system

Abstract

Provide a system. 【Solution means】 Means for collecting information, Means for preprocessing the collected information, Means for generating a business plan using a data model based on the preprocessed information, Means for creating business materials based on the generated business plan, Means for automatically verifying the created business materials and providing improvement information, Means for accepting input of business ideas, analyzing data, and extracting relevant information, Means for aggregating market information and competitive information and presenting a business model including proposals, Means for automatically creating presentation materials based on the above proposals, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When starting a new business, there is a problem that it takes a great deal of time and effort to create a business plan and prepare the accompanying presentation materials. Furthermore, since a process for evaluating the quality of these materials and making corrections requires specialized knowledge and advanced review techniques, it places a great psychological burden on entrepreneurs. Also, since the adoption rate of a proposal is greatly influenced by the quality of a business plan, there is a need for technical means to quickly generate accurate and attractive materials.

Means for Solving the Problems

[0005] This invention provides a means for automatically collecting and preprocessing information, and then using a generative model to generate a new business plan based on that information. Furthermore, by including a means for creating presentation materials based on the generated business plan, the system automates the creation of presentation materials. In addition, it is possible to improve the quality of these materials through means for reviewing them and providing feedback to the user. In this way, the system reduces the burden faced by entrepreneurs and realizes a system that enables the efficient and effective creation of business plans and presentation materials.

[0006] "Information" refers to data and knowledge collected for use by a system, and forms the basis of new business plans.

[0007] "Information acquisition means" refers to a technical device or process for automatically acquiring information, incorporating it into a system, and making it available for use.

[0008] "Preprocessing" refers to a series of steps that involve analyzing, formatting, and converting collected information into a format that can be used by generative models.

[0009] A "generative model" is a machine learning algorithm that generates new data or ideas based on previously learned data.

[0010] A "business plan" is a document that includes details such as the objectives, strategy, market analysis, and financial plan of a new business, and serves as a guide for business activities.

[0011] "Presentation materials" refer to slides or documents created to convey information in a visual or verbal format.

[0012] A "review method" is a technical method or process for evaluating the content of generated materials and pointing out areas for improvement or shortcomings.

[0013] "Feedback" refers to information or comments that communicate evaluation results to the user and provide guidance for correcting or improving the material. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, 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 system developed to streamline the automatic generation of new business plans and the creation of presentation materials. In the embodiment, the user, server, and terminal collaborate to smoothly execute processes from information gathering to document creation and review.

[0036] First, users input the business theme and related initial information through their terminal. This input information is sent to the server as the basis for the business plan. The server has means to collect necessary external information from the web and internal databases, and automatically performs large-scale data collection.

[0037] Next, the server formats the collected information and preprocesses the data using natural language processing techniques. This transforms the information into a format usable by the generative model. The generative model is trained on a dataset of past success stories and common business plans, and the server uses this model to generate new business plans.

[0038] Based on the generated business plan, the server provides tools for automatically creating presentation materials. Specifically, it automatically arranges slides based on templates and organizes key points to emphasize within the document. The completed presentation materials are displayed to the user in real time via their terminal, allowing them to review the content and make adjustments as needed.

[0039] Subsequently, the server runs an automated review function on the created document, analyzing its content and providing feedback indicating areas for improvement and additional information. This review function objectively evaluates the quality of the document.

[0040] As a concrete example, consider a scenario where a user develops a business plan for "sustainable energy solutions." The user inputs relevant keywords and ideas into a terminal, and the server gathers the latest market data and success stories related to environmental technologies based on this input. The generative model utilizes this information to generate a business plan proposing a new business model using renewable energy. Subsequently, the server automatically generates persuasive presentation materials for investors based on the proposed plan and presents them to the user via the terminal.

[0041] This system helps users create high-quality business plans and presentation materials in a short period of time, streamlining the startup process.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters the business theme and initial information into the terminal. This information includes keywords and ideas related to the business, as well as data on the target market. This information is sent to the server.

[0045] Step 2:

[0046] The server collects relevant data based on the information it receives. It obtains necessary external information through web scraping and access to internal databases. The collected data is used to generate business plans.

[0047] Step 3:

[0048] The server preprocesses the acquired data. Specifically, it performs tokenization and text cleaning, and formats the information using natural language processing techniques. The preprocessed data is then used as input to a generative model.

