system

Abstract

We provide the system. [Solution] A means of receiving business proposal information from users, A means for extracting and analyzing keywords using natural language processing based on the received information, Based on the analysis results, a means for searching for and matching suitable support companies, A means of automatically generating and presenting tasks suitable for the user, A means of receiving user feedback and re-evaluating tasks and supporting companies, 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 persona chatbot control method performed by at least one processor, the method 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 an individual or a company has a new business idea, it is difficult to find a suitable partner company or expert for commercializing it. Also, it is impossible to clarify the specific procedures and steps necessary for commercialization, and there is a high possibility that the plan will fail. Against this background, there is a demand for the development of a system that can support and efficiently promote the process of embodying a business idea.

Means for Solving the Problems

[0005] This invention enables a deeper understanding of ideas by receiving business proposal information from users and analyzing that information using natural language processing. It quickly searches for and matches relevant support companies based on the analyzed information, and automatically proposes tasks necessary for business progress using a generative model. Furthermore, based on user feedback, it re-evaluates appropriate support companies and task content, ensuring that the system always provides the latest support.

[0006] A "user" is an entity that inputs business proposal information into the system and receives support to commercialize their own ideas.

[0007] "Business proposal information" refers to the details and related information of a user's business idea, which the system analyzes and uses to propose support companies and tasks.

[0008] "Natural language processing" refers to technologies that analyze text data to enable keyword extraction and understanding the meaning of the data.

[0009] "Keywords" are words or phrases deemed particularly important from the business proposal information, and are extracted as a result of the analysis.

[0010] A "support company" is a company that provides expertise and services to assist users in their business development process.

[0011] "Matching" is the process of comparing a user's business proposal information with the characteristics of supporting companies to select the most suitable company.

[0012] A "task" refers to the specific actions and processes required by the user to commercialize their ideas, and these are proposed by the system.

[0013] A "generative model" is an algorithm that uses AI technology to automatically generate new information and suggestions from data.

[0014] "Feedback" refers to data that users provide to the system, such as task completion status and satisfaction levels, and is used as a basis for improving support services. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] As an embodiment of this invention, the following program is incorporated, and each component of the system works in cooperation with one another to provide support for effectively realizing the user's business idea.

[0037] First, the user inputs detailed information about their business idea or project through an interface installed on the terminal. This information includes an overview of the idea, the target market, and the type of partnership they are seeking. The terminal verifies the information received from the user, formats it in a format suitable for the system, and sends it to the server.

[0038] Next, the server analyzes the received data. Here, it utilizes natural language processing to analyze the input data as text, extract keywords, and understand the nature and purpose of the business idea. Based on this analysis, the server searches its database for suitable "support companies" and lists the companies best suited to the user's idea.

[0039] Furthermore, the server uses a generative model to propose the tasks necessary to realize the user's business idea. This includes specific action plans and progress steps for each phase of the business, allowing the user to obtain a concrete plan for moving their idea forward.

[0040] Furthermore, users can input progress and feedback on each task they complete via their device. This information is then sent back to the server and used to re-evaluate proposed tasks and strategies, and to rematch them with support companies as needed.

[0041] For example, if a user wants to commercialize "environmentally friendly fashion items," this system extracts information related to "sustainability," "fashion," and "product development" from the user's input and suggests appropriate suppliers and consultants. It also automatically generates tasks related to market research and prototype development, showing the user what actions to take next. This allows users to efficiently grow their ideas into businesses.

[0042] This system dynamically evolves through user interaction and feedback, continuously providing optimal support and facilitating a smooth commercialization process.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users access the terminal interface and enter details about their business idea or project. This information includes an overview of the idea, target market, and necessary support.

[0046] Step 2:

[0047] The terminal receives information entered by the user, formats its contents formally, and sends it to the server. Before sending the data, it checks the format of the input data and prompts the user to make corrections if necessary.

[0048] Step 3:

[0049] The server receives user information sent from the terminal and stores the data. Next, it analyzes the input data using natural language processing (NLP) techniques to extract keywords and their relationships.

[0050] Step 4:

[0051] The server queries a company database to find suitable support companies based on the analyzed keywords for the business idea. This picks out companies that can provide relevant support and generates a list of candidates.

[0052] Step 5:

[0053] The server proposes specific tasks necessary to advance the user's project. This process uses a generative model to automatically create actionable steps for each phase of the project.

[0054] Step 6:

[0055] The terminal displays a list of support companies and task suggestions sent from the server to the user. The list displayed on the screen shows detailed information about each company and guidelines for completing the tasks.

[0056] Step 7:

[0057] Based on the information provided, the user contacts the selected support company and performs the suggested tasks. They then input their progress and feedback into their device and report it to the system.

[0058] Step 8:

[0059] The server receives user feedback and progress information, and re-evaluates the suitability of the support company and the task content. This allows for new suggestions and adjustments to support as needed.

[0060] (Example 1)

[0061] 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."

[0062] Conventional business idea realization systems struggled to effectively utilize user feedback to advance business proposals, and had issues with the accuracy of matching with supporting organizations and the usefulness of the tasks they proposed. Furthermore, the analysis and processing of information took a long time, sometimes detracting from the user experience.

[0063] 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.

[0064] In this invention, the server includes means for collecting information from the user, means for confirming and formatting the information using a terminal and transferring it to the server, and means for analyzing the content of the received information using natural language processing and extracting keywords. This enables rapid and highly accurate analysis, appropriate matching with support organizations, and accurate task proposals for the user.

[0065] A "user" is a party who uses an information system to input and manipulate information in order to advance their own business ideas or projects.

[0066] "Information" refers to details about a business idea or project, including an overview of the idea, market target, and necessary partnerships.

[0067] A "terminal" is a device used by a user to input information and interact with a system.

[0068] A "server" is a central system that processes information received from users and performs necessary analysis and database searches.

[0069] Natural language processing is a technology used by computers to understand and analyze human language, and it involves extracting keywords and meanings from text data.

[0070] "Keywords" are important terms extracted to understand the nature and purpose of a business idea.

[0071] A "support organization" refers to a company or group that can potentially cooperate in realizing a user's business idea.

[0072] "Matching" is the process of identifying support organizations that meet the user's needs and linking them in a mutually beneficial way.

[0073] A "generative AI model" is an artificial intelligence technology used to automatically suggest tasks and processes that are suitable for the user's business progress.

[0074] A "task" is a specific action or activity that needs to be taken in order to realize a business idea.

[0075] "Feedback" refers to reports from users regarding the progress of tasks they have completed and any support they need.

[0076] This invention aims to effectively realize users' business ideas using an information system, with multiple components working in conjunction with each other. A concrete example is the commercialization of environmentally friendly fashion items.

[0077] The user first enters detailed information about their business idea through an interface installed on the terminal. This information includes a product overview, target market, and desired collaboration model. The terminal reviews the information entered by the user, formats the data as needed, and sends it to the server. This formatting process removes extraneous spaces and symbols and converts the data into a format that is easy to process.

[0078] The server applies natural language processing techniques to the received data to extract meaningful keywords. This analysis helps understand the characteristics and objectives of business ideas and clarify user needs. Based on the analysis results, the server consults an information database to search for and select the most suitable support organization.

[0079] Furthermore, the server uses a generative AI model to materialize the tasks required by the user. This includes specific action guidelines and progress steps for each business phase, allowing the user to advance the project accordingly. In this process, the generative AI model presents optimal tasks and support strategies based on prompt statements such as, "Please tell me the steps required to launch a new environmentally friendly fashion line."

[0080] The user then uses the terminal to input progress and feedback regarding the tasks they have completed. This feedback is sent back to the server, where the suggested tasks and support strategies are re-evaluated. If necessary, the server updates the list of support organizations, ensuring that the user always has the most up-to-date and best information.

[0081] This system is designed to dynamically reflect user actions and feedback, providing optimal support for smoothly realizing business ideas.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user enters detailed information about their business idea through the terminal's interface. This information includes a product overview, target market, and desired partnership structure. The terminal receives this raw data and performs specific actions to verify the input.

[0085] Step 2:

[0086] The terminal formats the information received from the user and converts it into a format that the system can easily understand. This process involves data standardization and the removal of unnecessary characters and spaces. Once the formatted data is output, the terminal prepares to send this data to the server.

[0087] Step 3:

[0088] The server receives formatted data sent from the terminal and performs analysis using natural language processing techniques. It extracts keywords and important phrases from the input text, thereby generating output that helps understand the nature and intent of the business idea.

[0089] Step 4:

[0090] The server searches its information database based on the analysis results. Specifically, it executes queries to identify support organizations that match the extracted keywords. A list of matching support organizations is output, and the server is ready to present the most suitable organizations or groups for the user's business plan.

[0091] Step 5:

[0092] The server utilizes a generative AI model to propose specific tasks necessary to realize the user's business idea. Based on the prompt, it executes an algorithm and generates output that produces action guidelines and process plans that match the user's needs.

[0093] Step 6:

[0094] Users perform the proposed tasks and send feedback on their progress and results via their device. This feedback includes details of completed tasks and any additional support information needed.

[0095] Step 7:

[0096] The server receives feedback from users and re-evaluates proposed tasks and support strategies. Based on the feedback, necessary adjustments are made, and the list of support organizations and task suggestions are updated. This ensures that users always receive appropriate support.

[0097] (Application Example 1)

[0098] 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."

[0099] When content creators try to distribute new ideas, they face the challenge of finding appropriate distribution methods and partners, effectively analyzing the novelty and trends of their content, and establishing clear procedures for increasing engagement.

[0100] 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.

[0101] In this invention, the server includes means for receiving data to be operated on from a user, means for extracting and analyzing key words using natural language processing based on the received data, and means for searching for and matching suitable support organizations based on the analysis results. This enables creators to efficiently find the optimal distribution method and partner.

[0102] "Data to be manipulated" refers to data containing various information and ideas entered by the user, which forms the basis for the system's analysis and recommendations.

[0103] "Keywords" are keywords extracted from user input data through natural language processing, and are important elements for understanding the characteristics and content of the data being manipulated.

[0104] A "support organization" is a company or group that can be utilized to realize the user's plan, and is appropriately matched by the system to support the user's activities.

[0105] "Operational guidelines" are guidelines that the system automatically generates, outlining the most effective action plans and procedures for users, and serve as a roadmap to promote the success of the content.

[0106] "Evaluation information" refers to feedback provided by users, which serves as a standard for re-evaluating and further optimizing business guidelines and the selection of support organizations.

[0107] "Distribution method" refers to the means by which content creators distribute their content widely, and involves the process of selecting the most suitable platform and method.

[0108] A "collaborator" is an individual or group that works together in the creation and distribution of content, and acts as a partner to effectively advance the project.

[0109] "Trend analysis" is the process of analyzing trends and popularity at a given point in time and providing information to enhance the appeal of content and user interest.

[0110] "Specific procedures" refer to detailed, practical steps generated by the system to increase engagement, including instructions that the user can immediately take to achieve their goals.

[0111] To implement this system, the server, terminals, and users must work together in coordination.

[0112] The server implements a program using the Python language and natural language processing libraries (e.g., spaCy, NLTK). This program analyzes the target data received from the user via the terminal and extracts key words. The extracted data is linked to a database on Google Cloud Platform to search for and match the most suitable support organization, delivery method, and collaborators. Furthermore, using OpenAI's generative model, it analyzes the uniqueness and trends of content related to the user's content and automatically generates business guidelines and specific procedures.

[0113] The terminal receives evaluation information provided by the user and sends it to the server, thereby re-evaluating the selection of business guidelines and support organizations, and supporting system optimization.

[0114] Users can input target data and evaluation information through the interface, and based on the operational guidelines suggested by the system, they can effectively distribute content and increase engagement. This system serves as a powerful support tool for content creators to successfully realize their ideas.

[0115] As a concrete example, if a creator wants to distribute a "relaxation music album themed around nature sounds," the system will extract key terms related to "nature sounds," "relaxation," and "music," and suggest suitable distribution methods and collaborators for the user. An example of a prompt to the generating AI model in this case is as follows:

[0116] Project information:

[0117] Title: Relaxation music album themed around nature sounds

[0118] Target audience: Adults seeking relaxation

[0119] Please suggest the next course of action:

[0120] In this way, users can leverage the system's suggestions to bring their ideas to life and achieve business success.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The user inputs data to be manipulated using a terminal. This data includes content ideas, target market, and objectives. The terminal converts this input data into an appropriate format and sends it to the server.

