system

Abstract

We provide the system. [Solution] A means of collecting past event data and accumulating it as a dataset, A generation AI means that analyzes input business plan information and generates business proposals, A means for conducting investment evaluations and generating evaluation results for potential investment entities, A display means for displaying the generated business proposal and evaluation results, An input method that accepts voice or text input, performs analysis, and presents a realistic and feasible strategy, An output means that analyzes information in real time and provides the information in report format on a mobile device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] For modern venture companies and investors, formulating effective business strategies and making appropriate investment decisions are important issues. However, with existing methods, it is difficult to quickly analyze a large amount of data and make accurate judgments, and as a result, decisions with a high degree of uncertainty may be made. In particular, in formulating plans at the time of starting a new business and evaluating investment targets, lack of experience and insufficient information become obstacles, and there is a risk of missing opportunities for success. The present invention aims to solve these problems.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that includes means for collecting and storing past event data as a dataset, generation AI means for analyzing input business plan information and generating business proposals, means for performing investment evaluations and generating evaluation results for potential investment entities, and display means for displaying the generated business proposals and evaluation results. This system enables entrepreneurs to quickly build strategies suited to the market environment and investors to make more accurate investment decisions. As a result, the success rate of businesses can be increased and uncertainty can be reduced.

[0006] A "data set of events" is a collection of data that includes information such as past business success stories, investment performance, and market trends.

[0007] A "dataset" is a collection of information that is organized from a group of collected event data and stored in an analyzable format.

[0008] "Business plan information" refers to information that includes the objectives, strategies, and market analysis of a business planned by an entrepreneur or company.

[0009] "Generative AI means" refers to a function that uses machine learning algorithms to analyze input data and generate business proposals that are beneficial to the user.

[0010] "Investment evaluation" is the process of analyzing the financial condition and market potential of a potential investment target company or business entity, and clarifying the benefits and risks of the investment.

[0011] "Evaluation results" refer to the culmination of specific numerical data and analytical findings obtained through the investment evaluation process.

[0012] A "business entity" refers to a legal entity or organization that engages in specific business activities.

[0013] "Display means" refers to a series of mechanisms, including displays and interfaces, for showing generated information to the user. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a generative AI system that supports venture companies in formulating business strategies and provides investors with information to make investment decisions. The system receives information input by users (entrepreneurs and investors), analyzes it, and generates optimal business plans and investment evaluations.

[0036] First, the server automatically collects a vast amount of historical case data, industry data, and market trend data from the internet and existing databases to build a dataset. This dataset is used as the foundational data for the generative AI to perform analysis.

[0037] Next, the user enters an outline of their business plan and investment interests into the terminal. The entered data is sent to the server and analyzed by the generating AI. On the server, the generating AI performs information analysis. Specifically, it has the function of generating an optimal strategy by comparing data of similar success stories based on the entered business plan information, and by performing market analysis and evaluation of the competitive environment.

[0038] The generated business strategy is analyzed in detail for each element and compiled into a report that includes recommended actions, expected outcomes, and potential risks. This allows users to have a realistic and actionable business plan.

[0039] Furthermore, investors will also undergo an investment evaluation process using a similar generation AI. The server evaluates the financial data and market potential of potential investment companies, and performs ROI forecasts and risk assessments. This provides investors with information to make investment decisions from multiple perspectives.

[0040] Finally, the generated business strategy and investment evaluation results are displayed to the user via a terminal. This allows the user to make necessary modifications or decide whether to proceed with the investment based on the provided information. Supporting business planning and investment decisions with unprecedented efficiency and accuracy is a specific embodiment of the present invention.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server automatically collects historical case data, industry data, and market trend data from the internet and existing databases to build a dataset. This dataset is then cleaned to remove noise and converted into a format suitable for analysis.

[0044] Step 2:

[0045] The user inputs information about their business plan outline and investment areas of interest into the terminal. The terminal formats the input information received from the user and sends it to the server in a format necessary for analysis.

[0046] Step 3:

[0047] The server initiates the generative AI process based on the received user information. The generative AI matches business plan information with the dataset and identifies similar success stories. This generates potential business models and market strategies.

[0048] Step 4:

[0049] The server evaluates the generated business model and strategy in detail and creates a report that includes recommended actions, expected outcomes, and anticipated risks. This report is designed to help users concretize their business plan.

[0050] Step 5:

[0051] The server evaluates financial data and market potential of potential investment companies, generating possible ROI forecasts and risk assessment results. These evaluation results are then compiled to serve as a multifaceted resource for investment decisions.

[0052] Step 6:

[0053] The terminal displays the results of business preparation and investment evaluations sent from the server to the user. The user reviews the displayed information and makes adjustments to their strategy or investment decisions. They can also input feedback into the terminal again as needed.

[0054] (Example 1)

[0055] 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."

[0056] In formulating new business strategies and making investment decisions, it is difficult to effectively utilize vast amounts of historical and market data to obtain accurate and actionable proposals. Furthermore, the process of removing invalid data and extracting only useful information is crucial, but this is not sufficiently automated using conventional methods. There is also a need for efficient comparative analysis of past success stories and current market data using generating AI.

[0057] 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.

[0058] In this invention, the server includes means for collecting past event data and storing it as a dataset, means for generating AI that analyzes input business plan information and generates proposals, means for performing investment evaluations and generating evaluation results for the organization, and means for generating an optimal business strategy based on past cases and market trends. This makes it possible to efficiently extract necessary information from a vast amount of data and provide accurate and actionable business strategies and investment decisions.

[0059] "Past event data" refers to a collection of information related to various events that have occurred in the past.

[0060] A "dataset" is a collection of data that has been organized and classified for a specific purpose.

[0061] "Generative AI methods" refer to artificial intelligence systems that analyze data and generate information and suggestions tailored to specific purposes.

[0062] "Investment evaluation" is the process of analyzing the value and risks of an investment target and evaluating the results.

[0063] An "organization" is a general term for any group or entity formed with a specific purpose.

[0064] A "business strategy" is a set of action plans and guidelines formulated by a company or organization to achieve its goals.

[0065] "Display means" refers to devices or interfaces used to visually present information to the user.

[0066] This invention is a generative AI system designed to support venture companies in formulating business strategies and investors in making investment decisions. The system performs sophisticated analysis based on a large amount of data and provides users with practical suggestions.

[0067] The server automatically collects historical event data from the internet and databases to build a dataset. This provides the server with the data foundation necessary for analysis in the generative AI model. Data collection uses a common client-server model and a web crawler, and leverages scraping tools written in programming languages ​​such as Python.

[0068] Users input details of their business plans and investments into a terminal. This terminal can be a PC or mobile device, and can utilize a dedicated web interface or application. The input information is quickly transmitted to a server and analyzed by the generating AI.

[0069] The generative AI model is trained using machine learning frameworks such as TENSORFLOW® and PyTorch, and compares and analyzes past success stories with current market data in generating business strategies. As a result of the analysis, optimal strategies and investment evaluations are generated that take into account past success patterns of similar businesses, the latest market trends, and the competitive environment.

[0070] The device displays this generated information to the user as a report. The report indicates recommended actions, expected outcomes, and potential risks, helping the user to develop a feasible plan.

[0071] For example, if a user is considering entering the market with a new e-commerce platform, the system will perform a detailed market analysis based on the prompt "Please tell me the optimal business strategy for entering the market with a new e-commerce platform," and then provide the user with a visual strategic proposal.

[0072] This system allows users to benefit from accurate and rapid information processing, enabling them to make efficient decisions.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server collects historical event data from the internet and existing databases to build a dataset. This process uses crawling tools and APIs to collect data, cleans the collected data to remove noise, and generates a consistent dataset. The input is raw data from web pages and databases, and the output is a dataset prepared in a format suitable for analysis.

[0076] Step 2:

[0077] Users input business plan outlines and investment interests using a terminal. Input can be done via a web browser or a dedicated application, and the entered information is transmitted to the server via a secure protocol. The input consists of text data including the user's intentions and requirements, while the output is data received by the server for analysis.

[0078] Step 3:

[0079] The server feeds the received user data into a generating AI model and begins analysis. Specifically, it uses natural language processing and machine learning algorithms to compare and analyze the input business information with past success stories and market data. The input in this step is business plan data from the user, and the output is a draft of the generated business strategy and investment evaluation.

[0080] Step 4:

[0081] The server compiles the business strategies and investment evaluations generated by the AI ​​model into a detailed report. The report includes recommended actions, expected outcomes, and risk analysis. The input is the analysis results, and the output is a completed report document for presentation to the user.

[0082] Step 5:

[0083] The terminal displays reports received from the server to the user. The user visually reviews this information on the terminal and makes decisions on how to incorporate it into their business plan. At this stage, the input is a detailed report, and the output is a visual output of strategic proposals provided to the user.

[0084] (Application Example 1)

[0085] 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."

[0086] In today's business environment, entrepreneurs and investors need to make quick and accurate decisions regarding optimal business strategies and investments. However, extracting useful data from vast amounts of information and analyzing it rapidly is not easy, and there is a particular need to efficiently obtain information while on the go or using various devices.

[0087] 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.

[0088] In this invention, the server includes means for collecting and storing past event data sets as a dataset, generation AI means for analyzing input business plan information and generating business proposals, means for performing investment evaluations and generating evaluation results for potential investment entities, input means for receiving and analyzing voice or text input and presenting realistic and actionable strategies, and output means for analyzing information in real time and providing information in report format on mobile devices. This enables entrepreneurs and investors to efficiently obtain information for business strategies and investment decisions regardless of location.

[0089] "Past events data" refers to a collection of information records of past events and phenomena related to business and markets.

[0090] "Means of accumulating as a dataset" refers to a system or process that stores collected information in a specific format and makes it easily accessible for analysis and use.

[0091] "Generative AI methods" refer to processes that use artificial intelligence to analyze data based on input information and automatically generate new business proposals and evaluations.

[0092] "Means for receiving and analyzing voice or text input" refers to a system and process in which a user provides information as voice or text, which is then analyzed to derive useful results.

[0093] "An output method that analyzes information in real time and provides information in report format on mobile devices" refers to a system that immediately analyzes information and displays the results on a mobile device, enabling users to make decisions about their actions.

[0094] This invention provides a system that enables entrepreneurs and investors to effectively formulate business strategies and make investment decisions. This system processes information based on input from each user's device and performs advanced analysis using a server-based AI model.

[0095] The server runs on an AWS® EC2 instance and collects a vast amount of historical event data, storing it as a dataset. AWS S3 and DynamoDB are used as databases for storing and managing the collected data. A customized AI model utilizing Hugging Face's Transformers library is used to analyze and generate business proposals and investment evaluations.

[0096] Users input information about business plans and investments via voice or text through a mobile application built with React Native on their device. The input information is sent to a server and analyzed by a generative AI model. The analysis results are displayed in real-time on the device in a report format, which users can use to formulate strategies and make investment decisions.

[0097] As a concrete example, consider a scenario where an entrepreneur wants to open a new restaurant and inputs their plan into the app using voice. In this case, the system instantly analyzes past success stories and market trends to generate suggestions for optimal locations and marketing strategies. The user can quickly obtain the necessary information using prompts such as, "Please suggest the best strategy for my new restaurant based on data from the past 10 years."

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] Users input information about business plans and investments using their devices in either voice or text format. In the case of voice input, the device uses speech recognition technology to convert it into text data. This input data serves as the basis for subsequent analysis.

[0101] Step 2:

[0102] The terminal processes the input data and sends it to the server via the internet. The server stores the received data in a database and prepares it for analysis through a generated AI model.