[0049] Step 4:

[0050] The server inputs pre-processed data into an AI generative model. This generative model generates an optimal business plan based on its knowledge base. The generated plan is saved on the server as the initial draft of the business plan.

[0051] Step 5:

[0052] The server automatically creates presentation materials based on the generated business plan. It generates the materials while considering visual aspects such as slide layout and highlighting of key points.

[0053] Step 6:

[0054] The terminal displays the completed presentation materials to the user in real time. The user can review the content of the materials and make any necessary adjustments.

[0055] Step 7:

[0056] The server activates an automated review function to quantitatively and qualitatively evaluate the content of the presentation materials. The algorithm scrutinizes the materials and suggests areas for improvement and additional information to the user.

[0057] Step 8:

[0058] Users revise documents via their devices based on feedback provided by the server. The finalized documents are saved and used in preparation for presentations and proposals.

[0059] (Example 1)

[0060] 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."

[0061] In today's business environment, creating new business plans and compelling presentation materials to effectively communicate those plans are essential. However, these processes are time-consuming and labor-intensive, requiring specialized knowledge and experience to achieve high-quality results. Furthermore, the inefficiency of gathering necessary information and reviewing relevant materials remains a challenge.

[0062] 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.

[0063] In this invention, the server includes means for collecting information, means for preprocessing the collected information, and means for generating a plan using a generative model. This makes it possible to efficiently and automatically perform the entire process from information collection to planning using a generative model, document creation, and automated review and feedback of the documents.

[0064] "Means of collecting information" refers to functions for collecting relevant information from external data sources or internal databases.

[0065] "Preprocessing methods" refer to functions that reshape collected raw data and convert it into a format that can be used for analysis and generative models.

[0066] A "generative model" is an algorithm that learns from past data and is used to automatically generate new business plans.

[0067] "Means for creating documents" refers to a function that automatically generates visual documents based on the generated business plan.

[0068] "Means for automatically reviewing and providing feedback" refers to a function that analyzes the content of created documents and indicates areas for improvement or information that should be added.

[0069] "Means for making adjustments" refers to functions that allow users to review generated materials and make changes or additions as needed.

[0070] "Referencing successful case studies" refers to a function that improves the quality and success rate of new plans by referring to data from past successful business plans.

[0071] This system aims to automatically generate new business plans and presentation materials. The implementation of this invention involves collaboration between the user, server, and terminal. First, the user uses a terminal to input business themes and related initial information. This data is then sent from the terminal to the server. The server collects external and internal company data using web scraping and database search techniques. The collected information is preprocessed using natural language processing libraries (e.g., NLTK and spaCy) with programming languages ​​such as Python and Java (registered trademark).

[0072] This preprocessing converts the information into a format usable by the generative AI model. The server leverages the generative AI model, trained on past success stories and business plan datasets, to generate innovative business plans. Based on the generated plans, presentation materials are automatically constructed using PowerPoint templates. This step utilizes APIs such as the Microsoft Office API.

[0073] The terminal displays the generated presentation materials to the user in real time, and the user can review and adjust the materials through a GUI-based user interface. The created materials are automatically reviewed by the server, and feedback is provided to objectively evaluate the quality of the materials. Grammar checks and consistency analysis functions are based on machine learning algorithms.

[0074] As a concrete example, when developing a business plan for "sustainable energy solutions," users can input keywords such as "sustainable energy," "renewable resources," and "environmental technology." The server then collects relevant market data and generates a business plan proposing a new business model utilizing renewable energy. Furthermore, presentation materials suitable for investors are automatically created, which the user can review. This system supports the efficient creation of business plans.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users input business themes and related initial information using a terminal. This includes business objectives, target markets, and competitor information. The entered data is sent to the server. The information entered here forms the basis of the new business plan.

[0078] Step 2:

[0079] Based on the received data, the server collects information using web scraping techniques and internal database searches. This process acquires a large amount of data, including relevant market data and industry trends. As output, a wide variety of information is stored on the server in an unprocessed state.

[0080] Step 3:

[0081] The server preprocesses the collected raw data using natural language processing techniques. This stage involves text cleansing, extraction of important keywords, and text summarization using Python. The input is the unprocessed data collected in the previous step, and the output is data in a format suitable for use in a generative AI model.

[0082] Step 4:

[0083] The server feeds pre-processed data into a generating AI model. This model has already learned from past success stories and business data, and generates new business plans. The input is formatted data, and the output is a draft of a concrete new business plan.