[0124] Step 2:

[0125] The server analyzes the received input data using natural language processing libraries (e.g., spaCy, NLTK). Specifically, it extracts key words from the data and identifies keywords. This analysis provides information to understand the nature of the user's content ideas.

[0126] Step 3:

[0127] The server uses these key terms to search a database on Google Cloud Platform. This search identifies and lists support organizations, distribution methods, and collaborators that are suitable for the content idea. This process helps prepare the most suitable external collaboration for the user.

[0128] Step 4:

[0129] Next, the server utilizes OpenAI's generative AI model to generate business guidelines and specific procedures based on key terms and database results. These procedures are tailored to the popularity of the content and the user's objectives, suggesting the next action the user should take.

[0130] Step 5:

[0131] Users receive work guidelines from the server via their terminal. Following these guidelines, they create and distribute content, and input evaluation information based on the results into their terminal. This evaluation information is then sent back to the server.

[0132] Step 6:

[0133] Based on the evaluation information received from the user, the server automatically reassesss the suitability of business guidelines and support organizations. If necessary, it reuses the generated AI model to provide new suggestions for greater user success. This process ensures the system is constantly optimized and ready to support the user.

[0134] 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.

[0135] As an embodiment of the present invention, a system incorporating an emotion engine is designed, and each component works in cooperation with one another to provide optimal business support according to the user's emotional state.

[0136] First, the user accesses the terminal's user interface and provides emotion input data along with business proposal information. This information can be text-based, but it is also possible to estimate emotions based on voice and facial expression data. The terminal processes this information, formats it in an appropriate format, and sends it to the server.

[0137] The server performs text analysis based on the received information and uses natural language processing (NLP) technology to analyze business proposal information. Simultaneously, the emotion engine analyzes the user's emotional state and determines what kind of support the user desires. This analysis visualizes the emotional state in numerical or other formats, influencing subsequent processing.

[0138] Based on the analysis results, the server extracts suitable support companies from its corporate database and performs matching. It also proposes tasks based on the progress and priority of the business. The proposed tasks are then listed in priority, taking into account the results of the emotion engine's analysis, and prioritizing those of high importance or those that the user can immediately work on.

[0139] The terminal presents the user with a list of support companies and tasks suggested by the server. This uses language appropriate to the user's emotional state to enable a more appropriate approach and feedback. For example, if the user is feeling stressed, the number of support company options is limited, and tasks are presented in stages to reduce the burden.

[0140] Ultimately, the user contacts a support company selected based on the provided information and performs the proposed tasks. They then input the results and feedback into their terminal and report them to the system. This feedback is used on the server for re-evaluation, and the support company and task details are updated as needed.

[0141] For example, if a user wants to launch a "new online education platform" but is overwhelmed by too much information, this system uses an emotion engine to reduce user pressure and provides a concise, results-oriented task list. This allows the user to efficiently advance the project while reducing emotional burden.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] Users input business proposal information and data related to their own emotions through the device's user interface. Emotional data is collected through text input, voice recording, and facial recognition.

[0145] Step 2:

[0146] The terminal temporarily stores the entered information and formats it into a predetermined data format. This formatted data is then structured to facilitate analysis and securely transmitted to the server.

[0147] Step 3:

[0148] The server analyzes the information received from the terminal. This analysis includes a process of extracting keywords from text data using natural language processing techniques to understand the content of the business proposal.

[0149] Step 4:

[0150] The server simultaneously uses an emotion engine to analyze the user's emotional state. This analysis quantifies emotional data and evaluates what emotions the user is feeling.

[0151] Step 5:

[0152] The server queries a corporate database based on the analyzed business proposal information and sentiment data to search for and match the most suitable support company with the user. This includes selecting a company that does not burden the user, using sentiment information.

[0153] Step 6:

[0154] The server incorporates sentiment analysis results when generating recommended task lists for the user's project. For example, if the user is experiencing stress, it will suggest simplifying tasks or adjusting their priorities.

[0155] Step 7:

[0156] Based on the analysis results, the device visually presents the user with a list of matched support companies and a coordinated task list. The display is presented in a user-friendly interface, taking into account the user's emotional state.

[0157] Step 8:

[0158] Based on the information provided, users can contact their preferred support companies and proceed with the suggested tasks. Feedback on the progress and results of their activities is collected and reported to the system via their device.

[0159] Step 9:

[0160] The server analyzes user feedback, suggests new tasks, and re-evaluates support companies. This feedback loop ensures that the system is constantly adjusted to provide users with the best possible support.

[0161] (Example 2)

[0162] 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".

[0163] Traditional business support systems often provide generic tasks and support information without considering the user's emotional state, leading to increased emotional burden and decreased motivation. Furthermore, the sheer volume of information provided made it difficult for users to determine what was optimal and effective for them. Therefore, there was a need for individualized support tailored to each user's emotional state.

[0164] 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.

[0165] In this invention, the server includes means for collecting business-related and emotional information from the user, means for analyzing the collected information using natural language processing and sentiment analysis technologies to extract keywords and emotional states, and means for searching for and matching the user with a suitable support organization based on the analysis results according to the user's emotional state. This enables the user to receive information tailored to their emotional state, thereby reducing their burden and achieving optimal business progress.

[0166] A "user" refers to an individual or organization that uses the system and receives business support.

[0167] "Emotional information" refers to data that indicates the user's emotional state, and includes formats such as voice, facial expressions, and text input.

[0168] "Natural language processing technology" refers to the technology that allows computers to understand and process human language, including text analysis and keyword extraction.

[0169] "Emotion analysis technology" refers to technology that evaluates and visualizes a user's emotional state numerically or qualitatively.

[0170] A "support organization" refers to a company or group that assists users in their business operations.

[0171] A "task" refers to a specific task or activity that a user must perform in order to achieve business objectives.

[0172] A "generative model" refers to an algorithm or technology that generates new information or suggestions based on data.

[0173] This system optimizes user business support based on their emotional state. First, the user provides business-related information and emotional data using a terminal. Emotional data consists of text input, voice, and facial expression data. The terminal collects this data and standardizes the format as needed. Voice data is converted to text via speech recognition software, and facial expression data is quantified using image analysis technology.

[0174] Next, the terminal sends the collected data to the server. The server analyzes the received data using natural language processing and sentiment analysis technologies. This analysis extracts important keywords from the business proposal information and further evaluates the user's emotional state numerically. Natural language processing technologies include Python's natural language processing libraries.

[0175] Based on the analysis results, the server searches the database for support organizations suitable for business assistance and matches them to the user's needs. Furthermore, it utilizes a generative model to generate tasks that take the user's emotional state into consideration, suggesting tasks that the user should perform. This reduces the user's emotional burden while promoting efficient business progress.

[0176] Finally, the user receives a list of suggested support organizations and tasks through the device. The device presents this information through a user interface that is sensitive to the user's feelings. The user's results and feedback are again sent to the server via the device and incorporated into subsequent processes.

[0177] As a concrete example, consider a user who wants to start a new online education business but is feeling stressed due to information overload. This system uses an emotion engine to reduce pressure and generate an effective task list tailored to the user's situation. An example of a prompt to the generating AI model would be, "Please tell me how you can support my new project proposal based on my emotions."

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] Users input business-related information and emotional data into the terminal. This input data includes various formats such as text, voice, and facial expressions. The terminal converts voice data to text and facial expression data to numerical data through image analysis. Specifically, this involves text conversion using voice recognition software and facial expression capture using a camera.

[0181] Step 2:

[0182] The terminal sends the formatted data to the server. The data is securely transferred using an encryption protocol (e.g., TLS / SSL). Specifically, the terminal encrypts the data before sending it, and then transfers it to the server via a secure communication channel.

[0183] Step 3:

[0184] The server analyzes business information using natural language processing technology based on the received data. The analysis involves extracting keywords from text data to identify key themes. For example, it uses a natural language processing library to extract business-related terms and evaluate their importance.

[0185] Step 4:

[0186] The server simultaneously evaluates the user's emotional state using emotion analysis technology. It quantifies emotions from voice and facial expression data and converts them into a visualized form. Specific operations include calculating an emotion score using a machine learning model.

[0187] Step 5:

[0188] The server searches for suitable support organizations based on the analysis results and matches the user with the most suitable one. It extracts support organizations that meet the criteria from the company database using SQL queries and prioritizes them according to their sentiment score.

[0189] Step 6:

[0190] The server uses an AI model to generate a task list that takes the user's emotional state into account. The model suggests specific tasks that are relevant to the user's progress. The generated task list is then broken down into feasible steps, taking into account the user's emotional burden.

[0191] Step 7:

[0192] The device presents the user with suggested support organization information and a task list. The user interface employs a user-friendly design that considers the user's emotions, displaying information in a visually appealing and easy-to-use format. Specific actions include utilizing user interface animations and notification features.

[0193] (Application Example 2)

[0194] 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".

[0195] Traditionally, business proposals and product recommendations made without considering the user's emotional state were not always optimal for the user and, as a result, did not contribute to efficient business development or an improved purchasing experience. The present invention aims to realize appropriate business support and product / service recommendations based on the user's emotional state.

[0196] 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.

[0197] In this invention, the server includes means for receiving business proposal information and emotional state data from the user, means for extracting and analyzing keywords using natural language processing based on the received information, and means for optimizing the content of product and service proposals based on the user's emotional state. This enables optimal business support and product recommendations tailored to the user's emotional state.

[0198] A "user" is an individual or organization that uses the system to provide business proposal information and emotional state data.

[0199] "Business proposal information" refers to information about business-related plans and ideas that users provide to the system.

[0200] "Emotional state data" refers to data that indicates the user's emotional state, and is information estimated from voice, facial expressions, and text.

[0201] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0202] A "keyword" is an important word or phrase extracted from the business proposal information.

[0203] A "supporting organization" refers to a company or group that cooperates in realizing a business proposal.

[0204] A "task" is a set of specific tasks that a user needs to perform to advance their business.

[0205] A "generative model" is an algorithm that proposes the optimal process based on the user's business progress and emotional state.

[0206] "Feedback" refers to evaluations and opinions that users return to a system.

[0207] "Product and service recommendations" refer to the specific details of products and services recommended to the user.

[0208] The system for implementing this invention provides business support and product recommendations that take into account the user's emotional state. The server receives business proposal information and emotional state data from the user and processes this information. Specifically, it uses speech recognition APIs and facial expression analysis APIs to estimate the emotional state from speech and facial expressions, and analyzes text information using natural language processing technology. Hardware used includes smartphones and tablet devices, and software includes Google Cloud Speech-to-Text and Amazon Rekognition. Based on the analysis results, the server utilizes a generative AI model to generate optimal business support processes and product recommendations that are tailored to the user's emotional state.

[0209] The device optimizes the suggestions presented to the user based on their emotional state. For example, if the user is relaxed, it suggests relaxing products; if they are stressed, it emphasizes limited-time coupons and discount information. This allows users to have a more satisfying and personalized purchasing experience.

[0210] Users act based on the suggested products and business support services, and provide the system with their results and feedback. This feedback is used on the server as data for further optimization and is reflected in future suggestions. As a specific example, an example of a prompt message to the generating AI model is: "If the system detects that the user is relaxed while shopping, generate a prompt that lists recommended relaxation products and provides a more comfortable shopping experience."

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The user inputs business proposal information and emotional state data via their device. The system captures the user's voice and facial expressions using the smartphone's microphone and camera. This input data is passed to a speech recognition API and a facial expression analysis API.

[0214] Step 2:

[0215] The server converts the received audio data into text using Google Cloud Speech-to-Text, and simultaneously extracts and analyzes keywords using natural language processing techniques. Facial expression data is analyzed using Amazon Rekognition to estimate emotional states. This process yields business proposals and emotional analysis results.

[0216] Step 3:

[0217] Based on the analysis results, the server utilizes a generative AI model to generate tasks and product recommendations tailored to the user's business progress and emotional state. Specifically, it receives keywords and emotional data from the analysis results as input, executes an algorithm to select appropriate processes and products, and creates a task list and product list as output.

[0218] Step 4:

[0219] The server sends the generated tasks and product list to the terminal. The terminal then displays optimized suggestions based on the user's emotional state. For example, if the user is relaxed, it suggests relaxation products; if they are stressed, it highlights discount information.