[0103] Step 3:

[0104] The server uses historical event data stored in the database and current market data to initiate analysis using a generative AI model. By using user input data as prompts, the model performs data calculations to generate business proposals and investment evaluations. As a result of the analysis, optimal business strategies and investment decisions are proposed.

[0105] Step 4:

[0106] The generated proposals and evaluation results are processed in real time by the server and sent to the terminal. The terminal displays the received data in an easy-to-understand report format, allowing the user to visually confirm the information.

[0107] Step 5:

[0108] Users review the reports displayed on their devices and make adjustments to their strategies or investment decisions as needed. This feedback can then be used as further input for the next data processing cycle.

[0109] 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.

[0110] This invention relates to a generative AI system incorporating an emotion engine that recognizes user emotions, enabling it to adjust analysis results by taking into account the user's emotional state when formulating business strategies and evaluating investments. This system is intended to analyze information input by users (entrepreneurs and investors) from an emotional perspective and present optimal business plans and investment decisions.

[0111] In addition to the normal data collection process, the server collects the context and expressions used when the user inputs data. This allows the emotion engine to effectively analyze the user's emotions and detect the underlying emotions contained in the input information.

[0112] Next, as users input business plan information and investment interests into their devices, the system detects their emotional state in real time. For example, it assesses their confidence in the business plan and their concerns about risks, and then fine-tunes the strategy based on that assessment.

[0113] The generating AI processes this input information along with the results of sentiment analysis to produce even more refined business proposals. This results in the presentation of realistic and optimal strategies that take emotional aspects into account, and enables an approach that is also considerate of the user's feelings.

[0114] Furthermore, the server can utilize an emotion engine to analyze user feedback emotionally and incorporate this analysis into future strategic proposals and investment evaluations. This enhances the system's ability to adapt to users' emotional needs, resulting in more personalized support.

[0115] For example, when a user is about to enter a new market, if the emotion engine reads positive emotions from the user's content, the generative AI will suggest a risky strategy. On the other hand, if anxiety is clearly evident, it can present a more conservative plan. This approach not only increases the likelihood of business success but also improves user satisfaction.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The server automatically collects historical case data, industry data, and market trend data to build a dataset. This dataset contains the data necessary for users to develop business strategies and evaluate investments.

[0119] Step 2:

[0120] Users input information about business plans and areas of investment interest into the device using natural language. During input, the device monitors the input data in real time to perform sentiment analysis of the user's expressions and context.

[0121] Step 3:

[0122] The device analyzes user emotion data acquired in real time to identify the user's emotional state. For example, it can detect positive or negative tones from word choices.

[0123] Step 4:

[0124] The server integrates the results of sentiment analysis with the input business data into a generating AI to produce business strategies or investment proposals that take the user's emotions into account. These proposals are then tailored to the appropriate tone and content based on those emotions.

[0125] Step 5:

[0126] The server formats the generated business proposals and investment evaluation results, preparing them as visually easy-to-understand reports. These reports include explanations of the rationale behind the proposals, expected outcomes, and potential risks.

[0127] Step 6:

[0128] The device displays the generated report to the user. The displayed content is feedback tailored to the user's emotional state. The user then reviews the displayed report and makes a decision based on their own emotions and the market situation.

[0129] Step 7:

[0130] When a user provides feedback on a report, the device analyzes the sentiment of that feedback and sends it to the server to be used in future suggestions. This allows the system to evolve and provide suggestions that are more tailored to the individual user.

[0131] (Example 2)

[0132] 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".

[0133] In recent years, a problem has arisen in business strategy formulation and investment evaluation where the accuracy of proposals decreases and user satisfaction declines due to the disregard of users' emotional states. Conventional technologies are limited to qualitative evaluations based on input information and cannot adequately consider the emotional aspects of users, making it difficult to present optimal strategies for real-world decision-making.

[0134] 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.

[0135] In this invention, the server includes means for collecting past event data and storing it as a dataset, an emotion engine means for analyzing the user's emotional state from the input expression, and means for adjusting the suggestions and evaluation results generated based on the analyzed emotional state. This makes it possible to present highly accurate business strategies and investment evaluations that take the user's emotional state into consideration.

[0136] A "data set of past events" refers to a dataset that compiles information about various events and occurrences that have happened up to now.

[0137] "Means of accumulating data as a dataset" refers to methods and devices for organizing and effectively storing collected data.

[0138] "Planning information" refers to data that shows detailed plans and concepts related to businesses and investments.

[0139] "Artificial intelligence means for generating suggestions" refers to algorithms and programs that analyze input information and automatically create optimal suggestions.

[0140] "Investment evaluation" is the process of assessing an investment and quantitatively estimating its value and risks.

[0141] "Evaluation results of the subject" refers to the results and judgments obtained through the evaluation process regarding the subject related to investment or business activities.

[0142] An "emotional engine" refers to software or algorithms that detect and analyze an emotional state based on information input by the user.

[0143] "Means for adjusting proposals and evaluation results" refer to technologies and methods for appropriately modifying proposals and evaluation results generated based on the user's emotional state, and guiding them to an optimal form.

[0144] "Means of display" refers to devices or technologies that visually provide generated information, such as displays and monitors.

[0145] This system is a generative AI system that analyzes business plan and investment information entered by users through an emotion engine to provide optimal business strategies and investment evaluations.

[0146] First, as part of data collection, the server stores text data entered by users on their devices. In this process, natural language processing is used to analyze the text and extract keywords that indicate context and sentiment. The extracted data is then structured as a set of past event data and stored as a dataset.

[0147] Next, the emotion engine uses machine learning techniques to analyze the user's emotional state from their input. Leveraging specific algorithms, the emotion engine quantifies positive and negative emotions present in the input and evaluates their state. For example, if the user's input indicates anxiety, the system recognizes that emotion and assigns an emotion score.

[0148] The generative AI model generates optimal suggestions tailored to the user's needs based on this sentiment data and input data. The generated business strategies and investment evaluations are presented to the user visually. For example, the generative AI model derives suggestions based on the prompt, "Explain how to analyze a user's confidence and anxieties when they are considering entering a new market, and then provide strategic suggestions based on that."

[0149] Furthermore, the server collects user feedback and uses it to improve the emotion engine and generative AI models. This feedback loop allows the system to continuously improve the accuracy of its suggestions and present more personalized strategies.

[0150] In this way, the system provides the most effective decision-making support for the user through real-time sentiment analysis.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The server collects business plans and investment information entered by users on their terminals as text data. Upon receiving this text data, it uses natural language processing techniques to extract keywords and sentiment indicators. During this process, it analyzes important elements and expressions within the text and accumulates the data. The extracted data is then output as structured data, which is used in later stages for sentiment analysis and proposal generation.

[0154] Step 2:

[0155] The server passes the accumulated text data to the emotion engine, which analyzes the user's emotional state. The input is structured text data, and the emotion engine uses a machine learning algorithm to calculate an emotion score. Specifically, it assigns emotion labels such as positive and negative, and quantifies the degree of each emotion. The analysis results are output as an emotion score.

[0156] Step 3:

[0157] The device provides users with real-time feedback based on sentiment scores from an emotion engine. It receives sentiment scores as input and generates visual information, such as graphs and messages, to present them clearly to the user. This feedback influences user decision-making, enabling them to select more emotionally appropriate strategies. The output is visual feedback on the device.

[0158] Step 4:

[0159] The server combines sentiment scores and user input data to generate business strategies and investment proposals using a generative AI model. Based on the sentiment scores and text data as input, the generative AI model generates scenarios and strategies based on prompt text. This process derives multiple options and outputs highly accurate proposals.

[0160] Step 5:

[0161] The server collects user feedback and uses it to make adjustments to improve the overall accuracy of the system. The feedback input is used to update the emotion engine and generative AI model, improving the accuracy of future analyses and strategic recommendations. Specifically, the feedback is stored in a database to help improve the algorithms. The output is an improved model and more accurate analytical data.

[0162] (Application Example 2)

[0163] 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".

[0164] Traditional business proposal and investment evaluation systems relied heavily on data analysis, making it difficult to consider users' emotional states when making proposals and evaluations. As a result, proposals often failed to adequately address users' emotional needs and psychological states, ultimately reducing the likelihood of business success.

[0165] 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.

[0166] In this invention, the server includes means for collecting past event data and storing it as a dataset; generation AI means for analyzing input business plan information and generating business proposals; means for performing investment evaluations and generating evaluation results for potential investment entities; emotion analysis means for analyzing the user's emotional state in real time and adjusting the proposals based on the analysis results; and display means for displaying the generated business proposals and evaluation results. This makes it possible to adjust business proposals and investment evaluations while taking the user's emotional state into consideration.

[0167] "Past event data" refers to a collection of data on various events that have occurred to date, and is information accumulated as a dataset.

[0168] A "generative AI method" is a mechanism that uses artificial intelligence to analyze input information and generate business proposals.

[0169] An "investment evaluation tool" is a process for evaluating potential investment targets and generating the results.

[0170] "Emotional analysis means" refers to technology that analyzes a user's emotional state in real time and adjusts suggestions based on the analysis results.

[0171] A "display means" is an interface for visually presenting the generated business proposals and evaluation results to the user.

[0172] The system for implementing this invention utilizes sentiment analysis to optimize business proposals and investment evaluations for users. The server collects historical event data and stores it as a dataset. This data is used as basic information for data analysis, and data cleaning such as noise reduction is performed on it.

[0173] The server analyzes the input business plan information through a generation AI mechanism and generates business proposals. This AI mechanism has an algorithm that compares and analyzes past success stories with current market data. As a result, the generated business proposals are tailored to the current market situation.

[0174] Furthermore, the server utilizes sentiment analysis tools to analyze the user's emotional state in real time based on the information they input. For example, when a user inputs information about a business plan, the server detects their emotions based on the content and context. Based on this analysis, the generated suggestions are adjusted to match the user's emotional state.

[0175] The display means provides an interface for visually presenting the generated business proposals and evaluation results to the user. This display includes comments and coupon suggestions that take into account the user's emotional state.

[0176] For example, when a user is about to enter a new market, if positive emotions are detected, the server will suggest a more aggressive strategy. On the other hand, if anxiety is indicated, a more conservative plan will be presented. In this way, the user can obtain a strategy that matches their emotional state.

[0177] An example of a prompt for a generative AI model is, "How do you feel about the product you are about to purchase? Are you happy or anxious?"

[0178] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0179] Step 1:

[0180] The server collects historical event data and stores it as a dataset. This enables comparative analysis based on user input data. The historical data includes success stories and failures, and analyzing them forms the basis for future predictions and proposals. The input is historical data, and the output is a clean dataset.

[0181] Step 2:

[0182] The user inputs business plan information into the terminal. The terminal receives input data from the user, including details of the business plan as text data. The input is text information provided by the user, and the output is structured data for analysis.

[0183] Step 3:

[0184] The server uses a generative AI to analyze the user's business plan information and generate business proposals. Based on the input information, the server compares past success stories and market data to optimize the proposals. The input consists of structured data and historical data, and the output is the proposed business plan.

[0185] Step 4:

[0186] The server uses sentiment analysis tools to analyze the user's emotional state in real time based on their input information. This analysis employs natural language processing algorithms to determine whether the user's emotions are positive, negative, or neutral. The input is the user's text data, and the output is the sentiment analysis result.

[0187] Step 5:

[0188] The server adjusts the generated business proposals based on the results of sentiment analysis. If the user expresses positive emotions, it proposes high-risk strategies; if they express anxiety, it suggests more conservative options. The input is the generated business proposal and the sentiment analysis results, and the output is the sentiment-adjusted proposal.

