system

Abstract

We provide the system. [Solution] A processing unit that receives information, A generation mechanism that automatically generates a commercialization procedure based on the information received by the calculation unit, A forecasting unit that performs profit forecasting based on the commercialization procedure automatically generated by the aforementioned generation mechanism, A means for selecting stakeholders based on the profit forecast made by the aforementioned forecasting unit, A means of determining whether or not to participate in the proposed project based on the information of the aforementioned stakeholders, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] When individuals or entrepreneurs interested in new businesses commercialize their ideas, they face difficulties in effectively finding suitable collaborators and resources, and also lack scalability and accuracy of profit prediction for judging the success or failure of the business. As a result, the commercialization process does not progress efficiently, and there are many cases where potentially successful ideas are not realized. The purpose of this invention is to solve these problems and provide a platform for idea providers and participants to cooperate efficiently and lead new businesses to success.

Means for Solving the Problems

[0005] This invention provides a generation mechanism that includes a calculation unit for receiving information and automatically generates a commercialization procedure based on the information received by the calculation unit. Furthermore, by including a prediction unit that performs profit forecasting based on the commercialization procedure automatically generated by the generation mechanism, it enables the visualization of profits at the planning stage. The means for selecting stakeholders enables the identification of optimal collaborators based on the profit forecasts made by the prediction unit. It also includes a means for deciding whether or not to participate in a proposed project based on the stakeholders' information, enabling the rapid construction of an actionable team. This enables an efficient commercialization process and supports the growth and success of new businesses by matching them with potential investors.

[0006] The "information receiving processing unit" is a part of a computer system used to process input data, and it has the function of reading information transmitted from an external source and initiating appropriate actions.

[0007] An "automatic generation mechanism" refers to a program and hardware configuration that analyzes input information and mechanically creates the necessary processes and procedures based on that information.

[0008] The "forecasting unit for profit forecasting" refers to a component that includes a calculation model for estimating the profitability of business activities and a computer system that performs calculations for that purpose.

[0009] "Means of selecting stakeholders" refers to functions that include algorithms and database management systems for finding the best people for a particular project or task and assigning them the necessary roles in the planning and execution of the project.

[0010] "Means of determining eligibility to participate" refers to processes and systems that provide information to stakeholders to help them decide whether or not to participate in a proposed project or task, and to support their final decision to participate. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0017] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention provides a system that offers an innovative platform for commercializing ideas. This system comprises a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, a prediction unit for profit forecasting, means for selecting stakeholders, and means for determining whether or not to participate. The following describes the program processing in natural language for embodiments of the invention.

[0033] Users log in to the platform using their devices and register their ideas. The idea information entered by the user is sent from the device to the server. Based on the received information, the server activates a generation AI and automatically generates the specific steps necessary for commercialization. Based on the generated commercialization steps, the server uses a prediction unit to perform profit forecasts and saves the results to a database.

[0034] The server then uses profit forecasts to select the necessary stakeholders. This process involves referencing a database of registered members' skills and experience to identify members suitable for the commercialization steps. Selected members are provided with project details and success-based compensation via their terminals.

[0035] In the decision-making process for participation, users (registered members) review the compensation conditions and project details to ensure they are satisfied before deciding to participate. This makes it possible to quickly assign the right members to projects.

[0036] As a concrete example, consider a scenario where a user proposes an idea for a new fitness app. The server uses AI to suggest the necessary steps from app prototyping to launch, and provides profit forecasts based on market data. Based on this, it selects experienced mobile app developers and marketing personnel and provides a means for them to commit to the project.

[0037] Furthermore, once a project reaches a certain stage of progress, the server selects potential investment targets and provides support to enable the project to scale up. This system allows users to efficiently commercialize their ideas and maximize potential business opportunities.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user logs into the platform using their device and opens the idea registration screen. The user enters information such as idea details, target market, and assumed problem, and registers the idea. The device sends the entered data to the server.

[0041] Step 2:

[0042] The server receives registered idea information and activates the generation AI. The server provides input information to the AI, which then automatically generates the specific steps necessary for commercialization. The generated step information is recorded in the server's database.

[0043] Step 3:

[0044] The server performs profit forecasts based on the generated commercialization steps. It references market data and past project data, and uses a profit forecasting model to simulate sales and profits. These profit forecast results are stored in a database.

[0045] Step 4:

[0046] The server selects the most suitable participants based on the projected profits. It searches a database of registered members' skills, experience, and desired compensation to select the members best suited to the commercialization steps. The selection results are sent to the terminal as information for deciding whether or not to participate.

[0047] Step 5:

[0048] Users (registered members) review project details and success fees via their devices. Users consider the provided information and decide whether or not to participate in the project. The decision is then sent from the device to the server.

[0049] Step 6:

[0050] The server forms teams based on user participation availability and performs initial setup to advance the project. It also periodically monitors progress data during project execution and makes adjustments as needed.

[0051] Step 7:

[0052] The server generates a list of potential investors at certain stages of the project's progress and matches them with investors. Investors are provided with information about the project's current status and future growth potential via their terminals.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] In the traditional process of commercializing an idea, many elements need to be coordinated, such as formulating specific procedures, forecasting profits, selecting appropriate personnel, and matching with investors, making it difficult to carry out efficiently. There is a need for a system that can automate these diverse processes and enable rapid and reliable commercialization.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes an information processing means for receiving information, an automatic generation means, an analysis means, a selection means, and a decision support means. This enables the efficient automation of the idea commercialization process, allowing users to easily realize commercialization.

[0058] "Information processing means" refers to a device or function that takes in various types of information received by a system and processes them appropriately.

[0059] An "automatic generation means" is a device or function that mechanically generates the specific steps for commercialization based on the received information.

[0060] "Analysis means" refers to a device or function for predicting and evaluating profits based on an automatically generated commercialization process.

[0061] "Selection method" refers to a device or function for identifying suitable stakeholders for a project based on profit forecast results.

[0062] A "decision-making support tool" is a device or function that provides stakeholders with detailed project information and compensation, thereby facilitating their decision to participate.

[0063] This invention is a system for efficiently commercializing ideas, encompassing the processes of information processing, process generation, profit forecasting, stakeholder selection, and decision support.

[0064] Users log in to the platform using their own devices and input their ideas. For example, they might input an idea for a new fitness app. The device sends this information to the server. The server uses a generative AI model based on the received idea information to automatically generate the steps necessary for commercialization. This AI model utilizes natural language processing and machine learning algorithms to generate steps such as prototype creation and market strategy development.

[0065] The server uses the generated processes to perform profit forecasts. This forecasting utilizes analytical software and market data reference tools (e.g., Google Analytics). Based on these profit forecasts, the server consults a database to select appropriate stakeholders, identifying the most suitable members based on their skills and experience. Stakeholders selected based on the results are then provided with detailed project information and compensation terms. This information is provided via terminals, allowing stakeholders to review the information on their own devices and make their participation decisions.

[0066] A concrete example of a prompt would be something like, "Please list the development steps required for a new fitness app." By inputting this prompt into the AI ​​generation model, the details of the commercialization process are smoothly presented.

[0067] In this way, users, terminals, and servers work together to streamline the process from idea to commercialization and provide a system that enables rapid realization.

[0068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0069] Step 1:

[0070] The user inputs their idea into the terminal. This input is done by using a text box to specifically describe the main points of the idea and confirming the information with a submit button. As a result, the user's idea information is sent from the terminal to the server.

[0071] Step 2:

[0072] The server processes the idea information received from the terminal using information processing tools. In this step, the received text data is analyzed and converted into prompt sentences to activate the generation AI model. This conversion allows the analyzed idea information to be used as input data to generate concrete steps for commercialization.

[0073] Step 3:

[0074] The server activates a generative AI model to automatically generate specific steps for commercialization. In this step, the necessary processes for commercialization (e.g., prototype development, user testing, market entry plan, etc.) are generated using machine learning algorithms. The output from the generative AI model is a clearly defined list of commercialization steps.

[0075] Step 4:

[0076] The server uses analytical tools to perform profit forecasts based on automatically generated business development steps. Profit forecasting utilizes market data and historical data from similar cases, applying a forecasting algorithm to simulate revenue. The output of the profit forecast is the projected revenue figure and the probability of success.

[0077] Step 5:

[0078] The server identifies stakeholders using selection criteria based on profit forecast results. This process involves referencing a stakeholder database to narrow down individuals and groups with the necessary skill sets and experience. The output is a list of stakeholders suitable for the project.

[0079] Step 6:

[0080] The server provides selected participants with project details and compensation terms using decision support tools. This includes sending notifications to the participants' terminals. Participants receive the notifications and decide whether or not to participate based on the information provided. The output is a history of participation decisions.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] When commercializing an idea into concrete content or services, it is necessary to develop appropriate processes, forecast profitability, and select the necessary stakeholders, but doing so efficiently and quickly is difficult. Furthermore, there is a need for methods to encourage participation in the project and increase the success rate by providing stakeholders with appropriate information and incentives.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes a processing unit for receiving information, components for automatically generating business procedures, an estimation device for forecasting revenue, and a device for selecting appropriate skill providers and providing detailed information via an information terminal. This makes it possible to efficiently commercialize ideas and optimize the participation of stakeholders.

[0086] A "processing device for receiving information" is a device that acquires data and information input from an external source and converts it into a format that can be used internally.

[0087] A "component for automatically generating business procedures" is an element that has the function of automatically creating specific procedures and processes for carrying out business based on the information received.

[0088] A "revenue forecasting device" is a device that calculates expected revenue based on business procedures and provides the results.

[0089] A "device for selecting appropriate skill providers and providing detailed information via information terminals" is a device that analyzes the skills and experience of those involved, selects the most suitable personnel for a project, and provides detailed project information to those personnel.

[0090] This invention provides a system for rapidly commercializing ideas, which receives information and, based on that information, automatically generates business procedures, forecasts revenue, and selects stakeholders. The system for implementing this invention is configured as follows.

[0091] The server processes incoming idea information. Specifically, users access the platform using information terminals such as smartphones or personal computers and input their own ideas into the platform. This information is then sent to the server and processed internally.

[0092] The generation AI model installed on the server (e.g., OpenAI's GPT-4®) automatically generates specific procedures necessary for business implementation based on the received idea information. The generated procedures are then subjected to revenue forecasting by an estimation device, and the results are stored in a database. The server utilizes this revenue forecasting data to select skilled providers who should be involved in the project from the database and provides detailed project information through information terminals.

[0093] This system allows users to more efficiently turn their ideas into businesses. For example, suppose a user wants to create a new educational animation. By registering their idea on the platform, an AI model automatically generates project steps, predicts profitability, selects a suitable animation production team, and provides necessary details. In this way, the project can proceed smoothly and effectively.

[0094] An example of a prompt message might be: "Generate the steps for creating an animated educational content. Include the target market, budget, and a list of required staff."

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] Users access the platform using an information terminal and input their ideas. The input ideas are sent to the server as text data. The input data is combined with the user's account information for initial processing and then stored in the database as registered ideas.

[0098] Step 2:

[0099] The server activates the AI ​​model and generates business procedures based on the received idea data. This model is given prompts such as "Generate the steps for creating animation for educational content." The AI ​​then creates a detailed procedure list based on these prompts and outputs it to the database.

[0100] Step 3:

[0101] The server refers to a procedure list and uses an estimation tool to make revenue forecasts. The procedure list is entered, and the estimation tool performs calculations by comparing it with market data and data from similar past projects. The predicted revenue results are saved to the database.

[0102] Step 4:

[0103] The server uses revenue forecast results to select skilled providers who should participate in the project. It filters candidates with the necessary skills and experience from the stakeholders database and outputs a list of optimal candidates. The selection results are then notified to the relevant parties.

[0104] Step 5:

[0105] Detailed project information and participation requests will be sent to selected skilled providers via information terminals. Specific project details, projected revenue, roles, and compensation conditions will be provided. This process is intended to confirm the participants' willingness to participate.