[0220] Step 5:

[0221] The user reviews the presented tasks and products and enters feedback into the terminal. The terminal sends this feedback to the server, which stores the feedback data in a database to optimize future suggestions and incorporates it into the analysis results.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] As an embodiment of this invention, the following program is incorporated, and each component of the system works in cooperation with one another to provide support for effectively realizing the user's business idea.

[0239] First, the user inputs detailed information about their business idea or project through an interface installed on the terminal. This information includes an overview of the idea, the target market, and the type of partnership they are seeking. The terminal verifies the information received from the user, formats it in a format suitable for the system, and sends it to the server.

[0240] Next, the server analyzes the received data. Here, it utilizes natural language processing to analyze the input data as text, extract keywords, and understand the nature and purpose of the business idea. Based on this analysis, the server searches its database for suitable "support companies" and lists the companies best suited to the user's idea.

[0241] Furthermore, the server uses a generative model to propose the tasks necessary to realize the user's business idea. This includes specific action plans and progress steps for each phase of the business, allowing the user to obtain a concrete plan for moving their idea forward.

[0242] Furthermore, users can input progress and feedback on each task they complete via their device. This information is then sent back to the server and used to re-evaluate proposed tasks and strategies, and to rematch them with support companies as needed.

[0243] For example, if a user wants to commercialize "environmentally friendly fashion items," this system extracts information related to "sustainability," "fashion," and "product development" from the user's input and suggests appropriate suppliers and consultants. It also automatically generates tasks related to market research and prototype development, showing the user what actions to take next. This allows users to efficiently grow their ideas into businesses.

[0244] This system dynamically evolves through user interaction and feedback, continuously providing optimal support and facilitating a smooth commercialization process.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] Users access the terminal interface and enter details about their business idea or project. This information includes an overview of the idea, target market, and necessary support.

[0248] Step 2:

[0249] The terminal receives information entered by the user, formats its contents formally, and sends it to the server. Before sending the data, it checks the format of the input data and prompts the user to make corrections if necessary.

[0250] Step 3:

[0251] The server receives user information sent from the terminal and stores the data. Next, it analyzes the input data using natural language processing (NLP) techniques to extract keywords and their relationships.

[0252] Step 4:

[0253] The server queries a company database to find suitable support companies based on the analyzed keywords for the business idea. This picks out companies that can provide relevant support and generates a list of candidates.

[0254] Step 5:

[0255] The server proposes specific tasks necessary to advance the user's project. This process uses a generative model to automatically create actionable steps for each phase of the project.

[0256] Step 6:

[0257] The terminal displays a list of support companies and task suggestions sent from the server to the user. The list displayed on the screen shows detailed information about each company and guidelines for completing the tasks.

[0258] Step 7:

[0259] Based on the information provided, the user contacts the selected support company and performs the suggested tasks. They then input their progress and feedback into their device and report it to the system.

[0260] Step 8:

[0261] The server receives user feedback and progress information, and re-evaluates the suitability of the support company and the task content. This allows for new suggestions and adjustments to support as needed.

[0262] (Example 1)

[0263] 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."

[0264] Conventional business idea realization systems struggled to effectively utilize user feedback to advance business proposals, and had issues with the accuracy of matching with supporting organizations and the usefulness of the tasks they proposed. Furthermore, the analysis and processing of information took a long time, sometimes detracting from the user experience.

[0265] 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.

[0266] In this invention, the server includes means for collecting information from the user, means for confirming and formatting the information using a terminal and transferring it to the server, and means for analyzing the content of the received information using natural language processing and extracting keywords. This enables rapid and highly accurate analysis, appropriate matching with support organizations, and accurate task proposals for the user.

[0267] A "user" is someone who uses an information system to input and manipulate information in order to advance their own business ideas or projects.

[0268] "Information" refers to details about a business idea or project, including an overview of the idea, market target, and necessary partnerships.

[0269] A "terminal" is a device used by a user to input information and interact with a system.

[0270] A "server" is a central system that processes information received from users and performs necessary analysis and database searches.

[0271] Natural language processing is a technology used by computers to understand and analyze human language, and it involves extracting keywords and meanings from text data.

[0272] "Keywords" are important terms extracted to understand the nature and purpose of a business idea.

[0273] A "support organization" refers to a company or group that can potentially cooperate in realizing a user's business idea.

[0274] "Matching" is the process of identifying support organizations that meet the user's needs and linking them in a mutually beneficial way.

[0275] A "generative AI model" is an artificial intelligence technology used to automatically suggest tasks and processes that are suitable for the user's business progress.

[0276] A "task" is a specific action or activity that needs to be taken in order to realize a business idea.

[0277] "Feedback" refers to reports from users regarding the progress of tasks they have completed and any support they need.

[0278] This invention aims to effectively realize users' business ideas using information systems, with multiple components working in conjunction with each other. A concrete example is the commercialization of environmentally friendly fashion items.

[0279] The user first enters detailed information about their business idea through an interface installed on the terminal. This information includes a product overview, target market, and desired collaboration model. The terminal reviews the information entered by the user, formats the data as needed, and sends it to the server. This formatting process removes extraneous spaces and symbols and converts the data into a format that is easy to process.

[0280] The server applies natural language processing techniques to the received data to extract meaningful keywords. This analysis helps understand the characteristics and objectives of business ideas and clarify user needs. Based on the analysis results, the server consults an information database to search for and select the most suitable support organization.

[0281] Furthermore, the server uses a generative AI model to materialize the tasks required by the user. This includes specific action guidelines and progress steps for each business phase, allowing the user to advance the project accordingly. In this process, the generative AI model presents optimal tasks and support strategies based on prompt statements such as, "Please tell me the steps required to launch a new environmentally friendly fashion line."

[0282] The user then uses the terminal to input progress and feedback regarding the tasks they have completed. This feedback is sent back to the server, where the suggested tasks and support strategies are re-evaluated. If necessary, the server updates the list of support organizations, ensuring that the user always has the most up-to-date and best information.

[0283] This system is designed to dynamically reflect user actions and feedback, providing optimal support for smoothly realizing business ideas.

[0284] The flow of a specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user inputs detailed information regarding the business idea through the interface of the terminal. The information to be input includes an overview of the product, the target market, the form of the desired partnership, etc. The terminal receives this raw data and performs specific operations to confirm the input content.

[0287] Step 2:

[0288] The terminal formats the information received from the user and converts it into a form that is easy for the system to understand. In this process, data normalization and the removal of unnecessary characters and blanks are performed. When the formatted data is output, the terminal prepares to transmit this data to the server.

[0289] Step 3:

[0290] The server receives the formatted data transmitted from the terminal and performs analysis using natural language processing technology. Operations are carried out to extract keywords and important phrases from the input text, thereby generating an output that understands the nature and intention of the business idea.

[0291] Step 4:

[0292] <G The server searches the information database based on the analysis results. Specifically, a query is executed to identify support organizations that match the extracted keywords. A list of matching support organizations is output, preparing to present the most suitable organizations and groups for the user's business plan.

[0293] Step 5:

[0294] The server utilizes a generative AI model to propose specific tasks necessary to realize the user's business idea. Based on the prompt, it executes an algorithm and generates output that produces action guidelines and process plans that match the user's needs.

[0295] Step 6:

[0296] Users perform the proposed tasks and send feedback on their progress and results via their device. This feedback includes details of completed tasks and any additional support information needed.

[0297] Step 7:

[0298] The server receives feedback from users and re-evaluates proposed tasks and support strategies. Based on the feedback, necessary adjustments are made, and the list of support organizations and task suggestions are updated. This ensures that users always receive appropriate support.

[0299] (Application Example 1)

[0300] 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."

[0301] When content creators try to distribute new ideas, they face the challenge of finding appropriate distribution methods and partners, effectively analyzing the novelty and trends of their content, and establishing clear procedures for increasing engagement.

[0302] 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.

[0303] In this invention, the server includes means for receiving operation target data from a user, means for extracting and analyzing main words using natural language processing based on the received data, and means for searching and matching suitable support organizations based on the analysis results. As a result, the creator can efficiently find the optimal distribution method and partners.

[0304] The "operation target data" is data that includes various information and ideas input by the user and serves as the basis for the system to perform analysis and propose.

[0305] The "main words" are keywords extracted from the user's input data through natural language processing and are important elements for understanding the characteristics and content of the operation target data.

[0306] The "support organization" is an enterprise or organization that can be utilized to realize the user's plan, is properly matched by the system, and has the role of supporting the user's activities.

[0307] The "business guidelines" are guidelines that indicate the most effective action plans and procedures for the user automatically generated by the system and serve as the path for promoting the success of the content.

[0308] The "evaluation information" is feedback provided by the user and serves as a criterion for re-evaluating the selection of business guidelines and support organizations and for further optimization.

[0309] The "distribution method" is a means for widely distributing the content created by the content creator and is related to the process of selecting the optimal platform and method.

[0310] The "co-operator" refers to an individual or organization that cooperates in the production and distribution of content and becomes a partner for effectively promoting the project.

[0311] "Trend analysis" is the process of analyzing trends and popularity at a given point in time and providing information to enhance the appeal of content and user interest.

[0312] "Specific procedures" refer to detailed, practical steps generated by the system to increase engagement, including instructions that the user can immediately take to achieve their goals.

[0313] To implement this system, the server, terminals, and users must work together in coordination.

[0314] The server implements a program using the Python language and natural language processing libraries (e.g., spaCy, NLTK). This program analyzes the target data received from the user via the terminal and extracts key words. The extracted data is linked to a database on Google Cloud Platform to search for and match the most suitable support organization, delivery method, and collaborators. Furthermore, it uses OpenAI's generative model to analyze the specificity and trends of content related to the user's content and automatically generates business guidelines and specific procedures.

[0315] The terminal receives evaluation information provided by the user and sends it to the server, thereby re-evaluating the selection of business guidelines and support organizations, and supporting system optimization.

[0316] Users can input target data and evaluation information through the interface, and based on the operational guidelines suggested by the system, they can effectively distribute content and increase engagement. This system serves as a powerful support tool for content creators to successfully realize their ideas.

[0317] As a concrete example, if a creator wants to distribute a "relaxation music album themed around nature sounds," the system will extract key terms related to "nature sounds," "relaxation," and "music," and suggest suitable distribution methods and collaborators for the user. An example of a prompt to the generating AI model in this case is as follows:

[0318] Project information:

[0319] Title: Relaxation music album themed around nature sounds

[0320] Target audience: Adults seeking relaxation

[0321] Please suggest the next course of action:

[0322] In this way, users can leverage the system's suggestions to bring their ideas to life and achieve business success.

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The user inputs data to be manipulated using a terminal. This data includes content ideas, target market, and objectives. The terminal converts this input data into an appropriate format and sends it to the server.

[0326] Step 2:

[0327] The server analyzes the received input data using natural language processing libraries (e.g., spaCy, NLTK). Specifically, it extracts key words from the data and identifies keywords. This analysis provides information to understand the nature of the user's content ideas.

[0328] Step 3:

[0329] The server uses these key terms to search a database on Google Cloud Platform. This search identifies and lists support organizations, distribution methods, and collaborators that are suitable for the content idea. This process helps prepare the most suitable external collaboration for the user.

[0330] Step 4:

[0331] Next, the server utilizes OpenAI's generative AI model to generate business guidelines and specific procedures based on key terms and database results. These procedures are tailored to the popularity of the content and the user's objectives, suggesting the next action the user should take.

[0332] Step 5:

[0333] Users receive work guidelines from the server via their terminal. Following these guidelines, they create and distribute content, and input evaluation information based on the results into their terminal. This evaluation information is then sent back to the server.

[0334] Step 6:

[0335] Based on the evaluation information received from the user, the server automatically reassesss the suitability of business guidelines and support organizations. If necessary, it reuses the generated AI model to provide new suggestions for greater user success. This process ensures the system is constantly optimized and ready to support the user.

[0336] 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.

[0337] As an embodiment of the present invention, a system incorporating an emotion engine is designed, and each component works in cooperation with one another to provide optimal business support according to the user's emotional state.

[0338] First, the user accesses the terminal's user interface and provides emotion input data along with business proposal information. This information can be text-based, but it is also possible to estimate emotions based on voice and facial expression data. The terminal processes this information, formats it in an appropriate format, and sends it to the server.

