis, plan generation, fundraising, project implementation, and evaluation of success cases.

[1318] Data collection methods

[1319] Users use their devices (smartphones or tablets) to input local situation data (e.g., soil data, climate data, crop growth status, etc.) through a dedicated app. This data is sent in real time via the Internet to a server, which then stores the data in a database.

[1320] Data Analysis Methods

[1321] The server passes the local data stored in the database to an AI algorithm for analysis. The results of the analysis identify and prioritize the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.). These analysis results are used in subsequent processes.

[1322] Plan Generation Method

[1323] The server automatically generates specific solutions based on the results of the AI ​​analysis. For example, it may suggest solutions such as using organic fertilizer or introducing a new irrigation system. This generated plan is then reviewed by experts and revised or supplemented as necessary.

[1324] Funding methods

[1325] The server automatically posts the finalized plan on crowdfunding sites and social impact investment fund platforms. Users (backers and investors) access these platforms, review the plan, and then donate or invest in support. The server manages the collected funds and distributes the necessary resources according to each phase of the project.

[1326] Project implementation method

[1327] The server uses the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment, etc.) to local experts and NGOs. The user (local project team) uses the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.).

[1328] Success case evaluation tools

[1329] The server periodically evaluates the project's results and records them in a database as success stories. This allows other impoverished areas facing similar challenges to implement projects based on these success stories. Users (project teams in other areas) can retrieve information from this database and apply it to projects in their own areas.

[1330] Specific examples

[1331] For example, consider the case of implementing an agricultural improvement project in a poor area.

[1332] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, and crop growth status into their terminals. This information is sent to the server and stored in a database.

[1333] 2. The server analyzes the stored data using AI algorithms to identify key problem factors, such as nutrient deficiencies in the soil or insufficient irrigation.

[1334] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer or introducing a new irrigation system. The generated plan is reviewed and revised by experts.

[1335] 4. The server posts the revised plan on the crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the posted plan.

[1336] 5. The server uses the funds raised to provide local farmers with organic fertilizer and arrange resources to install new irrigation systems. The users (local farmers) use these resources to improve agricultural efficiency.

[1337] 6. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[1338] In this way, this system can solve specific problems in poor areas and build sustainable socio-economic activities.

[1339] The processing flow will be explained below.

[1340] Step 1:

[1341] The device (a smartphone or tablet used by the user) uses a dedicated app to input local situation data, including soil condition, climate, and crop growth status. The device then transmits the data to a server via the internet along with a unique identifier.

[1342] Step 2:

[1343] The server stores the data received from the devices in a database that stores all data collected over time and is used for subsequent data analysis.

[1344] Step 3:

[1345] The server preprocesses the data stored in the database. Specifically, it removes incomplete data, corrects outliers, and prepares the data in a format suitable for analysis. The preprocessed data is then saved in the data store for analysis.

[1346] Step 4:

[1347] The server then passes the preprocessed data to an AI algorithm for data analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These results are used in the subsequent plan generation process.

[1348] Step 5:

[1349] The server automatically generates specific solutions based on the AI ​​analysis results, such as using organic fertilizer or introducing a new irrigation system, and displays the resulting plan on a dashboard for expert review.

[1350] Step 6:

[1351] The expert reviews the plan presented by the server through a dashboard and makes corrections or additions as necessary. The expert's feedback is sent to the server, and the final version of the plan is completed.

[1352] Step 7:

[1353] The server generates a page for posting the final plan on a crowdfunding site or social impact investment platform, including a summary of the plan, the funding goal, and the expected impact.

[1354] Step 8:

[1355] Users (backers and investors) access the crowdfunding site, check the posted plans, make donations or investments to support the project, and the funds are sent to the server.

[1356] Step 9:

[1357] The server manages the collected funds and distributes the necessary resources for each phase of the project to local experts and NGOs.

[1358] Step 10:

[1359] Users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.). Activity status is reported successively from the terminals to the server.

[1360] Step 11:

[1361] The server periodically evaluates the progress of the project and records success stories in a database. The evaluation is based on the project's progress, results, and effectiveness of problem-solving.

[1362] Step 12:

[1363] Users (project teams in other regions) access the database of successful cases to obtain information, which they can then apply to projects in their own regions, aiming to solve similar problems.

[1364] These are the specific steps of the program processing of this system, which will effectively solve various issues in poor areas and enable the creation of sustainable socio-economic activities.

[1365] Example 1

[1366] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1367] The purpose of this invention is to comprehensively solve various social and economic issues in poor regions and create sustainable economic activities. Conventional support systems often lack efficient data collection and analysis, appropriate plan creation, fundraising, and on-site implementation based on those plans, making it difficult to provide effective solutions.

[1368] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1369] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project implementation means, a success story evaluation means, a user terminal that inputs local situation data and transmits it to the server in real time, a means by which the server analyzes the data stored in the database and identifies social issues using an AI algorithm, a means for generating specific solutions based on the analysis results and having them reviewed by experts, a means for posting the plan on a crowdfunding site or investment fund and raising funds, a means for providing the necessary resources based on the raised funds and carrying out project activities on site, and a means for evaluating the project results and recording them in the database as success stories. This makes it possible to efficiently collect data tailored to the local situation in impoverished areas, generate optimal solutions based on the analysis results, effectively raise funds and implement projects based on them, and share success stories.

[1370] The "data collection means" is a means for inputting situation data from a local user terminal and transmitting it to a server in real time.

[1371] "Data analysis means" refers to the means by which the server passes data stored in the database to an AI algorithm for analysis.

[1372] The "plan generation means" is a means for automatically generating specific solutions based on the analysis results and having them reviewed by experts.

[1373] A "fundraising method" is a means of raising funds from supporters and investors by posting a finalized plan on a crowdfunding site or a social impact investment fund platform.