[0106] Step 6:

[0107] Participants send their confirmations of participation to the server via their terminals. The server aggregates this feedback and makes final adjustments to prepare for the next steps in the project. This includes finalizing the participant list and determining the project schedule.

[0108] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0109] The present invention is a system that incorporates an emotion engine to support project progress by taking into account the user's emotional state in the process of commercializing an idea. This system includes a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, a prediction unit for predicting profits, means for selecting stakeholders, means for deciding whether or not to participate, as well as an emotion engine for recognizing and processing the user's emotions.

[0110] When a user logs into the platform via their device and registers an idea, the device sends that information to the server. The server then activates a generative AI based on the registered information to determine the steps to commercialization and predict profits. An emotion engine is incorporated into this process, analyzing the user's emotional state in real time in response to their input.

[0111] The emotion engine analyzes the user's input speed, stress level, and tone of voice and text to determine their emotional tendencies. This information is used to optimize the commercialization process, adjusting it to maintain user motivation throughout the process. For example, if a user is stressed, the system prioritizes concise and easy-to-understand suggestions.

[0112] Furthermore, the emotion engine also influences the participant selection process. By adjusting the communication strategy with selected participants according to the user's emotional state, the aim is to ensure smoother project progress. In this way, the system facilitates smooth participant engagement and strengthens collaboration.

[0113] Furthermore, it is possible to flexibly adjust the reward conditions offered based on user emotional feedback. For example, if a user shows high motivation, their motivation to participate can be further increased by offering rewards in a different form.

[0114] For example, if a user registers an idea for a fitness app, and the emotion engine determines that the user is excited during the registration process, the generating AI will display a positive analysis result regarding the app's market success potential on the server. In this way, the entire system considers the user's emotions and supports the optimal commercialization process.

[0115] The following describes the processing flow.

[0116] Step 1:

[0117] The user logs into the platform using their device and opens the idea registration form. The user enters information in each field and registers their idea. The registration is sent from the device to the server.

[0118] Step 2:

[0119] The server receives registered idea information and activates the generation AI. The server passes the idea information to the generation AI, which then generates the steps necessary for commercialization. The generated step information is stored on the server.

[0120] Step 3:

[0121] The server uses an emotion engine to analyze data collected during user input (such as input speed and text tone) and evaluate the user's emotional state. Based on the emotional state, adjustments are made to the generated commercialization steps.

[0122] Step 4:

[0123] The server performs profit forecasts based on the commercialization steps. It references market data and past project data, and uses a forecasting model to simulate profits. The forecast results are recorded in a database.

[0124] Step 5:

[0125] Based on the user's emotional state determined by the emotion engine, the server selects the most suitable stakeholders and adjusts the communication strategy. The most appropriate stakeholders are then selected and sent to the terminal along with detailed project information.

[0126] Step 6:

[0127] The user (registered member) checks the proposed project information and success fee amount on their device. Based on the provided conditions, they decide to participate and send that information back to the server.

[0128] Step 7:

[0129] The server takes emotional feedback into consideration and flexibly adjusts reward conditions, thereby encouraging user participation. Further adjustments are made as the project progresses, based on emotional feedback.

[0130] Step 8:

[0131] The server performs regular progress checks during project execution and also facilitates matching with investors. It also considers data from an emotion engine to develop the optimal strategy for promoting project growth.

[0132] (Example 2)

[0133] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0134] Conventional business development support systems are automated based on fixed processes without considering user emotions, making it difficult to manage projects in accordance with the user's emotional state. As a result, user motivation may decrease, potentially lowering the success rate of the plan. Furthermore, emotional information could not be considered in the selection of team members or the adjustment of compensation.

[0135] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0136] In this invention, the server includes a computing device for receiving information, a generating device for automatically generating a planning procedure, a predictive device for making predictions, and an emotion analysis mechanism. This makes it possible to generate an optimal planning procedure while taking into account the user's emotional state, and to select members and adjust rewards.

[0137] A "computational device" is a device that has the function of receiving information and performing processing and analysis.

[0138] A "generation device" is a device that automatically generates plans and procedures based on received information.

[0139] A "predictive device" is a device that predicts results and benefits based on the generated plan procedures.

[0140] A "member" refers to a person who assumes a specific role or responsibility within a project.

[0141] An "emotion analysis mechanism" is a mechanism that analyzes the user's emotional state based on their input data and reflects the results in each process of the system.

[0142] An "optimization device" is a device that adjusts the planning procedures and project progress based on the analyzed information to bring them to an optimal state.

[0143] "Compensation" refers to the reward given for participating in or contributing to a project.

[0144] This invention begins with a user accessing the system's platform via a terminal and registering an idea. The information entered by the user is transmitted from the terminal to a server. The server analyzes the received information using a computing device and stores it in a database.

[0145] The server's computing system automatically generates plans and procedures using a generative AI model based on registered information. The generator formulates a concrete business plan based on the user's ideas and predicts profits. During this process, an emotion analysis mechanism analyzes the user's emotional state. The emotion analysis mechanism analyzes the tone, speed, and vocabulary of the input text in real time to determine the user's emotions.

[0146] Based on the results of the emotion analysis mechanism, the server uses a planning procedure optimization device to adjust the project's progress according to the user's emotional state. For example, if the user is feeling stressed, it will offer simpler and easier-to-understand suggestions to reduce the user's burden.

[0147] Furthermore, the server selects appropriate members based on the results obtained from the profit prediction device. The user's emotional state is also considered in member selection to facilitate smooth communication. Reward adjustments are also based on emotional feedback; special rewards can be offered if the user shows high motivation.

[0148] For example, if a user registers an idea for a fitness app and the emotion analysis mechanism determines that the user is in an excited state, the server will present a positive analysis result regarding the likelihood of market success, generated by an AI model. In this way, the entire system is designed to support the optimal commercialization process by taking the user's emotions into consideration.

[0149] An example of a prompt message could be: "I've submitted a fitness app idea. How would you analyze its market potential when the user is in an excited state?" This is how you can instruct the generative AI model.

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] Users log in to the platform on their devices and register their ideas. First, the user enters text details about the project. Once this is complete, the device sends the text data to the server. The entered information includes an overview of the idea, goals, and expected results.

[0153] Step 2:

[0154] The server analyzes the received text data using a computing device and stores it in a database. During this process, the data is cleaned and normalized, and saved in a standardized format. The output consists of the analysis results and the cleaned data.

[0155] Step 3:

[0156] The server activates an emotion analysis mechanism to analyze the user's emotional state. This mechanism evaluates the user's text tone, word choice, and typing speed to determine their emotional tendencies. The input for the analysis is text data, and the output is metrics indicating the user's emotional state. Based on this, emotional categories such as positive, neutral, and negative are established.

[0157] Step 4:

[0158] The server uses a generative AI model to automatically generate business development steps for registered ideas. It considers the user's emotional state and suggests the most suitable steps. It uses the user's idea and emotional metrics as input and provides a concrete business development plan as output.

[0159] Step 5:

[0160] The server uses a forecasting device to perform profit forecasts based on the generated commercialization steps. This process uses the commercialization plan as input and generates a profit forecast report as output. The forecast also includes market analysis and trend predictions.

[0161] Step 6:

[0162] The server initiates a process to select members. The selection criteria are based on the user's emotional state and profit forecast. Based on this input, it creates and outputs a list of suitable members.

[0163] Step 7:

[0164] The server generates prompt messages, providing instructions for the next steps within the system to the generating AI model. For example, a prompt message might say, "Since the user is positive, propose a proactive marketing strategy." The output of this process is the generated action plan.

[0165] Step 8:

[0166] The server periodically collects user feedback and adjusts system suggestions and reward conditions accordingly. It continuously provides more appropriate project support, taking into account changes in the user's emotional state. The output of this feedback process is a continuously adjusted commercialization plan.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] Conventional business development support systems often fail to consider user emotions, resulting in uniform business development procedures and product recommendations that do not adequately reflect user stress or purchasing intent. This can compromise the user experience and reduce the efficiency of business development. Furthermore, insufficient dynamic process adjustments based on user emotions can negatively impact the overall project success rate.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes a processing unit for receiving information, a generation mechanism for automatically generating a commercialization procedure, and means for analyzing the user's emotional state and optimizing the commercialization procedure based on the analysis results. This makes it possible to provide an optimal commercialization procedure that responds to the user's emotions.

[0172] The "information receiving processing unit" is a part that collects data and signals and converts them into an analyzable format as an initial step for processing.

[0173] A "generating mechanism that automatically generates commercialization procedures" is a device that includes algorithms and logic circuits that form an efficient commercialization flow based on the input information.

[0174] The "forecasting unit that performs profit forecasting" is an analytical engine that evaluates and judges future revenue and profit / loss based on the business plan.

[0175] "Means of selecting stakeholders" refers to a set of rules or programs for appropriately selecting individuals and organizations to participate in a project.

[0176] "Methods for analyzing a user's emotional state" refers to a system of technologies that infer emotions from a user's behavior and biometric information, and then convert that information into data for interpretation.

[0177] A "recommendation method" is a system that has a filtering function to guide users to appropriate products and services based on their needs and emotions.

[0178] The system for realizing this invention comprises a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, and a function for analyzing the user's emotional state. These components work together to optimize the user experience.

[0179] The server receives user input and biometric information in real time and extracts the user's emotional state using facial recognition and speech analysis technologies. Specifically, it uses libraries such as OpenCV for facial recognition and LibROSA for speech analysis. Natural Language Toolkit (NLTK) is used for text analysis. Based on the emotional data collected using these technologies, a generative AI model constructs the optimal business development procedure for the user.

[0180] The terminal provides an interface for the user to log in to the server and transmits the user's emotional input in real time. As the user browses products, the server recommends the most suitable products and services based on the analysis of their emotions. At this time, if the system detects that the user is feeling stressed, it automatically takes measures such as simplifying the purchase process and providing relaxing music.

[0181] As a concrete example, consider a scenario where a user is considering purchasing a new camera. If the user's sentiment analysis indicates they are enjoying themselves, the system will proactively recommend related accessories and shooting techniques to stimulate their desire to buy. Furthermore, by using a generative AI model and inputting a prompt such as, "Based on sentiment data indicating the user is experiencing stress, suggest ways to simplify the purchase process," the system can respond accordingly.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The terminal provides an interface for the user to log in to the system. When the user enters login information via the terminal, the terminal sends that information to the server. At this point, the input is the user's login information, and the output is the user ID and authentication token sent to the server.

[0185] Step 2:

[0186] The server verifies the login and prepares to monitor subsequent user interactions. Specifically, it receives user information sent from the terminal and collects product information viewed by the user and text data entered. The input for this process is real-time user operation information, and the output is an accumulation of various operation data.

[0187] Step 3:

[0188] The server uses facial recognition technology (e.g., OpenCV) to receive a user's facial image from the terminal and extracts facial expression data from it. The input is the user's facial expression image, and the output is data indicating the user's emotional state. This data represents the user's emotions (joy, anger, sadness, etc.) using numerical values ​​and labels.

[0189] Step 4:

[0190] The server processes user voice data received from the terminal using voice analysis technology (e.g., LibROSA). Input is an audio file or audio stream, and output is the result of emotion recognition based on voice tone and tempo. This emotion analysis result is then used to further refine the user's emotional tendencies.

[0191] Step 5:

[0192] The server uses the Natural Language Toolkit (NLTK) to analyze the text data entered by the user. The input is the user's text, and the output is sentiment analysis results obtained by extracting the sentiment tone and keywords from the text.

[0193] Step 6:

[0194] The server generates commercialization procedures using a generative AI model and further optimizes project progress by incorporating real-time sentiment data. The generative AI model uses this dataset, along with pre-configured prompts, to recommend the next action.

[0195] Step 7:

[0196] The server adaptively provides the purchase process and recommendations based on the user's emotional state. For example, if it determines that the user is browsing a new camera and their emotions are heightened, it will recommend information on related accessories. The input is the user's overall emotional state, and the output is the optimized purchase process and recommendation results.