[0339] The server performs text analysis based on the received information and uses natural language processing (NLP) technology to analyze business proposal information. Simultaneously, the emotion engine analyzes the user's emotional state and determines what kind of support the user desires. This analysis visualizes the emotional state in numerical or other formats, influencing subsequent processing.

[0340] Based on the analysis results, the server extracts suitable support companies from its corporate database and performs matching. It also proposes tasks based on the progress and priority of the business. The proposed tasks are then listed in priority, taking into account the results of the emotion engine's analysis, and prioritizing those of high importance or those that the user can immediately work on.

[0341] The terminal presents the user with a list of support companies and tasks suggested by the server. This uses language appropriate to the user's emotional state to enable a more appropriate approach and feedback. For example, if the user is feeling stressed, the number of support company options is limited, and tasks are presented in stages to reduce the burden.

[0342] Ultimately, the user contacts a support company selected based on the provided information and performs the proposed tasks. They then input the results and feedback into their terminal and report them to the system. This feedback is used on the server for re-evaluation, and the support company and task details are updated as needed.

[0343] For example, if a user wants to launch a "new online education platform" but is overwhelmed by too much information, this system uses an emotion engine to reduce user pressure and provides a concise, results-oriented task list. This allows the user to efficiently advance the project while reducing emotional burden.

[0344] The following describes the processing flow.

[0345] Step 1:

[0346] Users input business proposal information and data related to their own emotions through the device's user interface. Emotional data is collected through text input, voice recording, and facial recognition.

[0347] Step 2:

[0348] The terminal temporarily stores the entered information and formats it into a predetermined data format. This formatted data is then structured to facilitate analysis and securely transmitted to the server.

[0349] Step 3:

[0350] The server analyzes the information received from the terminal. This analysis includes a process of extracting keywords from text data using natural language processing techniques to understand the content of the business proposal.

[0351] Step 4:

[0352] The server simultaneously uses an emotion engine to analyze the user's emotional state. This analysis quantifies emotional data and evaluates what emotions the user is feeling.

[0353] Step 5:

[0354] The server queries a corporate database based on the analyzed business proposal information and sentiment data to search for and match the most suitable support company with the user. This includes selecting a company that does not burden the user, using sentiment information.

[0355] Step 6:

[0356] The server incorporates sentiment analysis results when generating recommended task lists for the user's project. For example, if the user is experiencing stress, it will suggest simplifying tasks or adjusting their priorities.

[0357] Step 7:

[0358] Based on the analysis results, the device visually presents the user with a list of matched support companies and a coordinated task list. The display is presented in a user-friendly interface, taking into account the user's emotional state.

[0359] Step 8:

[0360] Based on the information provided, users can contact their preferred support companies and proceed with the suggested tasks. Feedback on the progress and results of their activities is collected and reported to the system via their device.

[0361] Step 9:

[0362] The server analyzes user feedback, suggests new tasks, and re-evaluates support companies. This feedback loop ensures that the system is constantly adjusted to provide users with the best possible support.

[0363] (Example 2)

[0364] 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 glasses 214 will be referred to as the "terminal".

[0365] Traditional business support systems often provide generic tasks and support information without considering the user's emotional state, leading to increased emotional burden and decreased motivation. Furthermore, the sheer volume of information provided made it difficult for users to determine what was optimal and effective for them. Therefore, there was a need for individualized support tailored to each user's emotional state.

[0366] 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.

[0367] In this invention, the server includes means for collecting business-related and emotional information from the user, means for analyzing the collected information using natural language processing and sentiment analysis technologies to extract keywords and emotional states, and means for searching for and matching the user with a suitable support organization based on the analysis results according to the user's emotional state. This enables the user to receive information tailored to their emotional state, thereby reducing their burden and achieving optimal business progress.

[0368] A "user" refers to an individual or organization that uses the system and receives business support.

[0369] "Emotional information" refers to data that indicates the user's emotional state, and includes formats such as voice, facial expressions, and text input.

[0370] "Natural language processing technology" refers to the technology that allows computers to understand and process human language, including text analysis and keyword extraction.

[0371] "Emotion analysis technology" refers to technology that evaluates and visualizes a user's emotional state numerically or qualitatively.

[0372] A "support organization" refers to a company or group that assists users in their business operations.

[0373] A "task" refers to a specific task or activity that a user must perform in order to achieve business objectives.

[0374] A "generative model" refers to an algorithm or technology that generates new information or suggestions based on data.

[0375] This system optimizes user business support based on their emotional state. First, the user provides business-related information and emotional data using a terminal. Emotional data consists of text input, voice, and facial expression data. The terminal collects this data and standardizes the format as needed. Voice data is converted to text via speech recognition software, and facial expression data is quantified using image analysis technology.

[0376] Next, the terminal sends the collected data to the server. The server analyzes the received data using natural language processing and sentiment analysis technologies. This analysis extracts important keywords from the business proposal information and further evaluates the user's emotional state numerically. Natural language processing technologies include Python's natural language processing libraries.

[0377] Based on the analysis results, the server searches the database for support organizations suitable for business assistance and matches them to the user's needs. Furthermore, it utilizes a generative model to generate tasks that take the user's emotional state into consideration, suggesting tasks that the user should perform. This reduces the user's emotional burden while promoting efficient business progress.

[0378] Finally, the user receives a list of suggested support organizations and tasks through the device. The device presents this information through a user interface that is sensitive to the user's feelings. The user's results and feedback are again sent to the server via the device and incorporated into subsequent processes.

[0379] As a concrete example, consider a user who wants to start a new online education business but is feeling stressed due to information overload. This system uses an emotion engine to reduce pressure and generate an effective task list tailored to the user's situation. An example of a prompt to the generating AI model would be, "Please tell me how you can support my new project proposal based on my emotions."

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] Users input business-related information and emotional data into the terminal. This input data includes various formats such as text, voice, and facial expressions. The terminal converts voice data to text and facial expression data to numerical data through image analysis. Specifically, this involves text conversion using voice recognition software and facial expression capture using a camera.

[0383] Step 2:

[0384] The terminal sends the formatted data to the server. The data is securely transferred using an encryption protocol (e.g., TLS / SSL). Specifically, the terminal encrypts the data before sending it, and then transfers it to the server via a secure communication channel.

[0385] Step 3:

[0386] The server analyzes business information using natural language processing technology based on the received data. The analysis involves extracting keywords from text data to identify key themes. For example, it uses a natural language processing library to extract business-related terms and evaluate their importance.

[0387] Step 4:

[0388] The server simultaneously evaluates the user's emotional state using emotion analysis technology. It quantifies emotions from voice and facial expression data and converts them into a visualized form. Specific operations include calculating an emotion score using a machine learning model.

[0389] Step 5:

[0390] The server searches for suitable support organizations based on the analysis results and matches the user with the most suitable one. It extracts support organizations that meet the criteria from the company database using SQL queries and prioritizes them according to their sentiment score.

[0391] Step 6:

[0392] The server uses an AI model to generate a task list that takes the user's emotional state into account. The model suggests specific tasks that are relevant to the user's progress. The generated task list is then broken down into feasible steps, taking into account the user's emotional burden.

[0393] Step 7:

[0394] The device presents the user with suggested support organization information and a task list. The user interface employs a user-friendly design that considers the user's emotions, displaying information in a visually appealing and easy-to-use format. Specific actions include utilizing user interface animations and notification features.

[0395] (Application Example 2)

[0396] 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."

[0397] Traditionally, business proposals and product recommendations made without considering the user's emotional state were not always optimal for the user and, as a result, did not contribute to efficient business development or an improved purchasing experience. The present invention aims to realize appropriate business support and product / service recommendations based on the user's emotional state.

[0398] 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.

[0399] In this invention, the server includes means for receiving business proposal information and emotional state data from the user, means for extracting and analyzing keywords using natural language processing based on the received information, and means for optimizing the content of product and service proposals based on the user's emotional state. This enables optimal business support and product recommendations tailored to the user's emotional state.

[0400] A "user" is an individual or organization that uses the system to provide business proposal information and emotional state data.

[0401] "Business proposal information" refers to information about business-related plans and ideas that users provide to the system.

[0402] "Emotional state data" refers to data that indicates the user's emotional state, and is information estimated from voice, facial expressions, and text.

[0403] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0404] A "keyword" is an important word or phrase extracted from the business proposal information.

[0405] A "supporting organization" refers to a company or group that cooperates in realizing a business proposal.

[0406] A "task" is a set of specific tasks that a user needs to perform to advance their business.

[0407] A "generative model" is an algorithm that proposes the optimal process based on the user's business progress and emotional state.

[0408] "Feedback" refers to evaluations and opinions that users return to a system.

[0409] "Product and service recommendations" refer to the specific details of products and services recommended to the user.

[0410] The system for implementing this invention provides business support and product recommendations that take into account the user's emotional state. The server receives business proposal information and emotional state data from the user and processes this information. Specifically, it uses speech recognition APIs and facial expression analysis APIs to estimate the emotional state from speech and facial expressions, and analyzes text information using natural language processing technology. Hardware used includes smartphones and tablet devices, and software includes Google Cloud Speech-to-Text and Amazon Rekognition. Based on the analysis results, the server utilizes a generative AI model to generate optimal business support processes and product recommendations that are tailored to the user's emotional state.

[0411] The device optimizes the suggestions presented to the user based on their emotional state. For example, if the user is relaxed, it suggests relaxing products; if they are stressed, it emphasizes limited-time coupons and discount information. This allows users to have a more satisfying and personalized purchasing experience.

[0412] Users act based on the suggested products and business support services, and provide the system with their results and feedback. This feedback is used on the server as data for further optimization and is reflected in future suggestions. As a specific example, an example of a prompt message to the generating AI model is: "If the system detects that the user is relaxed while shopping, generate a prompt that lists recommended relaxation products and provides a more comfortable shopping experience."

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] The user inputs business proposal information and emotional state data via their device. The system captures the user's voice and facial expressions using the smartphone's microphone and camera. This input data is passed to a speech recognition API and a facial expression analysis API.

[0416] Step 2:

[0417] The server converts the received audio data into text using Google Cloud Speech-to-Text, and simultaneously extracts and analyzes keywords using natural language processing techniques. Facial expression data is analyzed using Amazon Rekognition to estimate emotional states. This process yields business proposals and emotional analysis results.

[0418] Step 3:

[0419] Based on the analysis results, the server utilizes a generative AI model to generate tasks and product recommendations tailored to the user's business progress and emotional state. Specifically, it receives keywords and emotional data from the analysis results as input, executes an algorithm to select appropriate processes and products, and creates a task list and product list as output.

[0420] Step 4:

[0421] The server sends the generated tasks and product list to the terminal. The terminal then displays optimized suggestions based on the user's emotional state. For example, if the user is relaxed, it suggests relaxation products; if they are stressed, it highlights discount information.

[0422] Step 5:

[0423] The user reviews the presented tasks and products and enters feedback into the terminal. The terminal sends this feedback to the server, which stores the feedback data in a database to optimize future suggestions and incorporates it into the analysis results.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] As an embodiment of this invention, the following program is incorporated, and each component of the system works in cooperation with one another to provide support for effectively realizing the user's business idea.

[0441] First, the user inputs detailed information about their business idea or project through an interface installed on the terminal. This information includes an overview of the idea, the target market, and the type of partnership they are seeking. The terminal verifies the information received from the user, formats it in a format suitable for the system, and sends it to the server.

[0442] Next, the server analyzes the received data. Here, it utilizes natural language processing to analyze the input data as text, extract keywords, and understand the nature and purpose of the business idea. Based on this analysis, the server searches its database for suitable "support companies" and lists the companies best suited to the user's idea.

[0443] Furthermore, the server uses a generative model to propose the tasks necessary to realize the user's business idea. This includes specific action plans and progress steps for each phase of the business, allowing the user to obtain a concrete plan for moving their idea forward.

[0444] Furthermore, users can input progress and feedback on each task they complete via their device. This information is then sent back to the server and used to re-evaluate proposed tasks and strategies, and to rematch them with support companies as needed.