[0084] Step 5:

[0085] The server automatically generates presentation materials based on the generated business plan. Here, Microsoft Office APIs are used to construct the slides and design visually appealing materials. The input is the business plan, and the output is a high-quality presentation document.

[0086] Step 6:

[0087] The terminal displays the completed presentation materials to the user in real time. The user can review the materials and make adjustments or additions as needed using the GUI. The input is the automatically generated presentation material, and the output is the final version adjusted by the user.

[0088] Step 7:

[0089] The server performs an automated review function on the completed document. It evaluates grammar, content consistency, and structural clarity, and provides feedback. The input is the finalized presentation material, and the output is specific improvement suggestions and feedback.

[0090] (Application Example 1)

[0091] 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."

[0092] Developing new business plans and creating effective presentation materials are tasks that require a tremendous amount of time and effort. Furthermore, in today's rapidly changing business environment, building business models that adapt quickly and accurately to the market is difficult. In particular, there is a lack of means to efficiently collect and analyze large amounts of information and then make persuasive proposals. This invention aims to provide a system to solve these problems.

[0093] 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.

[0094] In this invention, the server includes means for collecting information, means for pre-processing the collected information, means for generating a business plan using a data model based on the pre-processed information, means for creating commercial materials based on the generated business plan, means for automatically verifying the created commercial materials and providing improvement information, means for receiving input of business ideas, analyzing data, and extracting relevant information, means for aggregating market information and competitor information and presenting a business model including proposals, and means for automatically creating exhibition materials based on the above proposals. This makes it possible to quickly and efficiently generate high-quality business plans and presentation materials, and to smoothly advance proposals for new businesses.

[0095] "Means of collecting information" refers to functions for systematically collecting user input and related external data.

[0096] "Means of pre-processing collected information" refers to the process of preparing collected information into a format that can be analyzed.

[0097] A "data model" is a set of algorithms that have been trained to rationally generate business plans from information.

[0098] "Means of creating commercial materials" refers to the function of constructing visually clear and easy-to-understand materials based on the generated business plan.

[0099] "Means for automatically verifying and providing improvement information" refers to a process that objectively evaluates the created materials and indicates necessary improvements or additional information.

[0100] A "means for receiving business idea input" refers to an interface for users to input the basic concept or theme of a business they are considering.

[0101] "Methods for analyzing data and extracting relevant information" refers to the process of deriving relevant market and technological information based on the input business idea.

[0102] "A means of aggregating market and competitor information and presenting business models, including proposals" refers to a function that comprehensively analyzes collected data and proposes new business possibilities.

[0103] "Methods for automatically creating exhibition materials" refer to a process that visualizes generated proposals and automatically compiles them into a format suitable for presentations.

[0104] This invention provides a system that streamlines the generation of new business plans and the creation of presentation materials. This system automatically collects, analyzes, and creates materials based on input from a user's terminal. Specifically, the user inputs basic ideas or themes related to the business into the terminal. The input information is sent to the server, which then initiates the process of collecting information based on it.

[0105] The server receives data through a frontend developed using React Native and processes it in a backend built with Flask. The collected information is analyzed by AI models using libraries such as TENSORFLOW® and PyTorch to generate business plans. Natural language processing techniques are used to format the information and convert it into a form suitable for business-related data models.

[0106] Based on the generated business plan, the server automatically creates commercial materials. In this process, the materials are generated in LaTeX or PPTX format and provided in a visualized form as reports or presentations. Furthermore, the generated materials are automatically reviewed, and feedback is provided on areas for improvement. This evaluation aims to increase the objectivity and effectiveness of the materials.

[0107] For example, if a user proposes an "environmentally friendly payment solution," the server can instantly analyze relevant market data and technology trends through its configured program and build a new business model. The generated business plan is simultaneously formatted as appropriate presentation material. Below is a specific prompt for the generating AI model: "Generate a business plan for a new payment technology market based on the following information: Target market: Young people, Competitors: General companies, Features: Biometric authentication technology."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user inputs a basic business idea or theme through a terminal. The input information is sent from the terminal to the server. At this stage, text input data is used. Based on the business idea input, the user selects relevant keywords.

[0111] Step 2:

[0112] The server initiates the information gathering process based on the received data. Market data, technology trends, and competitor information are acquired via external APIs. This information is stored as primary data within the server. This process automatically collects and stores necessary information via the internet.