[1374] "Project implementation means" refers to the means by which the funds raised are used to provide the necessary resources to local experts and NGOs to carry out specific project activities.

[1375] The "success case evaluation method" is a method for evaluating the results of a project and recording them in a database as success cases.

[1376] "Local situation data" refers to data showing the current state of poor areas, such as soil data, climate data, and crop growth conditions.

[1377] A "user terminal" is a device such as a smartphone or tablet that is used to input local situation data and send it to a server.

[1378] A "server" is a computer system that receives data sent from the field, stores it in a database, analyzes the data, and executes various processes.

[1379] A "database" is a system for storing and managing data received by a server.

[1380] An "AI algorithm" is an algorithm that allows a server to analyze data stored in a database, identify important social issues, and generate solutions.

[1381] A "crowdfunding site" is a platform for raising funds from people who support a specific project via the Internet.

[1382] A "social impact investment fund" is a fund that specializes in investing in projects that have social value.

[1383] "Project activities" are concrete solutions implemented on the ground, such as spreading fertilizer or installing irrigation systems.

[1384] The present invention is an integrated system for solving various issues in impoverished regions and creating sustainable economic activities. This system is composed of data collection means, data analysis means, plan generation means, fundraising means, project implementation means, and success case evaluation means. Specific embodiments of each means are described below.

[1385] Data collection methods

[1386] The user's device (smartphone or tablet) inputs local situation data (e.g., soil data, climate data, crop growth status, etc.) through a dedicated app. This data is sent in real time to a server via the Internet. The server stores this data in a database. Specifically, the device sends the data to the server via an HTTP request, and the server stores the data using a database management system such as MySQL or PostgreSQL.

[1387] Data Analysis Methods

[1388] The server then passes the on-site data stored in the database to an AI algorithm for analysis. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to input the data into an AI model and identify key fault factors, such as soil nutrient deficiencies or insufficient irrigation. The results of this analysis are then used in subsequent processes.

[1389] Plan Generation Method

[1390] The server automatically generates specific solutions based on the results of the AI ​​analysis. For example, it may propose solutions such as using organic fertilizer or installing a new irrigation system. These plans are then reviewed by experts and revised or supplemented as necessary. Specifically, solutions are generated using natural language processing and then sent to the expert review team via email or a web portal.

[1391] Funding methods

[1392] The server automatically posts the finalized plan on crowdfunding sites and social impact investment fund platforms. Users (backers and investors) access these platforms, review the plan, and then donate or invest to support the project. The server manages the collected funds and distributes the necessary resources according to each phase of the project. Specifically, the server uses the platform's API to post plan information and provide donation and investment management functions.

[1393] Project implementation method

[1394] The server uses the funds raised to arrange for the provision of necessary resources to experts and NGOs working on the ground. The users (local project teams) use the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation facilities, etc.). Specifically, the server uses the funds to coordinate with supply suppliers, deliver the appropriate supplies to the ground, and provide specific instructions to the local project teams.

[1395] Success case evaluation tools

[1396] The server periodically evaluates project results and records them in a database as success stories. This allows other impoverished regions facing similar challenges to implement projects based on these success stories. Users (project teams in other regions) can retrieve information from this database and apply it to projects in their own regions. Specifically, they input the results data into an AI evaluation model, and periodically report and update the database.

[1397] Specific examples

[1398] For example, consider an agricultural improvement project in a poor region. Users (local farmers or researchers) input information such as soil condition, climate data, and crop growth status from their devices. This information is sent to a server and stored in a database. The server then analyzes the stored data using an AI algorithm to identify key obstacles, such as soil nutrient deficiencies and insufficient irrigation. The server then generates specific solutions to address the identified obstacles, such as the use of organic fertilizer and the introduction of a new irrigation system. The generated plan is reviewed and revised by experts. The server then posts the revised plan on a crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the published plan. The server uses the raised funds to provide organic fertilizer to local farmers and arrange resources for the introduction of a new irrigation system. The users (local farmers) use these resources to improve agricultural efficiency. The server evaluates the project's results and records them in a database as success stories. Users (farmers in other regions or project teams) can then refer to the success stories and apply them to their own local projects.

[1399] Prompt Sentence Examples

[1400] For example, the following prompt sentence is input to the generative AI model:

[1401] "The local soil data is as follows: pH: 5.5, humidity: 30%, temperature: 28°C. Please suggest the best agricultural improvement measures for this situation."

[1402] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1403] Step 1:

[1404] The user launches the dedicated app and uses a device (smartphone or tablet) to input data on the local situation. The input data includes soil pH value, temperature, humidity, and crop growth status. The input data is sent to the server in real time by the device. Input: Soil pH value, temperature, humidity, crop growth status, etc. Output: Data sent to the server.

[1405] Step 2:

[1406] The server receives the data sent from the terminal and stores it in a database. At this time, the server stores the data using a database management system such as MySQL or PostgreSQL. Input: Data sent by the user through the terminal. Output: Data stored in the database.

[1407] Step 3:

[1408] The server passes the local data stored in the database to an AI algorithm for analysis. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to input the stored data into an AI model and identify important local issues. Analysis results include soil nutrient deficiencies and insufficient irrigation. Input: Data stored in the database. Output: Analysis results (e.g., soil nutrient deficiencies, insufficient irrigation, etc.).

[1409] Step 4:

[1410] The server automatically generates specific solutions based on the analysis results of the AI ​​model. For example, it generates proposals such as using organic fertilizer or introducing a new irrigation system. This generated plan is sent to experts via email or a web portal. Input: Analysis results. Output: Automatically generated solution plan.

[1411] Step 5:

[1412] The expert reviews the plan sent from the server and corrects and completes it as necessary. The corrected plan is sent back to the server. Input: Generated solution plan. Output: Reviewed and corrected plan.