[0189] Step 6:

[0190] The display method presents the final business proposal and evaluation results to the user on the terminal. The display also includes sentiment analysis feedback, giving the user an opportunity to become aware of their own emotional state. The input is the final business proposal and sentiment feedback, and the output is a visual display on the user interface.

[0191] 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.

[0192] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), 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.

[0193] 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.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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".

[0207] This invention is a generative AI system that supports venture companies in formulating business strategies and provides investors with information to make investment decisions. The system receives information input by users (entrepreneurs and investors), analyzes it, and generates optimal business plans and investment evaluations.

[0208] First, the server automatically collects a vast amount of historical case data, industry data, and market trend data from the internet and existing databases to build a dataset. This dataset is used as the foundational data for the generative AI to perform analysis.

[0209] Next, the user enters an outline of their business plan and investment interests into the terminal. The entered data is sent to the server and analyzed by the generating AI. On the server, the generating AI performs information analysis. Specifically, it has the function of generating an optimal strategy by comparing data of similar success stories based on the entered business plan information, and by performing market analysis and evaluation of the competitive environment.

[0210] The generated business strategy is analyzed in detail for each element and compiled into a report that includes recommended actions, expected outcomes, and potential risks. This allows users to have a realistic and actionable business plan.

[0211] Furthermore, investors will also undergo an investment evaluation process using a similar generation AI. The server evaluates the financial data and market potential of potential investment companies, and performs ROI forecasts and risk assessments. This provides investors with information to make investment decisions from multiple perspectives.

[0212] Finally, the generated business strategy and investment evaluation results are displayed to the user via a terminal. This allows the user to make necessary modifications or decide whether to proceed with the investment based on the provided information. Supporting business planning and investment decisions with unprecedented efficiency and accuracy is a specific embodiment of the present invention.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] The server automatically collects historical case data, industry data, and market trend data from the internet and existing databases to build a dataset. This dataset is then cleaned to remove noise and converted into a format suitable for analysis.

[0216] Step 2:

[0217] The user inputs information about their business plan outline and investment areas of interest into the terminal. The terminal formats the input information received from the user and sends it to the server in a format necessary for analysis.

[0218] Step 3:

[0219] The server initiates the generative AI process based on the received user information. The generative AI matches business plan information with the dataset and identifies similar success stories. This generates potential business models and market strategies.

[0220] Step 4:

[0221] The server evaluates the generated business model and strategy in detail and creates a report that includes recommended actions, expected outcomes, and anticipated risks. This report is designed to help users concretize their business plan.

[0222] Step 5:

[0223] The server evaluates financial data and market potential of potential investment companies, generating possible ROI forecasts and risk assessment results. These evaluation results are then compiled to serve as a multifaceted resource for investment decisions.

[0224] Step 6:

[0225] The terminal displays the results of business preparation and investment evaluations sent from the server to the user. The user reviews the displayed information and makes adjustments to their strategy or investment decisions. They can also input feedback into the terminal again as needed.

[0226] (Example 1)

[0227] 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."

[0228] In formulating new business strategies and making investment decisions, it is difficult to effectively utilize vast amounts of historical and market data to obtain accurate and actionable proposals. Furthermore, the process of removing invalid data and extracting only useful information is crucial, but this is not sufficiently automated using conventional methods. There is also a need for efficient comparative analysis of past success stories and current market data using generating AI.

[0229] 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.

[0230] In this invention, the server includes means for collecting past event data and storing it as a dataset, means for generating AI that analyzes input business plan information and generates proposals, means for performing investment evaluations and generating evaluation results for the organization, and means for generating an optimal business strategy based on past cases and market trends. This makes it possible to efficiently extract necessary information from a vast amount of data and provide accurate and actionable business strategies and investment decisions.

[0231] "Past event data" refers to a collection of information related to various events that have occurred in the past.

[0232] A "dataset" is a collection of data that has been organized and classified for a specific purpose.

[0233] "Generative AI methods" refer to artificial intelligence systems that analyze data and generate information and suggestions tailored to specific purposes.

[0234] "Investment evaluation" is the process of analyzing the value and risks of an investment target and evaluating the results.

[0235] An "organization" is a general term for any group or entity formed with a specific purpose.

[0236] A "business strategy" is a set of action plans and guidelines formulated by a company or organization to achieve its goals.

[0237] "Display means" refers to devices or interfaces used to visually present information to the user.

[0238] This invention is a generative AI system designed to support venture companies in formulating business strategies and investors in making investment decisions. The system performs sophisticated analysis based on a large amount of data and provides users with practical suggestions.

[0239] The server automatically collects historical event data from the internet and databases to build a dataset. This provides the server with the data foundation necessary for analysis in the generative AI model. Data collection uses a common client-server model and a web crawler, and leverages scraping tools written in programming languages ​​such as Python.

[0240] Users input details of their business plans and investments into a terminal. This terminal can be a PC or mobile device, and can utilize a dedicated web interface or application. The input information is quickly transmitted to a server and analyzed by the generating AI.

[0241] The generative AI model is trained using machine learning frameworks such as TensorFlow and PyTorch, and compares and analyzes past success stories with current market data in generating business strategies. As a result of the analysis, optimal strategies and investment evaluations are generated that take into account past success patterns of similar businesses, the latest market trends, and the competitive environment.

[0242] The device displays this generated information to the user as a report. The report indicates recommended actions, expected outcomes, and potential risks, helping the user to develop a feasible plan.

[0243] For example, if a user is considering entering the market with a new e-commerce platform, the system will perform a detailed market analysis based on the prompt "Please tell me the optimal business strategy for entering the market with a new e-commerce platform," and then provide the user with a visual strategic proposal.

[0244] This system allows users to benefit from accurate and rapid information processing, enabling them to make efficient decisions.

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] The server collects historical event data from the internet and existing databases to build a dataset. This process uses crawling tools and APIs to collect data, cleans the collected data to remove noise, and generates a consistent dataset. The input is raw data from web pages and databases, and the output is a dataset prepared in a format suitable for analysis.

[0248] Step 2:

[0249] Users input business plan outlines and investment interests using a terminal. Input can be done via a web browser or a dedicated application, and the entered information is transmitted to the server via a secure protocol. The input consists of text data including the user's intentions and requirements, while the output is data received by the server for analysis.

[0250] Step 3:

[0251] The server feeds the received user data into a generating AI model and begins analysis. Specifically, it uses natural language processing and machine learning algorithms to compare and analyze the input business information with past success stories and market data. The input in this step is business plan data from the user, and the output is a draft of the generated business strategy and investment evaluation.

[0252] Step 4:

[0253] The server compiles the business strategies and investment evaluations generated by the AI ​​model into a detailed report. The report includes recommended actions, expected outcomes, and risk analysis. The input is the analysis results, and the output is a completed report document for presentation to the user.

[0254] Step 5:

[0255] The terminal displays reports received from the server to the user. The user visually reviews this information on the terminal and makes decisions on how to incorporate it into their business plan. At this stage, the input is a detailed report, and the output is a visual output of strategic proposals provided to the user.

[0256] (Application Example 1)

[0257] 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."

[0258] In today's business environment, entrepreneurs and investors need to make quick and accurate decisions regarding optimal business strategies and investments. However, extracting useful data from vast amounts of information and analyzing it rapidly is not easy, and there is a particular need to efficiently obtain information while on the go or using various devices.

[0259] 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.

[0260] In this invention, the server includes means for collecting and storing past event data sets as a dataset, generation AI means for analyzing input business plan information and generating business proposals, means for performing investment evaluations and generating evaluation results for potential investment entities, input means for receiving and analyzing voice or text input and presenting realistic and actionable strategies, and output means for analyzing information in real time and providing information in report format on mobile devices. This enables entrepreneurs and investors to efficiently obtain information for business strategies and investment decisions regardless of location.

[0261] "Past events data" refers to a collection of information records of past events and phenomena related to business and markets.

[0262] "Means of accumulating as a dataset" refers to a system or process that stores collected information in a specific format and makes it easily accessible for analysis and use.

[0263] "Generative AI methods" refer to processes that use artificial intelligence to analyze data based on input information and automatically generate new business proposals and evaluations.

[0264] "Means for receiving and analyzing voice or text input" refers to a system and process in which a user provides information as voice or text, which is then analyzed to derive useful results.

[0265] "An output method that analyzes information in real time and provides information in report format on mobile devices" refers to a system that immediately analyzes information and displays the results on a mobile device, enabling users to make decisions about their actions.

[0266] This invention provides a system that enables entrepreneurs and investors to effectively formulate business strategies and make investment decisions. This system processes information based on input from each user's device and performs advanced analysis using a server-based AI model.

[0267] The server runs on an AWS EC2 instance and collects a vast amount of historical event data, storing it as a dataset. AWS S3 and DynamoDB are used as databases for storing and managing the collected data. A customized AI model utilizing Hugging Face's Transformers library is used to analyze and generate business proposals and investment evaluations.

[0268] Users input information about business plans and investments via voice or text through a mobile application built with React Native on their device. The input information is sent to a server and analyzed by a generative AI model. The analysis results are displayed in real-time on the device in a report format, which users can use to formulate strategies and make investment decisions.

[0269] As a concrete example, consider a scenario where an entrepreneur wants to open a new restaurant and inputs their plan into the app using voice. In this case, the system instantly analyzes past success stories and market trends to generate suggestions for optimal locations and marketing strategies. The user can quickly obtain the necessary information using prompts such as, "Please suggest the best strategy for my new restaurant based on data from the past 10 years."

[0270] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0271] Step 1:

[0272] Users input information about business plans and investments using their devices in either voice or text format. In the case of voice input, the device uses speech recognition technology to convert it into text data. This input data serves as the basis for subsequent analysis.

[0273] Step 2:

[0274] The terminal processes the input data and sends it to the server via the internet. The server stores the received data in a database and prepares it for analysis through a generated AI model.

[0275] Step 3:

[0276] The server uses historical event data stored in the database and current market data to initiate analysis using a generative AI model. By using user input data as prompts, the model performs data calculations to generate business proposals and investment evaluations. As a result of the analysis, optimal business strategies and investment decisions are proposed.

[0277] Step 4:

[0278] The generated proposals and evaluation results are processed in real time by the server and sent to the terminal. The terminal displays the received data in an easy-to-understand report format, allowing the user to visually confirm the information.

[0279] Step 5:

[0280] Users review the reports displayed on their devices and make adjustments to their strategies or investment decisions as needed. This feedback can then be used as further input for the next data processing cycle.

[0281] 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.

[0282] This invention relates to a generative AI system incorporating an emotion engine that recognizes user emotions, enabling it to adjust analysis results by taking into account the user's emotional state when formulating business strategies and evaluating investments. This system is intended to analyze information input by users (entrepreneurs and investors) from an emotional perspective and present optimal business plans and investment decisions.

[0283] In addition to the normal data collection process, the server collects the context and expressions used when the user inputs. As a result, the emotion engine can effectively analyze the user's emotions and detect the potential emotions contained in the input information.

[0284] Next, when the user inputs information about business plans and investment-related matters into the terminal, it detects in real time what emotional state the input is in. For example, it evaluates the confidence in the business plan and the uneasiness about risks, and fine-tunes the strategy based on this.

[0285] The generative AI processes the input information and the results of the emotion analysis together, and generates a more refined business proposal. As a result, a realistic and optimal strategy that takes into account the emotional aspect is presented, and an approach that also considers the user's mood becomes possible.