[0113] Step 3:

[0114] The server preprocesses the collected information. Using natural language processing techniques, it analyzes the data, removing unnecessary data while extracting the information necessary for the business plan. This data processing includes text cleaning and keyword extraction. As a result, preprocessed data is generated.

[0115] Step 4:

[0116] The server generates business plans using an AI model based on pre-processed data. Scenario planning and marketing strategies are then constructed using the data model. Pre-processed data is the input, and the output is a structured business plan.

[0117] Step 5:

[0118] The server automatically generates commercial materials based on the generated business plan. Using templates, it presents the key points of the business plan in slide and document formats. This process converts the plan created by the AI ​​model into visual materials.

[0119] Step 6:

[0120] The server automatically validates commercial materials and generates feedback. This feedback includes suggestions for improvement and additional information. Based on the input commercial materials, improvement suggestions are provided as output.

[0121] Step 7:

[0122] Users fine-tune commercial materials using editing tools provided on their devices. They receive feedback from the server and perform final checks. This results in the completed commercial material.

[0123] 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.

[0124] This invention is a system that integrates information gathering, preprocessing, business plan generation using AI generative models, presentation material creation, automated review, and an emotion engine for recognizing user emotions. The aim of this system is to efficiently support the design and proposal of new businesses.

[0125] First, users input the business theme and initial relevant information through their terminals. This information is sent to the server and used as foundational data for effective business planning. Based on the collected information, the server retrieves relevant external data from the web and internal databases, accumulating knowledge by utilizing a wealth of information sources.

[0126] Next, the server preprocesses the acquired information. By utilizing natural language processing techniques, the data is organized and formatted, and converted into a form that can be used by the AI ​​generative model. The preprocessed data is input into the generative model, which then generates a business plan based on past success stories and existing business plans.

[0127] Next, the server automatically creates presentation materials based on the generated business plan. Slide generation based on templates and the arrangement of visual layouts are automated. The created materials are presented to the user via their terminal, allowing them to review the content in real time and make corrections as needed.

[0128] Furthermore, the server activates an automated review function to review the content of the document and provide feedback on areas for improvement and shortcomings. This helps to improve the quality of the document.

[0129] This is where the emotion engine plays a crucial role. The device recognizes the user's emotions through their facial expressions and tone of voice and sends that data to the server. The server can then analyze the user's feedback through the emotion engine and use it to improve the materials. For example, if a user expresses concern or dissatisfaction, the emotion engine identifies the contributing factors and suggests revisions to the materials. It also adjusts the interface and guides based on the user's emotional state to assist the user.

[0130] For example, when a user is developing a business plan regarding the sustainability of a new product, the emotion engine recognizes that the user has a positive reaction to "environmentally friendly" options, and the server recommends presentation materials that highlight relevant information based on that. In this way, the system can understand the complex information the user is faced with and provide appropriate advice.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] Users input new business themes, related keywords, and initial data through their terminals. This provides the server with the information that forms the basis of the business plan.

[0134] Step 2:

[0135] The server collects relevant information from external websites and internal databases based on the input data. This collection process is automated using APIs and web scraping techniques. The collected information is stored in a database.

[0136] Step 3:

[0137] The server preprocesses the collected data. Specifically, this involves text cleaning, extraction of important keywords, and data organization using natural language processing. The data prepared in this preprocessing step is then supplied to the AI ​​generative model.

[0138] Step 4:

[0139] The server inputs pre-processed data into an AI generative model. Based on the trained dataset, the generative model generates a new business plan. The generated plan is recorded in the database as an initial business plan.

[0140] Step 5:

[0141] The server creates presentation materials based on the generated business plan. Following the appropriate template, visually organized materials, including slide structure and graph placement, are automatically generated.

[0142] Step 6:

[0143] The terminal displays the generated presentation materials to the user in real time. The user can review the content of the materials and make adjustments as needed.

[0144] Step 7:

[0145] The server runs an automated review function to evaluate presentation materials. It analyzes the materials in detail, generates feedback with necessary corrections and improvement ideas, and provides them to the user.

[0146] Step 8:

[0147] The emotion engine built into the device analyzes the user's facial expressions and tone of voice to collect emotional data. This emotional data is sent to a server, providing clues to understanding the user's response.