[1413] Step 6:

[1414] The server automatically posts the finalized plan on the crowdfunding site or social impact investment fund platform. The server posts the plan information using the platform's API. Input: Reviewed and revised plan. Output: Posting on the crowdfunding site or investment fund.

[1415] Step 7:

[1416] Users (backers and investors) access crowdfunding sites and investment funds, check the posted plans, and then donate or invest to support the project. The server manages the funds and distributes resources according to each phase of the project. Input: Funds collected through crowdfunding sites and investment funds. Output: Distribution of required resources.

[1417] Step 8:

[1418] The server uses the raised funds to arrange for the provision of necessary resources to experts and NGOs working on the ground. The users (local project teams) use the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation facilities, etc.). Input: Funds raised. Output: Resources provided to the ground.

[1419] Step 9:

[1420] The server periodically evaluates the project's results and records them in a database as success stories. This results data is applied to similar projects in other poor areas. Users (project teams in other areas) retrieve the success stories from the database and apply them to projects in their own areas. Input: Project results data. Output: Success stories recorded in the database.

[1421] (Application example 1)

[1422] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1423] Traditional factory operations face many challenges, including excessive energy consumption and inefficient production processes. These challenges increase factory operating costs and contribute to a greater environmental impact. Furthermore, the process of proposing improvements and raising funds to resolve these issues is often done manually, requiring time and effort. Therefore, there is a need for a system that can collect and analyze data in real time and provide quick and effective improvement proposals.

[1424] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1425] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, a means for collecting data through a factory sensor system and analyzing the data in real time, a means for automatically generating factory improvement proposals based on the analysis results, a means for raising funds in cooperation with a crowdfunding platform, and a means for implementing a factory improvement project using the raised funds. This makes it possible to quickly and effectively solve problems while improving the operational efficiency of the factory.

[1426] A "data collection means" is a device that has the function of acquiring data in real time from sensors installed within the factory and sending it to a server.

[1427] The "data analysis means" is a device that uses AI algorithms to analyze collected data and identify problems related to factory operations.

[1428] The "plan generation means" is a device that automatically generates improvement proposals for the factory based on the analysis results of the data analysis means.

[1429] The "fundraising instrument" is a device that automatically posts the generated improvement proposals on crowdfunding platforms and social impact investment funds, thereby raising funds.

[1430] The "project implementation means" is a device that uses the procured funds to arrange resources for implementing the factory improvement project and carries out specific improvement activities.

[1431] The "success case evaluation means" is a device that has the function of evaluating the results of a project and recording them in a database as success cases.

[1432] A "factory sensor system" is a group of sensors that collect data from various devices and processes within a factory.

[1433] A "means for real-time analysis" is a device that has the ability to instantly pass collected data to an AI algorithm for analysis.

[1434] The "means for automatic generation" is a device that has the function of automatically creating optimal improvement proposals based on the analysis results of an AI algorithm.

[1435] A "crowdfunding platform" is a service for raising funds online, a website where project details are published and funding is sought from backers.

[1436] The "means for implementing improvement projects" refers to a device that has the function of arranging the necessary resources after fundraising and actually carrying out improvement activities in the factory.

[1437] The present invention is an integrated system for supporting efficiency in factory operations, which includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, a factory sensor system, a real-time analysis means, an automatic generation means, a crowdfunding platform, and a means for implementing an improvement project.

[1438] Hardware and Software Configuration

[1439] Hardware

[1440] Factory sensor system: A set of sensors used to collect data from each device and process in the factory. For example, temperature sensors, humidity sensors, vibration sensors, etc. are used.

[1441] Server: A central computer that handles data collection, analysis, plan generation, fundraising management, project execution, and success evaluation.

[1442] Smart glasses or terminals: Used for real-time monitoring and data entry on-site.

[1443] software

[1444] Python: A programming language used to build entire programs and process data.

[1445] Sci-kit Learn: A machine learning library for data analysis.

[1446] Requests: Used for API integration with crowdfunding platforms.

[1447] Generative AI model: An AI model that generates optimal plans from data analysis results.

[1448] Data processing and calculation

[1449] 1. Data Collection:

[1450] The server collects data in real time from the factory's sensor system. The sensors acquire various data (temperature, humidity, vibration, etc.) within the factory, and this data is sent to the server via smart glasses or terminals.

[1451] 2. Data Analysis:

[1452] The server analyzes the collected data using AI algorithms powered by Sci-kit Learn, which identifies key issues in factory operations (such as excessive energy consumption and production inefficiencies).

[1453] 3. Plan Generation:

[1454] Based on the analysis results, the server uses a generative AI model to automatically generate improvement suggestions, such as specific measures to reduce energy consumption or optimize production processes.

[1455] 4. Funding:

[1456] The server then posts the automatically generated plan on the crowdfunding platform, where investors and supporters can review the plan and provide funding.

[1457] 5. Project execution:

[1458] The server uses the raised funds to implement on-site factory improvement projects, specifically converting the funds into resources (such as new equipment and machinery) and supplying them to the factory.

[1459] 6. Success Case Evaluation:

[1460] The server evaluates the project results and records them in a database as success stories that can be used as references at other factories to solve similar problems.

[1461] Examples of concrete examples and prompts

[1462] Specific examples

[1463] If Factory A has a problem with high energy consumption, sensors collect real-time information on the factory's temperature and machine operation, and send the data to a server. The server then analyzes the data using an AI algorithm to identify the cause of the energy consumption. Based on the analysis results, improvement proposals are generated, such as replacing machines with more energy-efficient ones or introducing a new energy management system. These proposals are then posted on a crowdfunding platform, and once funds are raised, actual improvement activities are carried out on-site.

[1464] Prompt Sentence Examples

[1465] "Please suggest the best plan to reduce energy consumption in the factory. The sensor data is as follows: {sensor_data}"

[1466] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1467] Step 1:

[1468] Data collection

[1469] The server receives data in real time from sensors installed in the factory, including information on temperature, humidity, vibration, operating hours, etc. The input sensor data is sent to the server and stored in a database.