[0286] Also, the server can use the emotion engine to analyze the user's feedback in terms of emotions and reflect it in the next strategy proposal or investment evaluation. As a result, the system enhances its ability to adapt to the user's emotional needs, and more personalized support is realized.

[0287] As a specific example, when the user attempts to enter a new market, if the emotion engine reads positive emotions from the user's content, the generative AI proposes a strategy to take risks, while if uneasiness is显著 manifested, a more conservative plan can be presented. This approach can not only increase the likelihood of business success but also improve user satisfaction.

[0288] The following explains the processing flow.

[0289] Step one:

[0290] The server automatically collects historical case data, industry data, and market trend data to build a dataset. This dataset contains the data necessary for users to develop business strategies and evaluate investments.

[0291] Step 2:

[0292] Users input information about business plans and areas of investment interest into the device using natural language. During input, the device monitors the input data in real time to perform sentiment analysis of the user's expressions and context.

[0293] Step 3:

[0294] The device analyzes user emotion data acquired in real time to identify the user's emotional state. For example, it can detect positive or negative tones from word choices.

[0295] Step 4:

[0296] The server integrates the results of sentiment analysis with the input business data into a generating AI to produce business strategies or investment proposals that take the user's emotions into account. These proposals are then tailored to the appropriate tone and content based on those emotions.

[0297] Step 5:

[0298] The server formats the generated business proposals and investment evaluation results, preparing them as visually easy-to-understand reports. These reports include explanations of the rationale behind the proposals, expected outcomes, and potential risks.

[0299] Step 6:

[0300] The device displays the generated report to the user. The displayed content is feedback tailored to the user's emotional state. The user then reviews the displayed report and makes a decision based on their own emotions and the market situation.

[0301] Step 7:

[0302] When the user inputs feedback on the report, the terminal performs sentiment analysis on the feedback and sends it to the server for reflection in the next proposal. As a result, the system grows to be able to make more suitable proposals for the user himself / herself.

[0303] (Example 2)

[0304] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0305] In recent years, in business strategy formulation and investment evaluation, there has been a problem that the proposal accuracy decreases and the user satisfaction becomes low by ignoring the user's emotional state. In the conventional technology, it only stays at a qualitative evaluation based on the input information and cannot sufficiently consider the emotional aspect of the user, so there has been a problem that it is difficult to present an optimal strategy in real-world decision-making.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0307] In this invention, the server includes means for collecting a group of past event data and accumulating it as a data set, sentiment engine means for analyzing the user's emotional state from the expression at the time of input, and means for adjusting the proposal and evaluation results generated based on the analyzed emotional state. As a result, it becomes possible to present a highly accurate business strategy and investment evaluation considering the user's emotional state.

[0308] The "group of past event data" refers to a data set that accumulates information on various events and occurrences that have occurred so far.

[0309] The "means for accumulating as a data set" refers to a method or device for organizing and effectively storing the collected data.

[0310] "Planning information" refers to data that shows detailed plans and concepts related to businesses and investments.

[0311] "Artificial intelligence means for generating suggestions" refers to algorithms and programs that analyze input information and automatically create optimal suggestions.

[0312] "Investment evaluation" is the process of assessing an investment and quantitatively estimating its value and risks.

[0313] "Evaluation results of the subject" refers to the results and judgments obtained through the evaluation process regarding the subject related to investment or business activities.

[0314] An "emotional engine" refers to software or algorithms that detect and analyze an emotional state based on information input by the user.

[0315] "Means for adjusting proposals and evaluation results" refer to technologies and methods for appropriately modifying proposals and evaluation results generated based on the user's emotional state, and guiding them to an optimal form.

[0316] "Means of display" refers to devices or technologies that visually provide generated information, such as displays and monitors.

[0317] This system is a generative AI system that analyzes business plan and investment information entered by users through an emotion engine to provide optimal business strategies and investment evaluations.

[0318] First, as part of data collection, the server stores text data entered by users on their devices. In this process, natural language processing is used to analyze the text and extract keywords that indicate context and sentiment. The extracted data is then structured as a set of past event data and stored as a dataset.

[0319] Next, the emotion engine uses machine learning techniques to analyze the user's emotional state from their input. Leveraging specific algorithms, the emotion engine quantifies positive and negative emotions present in the input and evaluates their state. For example, if the user's input indicates anxiety, the system recognizes that emotion and assigns an emotion score.

[0320] The generative AI model generates optimal suggestions tailored to the user's needs based on this sentiment data and input data. The generated business strategies and investment evaluations are presented to the user visually. For example, the generative AI model derives suggestions based on the prompt, "Explain how to analyze a user's confidence and anxieties when they are considering entering a new market, and then provide strategic suggestions based on that."

[0321] Furthermore, the server collects user feedback and uses it to improve the emotion engine and generative AI models. This feedback loop allows the system to continuously improve the accuracy of its suggestions and present more personalized strategies.

[0322] In this way, the system provides the most effective decision-making support for the user through real-time sentiment analysis.

[0323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0324] Step 1:

[0325] The server collects business plans and investment information entered by users on their terminals as text data. Upon receiving this text data, it uses natural language processing techniques to extract keywords and sentiment indicators. During this process, it analyzes important elements and expressions within the text and accumulates the data. The extracted data is then output as structured data, which is used in later stages for sentiment analysis and proposal generation.

[0326] Step 2:

[0327] The server passes the accumulated text data to the emotion engine, which analyzes the user's emotional state. The input is structured text data, and the emotion engine uses a machine learning algorithm to calculate an emotion score. Specifically, it assigns emotion labels such as positive and negative, and quantifies the degree of each emotion. The analysis results are output as an emotion score.

[0328] Step 3:

[0329] The device provides users with real-time feedback based on sentiment scores from an emotion engine. It receives sentiment scores as input and generates visual information, such as graphs and messages, to present them clearly to the user. This feedback influences user decision-making, enabling them to select more emotionally appropriate strategies. The output is visual feedback on the device.

[0330] Step 4:

[0331] The server combines sentiment scores and user input data to generate business strategies and investment proposals using a generative AI model. Based on the sentiment scores and text data as input, the generative AI model generates scenarios and strategies based on prompt text. This process derives multiple options and outputs highly accurate proposals.

[0332] Step 5:

[0333] The server collects user feedback and uses it to make adjustments to improve the overall accuracy of the system. The feedback input is used to update the emotion engine and generative AI model, improving the accuracy of future analyses and strategic recommendations. Specifically, the feedback is stored in a database to help improve the algorithms. The output is an improved model and more accurate analytical data.

[0334] (Application Example 2)

[0335] 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."

[0336] Traditional business proposal and investment evaluation systems relied heavily on data analysis, making it difficult to consider users' emotional states when making proposals and evaluations. As a result, proposals often failed to adequately address users' emotional needs and psychological states, ultimately reducing the likelihood of business success.

[0337] 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.

[0338] In this invention, the server includes means for collecting past event data and storing it as a dataset; generation AI means for analyzing input business plan information and generating business proposals; means for performing investment evaluations and generating evaluation results for potential investment entities; emotion analysis means for analyzing the user's emotional state in real time and adjusting the proposals based on the analysis results; and display means for displaying the generated business proposals and evaluation results. This makes it possible to adjust business proposals and investment evaluations while taking the user's emotional state into consideration.

[0339] "Past event data" refers to a collection of data on various events that have occurred to date, and is information accumulated as a dataset.

[0340] A "generative AI method" is a mechanism that uses artificial intelligence to analyze input information and generate business proposals.

[0341] An "investment evaluation tool" is a process for evaluating potential investment targets and generating the results.

[0342] "Emotional analysis means" refers to technology that analyzes a user's emotional state in real time and adjusts suggestions based on the analysis results.

[0343] A "display means" is an interface for visually presenting the generated business proposals and evaluation results to the user.

[0344] The system for implementing this invention utilizes sentiment analysis to optimize business proposals and investment evaluations for users. The server collects historical event data and stores it as a dataset. This data is used as basic information for data analysis, and data cleaning such as noise reduction is performed on it.

[0345] The server analyzes the input business plan information through a generation AI mechanism and generates business proposals. This AI mechanism has an algorithm that compares and analyzes past success stories with current market data. As a result, the generated business proposals are tailored to the current market situation.

[0346] Furthermore, the server utilizes sentiment analysis tools to analyze the user's emotional state in real time based on the information they input. For example, when a user inputs information about a business plan, the server detects their emotions based on the content and context. Based on this analysis, the generated suggestions are adjusted to match the user's emotional state.

[0347] The display means provides an interface for visually presenting the generated business proposals and evaluation results to the user. This display includes comments and coupon suggestions that take into account the user's emotional state.

[0348] For example, when a user is about to enter a new market, if positive emotions are detected, the server will suggest a more aggressive strategy. On the other hand, if anxiety is indicated, a more conservative plan will be presented. In this way, the user can obtain a strategy that matches their emotional state.

[0349] An example of a prompt for a generative AI model is, "How do you feel about the product you are about to purchase? Are you happy or anxious?"

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0351] Step 1:

[0352] The server collects historical event data and stores it as a dataset. This enables comparative analysis based on user input data. The historical data includes success stories and failures, and analyzing them forms the basis for future predictions and proposals. The input is historical data, and the output is a clean dataset.

[0353] Step 2:

[0354] The user inputs business plan information into the terminal. The terminal receives input data from the user, including details of the business plan as text data. The input is text information provided by the user, and the output is structured data for analysis.

[0355] Step 3:

[0356] The server uses a generative AI to analyze the user's business plan information and generate business proposals. Based on the input information, the server compares past success stories and market data to optimize the proposals. The input consists of structured data and historical data, and the output is the proposed business plan.

[0357] Step 4:

[0358] The server uses sentiment analysis tools to analyze the user's emotional state in real time based on their input information. This analysis employs natural language processing algorithms to determine whether the user's emotions are positive, negative, or neutral. The input is the user's text data, and the output is the sentiment analysis result.

[0359] Step 5:

[0360] The server adjusts the generated business proposals based on the results of sentiment analysis. If the user expresses positive emotions, it proposes high-risk strategies; if they express anxiety, it suggests more conservative options. The input is the generated business proposal and the sentiment analysis results, and the output is the sentiment-adjusted proposal.

[0361] Step 6:

[0362] The display method presents the final business proposal and evaluation results to the user on the terminal. The display also includes sentiment analysis feedback, giving the user an opportunity to become aware of their own emotional state. The input is the final business proposal and sentiment feedback, and the output is a visual display on the user interface.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] [Third Embodiment]

[0367] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0368] 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.

[0369] 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).

[0370] 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.

[0371] 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.

[0372] 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).

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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".

[0379] This invention is a generative AI system that supports venture companies in formulating business strategies and provides investors with information to make investment decisions. The system receives information input by users (entrepreneurs and investors), analyzes it, and generates optimal business plans and investment evaluations.

[0380] First, the server automatically collects a vast amount of historical case data, industry data, and market trend data from the internet and existing databases to build a dataset. This dataset is used as the foundational data for the generative AI to perform analysis.

[0381] Next, the user enters an outline of their business plan and investment interests into the terminal. The entered data is sent to the server and analyzed by the generating AI. On the server, the generating AI performs information analysis. Specifically, it has the function of generating an optimal strategy by comparing data of similar success stories based on the entered business plan information, and by performing market analysis and evaluation of the competitive environment.

[0382] The generated business strategy is analyzed in detail for each element and compiled into a report that includes recommended actions, expected outcomes, and potential risks. This allows users to have a realistic and actionable business plan.

[0383] Furthermore, investors will also undergo an investment evaluation process using a similar generation AI. The server evaluates the financial data and market potential of potential investment companies, and performs ROI forecasts and risk assessments. This provides investors with information to make investment decisions from multiple perspectives.