[0445] For example, if a user wants to commercialize "environmentally friendly fashion items," this system extracts information related to "sustainability," "fashion," and "product development" from the user's input and suggests appropriate suppliers and consultants. It also automatically generates tasks related to market research and prototype development, showing the user what actions to take next. This allows users to efficiently grow their ideas into businesses.

[0446] This system dynamically evolves through user interaction and feedback, continuously providing optimal support and facilitating a smooth commercialization process.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] Users access the terminal interface and enter details about their business idea or project. This information includes an overview of the idea, target market, and necessary support.

[0450] Step 2:

[0451] The terminal receives information entered by the user, formats its contents formally, and sends it to the server. Before sending the data, it checks the format of the input data and prompts the user to make corrections if necessary.

[0452] Step 3:

[0453] The server receives user information sent from the terminal and stores the data. Next, it analyzes the input data using natural language processing (NLP) techniques to extract keywords and their relationships.

[0454] Step 4:

[0455] The server queries a company database to find suitable support companies based on the analyzed keywords for the business idea. This picks out companies that can provide relevant support and generates a list of candidates.

[0456] Step 5:

[0457] The server proposes specific tasks necessary to advance the user's project. This process uses a generative model to automatically create actionable steps for each phase of the project.

[0458] Step 6:

[0459] The terminal displays a list of support companies and task suggestions sent from the server to the user. The list displayed on the screen shows detailed information about each company and guidelines for completing the tasks.

[0460] Step 7:

[0461] Based on the information provided, the user contacts the selected support company and performs the suggested tasks. They then input their progress and feedback into their device and report it to the system.

[0462] Step 8:

[0463] The server receives user feedback and progress information, and re-evaluates the suitability of the support company and the task content. This allows for new suggestions and adjustments to support as needed.

[0464] (Example 1)

[0465] 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."

[0466] Conventional business idea realization systems struggled to effectively utilize user feedback to advance business proposals, and had issues with the accuracy of matching with supporting organizations and the usefulness of the tasks they proposed. Furthermore, the analysis and processing of information took a long time, sometimes detracting from the user experience.

[0467] 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.

[0468] In this invention, the server includes means for collecting information from the user, means for confirming and formatting the information using a terminal and transferring it to the server, and means for analyzing the content of the received information using natural language processing and extracting keywords. This enables rapid and highly accurate analysis, appropriate matching with support organizations, and accurate task proposals for the user.

[0469] A "user" is someone who uses an information system to input and manipulate information in order to advance their own business ideas or projects.

[0470] "Information" refers to details about a business idea or project, including an overview of the idea, market target, and necessary partnerships.

[0471] A "terminal" is a device used by a user to input information and interact with a system.

[0472] A "server" is a central system that processes information received from users and performs necessary analysis and database searches.

[0473] Natural language processing is a technology used by computers to understand and analyze human language, and it involves extracting keywords and meanings from text data.

[0474] "Keywords" are important terms extracted to understand the nature and purpose of a business idea.

[0475] A "support organization" refers to a company or group that can potentially cooperate in realizing a user's business idea.

[0476] "Matching" is the process of identifying support organizations that meet the user's needs and linking them in a mutually beneficial way.

[0477] A "generative AI model" is an artificial intelligence technology used to automatically suggest tasks and processes that are suitable for the user's business progress.

[0478] A "task" is a specific action or activity that needs to be taken in order to realize a business idea.

[0479] "Feedback" refers to reports from users regarding the progress of tasks they have completed and any support they need.

[0480] This invention aims to effectively realize users' business ideas using information systems, with multiple components working in conjunction with each other. A concrete example is the commercialization of environmentally friendly fashion items.

[0481] The user first enters detailed information about their business idea through an interface installed on the terminal. This information includes a product overview, target market, and desired collaboration model. The terminal reviews the information entered by the user, formats the data as needed, and sends it to the server. This formatting process removes extraneous spaces and symbols and converts the data into a format that is easy to process.

[0482] The server applies natural language processing techniques to the received data to extract meaningful keywords. This analysis helps understand the characteristics and objectives of business ideas and clarify user needs. Based on the analysis results, the server consults an information database to search for and select the most suitable support organization.

[0483] Furthermore, the server uses a generative AI model to materialize the tasks required by the user. This includes specific action guidelines and progress steps for each business phase, allowing the user to advance the project accordingly. In this process, the generative AI model presents optimal tasks and support strategies based on prompt statements such as, "Please tell me the steps required to launch a new environmentally friendly fashion line."

[0484] The user then uses the terminal to input progress and feedback regarding the tasks they have completed. This feedback is sent back to the server, where the suggested tasks and support strategies are re-evaluated. If necessary, the server updates the list of support organizations, ensuring that the user always has the most up-to-date and best information.

[0485] This system is designed to dynamically reflect user actions and feedback, providing optimal support for smoothly realizing business ideas.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user enters detailed information about their business idea through the terminal's interface. This information includes a product overview, target market, and desired partnership structure. The terminal receives this raw data and performs specific actions to verify the input.

[0489] Step 2:

[0490] The terminal formats the information received from the user and converts it into a format that the system can easily understand. This process involves data standardization and the removal of unnecessary characters and spaces. Once the formatted data is output, the terminal prepares to send this data to the server.

[0491] Step 3:

[0492] The server receives formatted data sent from the terminal and performs analysis using natural language processing techniques. It extracts keywords and important phrases from the input text, thereby generating output that helps understand the nature and intent of the business idea.

[0493] Step 4:

[0494] The server searches its information database based on the analysis results. Specifically, it executes queries to identify support organizations that match the extracted keywords. A list of matching support organizations is output, and the server is ready to present the most suitable organizations or groups for the user's business plan.

[0495] Step 5:

[0496] The server utilizes a generative AI model to propose specific tasks necessary to realize the user's business idea. Based on the prompt, it executes an algorithm and generates output that produces action plans and process plans that match the user's needs.

[0497] Step 6:

[0498] Users perform the proposed tasks and send feedback on their progress and results via their device. This feedback includes details of completed tasks and any additional support information needed.

[0499] Step 7:

[0500] The server receives feedback from users and re-evaluates proposed tasks and support strategies. Based on the feedback, necessary readjustments are made, and the list of support organizations and task suggestions are updated. This ensures that users always receive appropriate support.

[0501] (Application Example 1)

[0502] 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."

[0503] When content creators try to distribute new ideas, they face the challenge of finding appropriate distribution methods and partners, effectively analyzing the novelty and trends of their content, and establishing clear procedures for increasing engagement.

[0504] 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.

[0505] In this invention, the server includes means for receiving data to be operated on from a user, means for extracting and analyzing key words using natural language processing based on the received data, and means for searching for and matching suitable support organizations based on the analysis results. This enables creators to efficiently find the optimal distribution method and partner.

[0506] "Data to be manipulated" refers to data containing various information and ideas entered by the user, which forms the basis for the system's analysis and recommendations.

[0507] "Keywords" are keywords extracted from user input data through natural language processing, and are important elements for understanding the characteristics and content of the data being manipulated.

[0508] A "support organization" is a company or group that can be utilized to realize the user's plan, and is appropriately matched by the system to support the user's activities.

[0509] "Operational guidelines" are guidelines that the system automatically generates, outlining the most effective action plans and procedures for users, and serve as a roadmap to promote the success of the content.

[0510] "Evaluation information" refers to feedback provided by users, which serves as a standard for re-evaluating and further optimizing business guidelines and the selection of support organizations.

[0511] "Distribution method" refers to the means by which content creators distribute their content widely, and involves the process of selecting the most suitable platform and method.

[0512] A "collaborator" is an individual or group that works together in the creation and distribution of content, and acts as a partner to effectively advance the project.

[0513] "Trend analysis" is the process of analyzing trends and popularity at a given point in time and providing information to enhance the appeal of content and user interest.

[0514] "Specific procedures" refer to detailed, practical steps generated by the system to increase engagement, including instructions that the user can immediately take to achieve their goals.

[0515] To implement this system, the server, terminals, and users must work together in coordination.

[0516] The server implements a program using the Python language and natural language processing libraries (e.g., spaCy, NLTK). This program analyzes the target data received from the user via the terminal and extracts key words. The extracted data is linked to a database on Google Cloud Platform to search for and match the most suitable support organization, delivery method, and collaborators. Furthermore, it uses OpenAI's generative model to analyze the specificity and trends of content related to the user's content and automatically generates business guidelines and specific procedures.

[0517] The terminal receives evaluation information provided by the user and sends it to the server, thereby re-evaluating the selection of business guidelines and support organizations, and supporting system optimization.

[0518] Users can input target data and evaluation information through the interface, and based on the operational guidelines suggested by the system, they can effectively distribute content and increase engagement. This system serves as a powerful support tool for content creators to successfully realize their ideas.

[0519] As a concrete example, if a creator wants to distribute a "relaxation music album themed around nature sounds," the system will extract key terms related to "nature sounds," "relaxation," and "music," and suggest suitable distribution methods and collaborators for the user. An example of a prompt to the generating AI model in this case is as follows:

[0520] Project information:

[0521] Title: Relaxation music album themed around nature sounds

[0522] Target audience: Adults seeking relaxation

[0523] Please suggest the next course of action:

[0524] In this way, users can leverage the system's suggestions to bring their ideas to life and achieve business success.

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The user inputs data to be operated using a terminal. This includes content ideas, target market, and objectives. The terminal converts this input data into an appropriate format and sends it to the server.

[0528] Step 2:

[0529] The server analyzes the received input data using a natural language processing library (e.g., spaCy, NLTK). Specifically, it extracts key words from the data and identifies keywords. This analysis provides information to understand the nature of the user's content ideas.

[0530] Step 3:

[0531] The server uses these key terms to search a database on Google Cloud Platform. This search identifies and lists support organizations, distribution methods, and collaborators that are suitable for the content idea. This process helps prepare the most suitable external collaboration for the user.

[0532] Step 4:

[0533] Next, the server utilizes OpenAI's generative AI model to generate business guidelines and specific procedures based on key terms and database results. These procedures are tailored to the popularity of the content and the user's objectives, suggesting the next action the user should take.

[0534] Step 5:

[0535] Users receive work guidelines from the server via their terminal. Following these guidelines, they create and distribute content, and input evaluation information based on the results into their terminal. This evaluation information is then sent back to the server.

[0536] Step 6:

[0537] Based on the evaluation information received from the user, the server automatically reassesss the suitability of the operational guidelines and support organization. If necessary, it reuses the generated AI model to provide new suggestions for greater user success. This process ensures the system is constantly optimized and ready to support the user.

[0538] 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.

[0539] As an embodiment of the present invention, a system incorporating an emotion engine is designed, and each component works in cooperation with one another to provide optimal business support according to the user's emotional state.

[0540] First, the user accesses the terminal's user interface and provides emotion input data along with business proposal information. This information can be text-based, but it is also possible to estimate emotions based on voice and facial expression data. The terminal processes this information, formats it in an appropriate format, and sends it to the server.

[0541] The server performs text analysis based on the received information and uses natural language processing (NLP) technology to analyze business proposal information. Simultaneously, the emotion engine analyzes the user's emotional state and determines what kind of support the user desires. This analysis visualizes the emotional state in numerical or other formats, influencing subsequent processing.

[0542] Based on the analysis results, the server extracts suitable support companies from its corporate database and performs matching. It also proposes tasks based on the progress and priority of the business. The proposed tasks are then listed in priority, taking into account the results of the emotion engine's analysis, and prioritizing those of high importance or those that the user can immediately work on.

[0543] The terminal presents the user with a list of support companies and tasks suggested by the server. This uses language appropriate to the user's emotional state to enable a more appropriate approach and feedback. For example, if the user is feeling stressed, the number of support company options is limited, and tasks are presented in stages to reduce the burden.

[0544] Ultimately, the user contacts a support company selected based on the provided information and performs the proposed tasks. They then input the results and feedback into their terminal and report them to the system. This feedback is used on the server for re-evaluation, and the support company and task details are updated as needed.

[0545] For example, if a user wants to launch a "new online education platform" but is overwhelmed by too much information, this system uses an emotion engine to reduce user pressure and provides a concise, results-oriented task list. This allows the user to efficiently advance the project while reducing emotional burden.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] Users input business proposal information and data related to their own emotions through the device's user interface. Emotional data is collected through text input, voice recording, and facial recognition.

[0549] Step 2:

[0550] The terminal temporarily stores the entered information and formats it into a predetermined data format. This formatted data is then structured to facilitate analysis and securely transmitted to the server.