[1470] Step 2:

[1471] Data analysis

[1472] The server passes the collected data to an AI algorithm for analysis. Sci-kit Learn is used to apply machine learning models to identify problems in factory operations (e.g., excessive energy consumption, inefficient processes, etc.). The input is sensor data stored in a database, and the output is the identified problems as a result of the analysis.

[1473] Step 3:

[1474] Plan Generation

[1475] The server uses a generative AI model based on the results of data analysis to automatically generate improvement suggestions, including energy-saving measures, production process optimization, resource reallocation, etc. The input is the results of data analysis, and the output is detailed improvement suggestions.

[1476] Step 4:

[1477] Crowdfunding

[1478] The server automatically posts the generated improvement proposal on the crowdfunding platform. Specifically, it uses the Requests library to submit the plan in the appropriate format. The input is the generated improvement proposal, and the output is the project information posted on the crowdfunding platform.

[1479] Step 5:

[1480] Funding

[1481] Users (investors and supporters) review improvement proposals posted on the crowdfunding platform and provide funding. The server manages the collected funds and distributes resources according to the required phase. The input is funding information from the crowdfunding platform, and the output is the success of fundraising.

[1482] Step 6:

[1483] Project Execution

[1484] The server uses the raised funds to arrange resources to implement factory improvement projects. Resources can include new machinery, energy efficiency devices, improvement supplies, etc. The inputs are the successful fundraising and required resource information, and the outputs are the arranged resources and the implemented improvement projects.

[1485] Step 7:

[1486] Success Case Evaluation

[1487] The server evaluates the project results and records them in a database as success stories. It also provides the evaluation results to other factories as reference information. The input is the project execution result data, and the output is the evaluated success stories and their records.

[1488] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1489] This invention is an integrated system for solving social issues in impoverished areas and building sustainable economic activities. This system consists of a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a means for evaluating success cases, and an emotion engine that recognizes the user's emotions.

[1490] Data collection methods

[1491] The user's device (smartphone or tablet) inputs local situation data through a dedicated app. The input data includes soil condition, climate, crop growth status, and the user's emotions. The device then sends the data via the Internet to a server, which then stores the data in a database.

[1492] Data Analysis Methods

[1493] The server then passes the local data stored in the database to an AI algorithm for analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These analysis results are then used in the subsequent plan generation process.

[1494] Emotion Engine

[1495] The server uses an emotion engine to analyze data received from the local user terminal and recognize the user's emotion. The recognized emotion data is fed back to the data analysis means and plan generation means, which then generate and adjust plans that take the user's emotional state into consideration.

[1496] Plan Generation Method

[1497] The server automatically generates specific solutions based on the AI ​​analysis results and emotional data. For example, it may suggest solutions such as using organic fertilizer or installing a new irrigation system. The generated plan is displayed on a dashboard for expert review.

[1498] Funding methods

[1499] The server generates a page for posting the finalized plan on a crowdfunding site or social impact investment fund platform. This page includes an overview of the plan, the target funding amount, the expected impact, etc. Users (backers and investors) access the crowdfunding site, review the posted plan, and then make donations or investments to support the project.

[1500] Project implementation method

[1501] The server uses the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment, etc.) to local experts and NGOs. The user (local project team) uses the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, etc.).

[1502] Success case evaluation tools

[1503] The server periodically evaluates the project's results and records them in a database as success stories. This allows other impoverished areas facing similar challenges to implement projects based on these success stories. Users (project teams in other areas) can retrieve information from this database and apply it to projects in their own areas.

[1504] Specific examples

[1505] For example, consider the case of implementing an agricultural improvement project in a poor area.

[1506] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, crop growth status, and emotional state into their terminals. This information is sent to the server and stored in a database.

[1507] 2. The server analyzes the stored data using AI algorithms and an emotion engine to identify key fault factors, such as soil nutrient deficiency, insufficient irrigation, and the user's emotional state.

[1508] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer, introducing a new irrigation system, or support measures that take the user's emotional state into account. The generated plans are then reviewed and revised by experts.

[1509] 4. The server posts the revised plan on the crowdfunding site and raises the necessary funds. Users (backers and investors) provide funding based on the posted plan.

[1510] 5. The server uses the funds raised to provide local farmers with organic fertilizer and arrange resources to install new irrigation systems. The users (local farmers) use these resources to improve the efficiency and quality of their farming.

[1511] 6. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[1512] In this way, by solving specific problems in poor areas through this system and taking into account the emotional state of the user, it is possible to build more effective and sustainable socio-economic activities.

[1513] The processing flow will be explained below.

[1514] Step 1:

[1515] The device (a smartphone or tablet used by the user) inputs local situation data using a dedicated app. The input data includes soil condition, climate, crop growth status, and the user's emotional state. The device then transmits the data to a server via the Internet.

[1516] Step 2:

[1517] The server stores the data received from the devices in a database that stores all the data collected over time and stores the data for subsequent analysis and planning.

[1518] Step 3:

[1519] The server preprocesses the data stored in the database. Specifically, it removes incomplete data, corrects outliers, and prepares the data in a format suitable for analysis. The preprocessed data is then stored in the data store for analysis.

[1520] Step 4:

[1521] The server then passes the preprocessed data to an AI algorithm for data analysis. As a result of the analysis, the most important social issues (e.g., soil nutrient deficiency, insufficient irrigation, etc.) are identified and prioritized. These results are used in the subsequent plan generation process.

[1522] Step 5:

[1523] The server uses an emotion engine to analyze the user's emotional data. It detects the user's emotional state in real time based on their input and usage, and collects the data. The emotional data is then integrated with the data analysis results.