[0384] Finally, the generated business strategy and investment evaluation results are displayed to the user via a terminal. This allows the user to make necessary modifications or decide whether to proceed with the investment based on the provided information. Supporting business planning and investment decisions with unprecedented efficiency and accuracy is a specific embodiment of the present invention.

[0385] The following describes the processing flow.

[0386] Step 1:

[0387] The server automatically collects historical case data, industry data, and market trend data from the internet and existing databases to build a dataset. This dataset is then cleaned to remove noise and converted into a format suitable for analysis.

[0388] Step 2:

[0389] The user inputs information about their business plan outline and investment areas of interest into the terminal. The terminal formats the input information received from the user and sends it to the server in a format necessary for analysis.

[0390] Step 3:

[0391] The server initiates the generative AI process based on the received user information. The generative AI matches business plan information with the dataset and identifies similar success stories. This generates potential business models and market strategies.

[0392] Step 4:

[0393] The server evaluates the generated business model and strategy in detail and creates a report that includes recommended actions, expected outcomes, and anticipated risks. This report is designed to help users concretize their business plan.

[0394] Step 5:

[0395] The server evaluates financial data and market potential of potential investment companies, generating possible ROI forecasts and risk assessment results. These evaluation results are then compiled to serve as a multifaceted resource for investment decisions.

[0396] Step 6:

[0397] The terminal displays the results of business preparation and investment evaluations sent from the server to the user. The user reviews the displayed information and makes adjustments to their strategy or investment decisions. They can also input feedback into the terminal again as needed.

[0398] (Example 1)

[0399] 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."

[0400] In formulating new business strategies and making investment decisions, it is difficult to effectively utilize vast amounts of historical and market data to obtain accurate and actionable proposals. Furthermore, the process of removing invalid data and extracting only useful information is crucial, but this is not sufficiently automated using conventional methods. There is also a need for efficient comparative analysis of past success stories and current market data using generating AI.

[0401] 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.

[0402] In this invention, the server includes means for collecting past event data and storing it as a dataset, means for generating AI that analyzes input business plan information and generates proposals, means for performing investment evaluations and generating evaluation results for the organization, and means for generating an optimal business strategy based on past cases and market trends. This makes it possible to efficiently extract necessary information from a vast amount of data and provide accurate and actionable business strategies and investment decisions.

[0403] "Past event data" refers to a collection of information related to various events that have occurred in the past.

[0404] A "dataset" is a collection of data that has been organized and classified for a specific purpose.

[0405] "Generative AI methods" refer to artificial intelligence systems that analyze data and generate information and suggestions tailored to specific purposes.

[0406] "Investment evaluation" is the process of analyzing the value and risks of an investment target and evaluating the results.

[0407] An "organization" is a general term for any group or entity formed with a specific purpose.

[0408] A "business strategy" is a set of action plans and guidelines formulated by a company or organization to achieve its goals.

[0409] "Display means" refers to devices or interfaces used to visually present information to the user.

[0410] This invention is a generative AI system designed to support venture companies in formulating business strategies and investors in making investment decisions. The system performs sophisticated analysis based on a large amount of data and provides users with practical suggestions.

[0411] The server automatically collects historical event data from the internet and databases to build a dataset. This provides the server with the data foundation necessary for analysis in the generative AI model. Data collection uses a common client-server model and a web crawler, and leverages scraping tools written in programming languages ​​such as Python.

[0412] Users input details of their business plans and investments into a terminal. This terminal can be a PC or mobile device, and can utilize a dedicated web interface or application. The input information is quickly transmitted to a server and analyzed by the generating AI.

[0413] The generative AI model is trained using machine learning frameworks such as TensorFlow and PyTorch, and compares and analyzes past success stories with current market data in generating business strategies. As a result of the analysis, optimal strategies and investment evaluations are generated that take into account past success patterns of similar businesses, the latest market trends, and the competitive environment.

[0414] The device displays this generated information to the user as a report. The report indicates recommended actions, expected outcomes, and potential risks, helping the user to develop a feasible plan.

[0415] For example, if a user is considering entering the market with a new e-commerce platform, the system will perform a detailed market analysis based on the prompt "Please tell me the optimal business strategy for entering the market with a new e-commerce platform," and then provide the user with a visual strategic proposal.

[0416] This system allows users to benefit from accurate and rapid information processing, enabling them to make efficient decisions.

[0417] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0418] Step 1:

[0419] The server collects historical event data from the internet and existing databases to build a dataset. This process uses crawling tools and APIs to collect data, cleans the collected data to remove noise, and generates a consistent dataset. The input is raw data from web pages and databases, and the output is a dataset prepared in a format suitable for analysis.

[0420] Step 2:

[0421] Users input business plan outlines and investment interests using a terminal. Input can be done via a web browser or a dedicated application, and the entered information is transmitted to the server via a secure protocol. The input consists of text data including the user's intentions and requirements, while the output is data received by the server for analysis.

[0422] Step 3:

[0423] The server feeds the received user data into a generating AI model and begins analysis. Specifically, it uses natural language processing and machine learning algorithms to compare and analyze the input business information with past success stories and market data. The input in this step is business plan data from the user, and the output is a draft of the generated business strategy and investment evaluation.

[0424] Step 4:

[0425] The server compiles the business strategies and investment evaluations generated by the AI ​​model into a detailed report. The report includes recommended actions, expected outcomes, and risk analysis. The input is the analysis results, and the output is a completed report document for presentation to the user.

[0426] Step 5:

[0427] The terminal displays reports received from the server to the user. The user visually reviews this information on the terminal and makes decisions on how to incorporate it into their business plan. At this stage, the input is a detailed report, and the output is a visual output of strategic proposals provided to the user.

[0428] (Application Example 1)

[0429] 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."

[0430] In today's business environment, entrepreneurs and investors need to make quick and accurate decisions regarding optimal business strategies and investments. However, extracting useful data from vast amounts of information and analyzing it rapidly is not easy, and there is a particular need to efficiently obtain information while on the go or using various devices.

[0431] 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.

[0432] In this invention, the server includes means for collecting and storing past event data sets as a dataset, generation AI means for analyzing input business plan information and generating business proposals, means for performing investment evaluations and generating evaluation results for potential investment entities, input means for receiving and analyzing voice or text input and presenting realistic and actionable strategies, and output means for analyzing information in real time and providing information in report format on mobile devices. This enables entrepreneurs and investors to efficiently obtain information for business strategies and investment decisions regardless of location.

[0433] "Past events data" refers to a collection of information records of past events and phenomena related to business and markets.

[0434] "Means of accumulating as a dataset" refers to a system or process that stores collected information in a specific format and makes it easily accessible for analysis and use.

[0435] "Generative AI methods" refer to processes that use artificial intelligence to analyze data based on input information and automatically generate new business proposals and evaluations.

[0436] "Means for receiving and analyzing voice or text input" refers to a system and process in which a user provides information as voice or text, which is then analyzed to derive useful results.

[0437] "An output method that analyzes information in real time and provides information in report format on mobile devices" refers to a system that immediately analyzes information and displays the results on a mobile device, enabling users to make decisions about their actions.

[0438] This invention provides a system that enables entrepreneurs and investors to effectively formulate business strategies and make investment decisions. This system processes information based on input from each user's device and performs advanced analysis using a server-based AI model.

[0439] The server runs on an AWS EC2 instance and collects a vast amount of historical event data, storing it as a dataset. AWS S3 and DynamoDB are used as databases for storing and managing the collected data. A customized AI model utilizing Hugging Face's Transformers library is used to analyze and generate business proposals and investment evaluations.

[0440] Users input information about business plans and investments via voice or text through a mobile application built with React Native on their device. The input information is sent to a server and analyzed by a generative AI model. The analysis results are displayed in real-time on the device in a report format, which users can use to formulate strategies and make investment decisions.

[0441] As a concrete example, consider a scenario where an entrepreneur wants to open a new restaurant and inputs their plan into the app using voice. In this case, the system instantly analyzes past success stories and market trends to generate suggestions for optimal locations and marketing strategies. The user can quickly obtain the necessary information using prompts such as, "Please suggest the best strategy for my new restaurant based on data from the past 10 years."

[0442] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0443] Step 1:

[0444] Users input information about business plans and investments using their devices in either voice or text format. In the case of voice input, the device uses speech recognition technology to convert it into text data. This input data serves as the basis for subsequent analysis.

[0445] Step 2:

[0446] The terminal processes the input data and sends it to the server via the internet. The server stores the received data in a database and prepares it for analysis through a generated AI model.

[0447] Step 3:

[0448] The server uses historical event data stored in the database and current market data to initiate analysis using a generative AI model. By using user input data as prompts, the model performs data calculations to generate business proposals and investment evaluations. As a result of the analysis, optimal business strategies and investment decisions are proposed.

[0449] Step 4:

[0450] The generated proposals and evaluation results are processed in real time by the server and sent to the terminal. The terminal displays the received data in an easy-to-understand report format, allowing the user to visually confirm the information.

[0451] Step 5:

[0452] Users review the reports displayed on their devices and make adjustments to their strategies or investment decisions as needed. This feedback can then be used as further input for the next data processing cycle.

[0453] 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.

[0454] This invention relates to a generative AI system incorporating an emotion engine that recognizes user emotions, enabling it to adjust analysis results by taking into account the user's emotional state when formulating business strategies and evaluating investments. This system is intended to analyze information input by users (entrepreneurs and investors) from an emotional perspective and present optimal business plans and investment decisions.

[0455] In addition to the normal data collection process, the server collects the context and expressions used when the user inputs data. This allows the emotion engine to effectively analyze the user's emotions and detect the underlying emotions contained in the input information.

[0456] Next, as users input business plan information and investment interests into their devices, the system detects their emotional state in real time. For example, it assesses their confidence in the business plan and their concerns about risks, and then fine-tunes the strategy based on that assessment.

[0457] The generating AI processes this input information along with the results of sentiment analysis to produce even more refined business proposals. This results in the presentation of realistic and optimal strategies that take emotional aspects into account, and enables an approach that is also considerate of the user's feelings.

[0458] Furthermore, the server can utilize an emotion engine to analyze user feedback emotionally and incorporate this analysis into future strategic proposals and investment evaluations. This enhances the system's ability to adapt to users' emotional needs, resulting in more personalized support.

[0459] For example, when a user is about to enter a new market, if the emotion engine reads positive emotions from the user's content, the generative AI will suggest a risky strategy. On the other hand, if anxiety is clearly evident, it can present a more conservative plan. This approach not only increases the likelihood of business success but also improves user satisfaction.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The server automatically collects historical case data, industry data, and market trend data to build a dataset. This dataset contains the data necessary for users to develop business strategies and evaluate investments.

[0463] Step 2:

[0464] Users input information about business plans and areas of investment interest into the device using natural language. During input, the device monitors the input data in real time to perform sentiment analysis of the user's expressions and context.

[0465] Step 3:

[0466] The device analyzes user emotion data acquired in real time to identify the user's emotional state. For example, it can detect positive or negative tones from word choices.

[0467] Step 4:

[0468] The server integrates the results of sentiment analysis with the input business data into a generating AI to produce business strategies or investment proposals that take the user's emotions into account. These proposals are then tailored to the appropriate tone and content based on those emotions.

[0469] Step 5:

[0470] The server formats the generated business proposals and investment evaluation results, preparing them as visually easy-to-understand reports. These reports include explanations of the rationale behind the proposals, expected outcomes, and potential risks.