[0197] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0203] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0204] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0205] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0206] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0207] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0208] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0209] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0210] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0211] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0212] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0213] This invention provides a system that offers an innovative platform for commercializing ideas. This system comprises a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, a prediction unit for profit forecasting, means for selecting stakeholders, and means for determining whether or not to participate. The following describes the program processing in natural language for embodiments of the invention.

[0214] Users log in to the platform using their devices and register their ideas. The idea information entered by the user is sent from the device to the server. Based on the received information, the server activates a generation AI and automatically generates the specific steps necessary for commercialization. Based on the generated commercialization steps, the server uses a prediction unit to perform profit forecasts and saves the results to a database.

[0215] The server then uses profit forecasts to select the necessary stakeholders. This process involves referencing a database of registered members' skills and experience to identify members suitable for the commercialization steps. Selected members are provided with project details and success-based compensation via their terminals.

[0216] In the decision-making process for participation, users (registered members) review the compensation conditions and project details to ensure they are satisfied before deciding to participate. This makes it possible to quickly assign the right members to projects.

[0217] As a concrete example, consider a scenario where a user proposes an idea for a new fitness app. The server uses AI to suggest the necessary steps from app prototyping to launch, and provides profit forecasts based on market data. Based on this, it selects experienced mobile app developers and marketing personnel and provides a means for them to commit to the project.

[0218] Furthermore, once a project reaches a certain stage of progress, the server selects potential investment targets and provides support to enable the project to scale up. This system allows users to efficiently commercialize their ideas and maximize potential business opportunities.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The user logs into the platform using their device and opens the idea registration screen. The user enters information such as idea details, target market, and assumed problem, and registers the idea. The device sends the entered data to the server.

[0222] Step 2:

[0223] The server receives registered idea information and activates the generation AI. The server provides input information to the AI, which then automatically generates the specific steps necessary for commercialization. The generated step information is recorded in the server's database.

[0224] Step 3:

[0225] The server performs profit forecasts based on the generated commercialization steps. It references market data and past project data, and uses a profit forecasting model to simulate sales and profits. These profit forecast results are stored in a database.

[0226] Step 4:

[0227] The server selects the most suitable participants based on the projected profits. It searches a database of registered members' skills, experience, and desired compensation to select the members best suited to the commercialization steps. The selection results are sent to the terminal as information for deciding whether or not to participate.

[0228] Step 5:

[0229] Users (registered members) review project details and success fees via their devices. Users consider the provided information and decide whether or not to participate in the project. The decision is then sent from the device to the server.

[0230] Step 6:

[0231] The server forms teams based on user participation availability and performs initial setup to advance the project. It also periodically monitors progress data during project execution and makes adjustments as needed.

[0232] Step 7:

[0233] The server generates a list of potential investors at certain stages of the project's progress and matches them with investors. Investors are provided with information about the project's current status and future growth potential via their terminals.

[0234] (Example 1)

[0235] Next, we will describe Example 1. 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."

[0236] In the traditional process of commercializing an idea, many elements need to be coordinated, such as formulating specific procedures, forecasting profits, selecting appropriate personnel, and matching with investors, making it difficult to carry out efficiently. There is a need for a system that can automate these diverse processes and enable rapid and reliable commercialization.

[0237] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0238] In this invention, the server includes an information processing means for receiving information, an automatic generation means, an analysis means, a selection means, and a decision support means. This enables the efficient automation of the idea commercialization process, allowing users to easily realize commercialization.

[0239] "Information processing means" refers to a device or function that takes in various types of information received by a system and processes them appropriately.

[0240] An "automatic generation means" is a device or function that mechanically generates the specific steps for commercialization based on the received information.

[0241] "Analysis means" refers to a device or function for predicting and evaluating profits based on an automatically generated commercialization process.

[0242] "Selection method" refers to a device or function for identifying suitable stakeholders for a project based on profit forecast results.

[0243] A "decision-making support tool" is a device or function that provides stakeholders with detailed project information and compensation, thereby facilitating their decision to participate.

[0244] This invention is a system for efficiently commercializing ideas, encompassing the processes of information processing, process generation, profit forecasting, stakeholder selection, and decision support.

[0245] Users log in to the platform using their own devices and input their ideas. For example, they might input an idea for a new fitness app. The device sends this information to the server. The server uses a generative AI model based on the received idea information to automatically generate the steps necessary for commercialization. This AI model utilizes natural language processing and machine learning algorithms to generate steps such as prototype creation and market strategy development.

[0246] The server uses the generated processes to perform profit forecasts. Analytical software and market data reference tools (e.g., Google Analytics) are used for the forecasts. Based on these profit forecasts, the server consults a database to select appropriate stakeholders and identifies the most suitable members based on their skills and experience. Stakeholders selected based on the results are then provided with detailed project information and compensation terms. This information is provided via terminals, allowing stakeholders to review the information on their own devices and make their participation decisions.

[0247] A concrete example of a prompt would be something like, "Please list the development steps required for a new fitness app." By inputting this prompt into the AI ​​generation model, the details of the commercialization process are smoothly presented.

[0248] In this way, users, terminals, and servers work together to streamline the process from idea to commercialization and provide a system that enables rapid realization.

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] The user inputs their idea into the terminal. This input is done by using a text box to specifically describe the main points of the idea and confirming the information with a submit button. As a result, the user's idea information is sent from the terminal to the server.

[0252] Step 2:

[0253] The server processes the idea information received from the terminal using information processing tools. In this step, the received text data is analyzed and converted into prompt sentences to activate the generation AI model. This conversion allows the analyzed idea information to be used as input data to generate concrete steps for commercialization.

[0254] Step 3:

[0255] The server activates a generative AI model to automatically generate specific steps for commercialization. In this step, the necessary processes for commercialization (e.g., prototype development, user testing, market entry plan, etc.) are generated using machine learning algorithms. The output from the generative AI model is a clearly defined list of commercialization steps.

[0256] Step 4:

[0257] The server uses analytical tools to perform profit forecasts based on automatically generated business development steps. Profit forecasting utilizes market data and historical data from similar cases, applying a forecasting algorithm to simulate revenue. The output of the profit forecast is the projected revenue figure and the probability of success.

[0258] Step 5:

[0259] The server identifies stakeholders using selection criteria based on profit forecast results. This process involves referencing a stakeholder database to narrow down individuals and groups with the necessary skill sets and experience. The output is a list of stakeholders suitable for the project.

[0260] Step 6:

[0261] The server provides selected participants with project details and compensation terms using decision support tools. This includes sending notifications to the participants' terminals. Participants receive the notifications and decide whether or not to participate based on the information provided. The output is a history of participation decisions.

[0262] (Application Example 1)

[0263] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] When commercializing an idea into concrete content or services, it is necessary to develop appropriate processes, forecast profitability, and select the necessary stakeholders, but doing so efficiently and quickly is difficult. Furthermore, there is a need for methods to encourage participation in the project and increase the success rate by providing stakeholders with appropriate information and incentives.

[0265] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0266] In this invention, the server includes a processing unit for receiving information, components for automatically generating business procedures, an estimation device for forecasting revenue, and a device for selecting appropriate skill providers and providing detailed information via an information terminal. This makes it possible to efficiently commercialize ideas and optimize the participation of stakeholders.

[0267] A "processing device for receiving information" is a device that acquires data and information input from an external source and converts it into a format that can be used internally.

[0268] A "component for automatically generating business procedures" is an element that has the function of automatically creating specific procedures and processes for carrying out business based on the information received.

[0269] A "revenue forecasting device" is a device that calculates expected revenue based on business procedures and provides the results.

[0270] A "device for selecting appropriate skill providers and providing detailed information via information terminals" is a device that analyzes the skills and experience of those involved, selects the most suitable personnel for a project, and provides detailed project information to those personnel.

[0271] This invention provides a system for rapidly commercializing ideas, which receives information and, based on that information, automatically generates business procedures, forecasts revenue, and selects stakeholders. The system for implementing this invention is configured as follows.

[0272] The server processes incoming idea information. Specifically, users access the platform using information terminals such as smartphones or personal computers and input their own ideas into the platform. This information is then sent to the server and processed internally.

[0273] The generation AI model installed on the server (e.g., OpenAI's GPT-4) automatically generates specific procedures necessary for business implementation based on the received idea information. The generated procedures are then used by an estimation device to perform revenue forecasting, and the results are stored in a database. The server uses this revenue forecasting data to select skilled providers who should be involved in the project from the database and provides detailed project information through information terminals.

[0274] This system allows users to more efficiently turn their ideas into businesses. For example, suppose a user wants to create a new educational animation. By registering their idea on the platform, an AI model automatically generates project steps, predicts profitability, selects a suitable animation production team, and provides necessary details. In this way, the project can proceed smoothly and effectively.

[0275] An example of a prompt message might be: "Generate the steps for creating an animated educational content. Include the target market, budget, and a list of required staff."

[0276] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0277] Step 1:

[0278] Users access the platform using an information terminal and input their ideas. The input ideas are sent to the server as text data. The input data is combined with the user's account information for initial processing and then stored in the database as registered ideas.

[0279] Step 2:

[0280] The server starts the generative AI model and generates commercialization procedures based on the received idea data. Instructions such as "Please generate the steps for creating animations of educational content" are given to this model as prompt sentences. Based on this prompt, the AI creates a detailed list of procedures and outputs it to the database.

[0281] Step 3:

[0282] The server refers to the list of procedures and uses an inference device to perform revenue prediction. The list of procedures is input, and the inference device performs calculations in comparison with market data and data from past similar projects. The predicted revenue results are saved in the database.

[0283] Step 4:

[0284] The server uses the revenue prediction results to select skill providers who should participate in the project. From the participant database, candidates with the required skills and experience are filtered, and an optimal list of personnel is output. The selection results are notified to the relevant parties.

[0285] Step 5:

[0286] For the selected skill providers, detailed information about the project and an invitation to participate are sent via an information terminal. Specific project details, predicted revenue, roles, remuneration conditions, etc. are provided. This process is for confirming the participants' willingness to participate.

[0287] Step 6:

[0288] Replies regarding the acceptance or rejection of participation from the participants are sent back to the server through the terminal. The server aggregates these feedbacks and makes final adjustments to prepare for the next step of the project. The final determination of the participant list and the final decision of the project schedule are made.

[0289] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0290] The present invention is a system that incorporates an emotion engine to support project progress by taking into account the user's emotional state in the process of commercializing an idea. This system includes a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, a prediction unit for predicting profits, means for selecting stakeholders, means for deciding whether or not to participate, as well as an emotion engine for recognizing and processing the user's emotions.

[0291] When a user logs into the platform via their device and registers an idea, the device sends that information to the server. The server then activates a generative AI based on the registered information to determine the steps to commercialization and predict profits. An emotion engine is incorporated into this process, analyzing the user's emotional state in real time in response to their input.

[0292] The emotion engine analyzes the user's input speed, stress level, and tone of voice and text to determine their emotional tendencies. This information is used to optimize the commercialization process, adjusting it to maintain user motivation throughout the process. For example, if a user is stressed, the system prioritizes concise and easy-to-understand suggestions.

[0293] Furthermore, the emotion engine also influences the participant selection process. By adjusting the communication strategy with selected participants according to the user's emotional state, the aim is to ensure smoother project progress. In this way, the system facilitates smooth participant engagement and strengthens collaboration.

[0294] Furthermore, it is possible to flexibly adjust the reward conditions offered based on user emotional feedback. For example, if a user shows high motivation, their motivation to participate can be further increased by offering rewards in a different form.

[0295] For example, if a user registers an idea for a fitness app, and the emotion engine determines that the user is excited during the registration process, the generating AI will display a positive analysis result regarding the app's market success potential on the server. In this way, the entire system considers the user's emotions and supports the optimal commercialization process.

[0296] The following describes the processing flow.