[1524] Step 6:

[1525] The server automatically generates specific solutions based on the data analysis results and emotional data. For example, these could include using organic fertilizers or installing a new irrigation system, as well as psychological support and training sessions that take the user's emotional state into account. The generated plans are then displayed on a dashboard for expert review.

[1526] Step 7:

[1527] The expert reviews the plan presented by the server through a dashboard and makes corrections or additions as necessary. The expert's feedback is sent to the server, and the final version of the plan is completed.

[1528] Step 8:

[1529] The server generates a page for posting the final plan on a crowdfunding site or social impact investment platform, including a summary of the plan, the funding goal, and the expected impact.

[1530] Step 9:

[1531] Users (backers and investors) access the crowdfunding site, check the posted plans, make donations or investments to support the project, and the funds are sent to the server.

[1532] Step 10:

[1533] The server manages the collected funds and distributes the necessary resources for each phase of the project, including fertilizer, irrigation equipment, educational materials, psychological support resources, etc. The funds are distributed to experts and NGOs working on the ground.

[1534] Step 11:

[1535] Users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment, conducting psychological support sessions, etc.). Activity status is reported sequentially from the terminals to the server.

[1536] Step 12:

[1537] The server periodically evaluates the progress of the project and records it in a database as a success story. This evaluation is based on the project's progress, results, changes in user emotions, and the effectiveness of problem solving.

[1538] Step 13:

[1539] The server will publish a database of success stories and make them accessible to users (project teams in other regions). Success stories include specific solutions, implementation methods, results, and changes in user emotions.

[1540] Step 14:

[1541] Users (project teams in other regions) can obtain information from the database of success stories and apply it to projects in their own regions. Based on the information obtained, they can carry out projects aiming to solve similar problems.

[1542] These are the specific steps of the program processing of this system, which can effectively solve various problems in poor areas and, by taking into account the emotional state of the user, can build more effective and sustainable socio-economic activities.

[1543] Example 2

[1544] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1545] Building sustainable socioeconomic activities in impoverished areas requires a detailed understanding of specific local issues and the rapid and efficient provision of appropriate solutions based on that understanding. However, conventional methods lack an integrated process, from data collection and analysis to solution proposals, fundraising, implementation, and outcome evaluation, resulting in time-consuming information sharing and decision-making at each stage. Furthermore, they fail to take into account the emotional state of users, limiting the means for maximizing project effectiveness. Therefore, the present invention aims to provide an integrated system that solves these issues.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data analysis means, an emotion recognition means, a plan generation means, a fundraising means, a project execution means, and a success case evaluation means. This makes it possible to quickly and efficiently identify specific local issues and provide appropriate solutions based on the analysis results and the user's emotional state. Furthermore, by managing a series of processes from fundraising to project execution and outcome evaluation in an integrated manner, information sharing and decision-making can be accelerated, effectively promoting the establishment of sustainable socioeconomic activities in impoverished areas.

[1547] The "data collection means" is a device or system for receiving data from information processing devices used by local users.

[1548] A "data analysis means" is a device or system for analyzing collected data using machine learning algorithms.

[1549] An "emotion recognizer" is a device or system for identifying a user's emotional state from collected data.

[1550] The "plan generation means" is a device or system for generating specific solutions based on the data analysis results and emotion data.

[1551] A "funding vehicle" is a device or system for generating information for posting a generated plan on a funding platform.

[1552] A "Project Execution Vehicle" is a device or system that utilizes raised funds to provide resources and execute a project.

[1553] A "success case evaluation tool" is a device or system for evaluating the results of a project and recording the results in a database.

[1554] This invention is an integrated system for solving social issues in impoverished areas and building sustainable economic activities. This system consists of data collection means, data analysis means, emotion recognition means, plan generation means, fundraising means, project implementation means, and success case evaluation means.

[1555] Data collection methods

[1556] Users input data about their local conditions through a dedicated app installed on their smartphone or tablet. The data includes soil pH, temperature, precipitation, crop growth status, and the user's emotional state. The device then transmits this data to a server in real time via an internet connection.

[1557] Data Analysis Methods

[1558] The server stores the received data in a database (e.g., MySQL, PostgreSQL), then passes the data to an AI algorithm (e.g., TensorFlow, PyTorch) for analysis. Specific analysis results include determining the nutrient status of the soil and assessing the risk of drought.

[1559] emotion recognition means

[1560] The server uses an emotion engine (e.g., OpenAI's GPT-3) to recognize the user's emotions from the received data. The emotion data (e.g., anxiety, hope, irritation) is stored in a database and subsequent processing is based on it.

[1561] Plan Generation Method

[1562] The server automatically generates specific solutions based on the AI ​​analysis results and emotion data. The generated plan suggests measures such as using organic fertilizer, installing a new irrigation system, and providing psychological support. The plan is displayed on a dashboard (e.g., PowerBI) and awaits expert review.

[1563] Funding methods

[1564] The server automatically generates a page for posting the approved plan on a crowdfunding site (e.g., Kickstarter) or a social impact investment fund platform, detailing the plan's outline, target funding amount, and expected social impact.

[1565] Project implementation method

[1566] The server uses the funds raised to arrange for the provision of necessary resources (e.g., fertilizer, irrigation equipment) to local experts and NGOs. The provision of resources includes coordination with logistics centers and local vendors. The users (local project teams) utilize the provided resources to carry out specific project activities (e.g., spreading fertilizer, installing irrigation equipment).

[1567] Success case evaluation tools

[1568] The server periodically evaluates the project's performance and records the results in a database. Evaluation criteria include profitability, sustainability, and social impact. Success stories can also be used as reference for projects in other regions.

[1569] Specific examples

[1570] For example, consider the case of implementing an agricultural improvement project in a poor area.

[1571] 1. Users (local farmers or researchers) input information such as soil conditions, weather data, crop growth status, and emotional state into their devices. This information is sent to the server and stored in a database.