[0551] Step 3:

[0552] The server analyzes the information received from the terminal. This analysis includes a process of extracting keywords from text data using natural language processing techniques to understand the content of the business proposal.

[0553] Step 4:

[0554] The server simultaneously uses an emotion engine to analyze the user's emotional state. This analysis quantifies emotional data and evaluates what emotions the user is feeling.

[0555] Step 5:

[0556] The server queries a corporate database based on the analyzed business proposal information and sentiment data to search for and match the most suitable support company with the user. This includes selecting a company that does not burden the user, using sentiment information.

[0557] Step 6:

[0558] The server incorporates sentiment analysis results when generating recommended task lists for the user's project. For example, if the user is experiencing stress, it will suggest simplifying tasks or adjusting their priorities.

[0559] Step 7:

[0560] Based on the analysis results, the device visually presents the user with a list of matched support companies and a coordinated task list. The display is presented in a user-friendly interface, taking into account the user's emotional state.

[0561] Step 8:

[0562] Based on the information provided, users can contact their preferred support companies and proceed with the suggested tasks. Feedback on the progress and results of their activities is collected and reported to the system via their device.

[0563] Step 9:

[0564] The server analyzes user feedback, suggests new tasks, and re-evaluates support companies. This feedback loop ensures that the system is constantly adjusted to provide users with the best possible support.

[0565] (Example 2)

[0566] 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."

[0567] Traditional business support systems often provide generic tasks and support information without considering the user's emotional state, leading to increased emotional burden and decreased motivation. Furthermore, the sheer volume of information provided made it difficult for users to determine what was optimal and effective for them. Therefore, there was a need for individualized support tailored to each user's emotional state.

[0568] 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.

[0569] In this invention, the server includes means for collecting business-related and emotional information from the user, means for analyzing the collected information using natural language processing and sentiment analysis technologies to extract keywords and emotional states, and means for searching for and matching the user with a suitable support organization based on the analysis results according to the user's emotional state. This enables the user to receive information tailored to their emotional state, thereby reducing their burden and achieving optimal business progress.

[0570] A "user" refers to an individual or organization that uses the system and receives business support.

[0571] "Emotional information" refers to data that indicates the user's emotional state, and includes formats such as voice, facial expressions, and text input.

[0572] "Natural language processing technology" refers to the technology that allows computers to understand and process human language, including text analysis and keyword extraction.

[0573] "Emotion analysis technology" refers to technology that evaluates and visualizes a user's emotional state numerically or qualitatively.

[0574] A "support organization" refers to a company or group that assists users in their business operations.

[0575] A "task" refers to a specific task or activity that a user must perform in order to achieve business objectives.

[0576] A "generative model" refers to an algorithm or technology that generates new information or suggestions based on data.

[0577] This system optimizes user business support based on their emotional state. First, the user provides business-related information and emotional data using a terminal. Emotional data consists of text input, voice, and facial expression data. The terminal collects this data and standardizes the format as needed. Voice data is converted to text via speech recognition software, and facial expression data is quantified using image analysis technology.

[0578] Next, the terminal sends the collected data to the server. The server analyzes the received data using natural language processing and sentiment analysis technologies. This analysis extracts important keywords from the business proposal information and further evaluates the user's emotional state numerically. Natural language processing technologies include Python's natural language processing libraries.

[0579] Based on the analysis results, the server searches the database for support organizations suitable for business assistance and matches them to the user's needs. Furthermore, it utilizes a generative model to generate tasks that take the user's emotional state into consideration, suggesting tasks that the user should perform. This reduces the user's emotional burden while promoting efficient business progress.

[0580] Finally, the user receives a list of suggested support organizations and tasks through the device. The device presents this information through a user interface that is sensitive to the user's feelings. The user's results and feedback are again sent to the server via the device and incorporated into subsequent processes.

[0581] As a concrete example, consider a user who wants to start a new online education business but is feeling stressed due to information overload. This system uses an emotion engine to reduce pressure and generate an effective task list tailored to the user's situation. An example of a prompt to the generating AI model would be, "Please tell me how you can support my new project proposal based on my emotions."

[0582] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0583] Step 1:

[0584] Users input business-related information and emotional data into the terminal. This input data includes various formats such as text, voice, and facial expressions. The terminal converts voice data to text and facial expression data to numerical data through image analysis. Specifically, this involves text conversion using voice recognition software and facial expression capture using a camera.

[0585] Step 2:

[0586] The terminal sends the formatted data to the server. The data is securely transferred using an encryption protocol (e.g., TLS / SSL). Specifically, the terminal encrypts the data before sending it and then transfers it to the server via a secure communication channel.

[0587] Step 3:

[0588] The server analyzes business information using natural language processing technology based on the received data. The analysis involves extracting keywords from text data to identify key themes. For example, it uses a natural language processing library to extract business-related terms and evaluate their importance.

[0589] Step 4:

[0590] The server simultaneously evaluates the user's emotional state using emotion analysis technology. It quantifies emotions from voice and facial expression data and converts them into a visualized form. Specific operations include calculating an emotion score using a machine learning model.

[0591] Step 5:

[0592] The server searches for suitable support organizations based on the analysis results and matches the user with the most suitable one. It extracts support organizations that meet the criteria from the company database using SQL queries and prioritizes them according to their sentiment score.

[0593] Step 6:

[0594] The server uses an AI model to generate a task list that takes the user's emotional state into account. The model suggests specific tasks that are relevant to the user's progress. The generated task list is then broken down into feasible steps, taking into account the user's emotional burden.

[0595] Step 7:

[0596] The device presents the user with suggested support organization information and a task list. The user interface employs a user-friendly design that considers the user's emotions, displaying information in a visually appealing and easy-to-use format. Specific actions include utilizing user interface animations and notification features.

[0597] (Application Example 2)

[0598] 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."

[0599] Traditionally, business proposals and product recommendations made without considering the user's emotional state were not always optimal for the user and, as a result, did not contribute to efficient business development or an improved purchasing experience. The present invention aims to realize appropriate business support and product / service recommendations based on the user's emotional state.

[0600] 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.

[0601] In this invention, the server includes means for receiving business proposal information and emotional state data from the user, means for extracting and analyzing keywords using natural language processing based on the received information, and means for optimizing the content of product and service proposals based on the user's emotional state. This enables optimal business support and product recommendations tailored to the user's emotional state.

[0602] A "user" is an individual or organization that uses the system to provide business proposal information and emotional state data.

[0603] "Business proposal information" refers to information about business-related plans and ideas that users provide to the system.

[0604] "Emotional state data" refers to data that indicates the user's emotional state, and is information estimated from voice, facial expressions, and text.

[0605] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0606] A "keyword" is an important word or phrase extracted from the business proposal information.

[0607] A "supporting organization" refers to a company or group that cooperates in realizing a business proposal.

[0608] A "task" is a set of specific tasks that a user needs to perform to advance their business.

[0609] A "generative model" is an algorithm that proposes the optimal process based on the user's business progress and emotional state.

[0610] "Feedback" refers to evaluations and opinions that users return to a system.

[0611] "Product and service recommendations" refer to the specific details of products and services recommended to the user.

[0612] The system for implementing this invention provides business support and product recommendations that take into account the user's emotional state. The server receives business proposal information and emotional state data from the user and processes this information. Specifically, it uses speech recognition APIs and facial expression analysis APIs to estimate the emotional state from speech and facial expressions, and analyzes text information using natural language processing technology. Hardware used includes smartphones and tablet devices, and software includes Google Cloud Speech-to-Text and Amazon Rekognition. Based on the analysis results, the server utilizes a generative AI model to generate optimal business support processes and product recommendations that are tailored to the user's emotional state.

[0613] The device optimizes the suggestions presented to the user based on their emotional state. For example, if the user is relaxed, it suggests relaxing products; if they are stressed, it emphasizes limited-time coupons and discount information. This allows users to have a more satisfying and personalized purchasing experience.

[0614] Users act based on the suggested products and business support services, and provide the system with their results and feedback. This feedback is used on the server as data for further optimization and is reflected in future suggestions. As a specific example, an example of a prompt message to the generating AI model is: "If the system detects that the user is relaxed while shopping, generate a prompt that lists recommended relaxation products and provides a more comfortable shopping experience."

[0615] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0616] Step 1:

[0617] The user inputs business proposal information and emotional state data via their device. The system captures the user's voice and facial expressions using the smartphone's microphone and camera. This input data is passed to a speech recognition API and a facial expression analysis API.

[0618] Step 2:

[0619] The server converts the received audio data into text using Google Cloud Speech-to-Text, and simultaneously extracts and analyzes keywords using natural language processing techniques. Facial expression data is analyzed using Amazon Rekognition to estimate emotional states. This process yields business proposals and emotional analysis results.

[0620] Step 3:

[0621] Based on the analysis results, the server utilizes a generative AI model to generate tasks and product recommendations tailored to the user's business progress and emotional state. Specifically, it receives keywords and emotional data from the analysis results as input, executes an algorithm to select appropriate processes and products, and creates a task list and product list as output.

[0622] Step 4:

[0623] The server sends the generated tasks and product list to the terminal. The terminal then displays optimized suggestions based on the user's emotional state. For example, if the user is relaxed, it suggests relaxation products; if they are stressed, it highlights discount information.

[0624] Step 5:

[0625] The user reviews the presented tasks and products and enters feedback into the terminal. The terminal sends this feedback to the server, which stores the feedback data in a database to optimize future suggestions and incorporates it into the analysis results.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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".

[0643] As an embodiment of this invention, the following program is incorporated, and each component of the system works in cooperation with one another to provide support for effectively realizing the user's business idea.

[0644] First, the user inputs detailed information about their business idea or project through an interface installed on the terminal. This information includes an overview of the idea, the target market, and the type of partnership they are seeking. The terminal verifies the information received from the user, formats it in a format suitable for the system, and sends it to the server.

[0645] Next, the server analyzes the received data. Here, it utilizes natural language processing to analyze the input data as text, extract keywords, and understand the nature and purpose of the business idea. Based on this analysis, the server searches its database for suitable "support companies" and lists the companies best suited to the user's idea.

[0646] Furthermore, the server uses a generative model to propose the tasks necessary to realize the user's business idea. This includes specific action plans and progress steps for each phase of the business, allowing the user to obtain a concrete plan for moving their idea forward.

[0647] Furthermore, users can input progress and feedback on each task they complete via their device. This information is then sent back to the server and used to re-evaluate proposed tasks and strategies, and to rematch them with support companies as needed.

[0648] For example, if a user wants to commercialize "environmentally friendly fashion items," this system extracts information related to "sustainability," "fashion," and "product development" from the user's input and suggests appropriate suppliers and consultants. It also automatically generates tasks related to market research and prototype development, showing the user what actions to take next. This allows users to efficiently grow their ideas into businesses.

[0649] This system dynamically evolves through user interaction and feedback, continuously providing optimal support and facilitating a smooth commercialization process.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] Users access the terminal interface and enter details about their business idea or project. This information includes an overview of the idea, target market, and necessary support.

[0653] Step 2:

[0654] The terminal receives information entered by the user, formats its contents formally, and sends it to the server. Before sending the data, it checks the format of the input data and prompts the user to make corrections if necessary.

[0655] Step 3:

[0656] The server receives user information sent from the terminal and stores the data. Next, it analyzes the input data using natural language processing (NLP) techniques to extract keywords and their relationships.

[0657] Step 4:

[0658] The server queries a company database to find suitable support companies based on the analyzed keywords for the business idea. This picks out companies that can provide relevant support and generates a list of candidates.

[0659] Step 5:

[0660] The server proposes specific tasks necessary to advance the user's project. This process uses a generative model to automatically create actionable steps for each phase of the project.

[0661] Step 6:

[0662] The terminal displays a list of support companies and task suggestions sent from the server to the user. The list displayed on the screen shows detailed information about each company and guidelines for completing the tasks.

[0663] Step 7:

[0664] Based on the information provided, the user contacts the selected support company and performs the suggested tasks. They then input their progress and feedback into their device and report it to the system.

[0665] Step 8:

[0666] The server receives user feedback and progress information, and re-evaluates the suitability of the support company and the task content. This allows for new suggestions and adjustments to support as needed.