[0297] Step 1:

[0298] The user logs into the platform using their device and opens the idea registration form. The user enters information in each field and registers their idea. The registration is sent from the device to the server.

[0299] Step 2:

[0300] The server receives registered idea information and activates the generation AI. The server passes the idea information to the generation AI, which then generates the steps necessary for commercialization. The generated step information is stored on the server.

[0301] Step 3:

[0302] The server uses an emotion engine to analyze data collected during user input (such as input speed and text tone) and evaluate the user's emotional state. Based on the emotional state, adjustments are made to the generated commercialization steps.

[0303] Step 4:

[0304] The server performs profit forecasts based on the commercialization steps. It references market data and past project data, and uses a forecasting model to simulate profits. The forecast results are recorded in a database.

[0305] Step 5:

[0306] Based on the emotional state of the user determined by the server's emotion engine, select participants and adjust the communication strategy. Select the optimal participants and send them to the terminal together with the detailed project information.

[0307] Step 6:

[0308] The user (registered member) checks the project information and the amount of successful reward proposed on the terminal. Decide to participate based on the provided conditions and reply the information to the server.

[0309] Step 7:

[0310] The server considers the emotional feedback and flexibly adjusts the reward conditions. This stimulates the user's willingness to participate. During the progress of the project, further adjustments are made based on the emotional feedback.

[0311] Step 8:

[0312] The server regularly checks the progress during the project progress and also conducts matching with investors. Consider the data from the emotion engine and formulate an optimal strategy to promote the growth of the project.

[0313] (Example 2)

[0314] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0315] Since the conventional commercialization support system is automated based on a certain process without considering the user's emotions, it has been difficult to progress the project according to the user's emotional state. As a result, the user's motivation may decrease, and the success rate of the plan may decrease. Also, in the selection of team members and the adjustment of rewards, emotional information could not be considered.

[0316] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0317] In this invention, the server includes a computing device for receiving information, a generating device for automatically generating a planning procedure, a predictive device for making predictions, and an emotion analysis mechanism. This makes it possible to generate an optimal planning procedure while taking into account the user's emotional state, and to select members and adjust rewards.

[0318] A "computational device" is a device that has the function of receiving information and performing processing and analysis.

[0319] A "generation device" is a device that automatically generates plans and procedures based on received information.

[0320] A "predictive device" is a device that predicts results and benefits based on the generated plan procedures.

[0321] A "member" refers to a person who assumes a specific role or responsibility within a project.

[0322] An "emotion analysis mechanism" is a mechanism that analyzes the user's emotional state based on their input data and reflects the results in each process of the system.

[0323] An "optimization device" is a device that adjusts the planning procedures and project progress based on the analyzed information to bring them to an optimal state.

[0324] "Compensation" refers to the reward given for participating in or contributing to a project.

[0325] This invention begins with a user accessing the system's platform via a terminal and registering an idea. The information entered by the user is transmitted from the terminal to a server. The server analyzes the received information using a computing device and stores it in a database.

[0326] The server's computing system automatically generates plans and procedures using a generative AI model based on registered information. The generator formulates a concrete business plan based on the user's ideas and predicts profits. During this process, an emotion analysis mechanism analyzes the user's emotional state. The emotion analysis mechanism analyzes the tone, speed, and vocabulary of the input text in real time to determine the user's emotions.

[0327] Based on the results of the emotion analysis mechanism, the server uses a planning procedure optimization device to adjust the project's progress according to the user's emotional state. For example, if the user is feeling stressed, it will offer simpler and easier-to-understand suggestions to reduce the user's burden.

[0328] Furthermore, the server selects appropriate members based on the results obtained from the profit prediction device. The user's emotional state is also considered in member selection to facilitate smooth communication. Reward adjustments are also based on emotional feedback; special rewards can be offered if the user shows high motivation.

[0329] For example, if a user registers an idea for a fitness app and the emotion analysis mechanism determines that the user is in an excited state, the server will present a positive analysis result regarding the likelihood of market success, generated by an AI model. In this way, the entire system is designed to support the optimal commercialization process by taking the user's emotions into consideration.

[0330] An example of a prompt message could be: "I've submitted a fitness app idea. How would you analyze its market potential when the user is in an excited state?" This is how you can instruct the generative AI model.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] Users log in to the platform on their devices and register their ideas. First, the user enters text details about the project. Once this is complete, the device sends the text data to the server. The entered information includes an overview of the idea, goals, and expected results.

[0334] Step 2:

[0335] The server analyzes the received text data using a computing device and stores it in a database. During this process, the data is cleaned and normalized, and saved in a standardized format. The output consists of the analysis results and the cleaned data.

[0336] Step 3:

[0337] The server activates an emotion analysis mechanism to analyze the user's emotional state. This mechanism evaluates the user's text tone, word choice, and typing speed to determine their emotional tendencies. The input for the analysis is text data, and the output is metrics indicating the user's emotional state. Based on this, emotional categories such as positive, neutral, and negative are established.

[0338] Step 4:

[0339] The server uses a generative AI model to automatically generate business development steps for registered ideas. It considers the user's emotional state and suggests the most suitable steps. It uses the user's idea and emotional metrics as input and provides a concrete business development plan as output.

[0340] Step 5:

[0341] The server uses a forecasting device to perform profit forecasts based on the generated commercialization steps. This process uses the commercialization plan as input and generates a profit forecast report as output. The forecast also includes market analysis and trend predictions.

[0342] Step 6:

[0343] The server initiates a process to select members. The selection criteria are based on the user's emotional state and profit forecast. Based on this input, it creates and outputs a list of suitable members.

[0344] Step 7:

[0345] The server generates prompt messages, providing instructions for the next steps within the system to the generating AI model. For example, a prompt message might say, "Since the user is positive, propose a proactive marketing strategy." The output of this process is the generated action plan.

[0346] Step 8:

[0347] The server periodically collects user feedback and adjusts system suggestions and reward conditions accordingly. It continuously provides more appropriate project support, taking into account changes in the user's emotional state. The output of this feedback process is a continuously adjusted commercialization plan.

[0348] (Application Example 2)

[0349] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0350] Conventional business development support systems often fail to consider user emotions, resulting in uniform business development procedures and product recommendations that do not adequately reflect user stress or purchasing intent. This can compromise the user experience and reduce the efficiency of business development. Furthermore, insufficient dynamic process adjustments based on user emotions can negatively impact the overall project success rate.

[0351] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0352] In this invention, the server includes a processing unit for receiving information, a generation mechanism for automatically generating a commercialization procedure, and means for analyzing the user's emotional state and optimizing the commercialization procedure based on the analysis results. This makes it possible to provide an optimal commercialization procedure that responds to the user's emotions.

[0353] The "information receiving processing unit" is a part that collects data and signals and converts them into an analyzable format as an initial step for processing.

[0354] A "generating mechanism that automatically generates commercialization procedures" is a device that includes algorithms and logic circuits that form an efficient commercialization flow based on the input information.

[0355] The "forecasting unit that performs profit forecasting" is an analytical engine that evaluates and judges future revenue and profit / loss based on the business plan.

[0356] "Means of selecting stakeholders" refers to a set of rules or programs for appropriately selecting individuals and organizations to participate in a project.

[0357] "Methods for analyzing a user's emotional state" refers to a system of technologies that infer emotions from a user's behavior and biometric information, and then convert that information into data for interpretation.

[0358] A "recommendation method" is a system that has a filtering function to guide users to appropriate products and services based on their needs and emotions.

[0359] The system for realizing this invention comprises a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, and a function for analyzing the user's emotional state. These components work together to optimize the user experience.

[0360] The server receives user input and biometric information in real time and extracts the user's emotional state using facial recognition and speech analysis technologies. Specifically, it uses libraries such as OpenCV for facial recognition and LibROSA for speech analysis. Natural Language Toolkit (NLTK) is used for text analysis. Based on the emotional data collected using these technologies, a generative AI model constructs the optimal business development procedure for the user.

[0361] The terminal provides an interface for the user to log in to the server and transmits the user's emotional input in real time. As the user browses products, the server recommends the most suitable products and services based on the analysis of their emotions. At this time, if the system detects that the user is feeling stressed, it automatically takes measures such as simplifying the purchase process and providing relaxing music.

[0362] As a concrete example, consider a scenario where a user is considering purchasing a new camera. If the user's sentiment analysis indicates they are enjoying themselves, the system will proactively recommend related accessories and shooting techniques to stimulate their desire to buy. Furthermore, by using a generative AI model and inputting a prompt such as, "Based on sentiment data indicating the user is experiencing stress, suggest ways to simplify the purchase process," the system can respond accordingly.

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The terminal provides an interface for the user to log in to the system. When the user enters login information via the terminal, the terminal sends that information to the server. At this point, the input is the user's login information, and the output is the user ID and authentication token sent to the server.

[0366] Step 2:

[0367] The server verifies the login and prepares to monitor subsequent user interactions. Specifically, it receives user information sent from the terminal and collects product information viewed by the user and text data entered. The input for this process is real-time user operation information, and the output is an accumulation of various operation data.

[0368] Step 3:

[0369] The server uses facial recognition technology (e.g., OpenCV) to receive a user's facial image from the terminal and extracts facial expression data from it. The input is the user's facial expression image, and the output is data indicating the user's emotional state. This data represents the user's emotions (joy, anger, sadness, etc.) using numerical values ​​and labels.

[0370] Step 4:

[0371] The server processes user voice data received from the terminal using voice analysis technology (e.g., LibROSA). Input is an audio file or audio stream, and output is the result of emotion recognition based on voice tone and tempo. This emotion analysis result is then used to further refine the user's emotional tendencies.

[0372] Step 5:

[0373] The server uses the Natural Language Toolkit (NLTK) to analyze the text data entered by the user. The input is the user's text, and the output is sentiment analysis results obtained by extracting the sentiment tone and keywords from the text.

[0374] Step 6:

[0375] The server generates commercialization procedures using a generative AI model and further optimizes project progress by incorporating real-time sentiment data. The generative AI model uses this dataset, along with pre-configured prompts, to recommend the next action.

[0376] Step 7:

[0377] The server adaptively provides the purchase process and recommendations based on the user's emotional state. For example, if it determines that the user is browsing a new camera and their emotions are heightened, it will recommend information on related accessories. The input is the user's overall emotional state, and the output is the optimized purchase process and recommendation results.

[0378] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0379] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0380] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0384] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0385] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0386] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0387] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0388] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0389] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0390] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0391] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0392] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0393] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0394] This invention provides a system that offers an innovative platform for commercializing ideas. This system comprises a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, a prediction unit for profit forecasting, means for selecting stakeholders, and means for determining whether or not to participate. The following describes the program processing in natural language for embodiments of the invention.

[0395] Users log in to the platform using their devices and register their ideas. The idea information entered by the user is sent from the device to the server. Based on the received information, the server activates a generation AI and automatically generates the specific steps necessary for commercialization. Based on the generated commercialization steps, the server uses a prediction unit to perform profit forecasts and saves the results to a database.

[0396] The server then uses profit forecasts to select the necessary stakeholders. This process involves referencing a database of registered members' skills and experience to identify members suitable for the commercialization steps. Selected members are provided with project details and success-based compensation via their terminals.

[0397] In the decision-making process for participation, users (registered members) review the compensation conditions and project details to ensure they are satisfied before deciding to participate. This makes it possible to quickly assign the right members to projects.

[0398] As a concrete example, consider a scenario where a user proposes an idea for a new fitness app. The server uses AI to suggest the necessary steps from app prototyping to launch, and provides profit forecasts based on market data. Based on this, it selects experienced mobile app developers and marketing personnel and provides a means for them to commit to the project.

[0399] Furthermore, once a project reaches a certain stage of progress, the server selects potential investment targets and provides support to enable the project to scale up. This system allows users to efficiently commercialize their ideas and maximize potential business opportunities.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The user logs into the platform using their device and opens the idea registration screen. The user enters information such as idea details, target market, and assumed problem, and registers the idea. The device sends the entered data to the server.