[1572] 2. The server analyzes the stored data using AI algorithms and an emotion engine to identify key fault factors, such as soil nutrient deficiency, insufficient irrigation, and the user's emotional state.

[1573] 3. The server generates specific solutions to the identified obstacles, such as using organic fertilizer, introducing a new irrigation system, or support measures that take the user's emotional state into account. The generated plans are reviewed by experts and posted on a crowdfunding site.

[1574] 4. Users (backers and investors) access the crowdfunding site and provide funding.

[1575] 5. The server will use the funds raised to provide organic fertilizer to local farmers and arrange resources to install a new irrigation system.

[1576] 6. Users (local farmers) use these resources to improve the efficiency and quality of their farming.

[1577] 7. The server evaluates the results of this project and records them in a database as success stories. Users (farmers and project teams in other regions) can refer to these success stories and apply them to projects in their own regions.

[1578] Prompt Sentence Examples

[1579] Use the following data to generate a plan for improving agriculture in a poor area.

[1580] Soil condition: Highly acidic

[1581] Climate data: Short rainy season

[1582] Crop growth status: Poor growth

[1583] User Emotion: Anxiety

[1584] Please explain your specific solution and why.

[1585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1586] Step 1: Data collection

[1587] Users input local situation data, such as soil pH, temperature, precipitation, crop growth status, and the user's emotional state, through a dedicated app installed on a smartphone or tablet.

[1588] Input: Local condition data (e.g. soil pH, temperature, precipitation, crop condition, emotional state)

[1589] Output: Data stored on a smartphone or tablet

[1590] Step 2: Send data

[1591] The terminal sends the input data to the server in real time via the Internet. During transmission, encrypted communication (e.g., SSL / TLS) is used to maintain data integrity.

[1592] Input: Data stored on a smartphone or tablet

[1593] Output: Data sent to the server

[1594] Step 3: Save data

[1595] The server stores the received data in a database (e.g., MySQL, PostgreSQL). When storing the data, it also backs up the data to prevent data loss. It also monitors the data storage capacity.

[1596] Input: Received data

[1597] Output: Data stored in the database

[1598] Step 4: Data analysis

[1599] The server then passes the stored data to AI algorithms (e.g., TensorFlow, PyTorch) for analysis. The analysis includes determining the nutrient status of the soil and assessing the risk of drought. The most important social issues are identified and prioritized.

[1600] Input: Data stored in a database

[1601] Output: Analysis results (e.g., soil nutrient deficiency, insufficient irrigation)

[1602] Step 5: Emotion Recognition

[1603] The server uses an emotion engine (e.g., OpenAI's GPT-3) to recognize the user's emotion from the received data. The recognized emotion data is stored in a database and subsequent processing is based on it.

[1604] Input: Local situation data stored in a database

[1605] Output: Recognized emotion data

[1606] Step 6: Generate a plan

[1607] The server automatically generates specific solutions based on the AI ​​analysis results and emotional data. The generated plan suggests measures such as using organic fertilizer, installing a new irrigation system, and providing psychological support. The plan is displayed on a dashboard (e.g., PowerBI).

[1608] Input: Analysis results and emotion data

[1609] Output: Auto-generated solution (plan)

[1610] Step 7: Expert Review

[1611] The expert reviews the plan generated by the server via a dashboard, providing corrections or additional advice as necessary, and the reviewed plan moves on to the next step after receiving final approval.

[1612] Input: Auto-generated solution (plan)

[1613] Output: Reviewed and revised plan

[1614] Step 8: Generate your fundraising page

[1615] The server automatically generates a page for posting the approved plan on a crowdfunding site (e.g., Kickstarter) or a social impact investment fund platform, detailing the plan's outline, target funding amount, and expected social impact.

[1616] Input: Reviewed and revised plan

[1617] Output: Crowdfunding page

[1618] Step 9: Funding

[1619] Users (backers and investors) access crowdfunding sites, check the plans posted, and then donate or invest in support via credit card or digital payment systems (e.g., PayPal).

[1620] Input: Crowdfunding page

[1621] Output: Funds provided

[1622] Step 10: Resource Arrangements

[1623] The server will use the funds raised to provide necessary resources (e.g., fertilizer, irrigation equipment) to local experts and NGOs, including liaising with logistics centers and local vendors.

[1624] Input: Funds Provided

[1625] Output: Arranged resources

[1626] Step 11: Project execution

[1627] Users (local project teams) utilize the provided resources to carry out specific project activities, such as spreading fertilizer, installing irrigation systems, and implementing educational programs.

[1628] Input: Arranged resource

[1629] Output: Executed projects

[1630] Step 12: Success Case Evaluation

[1631] The server periodically evaluates the project's performance and records the results in a database. Evaluation criteria include profitability, sustainability, and social impact. Success stories can also be used as reference for projects in other regions.

[1632] Input: Data of the executed project

[1633] Output: Database of evaluation results and success stories

[1634] (Application example 2)

[1635] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1636] Traditional operations in poor areas and brick-and-mortar stores require efficient data collection and analysis, solution generation, fundraising, and project implementation, but there is a lack of systems that handle these elements in an integrated manner. Furthermore, while it is important to reflect the emotions and specific needs of users in solving local issues, few systems have this functionality. This makes it difficult to generate specific solutions tailored to local conditions and to raise funds effectively.

[1637] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1638] In this invention, the server includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, an emotion recognition means, and a crowdfunding page generation means, which enable the integrated collection and analysis of data, the generation of specific solutions that take into account user emotions, effective support for fundraising, and the execution and evaluation of projects.

[1639] The "data collection means" is a device or software that collects local situation data from the user's terminal and transmits it to the server.

[1640] "Data analysis tools" are devices or software that analyze collected data using AI algorithms and identify important social issues.

[1641] The "plan generation means" is a device or software that automatically generates specific solutions based on the results of data analysis and emotion data.