[0471] Step 6:

[0472] The device displays the generated report to the user. The displayed content is feedback tailored to the user's emotional state. The user then reviews the displayed report and makes a decision based on their own emotions and the market situation.

[0473] Step 7:

[0474] When a user provides feedback on a report, the device analyzes the sentiment of that feedback and sends it to the server to be used in future suggestions. This allows the system to evolve and provide suggestions that are more tailored to the individual user.

[0475] (Example 2)

[0476] 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."

[0477] In recent years, a problem has arisen in business strategy formulation and investment evaluation where the accuracy of proposals decreases and user satisfaction declines due to the disregard of users' emotional states. Conventional technologies are limited to qualitative evaluations based on input information and cannot adequately consider the emotional aspects of users, making it difficult to present optimal strategies for real-world decision-making.

[0478] 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.

[0479] In this invention, the server includes means for collecting past event data and storing it as a dataset, an emotion engine means for analyzing the user's emotional state from the input expression, and means for adjusting the suggestions and evaluation results generated based on the analyzed emotional state. This makes it possible to present highly accurate business strategies and investment evaluations that take the user's emotional state into consideration.

[0480] A "data set of past events" refers to a dataset that compiles information about various events and occurrences that have happened up to now.

[0481] "Means of accumulating data as a dataset" refers to methods and devices for organizing and effectively storing collected data.

[0482] "Planning information" refers to data that shows detailed plans and concepts related to businesses and investments.

[0483] "Artificial intelligence means for generating suggestions" refers to algorithms and programs that analyze input information and automatically create optimal suggestions.

[0484] "Investment evaluation" is the process of assessing an investment and quantitatively estimating its value and risks.

[0485] "Evaluation results of the subject" refers to the results and judgments obtained through the evaluation process regarding the subject related to investment or business activities.

[0486] An "emotional engine" refers to software or algorithms that detect and analyze an emotional state based on information input by the user.

[0487] "Means for adjusting proposals and evaluation results" refer to technologies and methods for appropriately modifying proposals and evaluation results generated based on the user's emotional state, and guiding them to an optimal form.

[0488] "Means of display" refers to devices or technologies that visually provide generated information, such as displays and monitors.

[0489] This system is a generative AI system that analyzes business plan and investment information entered by users through an emotion engine to provide optimal business strategies and investment evaluations.

[0490] First, as part of data collection, the server stores text data entered by users on their devices. In this process, natural language processing is used to analyze the text and extract keywords that indicate context and sentiment. The extracted data is then structured as a set of past event data and stored as a dataset.

[0491] Next, the emotion engine uses machine learning techniques to analyze the user's emotional state from their input. Leveraging specific algorithms, the emotion engine quantifies positive and negative emotions present in the input and evaluates their state. For example, if the user's input indicates anxiety, the system recognizes that emotion and assigns an emotion score.

[0492] The generative AI model generates optimal suggestions tailored to the user's needs based on this sentiment data and input data. The generated business strategies and investment evaluations are presented to the user visually. For example, the generative AI model derives suggestions based on the prompt, "Explain how to analyze a user's confidence and anxieties when they are considering entering a new market, and then provide strategic suggestions based on that."

[0493] Furthermore, the server collects user feedback and uses it to improve the emotion engine and generative AI models. This feedback loop allows the system to continuously improve the accuracy of its suggestions and present more personalized strategies.

[0494] In this way, the system provides the most effective decision-making support for the user through real-time sentiment analysis.

[0495] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0496] Step 1:

[0497] The server collects business plans and investment information entered by users on their terminals as text data. Upon receiving this text data, it uses natural language processing techniques to extract keywords and sentiment indicators. During this process, it analyzes important elements and expressions within the text and accumulates the data. The extracted data is then output as structured data, which is used in later stages for sentiment analysis and proposal generation.

[0498] Step 2:

[0499] The server passes the accumulated text data to the emotion engine, which analyzes the user's emotional state. The input is structured text data, and the emotion engine uses a machine learning algorithm to calculate an emotion score. Specifically, it assigns emotion labels such as positive and negative, and quantifies the degree of each emotion. The analysis results are output as an emotion score.

[0500] Step 3:

[0501] The device provides users with real-time feedback based on sentiment scores from an emotion engine. It receives sentiment scores as input and generates visual information, such as graphs and messages, to present them clearly to the user. This feedback influences user decision-making, enabling them to select more emotionally appropriate strategies. The output is visual feedback on the device.

[0502] Step 4:

[0503] The server combines sentiment scores and user input data to generate business strategies and investment proposals using a generative AI model. Based on the sentiment scores and text data as input, the generative AI model generates scenarios and strategies based on prompt text. This process derives multiple options and outputs highly accurate proposals.

[0504] Step 5:

[0505] The server collects user feedback and uses it to make adjustments to improve the overall accuracy of the system. The feedback input is used to update the emotion engine and generative AI model, improving the accuracy of future analyses and strategic recommendations. Specifically, the feedback is stored in a database to help improve the algorithms. The output is an improved model and more accurate analytical data.

[0506] (Application Example 2)

[0507] 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."

[0508] Traditional business proposal and investment evaluation systems relied heavily on data analysis, making it difficult to consider users' emotional states when making proposals and evaluations. As a result, proposals often failed to adequately address users' emotional needs and psychological states, ultimately reducing the likelihood of business success.

[0509] 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.

[0510] In this invention, the server includes means for collecting past event data and storing it as a dataset; generation AI means for analyzing input business plan information and generating business proposals; means for performing investment evaluations and generating evaluation results for potential investment entities; emotion analysis means for analyzing the user's emotional state in real time and adjusting the proposals based on the analysis results; and display means for displaying the generated business proposals and evaluation results. This makes it possible to adjust business proposals and investment evaluations while taking the user's emotional state into consideration.

[0511] "Past event data" refers to a collection of data on various events that have occurred to date, and is information accumulated as a dataset.

[0512] A "generative AI method" is a mechanism that uses artificial intelligence to analyze input information and generate business proposals.

[0513] An "investment evaluation tool" is a process for evaluating potential investment targets and generating the results.

[0514] "Emotional analysis means" refers to technology that analyzes a user's emotional state in real time and adjusts suggestions based on the analysis results.

[0515] A "display means" is an interface for visually presenting the generated business proposals and evaluation results to the user.

[0516] The system for implementing this invention utilizes sentiment analysis to optimize business proposals and investment evaluations for users. The server collects historical event data and stores it as a dataset. This data is used as basic information for data analysis, and data cleaning such as noise reduction is performed on it.

[0517] The server analyzes the input business plan information through a generation AI mechanism and generates business proposals. This AI mechanism has an algorithm that compares and analyzes past success stories with current market data. As a result, the generated business proposals are tailored to the current market situation.

[0518] Furthermore, the server utilizes sentiment analysis tools to analyze the user's emotional state in real time based on the information they input. For example, when a user inputs information about a business plan, the server detects their emotions based on the content and context. Based on this analysis, the generated suggestions are adjusted to match the user's emotional state.

[0519] The display means provides an interface for visually presenting the generated business proposals and evaluation results to the user. This display includes comments and coupon suggestions that take into account the user's emotional state.

[0520] For example, when a user is about to enter a new market, if positive emotions are detected, the server will suggest a more aggressive strategy. On the other hand, if anxiety is indicated, a more conservative plan will be presented. In this way, the user can obtain a strategy that matches their emotional state.

[0521] An example of a prompt for a generative AI model is, "How do you feel about the product you are about to purchase? Are you happy or anxious?"

[0522] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0523] Step 1:

[0524] The server collects historical event data and stores it as a dataset. This enables comparative analysis based on user input data. The historical data includes success stories and failures, and analyzing them forms the basis for future predictions and proposals. The input is historical data, and the output is a clean dataset.

[0525] Step 2:

[0526] The user inputs business plan information into the terminal. The terminal receives input data from the user, including details of the business plan as text data. The input is text information provided by the user, and the output is structured data for analysis.

[0527] Step 3:

[0528] The server uses a generative AI to analyze the user's business plan information and generate business proposals. Based on the input information, the server compares past success stories and market data to optimize the proposals. The input consists of structured data and historical data, and the output is the proposed business plan.

[0529] Step 4:

[0530] The server uses sentiment analysis tools to analyze the user's emotional state in real time based on their input information. This analysis employs natural language processing algorithms to determine whether the user's emotions are positive, negative, or neutral. The input is the user's text data, and the output is the sentiment analysis result.

[0531] Step 5:

[0532] The server adjusts the generated business proposals based on the results of sentiment analysis. If the user expresses positive emotions, it proposes high-risk strategies; if they express anxiety, it suggests more conservative options. The input is the generated business proposal and the sentiment analysis results, and the output is the sentiment-adjusted proposal.

[0533] Step 6:

[0534] The display method presents the final business proposal and evaluation results to the user on the terminal. The display also includes sentiment analysis feedback, giving the user an opportunity to become aware of their own emotional state. The input is the final business proposal and sentiment feedback, and the output is a visual display on the user interface.

[0535] 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.

[0536] 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.

[0537] 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.

[0538] [Fourth Embodiment]

[0539] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0540] 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.

[0541] 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).

[0542] 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.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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".

[0552] This invention is a generative AI system that supports venture companies in formulating business strategies and provides investors with information to make investment decisions. The system receives information input by users (entrepreneurs and investors), analyzes it, and generates optimal business plans and investment evaluations.

[0553] First, the server automatically collects a vast amount of historical case data, industry data, and market trend data from the internet and existing databases to build a dataset. This dataset is used as the foundational data for the generative AI to perform analysis.

[0554] Next, the user enters an outline of their business plan and investment interests into the terminal. The entered data is sent to the server and analyzed by the generating AI. On the server, the generating AI performs information analysis. Specifically, it has the function of generating an optimal strategy by comparing data of similar success stories based on the entered business plan information, and by performing market analysis and evaluation of the competitive environment.

[0555] The generated business strategy is analyzed in detail for each element and compiled into a report that includes recommended actions, expected outcomes, and potential risks. This allows users to have a realistic and actionable business plan.

[0556] Furthermore, investors will also undergo an investment evaluation process using a similar generation AI. The server evaluates the financial data and market potential of potential investment companies, and performs ROI forecasts and risk assessments. This provides investors with information to make investment decisions from multiple perspectives.

[0557] Finally, the generated business strategy and investment evaluation results are displayed to the user via a terminal. This allows the user to make necessary modifications or decide whether to proceed with the investment based on the provided information. Supporting business planning and investment decisions with unprecedented efficiency and accuracy is a specific embodiment of the present invention.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The server automatically collects historical case data, industry data, and market trend data from the internet and existing databases to build a dataset. This dataset is then cleaned to remove noise and converted into a format suitable for analysis.

[0561] Step 2:

[0562] The user inputs information about their business plan outline and investment areas of interest into the terminal. The terminal formats the input information received from the user and sends it to the server in a format necessary for analysis.

[0563] Step 3:

[0564] The server initiates the generative AI process based on the received user information. The generative AI matches business plan information with the dataset and identifies similar success stories. This generates potential business models and market strategies.

[0565] Step 4:

[0566] The server evaluates the generated business model and strategy in detail and creates a report that includes recommended actions, expected outcomes, and anticipated risks. This report is designed to help users concretize their business plan.

[0567] Step 5:

[0568] The server evaluates financial data and market potential of potential investment companies, generating possible ROI forecasts and risk assessment results. These evaluation results are then compiled to serve as a multifaceted resource for investment decisions.

[0569] Step 6:

[0570] The terminal displays the results of business preparation and investment evaluations sent from the server to the user. The user reviews the displayed information and makes adjustments to their strategy or investment decisions. They can also input feedback into the terminal again as needed.