[0403] Step 2:

[0404] The server receives registered idea information and activates the generation AI. The server provides input information to the AI, which then automatically generates the specific steps necessary for commercialization. The generated step information is recorded in the server's database.

[0405] Step 3:

[0406] The server performs profit forecasts based on the generated commercialization steps. It references market data and past project data, and uses a profit forecasting model to simulate sales and profits. These profit forecast results are stored in a database.

[0407] Step 4:

[0408] The server selects the most suitable participants based on the projected profits. It searches a database of registered members' skills, experience, and desired compensation to select the members best suited to the commercialization steps. The selection results are sent to the terminal as information for deciding whether or not to participate.

[0409] Step 5:

[0410] Users (registered members) review project details and success fees via their devices. Users consider the provided information and decide whether or not to participate in the project. The decision is then sent from the device to the server.

[0411] Step 6:

[0412] The server forms teams based on user participation availability and performs initial setup to advance the project. It also periodically monitors progress data during project execution and makes adjustments as needed.

[0413] Step 7:

[0414] The server generates a list of potential investors at certain stages of the project's progress and matches them with investors. Investors are provided with information about the project's current status and future growth potential via their terminals.

[0415] (Example 1)

[0416] Next, we will describe Example 1. 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."

[0417] In the traditional process of commercializing an idea, many elements need to be coordinated, such as formulating specific procedures, forecasting profits, selecting appropriate personnel, and matching with investors, making it difficult to carry out efficiently. There is a need for a system that can automate these diverse processes and enable rapid and reliable commercialization.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes an information processing means for receiving information, an automatic generation means, an analysis means, a selection means, and a decision support means. This enables the efficient automation of the idea commercialization process, allowing users to easily realize commercialization.

[0420] "Information processing means" refers to a device or function that takes in various types of information received by a system and processes them appropriately.

[0421] An "automatic generation means" is a device or function that mechanically generates the specific steps for commercialization based on the received information.

[0422] "Analysis means" refers to a device or function for predicting and evaluating profits based on an automatically generated commercialization process.

[0423] "Selection method" refers to a device or function for identifying suitable stakeholders for a project based on profit forecast results.

[0424] A "decision-making support tool" is a device or function that provides stakeholders with detailed project information and compensation, thereby facilitating their decision to participate.

[0425] This invention is a system for efficiently commercializing ideas, encompassing the processes of information processing, process generation, profit forecasting, stakeholder selection, and decision support.

[0426] Users log in to the platform using their own devices and input their ideas. For example, they might input an idea for a new fitness app. The device sends this information to the server. The server uses a generative AI model based on the received idea information to automatically generate the steps necessary for commercialization. This AI model utilizes natural language processing and machine learning algorithms to generate steps such as prototype creation and market strategy development.

[0427] The server uses the generated processes to perform profit forecasts. Analytical software and market data reference tools (e.g., Google Analytics) are used for the forecasts. Based on these profit forecasts, the server consults a database to select appropriate stakeholders and identifies the most suitable members based on their skills and experience. Stakeholders selected based on the results are then provided with detailed project information and compensation terms. This information is provided via terminals, allowing stakeholders to review the information on their own devices and make their participation decisions.

[0428] A concrete example of a prompt would be something like, "Please list the development steps required for a new fitness app." By inputting this prompt into the AI ​​generation model, the details of the commercialization process are smoothly presented.

[0429] In this way, users, terminals, and servers work together to streamline the process from idea to commercialization and provide a system that enables rapid realization.

[0430] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0431] Step 1:

[0432] The user inputs their idea into the terminal. This input is done by using a text box to specifically describe the main points of the idea and confirming the information with a submit button. As a result, the user's idea information is sent from the terminal to the server.

[0433] Step 2:

[0434] The server processes the idea information received from the terminal using information processing tools. In this step, the received text data is analyzed and converted into prompt sentences to activate the generation AI model. This conversion allows the analyzed idea information to be used as input data to generate concrete steps for commercialization.

[0435] Step 3:

[0436] The server activates a generative AI model to automatically generate specific steps for commercialization. In this step, the necessary processes for commercialization (e.g., prototype development, user testing, market entry plan, etc.) are generated using machine learning algorithms. The output from the generative AI model is a clearly defined list of commercialization steps.

[0437] Step 4:

[0438] The server uses analytical tools to perform profit forecasts based on automatically generated business development steps. Profit forecasting utilizes market data and historical data from similar cases, applying a forecasting algorithm to simulate revenue. The output of the profit forecast is the projected revenue figure and the probability of success.

[0439] Step 5:

[0440] The server identifies stakeholders using selection criteria based on profit forecast results. This process involves referencing a stakeholder database to narrow down individuals and groups with the necessary skill sets and experience. The output is a list of stakeholders suitable for the project.

[0441] Step 6:

[0442] The server provides selected participants with project details and compensation terms using decision support tools. This includes sending notifications to the participants' terminals. Participants receive the notifications and decide whether or not to participate based on the information provided. The output is a history of participation decisions.

[0443] (Application Example 1)

[0444] Next, we will explain Application Example 1. In the following explanation, 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."

[0445] When commercializing an idea into concrete content or services, it is necessary to develop appropriate processes, forecast profitability, and select the necessary stakeholders, but doing so efficiently and quickly is difficult. Furthermore, there is a need for methods to encourage participation in the project and increase the success rate by providing stakeholders with appropriate information and incentives.

[0446] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0447] In this invention, the server includes a processing unit for receiving information, components for automatically generating business procedures, an estimation device for forecasting revenue, and a device for selecting appropriate skill providers and providing detailed information via an information terminal. This makes it possible to efficiently commercialize ideas and optimize the participation of stakeholders.

[0448] A "processing device for receiving information" is a device that acquires data and information input from an external source and converts it into a format that can be used internally.

[0449] A "component for automatically generating business procedures" is an element that has the function of automatically creating specific procedures and processes for carrying out business based on the information received.

[0450] A "revenue forecasting device" is a device that calculates expected revenue based on business procedures and provides the results.

[0451] A "device for selecting appropriate skill providers and providing detailed information via information terminals" is a device that analyzes the skills and experience of those involved, selects the most suitable personnel for a project, and provides detailed project information to those personnel.

[0452] This invention provides a system for rapidly commercializing ideas, which receives information and, based on that information, automatically generates business procedures, forecasts revenue, and selects stakeholders. The system for implementing this invention is configured as follows.

[0453] The server processes incoming idea information. Specifically, users access the platform using information terminals such as smartphones or personal computers and input their own ideas into the platform. This information is then sent to the server and processed internally.

[0454] The generation AI model installed on the server (e.g., OpenAI's GPT-4) automatically generates specific procedures necessary for business implementation based on the received idea information. The generated procedures are then used by an estimation device to perform revenue forecasting, and the results are stored in a database. The server uses this revenue forecasting data to select skilled providers who should be involved in the project from the database and provides detailed project information through information terminals.

[0455] This system allows users to more efficiently turn their ideas into businesses. For example, suppose a user wants to create a new educational animation. By registering their idea on the platform, an AI model automatically generates project steps, predicts profitability, selects a suitable animation production team, and provides necessary details. In this way, the project can proceed smoothly and effectively.

[0456] An example of a prompt message might be: "Generate the steps for creating an animated educational content. Include the target market, budget, and a list of required staff."

[0457] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0458] Step 1:

[0459] Users access the platform using an information terminal and input their ideas. The input ideas are sent to the server as text data. The input data is combined with the user's account information for initial processing and then stored in the database as registered ideas.

[0460] Step 2:

[0461] The server activates the AI ​​model and generates business procedures based on the received idea data. This model is given prompts such as "Generate the steps for creating animation for educational content." The AI ​​then creates a detailed procedure list based on these prompts and outputs it to the database.

[0462] Step 3:

[0463] The server refers to a procedure list and uses an estimation tool to make revenue forecasts. The procedure list is entered, and the estimation tool performs calculations by comparing it with market data and data from similar past projects. The predicted revenue results are saved to the database.

[0464] Step 4:

[0465] The server uses revenue forecast results to select skilled providers who should participate in the project. It filters candidates with the necessary skills and experience from the stakeholders database and outputs a list of optimal candidates. The selection results are then notified to the relevant parties.

[0466] Step 5:

[0467] Detailed project information and participation requests will be sent to selected skilled providers via information terminals. Specific project details, projected revenue, roles, and compensation conditions will be provided. This process is intended to confirm the participants' willingness to participate.

[0468] Step 6:

[0469] Participants send their confirmations of participation to the server via their terminals. The server aggregates this feedback and makes final adjustments to prepare for the next steps in the project. This includes finalizing the participant list and determining the project schedule.

[0470] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0471] The present invention is a system that incorporates an emotion engine to support project progress by taking into account the user's emotional state in the process of commercializing an idea. This system includes a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, a prediction unit for predicting profits, means for selecting stakeholders, means for deciding whether or not to participate, as well as an emotion engine for recognizing and processing the user's emotions.

[0472] When a user logs into the platform via their device and registers an idea, the device sends that information to the server. The server then activates a generative AI based on the registered information to determine the steps to commercialization and predict profits. An emotion engine is incorporated into this process, analyzing the user's emotional state in real time in response to their input.

[0473] The emotion engine analyzes the user's input speed, stress level, and tone of voice and text to determine their emotional tendencies. This information is used to optimize the commercialization process, adjusting it to maintain user motivation throughout the process. For example, if a user is stressed, the system prioritizes concise and easy-to-understand suggestions.

[0474] Furthermore, the emotion engine also influences the participant selection process. By adjusting the communication strategy with selected participants according to the user's emotional state, the aim is to ensure smoother project progress. In this way, the system facilitates smooth participant engagement and strengthens collaboration.

[0475] Furthermore, it is possible to flexibly adjust the reward conditions offered based on user emotional feedback. For example, if a user shows high motivation, their motivation to participate can be further increased by offering rewards in a different form.

[0476] For example, if a user registers an idea for a fitness app, and the emotion engine determines that the user is excited during the registration process, the generating AI will display a positive analysis result regarding the app's market success potential on the server. In this way, the entire system considers the user's emotions and supports the optimal commercialization process.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The user logs into the platform using their device and opens the idea registration form. The user enters information in each field and registers their idea. The registration is sent from the device to the server.

[0480] Step 2:

[0481] The server receives registered idea information and activates the generation AI. The server passes the idea information to the generation AI, which then generates the steps necessary for commercialization. The generated step information is stored on the server.

[0482] Step 3:

[0483] The server uses an emotion engine to analyze data collected during user input (such as input speed and text tone) and evaluate the user's emotional state. Based on the emotional state, adjustments are made to the generated commercialization steps.

[0484] Step 4:

[0485] The server performs profit forecasts based on the commercialization steps. It references market data and past project data, and uses a forecasting model to simulate profits. The forecast results are recorded in a database.

[0486] Step 5:

[0487] Based on the user's emotional state determined by the emotion engine, the server selects the most suitable stakeholders and adjusts the communication strategy. The most appropriate stakeholders are then selected and sent to the terminal along with detailed project information.

[0488] Step 6:

[0489] The user (registered member) checks the proposed project information and success fee amount on their device. Based on the provided conditions, they decide to participate and send that information back to the server.

[0490] Step 7:

[0491] The server takes emotional feedback into consideration and flexibly adjusts reward conditions, thereby encouraging user participation. Further adjustments are made as the project progresses, based on emotional feedback.

[0492] Step 8:

[0493] The server performs regular progress checks during project execution and also facilitates matching with investors. It also considers data from an emotion engine to develop the optimal strategy for promoting project growth.

[0494] (Example 2)

[0495] Next, we will describe Example 2. 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."

[0496] Conventional business development support systems are automated based on fixed processes without considering user emotions, making it difficult to manage projects in accordance with the user's emotional state. As a result, user motivation may decrease, potentially lowering the success rate of the plan. Furthermore, emotional information could not be considered in the selection of team members or the adjustment of compensation.