[1642] A "fundraising vehicle" is a device or software used to post the generated plan on a crowdfunding site or investment platform and raise the necessary funds.

[1643] "Project Implementation Instrument" means equipment or software that provides resources to support the implementation of the Project on the ground, based on the funds raised.

[1644] A "success case evaluation tool" is a device or software that evaluates the results of a project and records them in a database as success cases.

[1645] An "emotion recognition means" is a device or software that analyzes the user's emotions and feeds the results back into data analysis and plan generation.

[1646] A "crowdfunding page generation means" is a device or software that automatically generates a page for a crowdfunding site based on the generated plan.

[1647] The present invention is an integrated system that supports the improvement of business management in poor areas and brick-and-mortar stores. The system includes a data collection means, a data analysis means, a plan generation means, a fundraising means, a project execution means, a success case evaluation means, an emotion recognition means, and a crowdfunding page generation means.

[1648] Data collection methods

[1649] Users use devices (smartphones or tablets) to input local situation data and store operation data through a dedicated app. The input data includes sales data, customer feedback, inventory status, and the user's emotional state. The devices then send the data via the Internet to a server, which stores the data in a database.

[1650] Data Analysis Methods

[1651] The server passes the local data and store operation data stored in the database to an AI algorithm for analysis. As a result of the analysis, the most important issues (e.g., declining sales, declining customer satisfaction, etc.) are identified and prioritized. The results of this analysis are used in the subsequent plan generation process.

[1652] emotion recognition means

[1653] The server uses the emotion recognition means to analyze emotions from the user's input data. The analysis results are fed back to the data analysis means and the plan generation means, and a plan is generated or adjusted taking the user's emotional state into consideration.

[1654] Plan Generation Method

[1655] The server automatically generates specific solutions based on the AI ​​analysis results and emotion data, such as suggestions for improving marketing strategies or optimizing inventory management. The generated plans are then displayed on a dashboard for expert review.

[1656] Funding methods

[1657] The server automatically generates a page to post the finalized plan on a crowdfunding site or social impact investment fund platform. This page includes an overview of the plan, the target funding amount, and the expected effects. Users (backers and investors) access the crowdfunding site, review the posted plan, and then make donations or investments to support the project.

[1658] Project implementation method

[1659] The server uses the funds raised to provide the necessary resources (e.g., equipment, materials, etc.) to the local team. The users (local project teams and store operators) use the provided resources to carry out specific project activities (e.g., introducing new equipment, training employees, etc.).

[1660] Success case evaluation tools

[1661] The server periodically evaluates the project results and records them in a database as success stories. This allows other regions and stores facing similar challenges to use these success stories as references for their own projects. Users (project teams and store managers in other regions) can retrieve information from this database and apply it to projects in their own regions and stores.

[1662] Hardware and software used

[1663] Hardware: Server

[1664] Software: Python, Flask, scikit-learn, SQLAlchemy, emotion recognition library

[1665] Examples of prompt statements

[1666] For example, say a cafe owner is experiencing declining sales and customer satisfaction. The owner enters the following data into the app:

[1667] Sales data: January - 100,000 yen, February - 80,000 yen

[1668] Customer feedback: "The coffee is cold" and "The store is crowded"

[1669] Availability: Coffee beans - 50 bags, Milk - 20 bottles

[1670] Operator's emotional state: "I feel stressed"

[1671] Enter this as the prompt.

[1672] Prompt Sentence Examples

[1673] Sales data: January - 100,000 yen, February - 80,000 yen

[1674] Customer feedback: "The coffee is cold" and "The store is crowded"

[1675] Availability: Coffee beans - 50 bags, Milk - 20 bottles

[1676] Operator's emotional state: "I feel stressed"

[1677] Use this data to automatically generate specific plans to improve sales and customer satisfaction.

[1678] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1679] Step 1:

[1680] The terminal collects local situation data and store operation data entered by the user.

[1681] Inputs: Sales data, customer feedback, inventory status, emotional state

[1682] Specific operation: The user enters data using the app, and the entered data is sent to the server by the device.

[1683] Step 2:

[1684] The server stores the received data in a database.

[1685] Input: Data sent from the terminal

[1686] Output: Data stored in the database

[1687] Specific operation: The server automatically stores the received data in the database.

[1688] Step 3:

[1689] The server uses data analysis means to analyze the data stored in the database using AI algorithms.

[1690] Input: Data stored in the database

[1691] Output: Analysis results (identification and prioritization of important issues)

[1692] What happens: The server runs an AI algorithm (e.g., random forest) to identify issues like declining sales or poor customer satisfaction.

[1693] Step 4:

[1694] The server uses an emotion recognition means to analyze the user's emotions.

[1695] Input: User emotional state data

[1696] Output: Recognized emotion data

[1697] Specific operation: The server uses an emotion recognition library to analyze the emotional state and saves the results.

[1698] Step 5:

[1699] The server generates a specific solution using a plan generation means based on the analysis results and emotion data.

[1700] Input: Analysis results, emotion data

[1701] Output: Specific solutions (e.g., marketing strategies, inventory management optimization)

[1702] Specific operation: The server executes the automatic generation algorithm and generates an improvement plan.

[1703] Step 6:

[1704] The server generates a page for posting the generated plan on the crowdfunding site using a crowdfunding page generation means.

[1705] Input: Specific solution

[1706] Output: Crowdfunding page

[1707] Specific operation: The server automatically generates a page outlining the plan, the target funding amount, and the expected results.

[1708] Step 7:

[1709] Users (supporters and investors) access the crowdfunding site, check the posted plans, and make donations or investments to support the project.

[1710] Enter: Crowdfunding page

[1711] Output: Support and investment

[1712] Specific actions: The user reviews the plan and makes a decision to support or invest.

[1713] Step 8:

[1714] The server provides resources using the project execution means based on the raised funds.