[0148] Step 9:

[0149] The server evaluates which parts of the material are influencing the user based on user sentiment data obtained from the sentiment engine. Based on the identified points, it suggests adjustments to the material.

[0150] Step 10:

[0151] Users receive feedback provided by the emotion engine and finalize the document on their device. The finalized document is saved for use in proposals and presentations and can be printed or shared electronically as needed.

[0152] (Example 2)

[0153] 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".

[0154] Traditional business planning systems often handled information gathering, plan generation, document creation, and evaluation processes separately, resulting in decreased overall work efficiency. Furthermore, it was difficult to immediately reflect users' emotions and reactions, making it challenging to create flexible plans and documents that reflected user intentions.

[0155] 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.

[0156] In this invention, the server includes means having a function for collecting information, means having a function for preprocessing the collected information, and means utilizing a generative model that generates a plan based on the preprocessed information. This makes it possible to perform the entire process from information collection to plan generation, document creation, and feedback provision in an integrated manner, enabling the immediate reflection of the user's feelings and the provision of optimized business plans and documents.

[0157] "Means that have the function of collecting information" refers to a mechanism for gathering relevant information from user input or external databases.

[0158] "Means having the function of pre-processing collected information" refers to technologies that organize and process acquired information using natural language processing techniques, etc., and convert it into a form suitable for subsequent processing.

[0159] "Using generative models to generate plans" refers to the process of creating business plans using AI models, based on past success stories and data.

[0160] "Means of creating materials" refers to a function that automatically creates presentation materials based on the generated plan, providing visually organized slides.

[0161] "Methods for automatically reviewing documents and providing feedback" refers to a process that analyzes generated documents and automatically provides feedback on areas for improvement and shortcomings in their content.

[0162] An "emotion engine that recognizes emotions and uses feedback to improve materials" is a technology that senses the user's facial expressions and tone of voice, evaluates their emotional state, and uses that feedback to improve materials and optimize the user interface.

[0163] This invention is an integrated system for efficiently creating and proposing business plans. Specifically, it incorporates technologies that automate information gathering, preprocessing, plan generation, document creation, and review. Furthermore, it includes an emotion engine that recognizes user emotions and uses them to improve the documents.

[0164] The system is structured as follows: First, users input business themes and related information via a terminal. This information is entered using an internet-connected terminal device and transmitted to the server in real time. The server has the function of collecting information, and strengthens the information base by obtaining additional data from external databases and web APIs.

[0165] The server then preprocesses the collected information. The primary software technology used here is natural language processing (NLP), through which the information is organized and formatted into a predetermined format.

[0166] The processed information is passed to a generating AI model. The server can then use this AI model to automatically generate a business plan based on historical data. The generated plan includes business objectives, strategies, and risk assessments.

[0167] Based on the generated plan, the server automatically creates presentation materials. The materials are provided in slide format with a visual layout based on a template. This completed material is presented to the user via a terminal, allowing the user to review the content and make corrections as needed.

[0168] Furthermore, the server automatically reviews the materials and provides feedback on areas for improvement. In this process, the emotion engine analyzes emotional data based on the user's facial expressions and tone of voice captured by the terminal, which can then be used to improve the materials. Based on what the emotion engine recognizes, the system makes adjustments to the user interface to improve user convenience.

[0169] For example, when a user is developing a sustainability business plan for a new product, the emotion engine can capture a positive response to "environmentally friendly" options. Based on this data, the server creates materials that emphasize that element. An example of a prompt might be, "Please create a business plan for a new product that takes sustainability into consideration. I would especially like to emphasize environmentally friendly options."

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user uses a terminal to input themes and initial information related to a new business. This input information serves as the basis for creating a business plan. The terminal packets this information and sends it to the server via a secure communication path.

[0173] Step 2:

[0174] The server collects relevant information from external databases and web APIs based on the user information it receives. During this process, the server generates queries and retrieves the necessary data from the databases based on those queries. The collected data is stored as additional foundational data.

[0175] Step 3:

[0176] The server preprocesses the collected data. This process utilizes natural language processing techniques to normalize, classify, and summarize the text data. As a result of the preprocessing, the information is converted into a format usable by generative AI models, and only the most relevant parts are extracted.

[0177] Step 4:

[0178] The server inputs pre-processed information into a generating AI model to create a business plan. The AI ​​model automatically creates the plan based on prompts and historical data. The generated business plan includes detailed information such as business goals, strategies, and anticipated challenges.