[0497] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0498] In this invention, the server includes a computing device for receiving information, a generating device for automatically generating a planning procedure, a predictive device for making predictions, and an emotion analysis mechanism. This makes it possible to generate an optimal planning procedure while taking into account the user's emotional state, and to select members and adjust rewards.

[0499] A "computational device" is a device that has the function of receiving information and performing processing and analysis.

[0500] A "generation device" is a device that automatically generates plans and procedures based on received information.

[0501] A "predictive device" is a device that predicts results and benefits based on the generated plan procedures.

[0502] A "member" refers to a person who assumes a specific role or responsibility within a project.

[0503] An "emotion analysis mechanism" is a mechanism that analyzes the user's emotional state based on their input data and reflects the results in each process of the system.

[0504] An "optimization device" is a device that adjusts the planning procedures and project progress based on the analyzed information to bring them to an optimal state.

[0505] "Compensation" refers to the reward given for participating in or contributing to a project.

[0506] This invention begins with a user accessing the system's platform via a terminal and registering an idea. The information entered by the user is transmitted from the terminal to a server. The server analyzes the received information using a computing device and stores it in a database.

[0507] The server's computing system automatically generates plans and procedures using a generative AI model based on registered information. The generator formulates a concrete business plan based on the user's ideas and predicts profits. During this process, an emotion analysis mechanism analyzes the user's emotional state. The emotion analysis mechanism analyzes the tone, speed, and vocabulary of the input text in real time to determine the user's emotions.

[0508] Based on the results of the emotion analysis mechanism, the server uses a planning procedure optimization device to adjust the project's progress according to the user's emotional state. For example, if the user is feeling stressed, it will offer simpler and easier-to-understand suggestions to reduce the user's burden.

[0509] Furthermore, the server selects appropriate members based on the results obtained from the profit prediction device. The user's emotional state is also considered in member selection to facilitate smooth communication. Reward adjustments are also based on emotional feedback; special rewards can be offered if the user shows high motivation.

[0510] For example, if a user registers an idea for a fitness app and the emotion analysis mechanism determines that the user is in an excited state, the server will present a positive analysis result regarding the likelihood of market success, generated by an AI model. In this way, the entire system is designed to support the optimal commercialization process by taking the user's emotions into consideration.

[0511] An example of a prompt message could be: "I've submitted a fitness app idea. How would you analyze its market potential when the user is in an excited state?" This is how you can instruct the generative AI model.

[0512] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0513] Step 1:

[0514] Users log in to the platform on their devices and register their ideas. First, the user enters text details about the project. Once this is complete, the device sends the text data to the server. The entered information includes an overview of the idea, goals, and expected results.

[0515] Step 2:

[0516] The server analyzes the received text data using a computing device and stores it in a database. During this process, the data is cleaned and normalized, and saved in a standardized format. The output consists of the analysis results and the cleaned data.

[0517] Step 3:

[0518] The server activates an emotion analysis mechanism to analyze the user's emotional state. This mechanism evaluates the user's text tone, word choice, and typing speed to determine their emotional tendencies. The input for the analysis is text data, and the output is metrics indicating the user's emotional state. Based on this, emotional categories such as positive, neutral, and negative are established.

[0519] Step 4:

[0520] The server uses a generative AI model to automatically generate business development steps for registered ideas. It considers the user's emotional state and suggests the most suitable steps. It uses the user's idea and emotional metrics as input and provides a concrete business development plan as output.

[0521] Step 5:

[0522] The server uses a forecasting device to perform profit forecasts based on the generated commercialization steps. This process uses the commercialization plan as input and generates a profit forecast report as output. The forecast also includes market analysis and trend predictions.

[0523] Step 6:

[0524] The server initiates a process to select members. The selection criteria are based on the user's emotional state and profit forecast. Based on this input, it creates and outputs a list of suitable members.

[0525] Step 7:

[0526] The server generates prompt messages, providing instructions for the next steps within the system to the generating AI model. For example, a prompt message might say, "Since the user is positive, propose a proactive marketing strategy." The output of this process is the generated action plan.

[0527] Step 8:

[0528] The server periodically collects user feedback and adjusts system suggestions and reward conditions accordingly. It continuously provides more appropriate project support, taking into account changes in the user's emotional state. The output of this feedback process is a continuously adjusted commercialization plan.

[0529] (Application Example 2)

[0530] Next, we will explain Application Example 2. In the following explanation, 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."

[0531] Conventional business development support systems often fail to consider user emotions, resulting in uniform business development procedures and product recommendations that do not adequately reflect user stress or purchasing intent. This can compromise the user experience and reduce the efficiency of business development. Furthermore, insufficient dynamic process adjustments based on user emotions can negatively impact the overall project success rate.

[0532] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0533] In this invention, the server includes a processing unit for receiving information, a generation mechanism for automatically generating a commercialization procedure, and means for analyzing the user's emotional state and optimizing the commercialization procedure based on the analysis results. This makes it possible to provide an optimal commercialization procedure that responds to the user's emotions.

[0534] The "information receiving processing unit" is a part that collects data and signals and converts them into an analyzable format as an initial step for processing.

[0535] A "generating mechanism that automatically generates commercialization procedures" is a device that includes algorithms and logic circuits that form an efficient commercialization flow based on the input information.

[0536] The "forecasting unit that performs profit forecasting" is an analytical engine that evaluates and judges future revenue and profit / loss based on the business plan.

[0537] "Means of selecting stakeholders" refers to a set of rules or programs for appropriately selecting individuals and organizations to participate in a project.

[0538] "Methods for analyzing a user's emotional state" refers to a system of technologies that infer emotions from a user's behavior and biometric information, and then convert that information into data for interpretation.

[0539] A "recommendation method" is a system that has a filtering function to guide users to appropriate products and services based on their needs and emotions.

[0540] The system for realizing this invention comprises a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, and a function for analyzing the user's emotional state. These components work together to optimize the user experience.

[0541] The server receives user input and biometric information in real time and extracts the user's emotional state using facial recognition and speech analysis technologies. Specifically, it uses libraries such as OpenCV for facial recognition and LibROSA for speech analysis. Natural Language Toolkit (NLTK) is used for text analysis. Based on the emotional data collected using these technologies, a generative AI model constructs the optimal business development procedure for the user.

[0542] The terminal provides an interface for the user to log in to the server and transmits the user's emotional input in real time. As the user browses products, the server recommends the most suitable products and services based on the analysis of their emotions. At this time, if the system detects that the user is feeling stressed, it automatically takes measures such as simplifying the purchase process and providing relaxing music.

[0543] As a concrete example, consider a scenario where a user is considering purchasing a new camera. If the user's sentiment analysis indicates they are enjoying themselves, the system will proactively recommend related accessories and shooting techniques to stimulate their desire to buy. Furthermore, by using a generative AI model and inputting a prompt such as, "Based on sentiment data indicating the user is experiencing stress, suggest ways to simplify the purchase process," the system can respond accordingly.

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] The terminal provides an interface for the user to log in to the system. When the user enters login information via the terminal, the terminal sends that information to the server. At this point, the input is the user's login information, and the output is the user ID and authentication token sent to the server.

[0547] Step 2:

[0548] The server verifies the login and prepares to monitor subsequent user interactions. Specifically, it receives user information sent from the terminal and collects product information viewed by the user and text data entered. The input for this process is real-time user operation information, and the output is an accumulation of various operation data.

[0549] Step 3:

[0550] The server uses facial recognition technology (e.g., OpenCV) to receive a user's facial image from the terminal and extracts facial expression data from it. The input is the user's facial expression image, and the output is data indicating the user's emotional state. This data represents the user's emotions (joy, anger, sadness, etc.) using numerical values ​​and labels.

[0551] Step 4:

[0552] The server processes user voice data received from the terminal using voice analysis technology (e.g., LibROSA). Input is an audio file or audio stream, and output is the result of emotion recognition based on voice tone and tempo. This emotion analysis result is then used to further refine the user's emotional tendencies.

[0553] Step 5:

[0554] The server uses the Natural Language Toolkit (NLTK) to analyze the text data entered by the user. The input is the user's text, and the output is sentiment analysis results obtained by extracting the sentiment tone and keywords from the text.

[0555] Step 6:

[0556] The server generates commercialization procedures using a generative AI model and further optimizes project progress by incorporating real-time sentiment data. The generative AI model uses this dataset, along with pre-configured prompts, to recommend the next action.

[0557] Step 7:

[0558] The server adaptively provides the purchase process and recommendations based on the user's emotional state. For example, if it determines that the user is browsing a new camera and their emotions are heightened, it will recommend information on related accessories. The input is the user's overall emotional state, and the output is the optimized purchase process and recommendation results.

[0559] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0560] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0561] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0562] [Fourth Embodiment]

[0563] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0564] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0565] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0566] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0567] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0568] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0569] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0570] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0571] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0572] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0573] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0574] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0575] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0576] This invention provides a system that offers an innovative platform for commercializing ideas. This system comprises a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, a prediction unit for profit forecasting, means for selecting stakeholders, and means for determining whether or not to participate. The following describes the program processing in natural language for embodiments of the invention.

[0577] Users log in to the platform using their devices and register their ideas. The idea information entered by the user is sent from the device to the server. Based on the received information, the server activates a generation AI and automatically generates the specific steps necessary for commercialization. Based on the generated commercialization steps, the server uses a prediction unit to perform profit forecasts and saves the results to a database.

[0578] The server then uses profit forecasts to select the necessary stakeholders. This process involves referencing a database of registered members' skills and experience to identify members suitable for the commercialization steps. Selected members are provided with project details and success-based compensation via their terminals.

[0579] In the decision-making process for participation, users (registered members) review the compensation conditions and project details to ensure they are satisfied before deciding to participate. This makes it possible to quickly assign the right members to projects.

[0580] As a concrete example, consider a scenario where a user proposes an idea for a new fitness app. The server uses AI to suggest the necessary steps from app prototyping to launch, and provides profit forecasts based on market data. Based on this, it selects experienced mobile app developers and marketing personnel and provides a means for them to commit to the project.

[0581] Furthermore, once a project reaches a certain stage of progress, the server selects potential investment targets and provides support to enable the project to scale up. This system allows users to efficiently commercialize their ideas and maximize potential business opportunities.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The user logs into the platform using their device and opens the idea registration screen. The user enters information such as idea details, target market, and assumed problem, and registers the idea. The device sends the entered data to the server.

[0585] Step 2:

[0586] The server receives registered idea information and activates the generation AI. The server provides input information to the AI, which then automatically generates the specific steps necessary for commercialization. The generated step information is recorded in the server's database.

[0587] Step 3:

[0588] The server performs profit forecasts based on the generated commercialization steps. It references market data and past project data, and uses a profit forecasting model to simulate sales and profits. These profit forecast results are stored in a database.

[0589] Step 4:

[0590] The server selects the most suitable participants based on the projected profits. It searches a database of registered members' skills, experience, and desired compensation to select the members best suited to the commercialization steps. The selection results are sent to the terminal as information for deciding whether or not to participate.

[0591] Step 5:

[0592] Users (registered members) review project details and success fees via their devices. Users consider the provided information and decide whether or not to participate in the project. The decision is then sent from the device to the server.

[0593] Step 6:

[0594] The server forms teams based on user participation availability and performs initial setup to advance the project. It also periodically monitors progress data during project execution and makes adjustments as needed.

[0595] Step 7:

[0596] The server generates a list of potential investors at certain stages of the project's progress and matches them with investors. Investors are provided with information about the project's current status and future growth potential via their terminals.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] In the traditional process of commercializing an idea, many elements need to be coordinated, such as formulating specific procedures, forecasting profits, selecting appropriate personnel, and matching with investors, making it difficult to carry out efficiently. There is a need for a system that can automate these diverse processes and enable rapid and reliable commercialization.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes an information processing means for receiving information, an automatic generation means, an analysis means, a selection means, and a decision support means. This enables the efficient automation of the idea commercialization process, allowing users to easily realize commercialization.