[1715] Input: Funds raised

[1716] Output: Resources provided (e.g., equipment, materials)

[1717] Specific actions: The server arranges for the necessary resources and provides them to the local project team and store operators.

[1718] Step 9:

[1719] The server evaluates the results of the project using the success case evaluation means and records the results in a database.

[1720] Input: Project execution data

[1721] Output: Recorded data of success stories

[1722] Specific actions: The server evaluates the project's results and stores them in a database as success stories to help with similar projects.

[1723] Step 10:

[1724] Users (project teams and store operators in other regions) retrieve success stories from the database and apply them to projects in their own regions or stores.

[1725] Input: Success story data retrieved from the database

[1726] Output: Specific application of the project

[1727] Specific actions: Users refer to success stories and implement projects tailored to the characteristics of their local area.

[1728] 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.

[1729] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1730] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1731] 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.

[1732] 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 includes both affect 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.

[1733] 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.

[1734] 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).

[1735] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1736] 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."

[1737] 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.

[1738] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1739] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1740] 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.

[1741] 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.

[1742] 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.

[1743] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1744] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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.

[1749] The following is further disclosed regarding the above embodiment.

[1750] (Claim 1)

[1751] data collection means;

[1752] Data analysis means;

[1753] plan generation means;

[1754] Funding methods and

[1755] Project implementation methods,

[1756] A success story evaluation tool;

[1757] A system including:

[1758] (Claim 2)

[1759] 2. The system of claim 1, wherein the data collection means receives data from a local user terminal.

[1760] (Claim 3)

[1761] 2. The system according to claim 1, wherein the data analysis means analyzes the collected data using an AI algorithm.

[1762] "Example 1"

[1763] (Claim 1)

[1764] data collection means;

[1765] Data analysis means;

[1766] plan generation means;

[1767] Funding methods and

[1768] Project implementation methods,

[1769] A success story evaluation tool;

[1770] A user terminal that inputs local situation data and transmits it to a server in real time;

[1771] The server analyzes the data stored in the database and identifies social issues using AI algorithms.

[1772] A means to generate concrete solutions based on the analysis results and have them reviewed by experts;

[1773] You can raise funds by posting your plan on crowdfunding sites and investment funds,

[1774] The means to provide the necessary resources based on the funds raised to carry out project activities on the ground;

[1775] A means to evaluate project results and record them as success stories in a database;

[1776] A system including:

[1777] (Claim 2)

[1778] 2. The system according to claim 1, wherein the data collection means comprises means for receiving data from local user terminals and storing the data in a database via a server.

[1779] (Claim 3)

[1780] 2. The system of claim 1, wherein the data analysis means includes means for analyzing the collected data using an AI algorithm to identify and prioritize important social issues.

[1781] "Application Example 1"

[1782] (Claim 1)

[1783] data collection means;

[1784] Data analysis means;

[1785] plan generation means;

[1786] Funding methods and

[1787] Project implementation methods,

[1788] A success story evaluation tool;

[1789] A means of collecting data through the factory's sensor system and analyzing it in real time,

[1790] A means for automatically generating factory improvement proposals based on the analysis results;

[1791] A means of raising funds in collaboration with crowdfunding platforms,

[1792] the means to use the funds obtained to implement factory improvement projects;

[1793] A system including:

[1794] (Claim 2)

[1795] 10. The system of claim 1, wherein the data collection means receives data from sensors installed within the factory.

[1796] (Claim 3)

[1797] 2. The system of claim 1, wherein the data analysis means analyzes the collected data with an AI algorithm and generates optimal improvement suggestions using a generative AI model.

[1798] "Example 2: Combining Emotion Engines"

[1799] (Claim 1)

[1800] data collection means;

[1801] Data analysis means;

[1802] An emotion recognition means;

[1803] plan generation means;

[1804] Funding methods and

[1805] Project implementation methods,

[1806] A success story evaluation tool;

[1807] A system including:

[1808] (Claim 2)

[1809] 2. The system according to claim 1, wherein the data collection means receives data from an information processing device operated by a local user.

[1810] (Claim 3)

[1811] 2. The system of claim 1, wherein the data analysis means analyzes the collected data using a machine learning algorithm.

[1812] (Claim 4)

[1813] 10. The system of claim 1, wherein the emotion recognition means identifies an emotional state from the collected data.

[1814] (Claim 5)

[1815] 2. The system of claim 1, wherein the plan generating means generates a solution based on the data analysis results and the sentiment data.

[1816] (Claim 6)

[1817] The system of claim 1 , wherein the fundraising means generates information for posting the generated plan on a fundraising platform.

[1818] (Claim 7)

[1819] 2. The system according to claim 1, wherein the project execution means utilizes the procured funds to provide resources and execute the project.

[1820] (Claim 8)

[1821] 2. The system according to claim 1, wherein the success case evaluation means evaluates the results of the project and records the results in a database.

[1822] "Application example 2 when combining emotion engines"

[1823] (Claim 1)

[1824] data collection means;

[1825] Data analysis means;

[1826] plan generation means;

[1827] Funding methods and

[1828] Project implementation methods,

[1829] A success story evaluation tool;

[1830] An emotion recognition means;

[1831] A crowdfunding page generation means;

[1832] A system including:

[1833] (Claim 2)

[1834] 2. The system of claim 1, wherein the data collection means receives data from a local user terminal.

[1835] (Claim 3)

[1836] 2. The system according to claim 1, wherein the data analysis means analyzes the collected data using an AI algorithm. [Explanation of symbols]

[1837] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. data collection means; Data analysis means; plan generation means; Funding methods and Project implementation methods, A success story evaluation tool; A system including:

2. 2. The system of claim 1, wherein said data collection means receives data from a local user terminal.

3. 2. The system according to claim 1, wherein the data analysis means analyzes the collected data using an AI algorithm.

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