[0571] (Example 1)

[0572] 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".

[0573] In formulating new business strategies and making investment decisions, it is difficult to effectively utilize vast amounts of historical and market data to obtain accurate and actionable proposals. Furthermore, the process of removing invalid data and extracting only useful information is crucial, but this is not sufficiently automated using conventional methods. There is also a need for efficient comparative analysis of past success stories and current market data using generating AI.

[0574] 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.

[0575] In this invention, the server includes means for collecting past event data and storing it as a dataset, means for generating AI that analyzes input business plan information and generates proposals, means for performing investment evaluations and generating evaluation results for the organization, and means for generating an optimal business strategy based on past cases and market trends. This makes it possible to efficiently extract necessary information from a vast amount of data and provide accurate and actionable business strategies and investment decisions.

[0576] "Past event data" refers to a collection of information related to various events that have occurred in the past.

[0577] A "dataset" is a collection of data that has been organized and classified for a specific purpose.

[0578] "Generative AI methods" refer to artificial intelligence systems that analyze data and generate information and suggestions tailored to specific purposes.

[0579] "Investment evaluation" is the process of analyzing the value and risks of an investment target and evaluating the results.

[0580] An "organization" is a general term for any group or entity formed with a specific purpose.

[0581] A "business strategy" is a set of action plans and guidelines formulated by a company or organization to achieve its goals.

[0582] "Display means" refers to devices or interfaces used to visually present information to the user.

[0583] This invention is a generative AI system designed to support venture companies in formulating business strategies and investors in making investment decisions. The system performs sophisticated analysis based on a large amount of data and provides users with practical suggestions.

[0584] The server automatically collects historical event data from the internet and databases to build a dataset. This provides the server with the data foundation necessary for analysis in the generative AI model. Data collection uses a common client-server model and a web crawler, and leverages scraping tools written in programming languages ​​such as Python.

[0585] Users input details of their business plans and investments into a terminal. This terminal can be a PC or mobile device, and can utilize a dedicated web interface or application. The input information is quickly transmitted to a server and analyzed by the generating AI.

[0586] The generative AI model is trained using machine learning frameworks such as TensorFlow and PyTorch, and compares and analyzes past success stories with current market data in generating business strategies. As a result of the analysis, optimal strategies and investment evaluations are generated that take into account past success patterns of similar businesses, the latest market trends, and the competitive environment.

[0587] The device displays this generated information to the user as a report. The report indicates recommended actions, expected outcomes, and potential risks, helping the user to develop a feasible plan.

[0588] For example, if a user is considering entering the market with a new e-commerce platform, the system will perform a detailed market analysis based on the prompt "Please tell me the optimal business strategy for entering the market with a new e-commerce platform," and then provide the user with a visual strategic proposal.

[0589] This system allows users to benefit from accurate and rapid information processing, enabling them to make efficient decisions.

[0590] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0591] Step 1:

[0592] The server collects historical event data from the internet and existing databases to build a dataset. This process uses crawling tools and APIs to collect data, cleans the collected data to remove noise, and generates a consistent dataset. The input is raw data from web pages and databases, and the output is a dataset prepared in a format suitable for analysis.

[0593] Step 2:

[0594] Users input business plan outlines and investment interests using a terminal. Input can be done via a web browser or a dedicated application, and the entered information is transmitted to the server via a secure protocol. The input consists of text data including the user's intentions and requirements, while the output is data received by the server for analysis.

[0595] Step 3:

[0596] The server feeds the received user data into a generating AI model and begins analysis. Specifically, it uses natural language processing and machine learning algorithms to compare and analyze the input business information with past success stories and market data. The input in this step is business plan data from the user, and the output is a draft of the generated business strategy and investment evaluation.

[0597] Step 4:

[0598] The server compiles the business strategies and investment evaluations generated by the AI ​​model into a detailed report. The report includes recommended actions, expected outcomes, and risk analysis. The input is the analysis results, and the output is a completed report document for presentation to the user.

[0599] Step 5:

[0600] The terminal displays reports received from the server to the user. The user visually reviews this information on the terminal and makes decisions on how to incorporate it into their business plan. At this stage, the input is a detailed report, and the output is a visual output of strategic proposals provided to the user.

[0601] (Application Example 1)

[0602] 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".

[0603] In today's business environment, entrepreneurs and investors need to make quick and accurate decisions regarding optimal business strategies and investments. However, extracting useful data from vast amounts of information and analyzing it rapidly is not easy, and there is a particular need to efficiently obtain information while on the go or using various devices.

[0604] 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.

[0605] In this invention, the server includes means for collecting and storing past event data sets as a dataset, generation AI means for analyzing input business plan information and generating business proposals, means for performing investment evaluations and generating evaluation results for potential investment entities, input means for receiving and analyzing voice or text input and presenting realistic and actionable strategies, and output means for analyzing information in real time and providing information in report format on mobile devices. This enables entrepreneurs and investors to efficiently obtain information for business strategies and investment decisions regardless of location.

[0606] "Past events data" refers to a collection of information records of past events and phenomena related to business and markets.

[0607] "Means of accumulating as a dataset" refers to a system or process that stores collected information in a specific format and makes it easily accessible for analysis and use.

[0608] "Generative AI methods" refer to processes that use artificial intelligence to analyze data based on input information and automatically generate new business proposals and evaluations.

[0609] "Means for receiving and analyzing voice or text input" refers to a system and process in which a user provides information as voice or text, which is then analyzed to derive useful results.

[0610] "An output method that analyzes information in real time and provides information in report format on mobile devices" refers to a system that immediately analyzes information and displays the results on a mobile device, enabling users to make decisions about their actions.

[0611] This invention provides a system that enables entrepreneurs and investors to effectively formulate business strategies and make investment decisions. This system processes information based on input from each user's device and performs advanced analysis using a server-based AI model.

[0612] The server runs on an AWS EC2 instance and collects a vast amount of historical event data, storing it as a dataset. AWS S3 and DynamoDB are used as databases for storing and managing the collected data. A customized AI model utilizing Hugging Face's Transformers library is used to analyze and generate business proposals and investment evaluations.

[0613] Users input information about business plans and investments via voice or text through a mobile application built with React Native on their device. The input information is sent to a server and analyzed by a generative AI model. The analysis results are displayed in real-time on the device in a report format, which users can use to formulate strategies and make investment decisions.

[0614] As a concrete example, consider a scenario where an entrepreneur wants to open a new restaurant and inputs their plan into the app using voice. In this case, the system instantly analyzes past success stories and market trends to generate suggestions for optimal locations and marketing strategies. The user can quickly obtain the necessary information using prompts such as, "Please suggest the best strategy for my new restaurant based on data from the past 10 years."

[0615] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0616] Step 1:

[0617] Users input information about business plans and investments using their devices in either voice or text format. In the case of voice input, the device uses speech recognition technology to convert it into text data. This input data serves as the basis for subsequent analysis.

[0618] Step 2:

[0619] The terminal processes the input data and sends it to the server via the internet. The server stores the received data in a database and prepares it for analysis through a generated AI model.

[0620] Step 3:

[0621] The server uses historical event data stored in the database and current market data to initiate analysis using a generative AI model. By using user input data as prompts, the model performs data calculations to generate business proposals and investment evaluations. As a result of the analysis, optimal business strategies and investment decisions are proposed.

[0622] Step 4:

[0623] The generated proposals and evaluation results are processed in real time by the server and sent to the terminal. The terminal displays the received data in an easy-to-understand report format, allowing the user to visually confirm the information.

[0624] Step 5:

[0625] Users review the reports displayed on their devices and make adjustments to their strategies or investment decisions as needed. This feedback can then be used as further input for the next data processing cycle.

[0626] 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.

[0627] This invention relates to a generative AI system incorporating an emotion engine that recognizes user emotions, enabling it to adjust analysis results by taking into account the user's emotional state when formulating business strategies and evaluating investments. This system is intended to analyze information input by users (entrepreneurs and investors) from an emotional perspective and present optimal business plans and investment decisions.

[0628] In addition to the normal data collection process, the server collects the context and expressions used when the user inputs data. This allows the emotion engine to effectively analyze the user's emotions and detect the underlying emotions contained in the input information.

[0629] Next, as users input business plan information and investment interests into their devices, the system detects their emotional state in real time. For example, it assesses their confidence in the business plan and their concerns about risks, and then fine-tunes the strategy based on that assessment.

[0630] The generating AI processes this input information along with the results of sentiment analysis to produce even more refined business proposals. This results in the presentation of realistic and optimal strategies that take emotional aspects into account, and enables an approach that is also considerate of the user's feelings.

[0631] Furthermore, the server can utilize an emotion engine to analyze user feedback emotionally and incorporate this analysis into future strategic proposals and investment evaluations. This enhances the system's ability to adapt to users' emotional needs, resulting in more personalized support.

[0632] For example, when a user is about to enter a new market, if the emotion engine reads positive emotions from the user's content, the generative AI will suggest a risky strategy. On the other hand, if anxiety is clearly evident, it can present a more conservative plan. This approach not only increases the likelihood of business success but also improves user satisfaction.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The server automatically collects historical case data, industry data, and market trend data to build a dataset. This dataset contains the data necessary for users to develop business strategies and evaluate investments.

[0636] Step 2:

[0637] Users input information about business plans and areas of investment interest into the device using natural language. During input, the device monitors the input data in real time to perform sentiment analysis of the user's expressions and context.

[0638] Step 3:

[0639] The device analyzes user emotion data acquired in real time to identify the user's emotional state. For example, it can detect positive or negative tones from word choices.

[0640] Step 4:

[0641] The server integrates the results of sentiment analysis with the input business data into a generating AI to produce business strategies or investment proposals that take the user's emotions into account. These proposals are then tailored to the appropriate tone and content based on those emotions.

[0642] Step 5:

[0643] The server formats the generated business proposals and investment evaluation results, preparing them as visually easy-to-understand reports. These reports include explanations of the rationale behind the proposals, expected outcomes, and potential risks.

[0644] Step 6:

[0645] The device displays the generated report to the user. The displayed content is feedback tailored to the user's emotional state. The user then reviews the displayed report and makes a decision based on their own emotions and the market situation.

[0646] Step 7:

[0647] When a user provides feedback on a report, the device analyzes the sentiment of that feedback and sends it to the server to be used in future suggestions. This allows the system to evolve and provide suggestions that are more tailored to the individual user.

[0648] (Example 2)

[0649] 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".

[0650] In recent years, a problem has arisen in business strategy formulation and investment evaluation where the accuracy of proposals decreases and user satisfaction declines due to the disregard of users' emotional states. Conventional technologies are limited to qualitative evaluations based on input information and cannot adequately consider the emotional aspects of users, making it difficult to present optimal strategies for real-world decision-making.

[0651] 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.

[0652] In this invention, the server includes means for collecting past event data and storing it as a dataset, an emotion engine means for analyzing the user's emotional state from the input expression, and means for adjusting the suggestions and evaluation results generated based on the analyzed emotional state. This makes it possible to present highly accurate business strategies and investment evaluations that take the user's emotional state into consideration.

[0653] A "data set of past events" refers to a dataset that compiles information about various events and occurrences that have happened up to now.

[0654] "Means of accumulating data as a dataset" refers to methods and devices for organizing and effectively storing collected data.

[0655] "Planning information" refers to data that shows detailed plans and concepts related to businesses and investments.

[0656] "Artificial intelligence means for generating suggestions" refers to algorithms and programs that analyze input information and automatically create optimal suggestions.