[0602] "Information processing means" refers to a device or function that takes in various types of information received by a system and processes them appropriately.

[0603] An "automatic generation means" is a device or function that mechanically generates the specific steps for commercialization based on the received information.

[0604] "Analysis means" refers to a device or function for predicting and evaluating profits based on an automatically generated commercialization process.

[0605] "Selection method" refers to a device or function for identifying suitable stakeholders for a project based on profit forecast results.

[0606] A "decision-making support tool" is a device or function that provides stakeholders with detailed project information and compensation, thereby facilitating their decision to participate.

[0607] This invention is a system for efficiently commercializing ideas, encompassing the processes of information processing, process generation, profit forecasting, stakeholder selection, and decision support.

[0608] Users log in to the platform using their own devices and input their ideas. For example, they might input an idea for a new fitness app. The device sends this information to the server. The server uses a generative AI model based on the received idea information to automatically generate the steps necessary for commercialization. This AI model utilizes natural language processing and machine learning algorithms to generate steps such as prototype creation and market strategy development.

[0609] The server uses the generated processes to perform profit forecasts. Analytical software and market data reference tools (e.g., Google Analytics) are used for the forecasts. Based on these profit forecasts, the server consults a database to select appropriate stakeholders and identifies the most suitable members based on their skills and experience. Stakeholders selected based on the results are then provided with detailed project information and compensation terms. This information is provided via terminals, allowing stakeholders to review the information on their own devices and make their participation decisions.

[0610] A concrete example of a prompt would be something like, "Please list the development steps required for a new fitness app." By inputting this prompt into the AI ​​generation model, the details of the commercialization process are smoothly presented.

[0611] In this way, users, terminals, and servers work together to streamline the process from idea to commercialization and provide a system that enables rapid realization.

[0612] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0613] Step 1:

[0614] The user inputs their idea into the terminal. This input is done by using a text box to specifically describe the main points of the idea and confirming the information with a submit button. As a result, the user's idea information is sent from the terminal to the server.

[0615] Step 2:

[0616] The server processes the idea information received from the terminal using information processing tools. In this step, the received text data is analyzed and converted into prompt sentences to activate the generation AI model. This conversion allows the analyzed idea information to be used as input data to generate concrete steps for commercialization.

[0617] Step 3:

[0618] The server activates a generative AI model to automatically generate specific steps for commercialization. In this step, the necessary processes for commercialization (e.g., prototype development, user testing, market entry plan, etc.) are generated using machine learning algorithms. The output from the generative AI model is a clearly defined list of commercialization steps.

[0619] Step 4:

[0620] The server uses analytical tools to perform profit forecasts based on automatically generated business development steps. Profit forecasting utilizes market data and historical data from similar cases, applying a forecasting algorithm to simulate revenue. The output of the profit forecast is the projected revenue figure and the probability of success.

[0621] Step 5:

[0622] The server identifies stakeholders using selection criteria based on profit forecast results. This process involves referencing a stakeholder database to narrow down individuals and groups with the necessary skill sets and experience. The output is a list of stakeholders suitable for the project.

[0623] Step 6:

[0624] The server provides selected participants with project details and compensation terms using decision support tools. This includes sending notifications to the participants' terminals. Participants receive the notifications and decide whether or not to participate based on the information provided. The output is a history of participation decisions.

[0625] (Application Example 1)

[0626] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0627] When commercializing an idea into concrete content or services, it is necessary to develop appropriate processes, forecast profitability, and select the necessary stakeholders, but doing so efficiently and quickly is difficult. Furthermore, there is a need for methods to encourage participation in the project and increase the success rate by providing stakeholders with appropriate information and incentives.

[0628] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0629] In this invention, the server includes a processing unit for receiving information, components for automatically generating business procedures, an estimation device for forecasting revenue, and a device for selecting appropriate skill providers and providing detailed information via an information terminal. This makes it possible to efficiently commercialize ideas and optimize the participation of stakeholders.

[0630] A "processing device for receiving information" is a device that acquires data and information input from an external source and converts it into a format that can be used internally.

[0631] A "component for automatically generating business procedures" is an element that has the function of automatically creating specific procedures and processes for carrying out business based on the information received.

[0632] A "revenue forecasting device" is a device that calculates expected revenue based on business procedures and provides the results.

[0633] A "device for selecting appropriate skill providers and providing detailed information via information terminals" is a device that analyzes the skills and experience of those involved, selects the most suitable personnel for a project, and provides detailed project information to those personnel.

[0634] This invention provides a system for rapidly commercializing ideas, which receives information and, based on that information, automatically generates business procedures, forecasts revenue, and selects stakeholders. The system for implementing this invention is configured as follows.

[0635] The server processes incoming idea information. Specifically, users access the platform using information terminals such as smartphones or personal computers and input their own ideas into the platform. This information is then sent to the server and processed internally.

[0636] The generation AI model installed on the server (e.g., OpenAI's GPT-4) automatically generates specific procedures necessary for business implementation based on the received idea information. The generated procedures are then used by an estimation device to perform revenue forecasting, and the results are stored in a database. The server uses this revenue forecasting data to select skilled providers who should be involved in the project from the database and provides detailed project information through information terminals.

[0637] This system allows users to more efficiently turn their ideas into businesses. For example, suppose a user wants to create a new educational animation. By registering their idea on the platform, an AI model automatically generates project steps, predicts profitability, selects a suitable animation production team, and provides necessary details. In this way, the project can proceed smoothly and effectively.

[0638] An example of a prompt message might be: "Generate the steps for creating an animated educational content. Include the target market, budget, and a list of required staff."

[0639] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0640] Step 1:

[0641] Users access the platform using an information terminal and input their ideas. The input ideas are sent to the server as text data. The input data is combined with the user's account information for initial processing and then stored in the database as registered ideas.

[0642] Step 2:

[0643] The server activates the AI ​​model and generates business procedures based on the received idea data. This model is given prompts such as "Generate the steps for creating animation for educational content." The AI ​​then creates a detailed procedure list based on these prompts and outputs it to the database.

[0644] Step 3:

[0645] The server refers to a procedure list and uses an estimation tool to make revenue forecasts. The procedure list is entered, and the estimation tool performs calculations by comparing it with market data and data from similar past projects. The predicted revenue results are saved to the database.

[0646] Step 4:

[0647] The server uses revenue forecast results to select skilled providers who should participate in the project. It filters candidates with the necessary skills and experience from the stakeholders database and outputs a list of optimal candidates. The selection results are then notified to the relevant parties.

[0648] Step 5:

[0649] Detailed project information and participation requests will be sent to selected skilled providers via information terminals. Specific project details, projected revenue, roles, and compensation conditions will be provided. This process is intended to confirm the participants' willingness to participate.

[0650] Step 6:

[0651] Participants send their confirmations of participation to the server via their terminals. The server aggregates this feedback and makes final adjustments to prepare for the next steps in the project. This includes finalizing the participant list and determining the project schedule.

[0652] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0653] The present invention is a system that incorporates an emotion engine to support project progress by taking into account the user's emotional state in the process of commercializing an idea. This system includes a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, a prediction unit for predicting profits, means for selecting stakeholders, means for deciding whether or not to participate, as well as an emotion engine for recognizing and processing the user's emotions.

[0654] When a user logs into the platform via their device and registers an idea, the device sends that information to the server. The server then activates a generative AI based on the registered information to determine the steps to commercialization and predict profits. An emotion engine is incorporated into this process, analyzing the user's emotional state in real time in response to their input.

[0655] The emotion engine analyzes the user's input speed, stress level, and tone of voice and text to determine their emotional tendencies. This information is used to optimize the commercialization process, adjusting it to maintain user motivation throughout the process. For example, if a user is stressed, the system prioritizes concise and easy-to-understand suggestions.

[0656] Furthermore, the emotion engine also influences the participant selection process. By adjusting the communication strategy with selected participants according to the user's emotional state, the aim is to ensure smoother project progress. In this way, the system facilitates smooth participant engagement and strengthens collaboration.

[0657] Furthermore, it is possible to flexibly adjust the reward conditions offered based on user emotional feedback. For example, if a user shows high motivation, their motivation to participate can be further increased by offering rewards in a different form.

[0658] For example, if a user registers an idea for a fitness app, and the emotion engine determines that the user is excited during the registration process, the generating AI will display a positive analysis result regarding the app's market success potential on the server. In this way, the entire system considers the user's emotions and supports the optimal commercialization process.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The user logs into the platform using their device and opens the idea registration form. The user enters information in each field and registers their idea. The registration is sent from the device to the server.

[0662] Step 2:

[0663] The server receives registered idea information and activates the generation AI. The server passes the idea information to the generation AI, which then generates the steps necessary for commercialization. The generated step information is stored on the server.

[0664] Step 3:

[0665] The server uses an emotion engine to analyze data collected during user input (such as input speed and text tone) and evaluate the user's emotional state. Based on the emotional state, adjustments are made to the generated commercialization steps.

[0666] Step 4:

[0667] The server performs profit forecasts based on the commercialization steps. It references market data and past project data, and uses a forecasting model to simulate profits. The forecast results are recorded in a database.

[0668] Step 5:

[0669] Based on the user's emotional state determined by the emotion engine, the server selects the most suitable stakeholders and adjusts the communication strategy. The most appropriate stakeholders are then selected and sent to the terminal along with detailed project information.

[0670] Step 6:

[0671] The user (registered member) checks the proposed project information and success fee amount on their device. Based on the provided conditions, they decide to participate and send that information back to the server.

[0672] Step 7:

[0673] The server takes emotional feedback into consideration and flexibly adjusts reward conditions, thereby encouraging user participation. Further adjustments are made as the project progresses, based on emotional feedback.

[0674] Step 8:

[0675] The server performs regular progress checks during project execution and also facilitates matching with investors. It also considers data from an emotion engine to develop the optimal strategy for promoting project growth.

[0676] (Example 2)

[0677] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0678] Conventional business development support systems are automated based on fixed processes without considering user emotions, making it difficult to manage projects in accordance with the user's emotional state. As a result, user motivation may decrease, potentially lowering the success rate of the plan. Furthermore, emotional information could not be considered in the selection of team members or the adjustment of compensation.

[0679] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0680] In this invention, the server includes a computing device for receiving information, a generating device for automatically generating a planning procedure, a predictive device for making predictions, and an emotion analysis mechanism. This makes it possible to generate an optimal planning procedure while taking into account the user's emotional state, and to select members and adjust rewards.

[0681] A "computational device" is a device that has the function of receiving information and performing processing and analysis.

[0682] A "generation device" is a device that automatically generates plans and procedures based on received information.

[0683] A "predictive device" is a device that predicts results and benefits based on the generated plan procedures.

[0684] A "member" refers to a person who assumes a specific role or responsibility within a project.

[0685] An "emotion analysis mechanism" is a mechanism that analyzes the user's emotional state based on their input data and reflects the results in each process of the system.

[0686] An "optimization device" is a device that adjusts the planning procedures and project progress based on the analyzed information to bring them to an optimal state.

[0687] "Compensation" refers to the reward given for participating in or contributing to a project.

[0688] This invention begins with a user accessing the system's platform via a terminal and registering an idea. The information entered by the user is transmitted from the terminal to a server. The server analyzes the received information using a computing device and stores it in a database.

[0689] The server's computing system automatically generates plans and procedures using a generative AI model based on registered information. The generator formulates a concrete business plan based on the user's ideas and predicts profits. During this process, an emotion analysis mechanism analyzes the user's emotional state. The emotion analysis mechanism analyzes the tone, speed, and vocabulary of the input text in real time to determine the user's emotions.

[0690] Based on the results of the emotion analysis mechanism, the server uses a planning procedure optimization device to adjust the project's progress according to the user's emotional state. For example, if the user is feeling stressed, it will offer simpler and easier-to-understand suggestions to reduce the user's burden.