[0667] (Example 1)

[0668] 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".

[0669] Conventional business idea realization systems struggled to effectively utilize user feedback to advance business proposals, and had issues with the accuracy of matching with supporting organizations and the usefulness of the tasks they proposed. Furthermore, the analysis and processing of information took a long time, sometimes detracting from the user experience.

[0670] 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.

[0671] In this invention, the server includes means for collecting information from the user, means for confirming and formatting the information using a terminal and transferring it to the server, and means for analyzing the content of the received information using natural language processing and extracting keywords. This enables rapid and highly accurate analysis, appropriate matching with support organizations, and accurate task proposals for the user.

[0672] A "user" is someone who uses an information system to input and manipulate information in order to advance their own business ideas or projects.

[0673] "Information" refers to details about a business idea or project, including an overview of the idea, market target, and necessary partnerships.

[0674] A "terminal" is a device used by a user to input information and interact with a system.

[0675] A "server" is a central system that processes information received from users and performs necessary analysis and database searches.

[0676] Natural language processing is a technology used by computers to understand and analyze human language, and it involves extracting keywords and meanings from text data.

[0677] "Keywords" are important terms extracted to understand the nature and purpose of a business idea.

[0678] A "support organization" refers to a company or group that can potentially cooperate in realizing a user's business idea.

[0679] "Matching" is the process of identifying support organizations that meet the user's needs and linking them in a mutually beneficial way.

[0680] A "generative AI model" is an artificial intelligence technology used to automatically suggest tasks and processes that are suitable for the user's business progress.

[0681] A "task" is a specific action or activity that needs to be taken in order to realize a business idea.

[0682] "Feedback" refers to reports from users regarding the progress of tasks they have completed and any support they need.

[0683] This invention aims to effectively realize users' business ideas using information systems, with multiple components working in conjunction with each other. A concrete example is the commercialization of environmentally friendly fashion items.

[0684] The user first enters detailed information about their business idea through an interface installed on the terminal. This information includes a product overview, target market, and desired collaboration model. The terminal reviews the information entered by the user, formats the data as needed, and sends it to the server. This formatting process removes extraneous spaces and symbols and converts the data into a format that is easy to process.

[0685] The server applies natural language processing techniques to the received data to extract meaningful keywords. This analysis helps understand the characteristics and objectives of business ideas and clarify user needs. Based on the analysis results, the server consults an information database to search for and select the most suitable support organization.

[0686] Furthermore, the server uses a generative AI model to materialize the tasks required by the user. This includes specific action guidelines and progress steps for each business phase, allowing the user to advance the project accordingly. In this process, the generative AI model presents optimal tasks and support strategies based on prompt statements such as, "Please tell me the steps required to launch a new environmentally friendly fashion line."

[0687] The user then uses the terminal to input progress and feedback regarding the tasks they have completed. This feedback is sent back to the server, where the suggested tasks and support strategies are re-evaluated. If necessary, the server updates the list of support organizations, ensuring that the user always has the most up-to-date and best information.

[0688] This system is designed to dynamically reflect user actions and feedback, providing optimal support for smoothly realizing business ideas.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] The user enters detailed information about their business idea through the terminal's interface. This information includes a product overview, target market, and desired partnership structure. The terminal receives this raw data and performs specific actions to verify the input.

[0692] Step 2:

[0693] The terminal formats the information received from the user and converts it into a format that the system can easily understand. This process involves data standardization and the removal of unnecessary characters and spaces. Once the formatted data is output, the terminal prepares to send this data to the server.

[0694] Step 3:

[0695] The server receives formatted data sent from the terminal and performs analysis using natural language processing techniques. It extracts keywords and important phrases from the input text, thereby generating output that helps understand the nature and intent of the business idea.

[0696] Step 4:

[0697] The server searches its information database based on the analysis results. Specifically, it executes queries to identify support organizations that match the extracted keywords. A list of matching support organizations is output, and the server is ready to present the most suitable organizations or groups for the user's business plan.

[0698] Step 5:

[0699] The server utilizes a generative AI model to propose specific tasks necessary to realize the user's business idea. Based on the prompt, it executes an algorithm and generates output that produces action plans and process plans that match the user's needs.

[0700] Step 6:

[0701] Users perform the proposed tasks and send feedback on their progress and results via their device. This feedback includes details of completed tasks and any additional support information needed.

[0702] Step 7:

[0703] The server receives feedback from users and re-evaluates proposed tasks and support strategies. Based on the feedback, necessary readjustments are made, and the list of support organizations and task suggestions are updated. This ensures that users always receive appropriate support.

[0704] (Application Example 1)

[0705] 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".

[0706] When content creators try to distribute new ideas, they face the challenge of finding appropriate distribution methods and partners, effectively analyzing the novelty and trends of their content, and establishing clear procedures for increasing engagement.

[0707] 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.

[0708] In this invention, the server includes means for receiving data to be operated on from a user, means for extracting and analyzing key words using natural language processing based on the received data, and means for searching for and matching suitable support organizations based on the analysis results. This enables creators to efficiently find the optimal distribution method and partner.

[0709] "Data to be manipulated" refers to data containing various information and ideas entered by the user, which forms the basis for the system's analysis and recommendations.

[0710] "Keywords" are keywords extracted from user input data through natural language processing, and are important elements for understanding the characteristics and content of the data being manipulated.

[0711] A "support organization" is a company or group that can be utilized to realize the user's plan, and is appropriately matched by the system to support the user's activities.

[0712] "Operational guidelines" are guidelines that the system automatically generates, outlining the most effective action plans and procedures for users, and serve as a roadmap to promote the success of the content.

[0713] "Evaluation information" refers to feedback provided by users, which serves as a standard for re-evaluating and further optimizing business guidelines and the selection of support organizations.

[0714] "Distribution method" refers to the means by which content creators distribute their content widely, and involves the process of selecting the most suitable platform and method.

[0715] A "collaborator" is an individual or group that works together in the creation and distribution of content, and acts as a partner to effectively advance the project.

[0716] "Trend analysis" is the process of analyzing trends and popularity at a given point in time and providing information to enhance the appeal of content and user interest.

[0717] "Specific procedures" refer to detailed, practical steps generated by the system to increase engagement, including instructions that the user can immediately take to achieve their goals.

[0718] To implement this system, the server, terminals, and users must work together in coordination.

[0719] The server implements a program using the Python language and natural language processing libraries (e.g., spaCy, NLTK). This program analyzes the target data received from the user via the terminal and extracts key words. The extracted data is linked to a database on Google Cloud Platform to search for and match the most suitable support organization, delivery method, and collaborators. Furthermore, it uses OpenAI's generative model to analyze the specificity and trends of content related to the user's content and automatically generates business guidelines and specific procedures.

[0720] The terminal receives evaluation information provided by the user and sends it to the server, thereby re-evaluating the selection of business guidelines and support organizations, and supporting system optimization.

[0721] Users can input target data and evaluation information through the interface, and based on the operational guidelines suggested by the system, they can effectively distribute content and increase engagement. This system serves as a powerful support tool for content creators to successfully realize their ideas.

[0722] As a concrete example, if a creator wants to distribute a "relaxation music album themed around nature sounds," the system will extract key terms related to "nature sounds," "relaxation," and "music," and suggest suitable distribution methods and collaborators for the user. An example of a prompt to the generating AI model in this case is as follows:

[0723] Project information:

[0724] Title: Relaxation music album themed around nature sounds

[0725] Target audience: Adults seeking relaxation

[0726] Please suggest the next course of action:

[0727] In this way, users can leverage the system's suggestions to bring their ideas to life and achieve business success.

[0728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0729] Step 1:

[0730] The user inputs data to be operated using a terminal. This includes content ideas, target market, and objectives. The terminal converts this input data into an appropriate format and sends it to the server.

[0731] Step 2:

[0732] The server analyzes the received input data using a natural language processing library (e.g., spaCy, NLTK). Specifically, it extracts key words from the data and identifies keywords. This analysis provides information to understand the nature of the user's content ideas.

[0733] Step 3:

[0734] The server uses these key terms to search a database on Google Cloud Platform. This search identifies and lists support organizations, distribution methods, and collaborators that are suitable for the content idea. This process helps prepare the most suitable external collaboration for the user.

[0735] Step 4:

[0736] Next, the server utilizes OpenAI's generative AI model to generate business guidelines and specific procedures based on key terms and database results. These procedures are tailored to the popularity of the content and the user's objectives, suggesting the next action the user should take.

[0737] Step 5:

[0738] Users receive work guidelines from the server via their terminal. Following these guidelines, they create and distribute content, and input evaluation information based on the results into their terminal. This evaluation information is then sent back to the server.

[0739] Step 6:

[0740] Based on the evaluation information received from the user, the server automatically reassesss the suitability of the operational guidelines and support organization. If necessary, it reuses the generated AI model to provide new suggestions for greater user success. This process ensures the system is constantly optimized and ready to support the user.

[0741] 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.

[0742] As an embodiment of the present invention, a system incorporating an emotion engine is designed, and each component works in cooperation with one another to provide optimal business support according to the user's emotional state.

[0743] First, the user accesses the terminal's user interface and provides emotion input data along with business proposal information. This information can be text-based, but it is also possible to estimate emotions based on voice and facial expression data. The terminal processes this information, formats it in an appropriate format, and sends it to the server.

[0744] The server performs text analysis based on the received information and uses natural language processing (NLP) technology to analyze business proposal information. Simultaneously, the emotion engine analyzes the user's emotional state and determines what kind of support the user desires. This analysis visualizes the emotional state in numerical or other formats, influencing subsequent processing.

[0745] Based on the analysis results, the server extracts suitable support companies from its corporate database and performs matching. It also proposes tasks based on the progress and priority of the business. The proposed tasks are then listed in priority, taking into account the results of the emotion engine's analysis, and prioritizing those of high importance or those that the user can immediately work on.

[0746] The terminal presents the user with a list of support companies and tasks suggested by the server. This uses language appropriate to the user's emotional state to enable a more appropriate approach and feedback. For example, if the user is feeling stressed, the number of support company options is limited, and tasks are presented in stages to reduce the burden.

[0747] Ultimately, the user contacts a support company selected based on the provided information and performs the proposed tasks. They then input the results and feedback into their terminal and report them to the system. This feedback is used on the server for re-evaluation, and the support company and task details are updated as needed.

[0748] For example, if a user wants to launch a "new online education platform" but is overwhelmed by too much information, this system uses an emotion engine to reduce user pressure and provides a concise, results-oriented task list. This allows the user to efficiently advance the project while reducing emotional burden.

[0749] The following describes the processing flow.

[0750] Step 1:

[0751] Users input business proposal information and data related to their own emotions through the device's user interface. Emotional data is collected through text input, voice recording, and facial recognition.

[0752] Step 2:

[0753] The terminal temporarily stores the entered information and formats it into a predetermined data format. This formatted data is then structured to facilitate analysis and securely transmitted to the server.

[0754] Step 3:

[0755] The server analyzes the information received from the terminal. This analysis includes a process of extracting keywords from text data using natural language processing techniques to understand the content of the business proposal.

[0756] Step 4:

[0757] The server simultaneously uses an emotion engine to analyze the user's emotional state. This analysis quantifies emotional data and evaluates what emotions the user is feeling.

[0758] Step 5:

[0759] The server queries a corporate database based on the analyzed business proposal information and sentiment data to search for and match the most suitable support company with the user. This includes selecting a company that does not burden the user, using sentiment information.

[0760] Step 6:

[0761] The server incorporates sentiment analysis results when generating recommended task lists for the user's project. For example, if the user is experiencing stress, it will suggest simplifying tasks or adjusting their priorities.

[0762] Step 7:

[0763] Based on the analysis results, the device visually presents the user with a list of matched support companies and a coordinated task list. The display is presented in a user-friendly interface, taking into account the user's emotional state.

[0764] Step 8:

[0765] Based on the information provided, users can contact their preferred support companies and proceed with the suggested tasks. Feedback on the progress and results of their activities is collected and reported to the system via their device.

[0766] Step 9:

[0767] The server analyzes user feedback, suggests new tasks, and re-evaluates support companies. This feedback loop ensures that the system is constantly adjusted to provide users with the best possible support.

[0768] (Example 2)

[0769] 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".