[0657] "Investment evaluation" is the process of assessing an investment and quantitatively estimating its value and risks.

[0658] "Evaluation results of the subject" refers to the results and judgments obtained through the evaluation process regarding the subject related to investment or business activities.

[0659] An "emotional engine" refers to software or algorithms that detect and analyze an emotional state based on information input by the user.

[0660] "Means for adjusting proposals and evaluation results" refer to technologies and methods for appropriately modifying proposals and evaluation results generated based on the user's emotional state, and guiding them to an optimal form.

[0661] "Means of display" refers to devices or technologies that visually provide generated information, such as displays and monitors.

[0662] This system is a generative AI system that analyzes business plan and investment information entered by users through an emotion engine to provide optimal business strategies and investment evaluations.

[0663] First, as part of data collection, the server stores text data entered by users on their devices. In this process, natural language processing is used to analyze the text and extract keywords that indicate context and sentiment. The extracted data is then structured as a set of past event data and stored as a dataset.

[0664] Next, the emotion engine uses machine learning techniques to analyze the user's emotional state from their input. Leveraging specific algorithms, the emotion engine quantifies positive and negative emotions present in the input and evaluates their state. For example, if the user's input indicates anxiety, the system recognizes that emotion and assigns an emotion score.

[0665] The generative AI model generates optimal suggestions tailored to the user's needs based on this sentiment data and input data. The generated business strategies and investment evaluations are presented to the user visually. For example, the generative AI model derives suggestions based on the prompt, "Explain how to analyze a user's confidence and anxieties when they are considering entering a new market, and then provide strategic suggestions based on that."

[0666] Furthermore, the server collects user feedback and uses it to improve the emotion engine and generative AI models. This feedback loop allows the system to continuously improve the accuracy of its suggestions and present more personalized strategies.

[0667] In this way, the system provides the most effective decision-making support for the user through real-time sentiment analysis.

[0668] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0669] Step 1:

[0670] The server collects business plans and investment information entered by users on their terminals as text data. Upon receiving this text data, it uses natural language processing techniques to extract keywords and sentiment indicators. During this process, it analyzes important elements and expressions within the text and accumulates the data. The extracted data is then output as structured data, which is used in later stages for sentiment analysis and proposal generation.

[0671] Step 2:

[0672] The server passes the accumulated text data to the emotion engine, which analyzes the user's emotional state. The input is structured text data, and the emotion engine uses a machine learning algorithm to calculate an emotion score. Specifically, it assigns emotion labels such as positive and negative, and quantifies the degree of each emotion. The analysis results are output as an emotion score.

[0673] Step 3:

[0674] The device provides users with real-time feedback based on sentiment scores from an emotion engine. It receives sentiment scores as input and generates visual information, such as graphs and messages, to present them clearly to the user. This feedback influences user decision-making, enabling them to select more emotionally appropriate strategies. The output is visual feedback on the device.

[0675] Step 4:

[0676] The server combines sentiment scores and user input data to generate business strategies and investment proposals using a generative AI model. Based on the sentiment scores and text data as input, the generative AI model generates scenarios and strategies based on prompt text. This process derives multiple options and outputs highly accurate proposals.

[0677] Step 5:

[0678] The server collects user feedback and uses it to make adjustments to improve the overall accuracy of the system. The feedback input is used to update the emotion engine and generative AI model, improving the accuracy of future analyses and strategic recommendations. Specifically, the feedback is stored in a database to help improve the algorithms. The output is an improved model and more accurate analytical data.

[0679] (Application Example 2)

[0680] 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".

[0681] Traditional business proposal and investment evaluation systems relied heavily on data analysis, making it difficult to consider users' emotional states when making proposals and evaluations. As a result, proposals often failed to adequately address users' emotional needs and psychological states, ultimately reducing the likelihood of business success.

[0682] 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.

[0683] In this invention, the server includes means for collecting past event data and storing it as a dataset; generation AI means for analyzing input business plan information and generating business proposals; means for performing investment evaluations and generating evaluation results for potential investment entities; emotion analysis means for analyzing the user's emotional state in real time and adjusting the proposals based on the analysis results; and display means for displaying the generated business proposals and evaluation results. This makes it possible to adjust business proposals and investment evaluations while taking the user's emotional state into consideration.

[0684] "Past event data" refers to a collection of data on various events that have occurred to date, and is information accumulated as a dataset.

[0685] A "generative AI method" is a mechanism that uses artificial intelligence to analyze input information and generate business proposals.

[0686] An "investment evaluation tool" is a process for evaluating potential investment targets and generating the results.

[0687] "Emotional analysis means" refers to technology that analyzes a user's emotional state in real time and adjusts suggestions based on the analysis results.

[0688] A "display means" is an interface for visually presenting the generated business proposals and evaluation results to the user.

[0689] The system for implementing this invention utilizes sentiment analysis to optimize business proposals and investment evaluations for users. The server collects historical event data and stores it as a dataset. This data is used as basic information for data analysis, and data cleaning such as noise reduction is performed on it.

[0690] The server analyzes the input business plan information through a generation AI mechanism and generates business proposals. This AI mechanism has an algorithm that compares and analyzes past success stories with current market data. As a result, the generated business proposals are tailored to the current market situation.

[0691] Furthermore, the server utilizes sentiment analysis tools to analyze the user's emotional state in real time based on the information they input. For example, when a user inputs information about a business plan, the server detects their emotions based on the content and context. Based on this analysis, the generated suggestions are adjusted to match the user's emotional state.

[0692] The display means provides an interface for visually presenting the generated business proposals and evaluation results to the user. This display includes comments and coupon suggestions that take into account the user's emotional state.

[0693] For example, when a user is about to enter a new market, if positive emotions are detected, the server will suggest a more aggressive strategy. On the other hand, if anxiety is indicated, a more conservative plan will be presented. In this way, the user can obtain a strategy that matches their emotional state.

[0694] An example of a prompt for a generative AI model is, "How do you feel about the product you are about to purchase? Are you happy or anxious?"

[0695] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0696] Step 1:

[0697] The server collects historical event data and stores it as a dataset. This enables comparative analysis based on user input data. The historical data includes success stories and failures, and analyzing them forms the basis for future predictions and proposals. The input is historical data, and the output is a clean dataset.

[0698] Step 2:

[0699] The user inputs business plan information into the terminal. The terminal receives input data from the user, including details of the business plan as text data. The input is text information provided by the user, and the output is structured data for analysis.

[0700] Step 3:

[0701] The server uses a generative AI to analyze the user's business plan information and generate business proposals. Based on the input information, the server compares past success stories and market data to optimize the proposals. The input consists of structured data and historical data, and the output is the proposed business plan.

[0702] Step 4:

[0703] The server uses sentiment analysis tools to analyze the user's emotional state in real time based on their input information. This analysis employs natural language processing algorithms to determine whether the user's emotions are positive, negative, or neutral. The input is the user's text data, and the output is the sentiment analysis result.

[0704] Step 5:

[0705] The server adjusts the generated business proposals based on the results of sentiment analysis. If the user expresses positive emotions, it proposes high-risk strategies; if they express anxiety, it suggests more conservative options. The input is the generated business proposal and the sentiment analysis results, and the output is the sentiment-adjusted proposal.

[0706] Step 6:

[0707] The display method presents the final business proposal and evaluation results to the user on the terminal. The display also includes sentiment analysis feedback, giving the user an opportunity to become aware of their own emotional state. The input is the final business proposal and sentiment feedback, and the output is a visual display on the user interface.

[0708] 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.

[0709] 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.

[0710] 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 robot 414.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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."

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] The following is further disclosed regarding the embodiments described above.

[0730] (Claim 1)

[0731] A means of collecting past event data and accumulating it as a dataset,

[0732] A generation AI means that analyzes input business plan information and generates business proposals,

[0733] A means for conducting investment evaluations and generating evaluation results for potential investment entities,

[0734] A display means for displaying the generated business proposal and evaluation results,

[0735] A system that includes this.

[0736] (Claim 2)

[0737] The system according to claim 1, comprising steps for cleaning the collected data and removing noise.

[0738] (Claim 3)

[0739] The system according to claim 1, wherein the generating AI means includes an algorithm for comparing and analyzing past success data with current market data.

[0740] "Example 1"

[0741] (Claim 1)

[0742] A means of collecting past event data and accumulating it as a dataset,

[0743] A generation AI means that analyzes input business plan information and generates proposals,

[0744] A means for conducting investment evaluations and generating evaluation results for potential investment organizations,

[0745] A means of generating the optimal business strategy based on past cases and market trends,

[0746] A display means for displaying the generated proposals and evaluation results,

[0747] A system that includes this.

[0748] (Claim 2)

[0749] The system according to claim 1, comprising a procedure for scrutinizing collected data and deleting invalid data.

[0750] (Claim 3)

[0751] The system according to claim 1, wherein the generating AI means includes an algorithm for comparing and analyzing past success story data with current market data.

[0752] "Application Example 1"

[0753] (Claim 1)

[0754] A means of collecting past event data and accumulating it as a dataset,

[0755] A generation AI means that analyzes input business plan information and generates business proposals,

[0756] A means for conducting investment evaluations and generating evaluation results for potential investment entities,

[0757] A display means for displaying the generated business proposal and evaluation results,

[0758] An input method that accepts voice or text input, performs analysis, and presents a realistic and feasible strategy,

[0759] An output means that analyzes information in real time and provides the information in report format on a mobile device,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, comprising steps for cleaning the collected data and removing noise.

[0763] (Claim 3)

[0764] The system according to claim 1, wherein the generating AI means includes an algorithm for comparing and analyzing past success data with current market data.

[0765] "Example 2 of combining an emotion engine"

[0766] (Claim 1)

[0767] A means of collecting past event data and accumulating it as a dataset,

[0768] An artificial intelligence means that analyzes input planning information and generates proposals,

[0769] A means for conducting investment evaluations and generating evaluation results for candidate targets,

[0770] An emotion engine that analyzes the user's emotional state from the expressions used during input,

[0771] Means for adjusting the suggestions and evaluation results generated based on the analyzed emotional state,

[0772] Means for displaying the generated proposals and adjusted evaluation results,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] The system according to claim 1, comprising steps for cleaning the collected data and removing noise.

[0776] (Claim 3)

[0777] The system according to claim 1, wherein the artificial intelligence means includes an algorithm for comparing and analyzing past success data with current market data.

[0778] "Application example 2 when combining with an emotional engine"

[0779] (Claim 1)

[0780] A means of collecting past event data and accumulating it as a dataset,

[0781] A generation AI means that analyzes input business plan information and generates business proposals,

[0782] A means for conducting investment evaluations and generating evaluation results for potential investment entities,

[0783] A sentiment analysis tool that analyzes the user's emotional state in real time and adjusts suggestions based on the analysis results,

[0784] A display means for displaying the generated business proposal and evaluation results,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, comprising steps for cleaning the collected data and removing noise.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein the generating AI means includes an algorithm for comparing and analyzing past success data with current market data. [Explanation of Symbols]

[0790] 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 collecting past event data and accumulating it as a dataset, A generation AI means that analyzes input business plan information and generates business proposals, A means for conducting investment evaluations and generating evaluation results for potential investment entities, A display means for displaying the generated business proposal and evaluation results, An input method that accepts voice or text input, performs analysis, and presents a realistic and feasible strategy, An output means that analyzes information in real time and provides the information in report format on a mobile device, A system that includes this.

2. The system according to claim 1, comprising steps for cleaning the collected data and removing noise.

3. The system according to claim 1, wherein the generating AI means includes an algorithm for comparing and analyzing past success case data with current market data.

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