[0691] Furthermore, the server selects appropriate members based on the results obtained from the profit prediction device. The user's emotional state is also considered in member selection to facilitate smooth communication. Reward adjustments are also based on emotional feedback; special rewards can be offered if the user shows high motivation.

[0692] For example, if a user registers an idea for a fitness app and the emotion analysis mechanism determines that the user is in an excited state, the server will present a positive analysis result regarding the likelihood of market success, generated by an AI model. In this way, the entire system is designed to support the optimal commercialization process by taking the user's emotions into consideration.

[0693] An example of a prompt message could be: "I've submitted a fitness app idea. How would you analyze its market potential when the user is in an excited state?" This is how you can instruct the generative AI model.

[0694] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0695] Step 1:

[0696] Users log in to the platform on their devices and register their ideas. First, the user enters text details about the project. Once this is complete, the device sends the text data to the server. The entered information includes an overview of the idea, goals, and expected results.

[0697] Step 2:

[0698] The server analyzes the received text data using a computing device and stores it in a database. During this process, the data is cleaned and normalized, and saved in a standardized format. The output consists of the analysis results and the cleaned data.

[0699] Step 3:

[0700] The server activates an emotion analysis mechanism to analyze the user's emotional state. This mechanism evaluates the user's text tone, word choice, and typing speed to determine their emotional tendencies. The input for the analysis is text data, and the output is metrics indicating the user's emotional state. Based on this, emotional categories such as positive, neutral, and negative are established.

[0701] Step 4:

[0702] The server uses a generative AI model to automatically generate business development steps for registered ideas. It considers the user's emotional state and suggests the most suitable steps. It uses the user's idea and emotional metrics as input and provides a concrete business development plan as output.

[0703] Step 5:

[0704] The server uses a forecasting device to perform profit forecasts based on the generated commercialization steps. This process uses the commercialization plan as input and generates a profit forecast report as output. The forecast also includes market analysis and trend predictions.

[0705] Step 6:

[0706] The server initiates a process to select members. The selection criteria are based on the user's emotional state and profit forecast. Based on this input, it creates and outputs a list of suitable members.

[0707] Step 7:

[0708] The server generates prompt messages, providing instructions for the next steps within the system to the generating AI model. For example, a prompt message might say, "Since the user is positive, propose a proactive marketing strategy." The output of this process is the generated action plan.

[0709] Step 8:

[0710] The server periodically collects user feedback and adjusts system suggestions and reward conditions accordingly. It continuously provides more appropriate project support, taking into account changes in the user's emotional state. The output of this feedback process is a continuously adjusted commercialization plan.

[0711] (Application Example 2)

[0712] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] Conventional business development support systems often fail to consider user emotions, resulting in uniform business development procedures and product recommendations that do not adequately reflect user stress or purchasing intent. This can compromise the user experience and reduce the efficiency of business development. Furthermore, insufficient dynamic process adjustments based on user emotions can negatively impact the overall project success rate.

[0714] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0715] In this invention, the server includes a processing unit for receiving information, a generation mechanism for automatically generating a commercialization procedure, and means for analyzing the user's emotional state and optimizing the commercialization procedure based on the analysis results. This makes it possible to provide an optimal commercialization procedure that responds to the user's emotions.

[0716] The "information receiving processing unit" is a part that collects data and signals and converts them into an analyzable format as an initial step for processing.

[0717] A "generating mechanism that automatically generates commercialization procedures" is a device that includes algorithms and logic circuits that form an efficient commercialization flow based on the input information.

[0718] The "forecasting unit that performs profit forecasting" is an analytical engine that evaluates and judges future revenue and profit / loss based on the business plan.

[0719] "Means of selecting stakeholders" refers to a set of rules or programs for appropriately selecting individuals and organizations to participate in a project.

[0720] "Methods for analyzing a user's emotional state" refers to a system of technologies that infer emotions from a user's behavior and biometric information, and then convert that information into data for interpretation.

[0721] A "recommendation method" is a system that has a filtering function to guide users to appropriate products and services based on their needs and emotions.

[0722] The system for realizing this invention comprises a processing unit for receiving information, a generation mechanism for automatically generating commercialization procedures, and a function for analyzing the user's emotional state. These components work together to optimize the user experience.

[0723] The server receives user input and biometric information in real time and extracts the user's emotional state using facial recognition and speech analysis technologies. Specifically, it uses libraries such as OpenCV for facial recognition and LibROSA for speech analysis. Natural Language Toolkit (NLTK) is used for text analysis. Based on the emotional data collected using these technologies, a generative AI model constructs the optimal business development procedure for the user.

[0724] The terminal provides an interface for the user to log in to the server and transmits the user's emotional input in real time. As the user browses products, the server recommends the most suitable products and services based on the analysis of their emotions. At this time, if the system detects that the user is feeling stressed, it automatically takes measures such as simplifying the purchase process and providing relaxing music.

[0725] As a concrete example, consider a scenario where a user is considering purchasing a new camera. If the user's sentiment analysis indicates they are enjoying themselves, the system will proactively recommend related accessories and shooting techniques to stimulate their desire to buy. Furthermore, by using a generative AI model and inputting a prompt such as, "Based on sentiment data indicating the user is experiencing stress, suggest ways to simplify the purchase process," the system can respond accordingly.

[0726] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0727] Step 1:

[0728] The terminal provides an interface for the user to log in to the system. When the user enters login information via the terminal, the terminal sends that information to the server. At this point, the input is the user's login information, and the output is the user ID and authentication token sent to the server.

[0729] Step 2:

[0730] The server verifies the login and prepares to monitor subsequent user interactions. Specifically, it receives user information sent from the terminal and collects product information viewed by the user and text data entered. The input for this process is real-time user operation information, and the output is an accumulation of various operation data.

[0731] Step 3:

[0732] The server uses facial recognition technology (e.g., OpenCV) to receive a user's facial image from the terminal and extracts facial expression data from it. The input is the user's facial expression image, and the output is data indicating the user's emotional state. This data represents the user's emotions (joy, anger, sadness, etc.) using numerical values ​​and labels.

[0733] Step 4:

[0734] The server processes user voice data received from the terminal using voice analysis technology (e.g., LibROSA). Input is an audio file or audio stream, and output is the result of emotion recognition based on voice tone and tempo. This emotion analysis result is then used to further refine the user's emotional tendencies.

[0735] Step 5:

[0736] The server uses the Natural Language Toolkit (NLTK) to analyze the text data entered by the user. The input is the user's text, and the output is sentiment analysis results obtained by extracting the sentiment tone and keywords from the text.

[0737] Step 6:

[0738] The server generates commercialization procedures using a generative AI model and further optimizes project progress by incorporating real-time sentiment data. The generative AI model uses this dataset, along with pre-configured prompts, to recommend the next action.

[0739] Step 7:

[0740] The server adaptively provides the purchase process and recommendations based on the user's emotional state. For example, if it determines that the user is browsing a new camera and their emotions are heightened, it will recommend information on related accessories. The input is the user's overall emotional state, and the output is the optimized purchase process and recommendation results.

[0741] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0742] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0743] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0744] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0745] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0746] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0747] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0748] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0749] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0750] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0751] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0752] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0753] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0754] 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.

[0755] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0756] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0757] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0758] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0759] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0760] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0761] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0762] The following is further disclosed regarding the embodiments described above.

[0763] (Claim 1)

[0764] A processing unit that receives information,

[0765] A generation mechanism that automatically generates a commercialization procedure based on the information received by the calculation unit,

[0766] A forecasting unit that performs profit forecasting based on the commercialization procedure automatically generated by the aforementioned generation mechanism,

[0767] A means for selecting stakeholders based on the profit forecast made by the aforementioned forecasting unit,

[0768] A means of determining whether or not to participate in the proposed project based on the information of the aforementioned stakeholders,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, characterized in that the calculation unit includes means for performing evaluations to match investors with projects.

[0772] (Claim 3)

[0773] The system according to claim 1, characterized in that it includes means for changing the reward depending on whether or not an participant is able to participate.

[0774] "Example 1"

[0775] (Claim 1)

[0776] Information processing means for receiving information,

[0777] An automatic generation means that automatically generates a commercialization process based on the information acquired by the aforementioned information processing means,

[0778] An analysis means for predicting profits based on the commercialization process generated by the aforementioned automatic generation means,

[0779] Selection means for identifying stakeholders based on the profit prediction results obtained by the aforementioned analysis means,

[0780] A decision-making support mechanism that provides identified stakeholders with detailed project information and compensation, and encourages them to decide whether to participate.

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, which enables business expansion by having the information processing means evaluate investment candidates and provide support for project progress.

[0784] (Claim 3)

[0785] The system according to claim 1, comprising a reward adjustment means for adjusting rewards based on the willingness of participants.

[0786] "Application Example 1"

[0787] (Claim 1)

[0788] A processing unit that receives information,

[0789] A component that automatically generates business procedures based on information received by the aforementioned processing device,

[0790] The aforementioned components include a prediction device that performs revenue forecasting based on automatically generated business procedures,

[0791] A device for selecting stakeholders based on the revenue forecast made by the aforementioned estimation device,

[0792] A device that determines whether or not to participate in the proposed activity based on the data of the aforementioned participants,

[0793] A device for selecting appropriate skilled providers for the aforementioned activities and providing detailed information via an information terminal,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, characterized in that the processing device has a function for performing evaluations to match supporters with activities.

[0797] (Claim 3)

[0798] The system according to claim 1, characterized in that it has a function to change the compensation depending on whether or not the participants are able to take part.

[0799] "Example 2 of combining an emotion engine"

[0800] (Claim 1)

[0801] A computing device that receives information,

[0802] A generation device that automatically generates a plan procedure based on information received by the aforementioned computing device,

[0803] A prediction device that predicts results based on the plan procedure automatically generated by the generation device,

[0804] A device for selecting members based on the prediction made by the aforementioned prediction device,

[0805] A device for determining whether or not to participate in a proposed project based on the information of the aforementioned members,

[0806] An emotion analysis mechanism that analyzes the user's emotional state,

[0807] A device that optimizes the planning procedure based on the emotional state analyzed by the aforementioned emotion analysis mechanism,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, characterized in that the computing device includes a device for performing evaluations to connect investors with projects.

[0811] (Claim 3)

[0812] The system according to claim 1, characterized by comprising a device that adjusts the reward depending on whether or not a member can participate.

[0813] "Application example 2 when combining with an emotional engine"

[0814] (Claim 1)

[0815] A processing unit that receives information,

[0816] A generation mechanism that automatically generates a commercialization procedure based on the information received by the calculation unit,

[0817] A forecasting unit that performs profit forecasting based on the commercialization procedure automatically generated by the aforementioned generation mechanism,

[0818] A means for selecting stakeholders based on the profit forecast made by the aforementioned forecasting unit,

[0819] A means for analyzing the emotional state of users and optimizing the commercialization process based on the analysis results,

[0820] A means of recommending products and services that match the user's emotional state,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, characterized in that the calculation unit includes means for performing evaluations to match investors with projects.

[0824] (Claim 3)

[0825] The system according to claim 1, characterized by having means to change the reward depending on whether or not an participant is able to participate, and having means to adaptively adjust the purchase process based on the user's emotional state. [Explanation of symbols]

[0826] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A processing unit that receives information, A generation mechanism that automatically generates a commercialization procedure based on the information received by the calculation unit, A forecasting unit that performs profit forecasting based on the commercialization procedure automatically generated by the aforementioned generation mechanism, A means for selecting stakeholders based on the profit forecast made by the aforementioned forecasting unit, A means of determining whether or not to participate in the proposed project based on the information of the aforementioned stakeholders, A system that includes this.

2. The system according to claim 1, characterized in that the calculation unit includes means for performing evaluations to match investors with projects.

3. The system according to claim 1, characterized in that it includes means for changing the reward depending on whether or not an participant is able to participate.

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