[0770] Traditional business support systems often provide generic tasks and support information without considering the user's emotional state, leading to increased emotional burden and decreased motivation. Furthermore, the sheer volume of information provided made it difficult for users to determine what was optimal and effective for them. Therefore, there was a need for individualized support tailored to each user's emotional state.

[0771] 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.

[0772] In this invention, the server includes means for collecting business-related and emotional information from the user, means for analyzing the collected information using natural language processing and sentiment analysis technologies to extract keywords and emotional states, and means for searching for and matching the user with a suitable support organization based on the analysis results according to the user's emotional state. This enables the user to receive information tailored to their emotional state, thereby reducing their burden and achieving optimal business progress.

[0773] A "user" refers to an individual or organization that uses the system and receives business support.

[0774] "Emotional information" refers to data that indicates the user's emotional state, and includes formats such as voice, facial expressions, and text input.

[0775] "Natural language processing technology" refers to the technology that allows computers to understand and process human language, including text analysis and keyword extraction.

[0776] "Emotion analysis technology" refers to technology that evaluates and visualizes a user's emotional state numerically or qualitatively.

[0777] A "support organization" refers to a company or group that assists users in their business operations.

[0778] A "task" refers to a specific task or activity that a user must perform in order to achieve business objectives.

[0779] A "generative model" refers to an algorithm or technology that generates new information or suggestions based on data.

[0780] This system optimizes user business support based on their emotional state. First, the user provides business-related information and emotional data using a terminal. Emotional data consists of text input, voice, and facial expression data. The terminal collects this data and standardizes the format as needed. Voice data is converted to text via speech recognition software, and facial expression data is quantified using image analysis technology.

[0781] Next, the terminal sends the collected data to the server. The server analyzes the received data using natural language processing and sentiment analysis technologies. This analysis extracts important keywords from the business proposal information and further evaluates the user's emotional state numerically. Natural language processing technologies include Python's natural language processing libraries.

[0782] Based on the analysis results, the server searches the database for support organizations suitable for business assistance and matches them to the user's needs. Furthermore, it utilizes a generative model to generate tasks that take the user's emotional state into consideration, suggesting tasks that the user should perform. This reduces the user's emotional burden while promoting efficient business progress.

[0783] Finally, the user receives a list of suggested support organizations and tasks through the device. The device presents this information through a user interface that is sensitive to the user's feelings. The user's results and feedback are again sent to the server via the device and incorporated into subsequent processes.

[0784] As a concrete example, consider a user who wants to start a new online education business but is feeling stressed due to information overload. This system uses an emotion engine to reduce pressure and generate an effective task list tailored to the user's situation. An example of a prompt to the generating AI model would be, "Please tell me how you can support my new project proposal based on my emotions."

[0785] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0786] Step 1:

[0787] Users input business-related information and emotional data into the terminal. This input data includes various formats such as text, voice, and facial expressions. The terminal converts voice data to text and facial expression data to numerical data through image analysis. Specifically, this involves text conversion using voice recognition software and facial expression capture using a camera.

[0788] Step 2:

[0789] The terminal sends the formatted data to the server. The data is securely transferred using an encryption protocol (e.g., TLS / SSL). Specifically, the terminal encrypts the data before sending it and then transfers it to the server via a secure communication channel.

[0790] Step 3:

[0791] The server analyzes business information using natural language processing technology based on the received data. The analysis involves extracting keywords from text data to identify key themes. For example, it uses a natural language processing library to extract business-related terms and evaluate their importance.

[0792] Step 4:

[0793] The server simultaneously evaluates the user's emotional state using emotion analysis technology. It quantifies emotions from voice and facial expression data and converts them into a visualized form. Specific operations include calculating an emotion score using a machine learning model.

[0794] Step 5:

[0795] The server searches for suitable support organizations based on the analysis results and matches the user with the most suitable one. It extracts support organizations that meet the criteria from the company database using SQL queries and prioritizes them according to their sentiment score.

[0796] Step 6:

[0797] The server uses an AI model to generate a task list that takes the user's emotional state into account. The model suggests specific tasks that are relevant to the user's progress. The generated task list is then broken down into feasible steps, taking into account the user's emotional burden.

[0798] Step 7:

[0799] The device presents the user with suggested support organization information and a task list. The user interface employs a user-friendly design that considers the user's emotions, displaying information in a visually appealing and easy-to-use format. Specific actions include utilizing user interface animations and notification features.

[0800] (Application Example 2)

[0801] 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".

[0802] Traditionally, business proposals and product recommendations made without considering the user's emotional state were not always optimal for the user and, as a result, did not contribute to efficient business development or an improved purchasing experience. The present invention aims to realize appropriate business support and product / service recommendations based on the user's emotional state.

[0803] 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.

[0804] In this invention, the server includes means for receiving business proposal information and emotional state data from the user, means for extracting and analyzing keywords using natural language processing based on the received information, and means for optimizing the content of product and service proposals based on the user's emotional state. This enables optimal business support and product recommendations tailored to the user's emotional state.

[0805] A "user" is an individual or organization that uses the system to provide business proposal information and emotional state data.

[0806] "Business proposal information" refers to information about business-related plans and ideas that users provide to the system.

[0807] "Emotional state data" refers to data that indicates the user's emotional state, and is information estimated from voice, facial expressions, and text.

[0808] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0809] A "keyword" is an important word or phrase extracted from the business proposal information.

[0810] A "supporting organization" refers to a company or group that cooperates in realizing a business proposal.

[0811] A "task" is a set of specific tasks that a user needs to perform to advance their business.

[0812] A "generative model" is an algorithm that proposes the optimal process based on the user's business progress and emotional state.

[0813] "Feedback" refers to evaluations and opinions that users return to a system.

[0814] "Product and service recommendations" refer to the specific details of products and services recommended to the user.

[0815] The system for implementing this invention provides business support and product recommendations that take into account the user's emotional state. The server receives business proposal information and emotional state data from the user and processes this information. Specifically, it uses speech recognition APIs and facial expression analysis APIs to estimate the emotional state from speech and facial expressions, and analyzes text information using natural language processing technology. Hardware used includes smartphones and tablet devices, and software includes Google Cloud Speech-to-Text and Amazon Rekognition. Based on the analysis results, the server utilizes a generative AI model to generate optimal business support processes and product recommendations that are tailored to the user's emotional state.

[0816] The device optimizes the suggestions presented to the user based on their emotional state. For example, if the user is relaxed, it suggests relaxing products; if they are stressed, it emphasizes limited-time coupons and discount information. This allows users to have a more satisfying and personalized purchasing experience.

[0817] Users act based on the suggested products and business support services, and provide the system with their results and feedback. This feedback is used on the server as data for further optimization and is reflected in future suggestions. As a specific example, an example of a prompt message to the generating AI model is: "If the system detects that the user is relaxed while shopping, generate a prompt that lists recommended relaxation products and provides a more comfortable shopping experience."

[0818] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0819] Step 1:

[0820] The user inputs business proposal information and emotional state data via their device. The system captures the user's voice and facial expressions using the smartphone's microphone and camera. This input data is passed to a speech recognition API and a facial expression analysis API.

[0821] Step 2:

[0822] The server converts the received audio data into text using Google Cloud Speech-to-Text, and simultaneously extracts and analyzes keywords using natural language processing techniques. Facial expression data is analyzed using Amazon Rekognition to estimate emotional states. This process yields business proposals and emotional analysis results.

[0823] Step 3:

[0824] Based on the analysis results, the server utilizes a generative AI model to generate tasks and product recommendations tailored to the user's business progress and emotional state. Specifically, it receives keywords and emotional data from the analysis results as input, executes an algorithm to select appropriate processes and products, and creates a task list and product list as output.

[0825] Step 4:

[0826] The server sends the generated tasks and product list to the terminal. The terminal then displays optimized suggestions based on the user's emotional state. For example, if the user is relaxed, it suggests relaxation products; if they are stressed, it highlights discount information.

[0827] Step 5:

[0828] The user reviews the presented tasks and products and enters feedback into the terminal. The terminal sends this feedback to the server, which stores the feedback data in a database to optimize future suggestions and incorporates it into the analysis results.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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."

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0850] The following is further disclosed regarding the embodiments described above.

[0851] (Claim 1)

[0852] A means of receiving business proposal information from users,

[0853] A means for extracting and analyzing keywords using natural language processing based on the received information,

[0854] Based on the analysis results, a means for searching for and matching suitable support companies,

[0855] A means of automatically generating and presenting tasks suitable for the user,

[0856] A means of receiving user feedback and re-evaluating tasks and supporting companies,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, characterized in that the analysis means improves the search efficiency from the corporate database by converting business proposal information into formalized data.

[0860] (Claim 3)

[0861] The system according to claim 1, characterized in that the task generation means proposes a process suitable for the user's business progress using a generation model.

[0862] "Example 1"

[0863] (Claim 1)

[0864] Means of collecting information from users,

[0865] A means of verifying and formatting information on a terminal and transferring it to a server,

[0866] A method for analyzing the content using natural language processing based on received information and extracting keywords,

[0867] A means for searching and matching relevant support organizations from an information database according to the analysis results,

[0868] A means of automatically generating and presenting tasks suitable for the user using a generative AI model,

[0869] A means of receiving user work results and feedback, and re-evaluating and adjusting the support strategy,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, characterized in that the analysis means improves the efficiency of searching from the organizational database by converting the information into structured data.

[0873] (Claim 3)

[0874] The system according to claim 1, characterized in that the generation means proposes a process that is adapted to the user's business progress using a generation model.

[0875] "Application Example 1"

[0876] (Claim 1)

[0877] A means of receiving data to be manipulated from the user,

[0878] A means for extracting and analyzing key words using natural language processing based on the received data,

[0879] A means for searching for and matching suitable support organizations based on the analysis results,

[0880] A means of automatically generating and presenting business guidelines suitable for the user,

[0881] A means of receiving user evaluation information and re-evaluating business guidelines and support organizations,

[0882] A means of suggesting suitable distribution methods and collaborators based on specific information,

[0883] A means to automatically generate specific procedures to increase engagement by analyzing the uniqueness and trends of the content,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, characterized in that the analysis means improves the search efficiency from the information database by converting the data to be operated on into formalized information.

[0887] (Claim 3)

[0888] The system according to claim 1, characterized in that the business guideline generation means proposes procedures suitable for user operation distribution using a generation AI model.

[0889] "Example 2 of combining an emotion engine"

[0890] (Claim 1)

[0891] Means for collecting information about the business and emotions from users,

[0892] A means for analyzing collected information using natural language processing and sentiment analysis techniques to extract keywords and emotional states,

[0893] A means for searching for and matching a suitable support organization based on the user's emotional state, based on the analysis results,

[0894] A means of automatically generating and presenting appropriate tasks, taking into account the user's emotional state,

[0895] A means of receiving user feedback and re-evaluating tasks and support organizations,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, characterized in that the analysis means optimizes business-related information using the results of sentiment analysis and improves information processing efficiency.

[0899] (Claim 3)

[0900] The system according to claim 1, characterized in that the task generation means proposes a process specialized for the user's emotional state using a generation model.

[0901] "Application example 2 when combining with an emotional engine"

[0902] (Claim 1)

[0903] A means of receiving business proposal information and emotional state data from users,

[0904] A means for extracting and analyzing keywords using natural language processing based on the received information,

[0905] A means for searching for and matching suitable support organizations based on the analysis results,

[0906] A means of automatically generating and presenting tasks suitable for the user,

[0907] A means of optimizing product and service recommendations based on the user's emotional state,

[0908] A means of receiving user feedback and re-evaluating tasks and support organizations,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, characterized in that the analysis means improves the search efficiency from the information database by converting business proposal information and emotional state data into formalized data.

[0912] (Claim 3)

[0913] The system according to claim 1, characterized in that the task generation means proposes a process suitable for the user's business progress and emotional state using a generation model. [Explanation of symbols]

[0914] 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 means of receiving business proposal information from users, A means for extracting and analyzing keywords using natural language processing based on the received information, Based on the analysis results, a means for searching for and matching suitable support companies, A means of automatically generating and presenting tasks suitable for the user, A means of receiving user feedback and re-evaluating tasks and supporting companies, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means improves the search efficiency from the corporate database by converting business proposal information into formalized data.

3. The system according to claim 1, characterized in that the task generation means proposes a process suitable for the user's business progress using a generation model.

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