[0179] Step 5:

[0180] The server automatically creates presentation materials based on the generated business plan. It applies templates and generates visually consistent slides. This provides an easy-to-understand output for the user, and the materials are sent to the terminal.

[0181] Step 6:

[0182] The terminal provides an environment where users can view the presented presentation materials and provide real-time feedback. If a user makes corrections to the materials, those corrections are sent from the terminal to the server.

[0183] Step 7:

[0184] The server activates an automated review function to analyze the content of the presentation materials, identify areas for improvement, and provide feedback. Advice on what needs to be corrected and strengthened is generated and presented to the user.

[0185] Step 8:

[0186] The device recognizes the user's emotions from their facial expressions and voice and sends this information to the server. The server utilizes an emotion engine to analyze the user's feedback. Specifically, the emotion data is used to improve the materials, enabling the server to suggest the most suitable materials that match the user's intentions and feelings. Through this process, the interface is also adjusted based on user feedback.

[0187] (Application Example 2)

[0188] 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 device 14 will be referred to as the "terminal."

[0189] In today's world, the process of planning and proposing new businesses is fraught with challenges, as it requires extensive information gathering and document creation, making it difficult to conduct efficiently. Furthermore, proposals that do not utilize users' past activity data fail to provide information tailored to their needs. A system is needed to address these challenges and efficiently and effectively support the design and proposal of new businesses.

[0190] 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.

[0191] In this invention, the server includes means for collecting information, means for preprocessing the collected information, and means for utilizing a generative model that generates a business plan based on the preprocessed information. This enables efficient aggregation and processing of information. Furthermore, it includes means for recognizing the user's emotions and adjusting the system's output based on that data, and means for analyzing the user's past activity data and providing optimized suggestions, thereby realizing the provision of optimal information and suggestions to the user.

[0192] "Means of collecting information" refers to the process of gathering necessary data from initial information provided by the user and from external databases.

[0193] "Preprocessing" refers to the process of organizing collected information using natural language processing techniques or similar methods in order to convert it into a format suitable for use in generative models.

[0194] "Methods of using generative models to generate business plans" refers to technologies that utilize AI technology and pre-processed information to generate new business plans based on past success stories and other factors.

[0195] "Methods for creating presentation materials" refers to the process of automatically generating visual materials for presentations based on the generated business plan.

[0196] "Means of reviewing and providing feedback" refers to an automated diagnostic process that evaluates the content of created materials and identifies areas for improvement.

[0197] "Means of recognizing emotions" refers to technologies that analyze a user's facial expressions and voice to identify their emotional state.

[0198] "A means of analyzing past activity data and providing optimized suggestions" refers to a process that analyzes a user's past data and automatically provides suggestions that are best suited to the user's interests and needs.

[0199] This invention is a system that effectively collects various types of information and generates an optimal business plan for the user. This system operates through the collaborative efforts of a server and a terminal. The user inputs the business theme and related information via their terminal. This information is sent to the server, which collects additional information from external databases and the web to build a comprehensive dataset.

[0200] The server then performs preprocessing using natural language processing techniques. In this process, the Python programming language and its libraries, NLTK and spaCy, are used to organize and format the unstructured data, converting it into a form that can be easily processed as input by the generative AI model. In addition, data on the user's past activity is also collected and analyzed by the generative model.

[0201] The generative model is implemented using machine learning frameworks such as TensorFlow and PyTorch, and automatically generates a business plan optimized for the user. The server then creates presentation materials based on this, arranging them into templated slides. These materials are sent to the user's device, where the user can view them in real time and provide feedback as needed.

[0202] Furthermore, the device's built-in camera and microphone are used to analyze the user's facial expressions and voice, and the emotion engine sends this data to the server. OpenCV and Google® Cloud Speech-to-Text API are used to analyze emotions, and the server adjusts the presentation content based on the results. The content of the presentation is optimized according to the user's reaction to the materials.

[0203] Let's look at a concrete example. For instance, when a user starts a new restaurant business plan, the system can generate a "promotion that will pique their interest in recipes using local ingredients" based on the user's preferences and past payment history. An example of an input prompt for the AI ​​model during this generation process would be: "Generate the most suitable promotional offer based on the user's recent payment history. The user particularly likes to pay for restaurants."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user's terminal inputs the business theme and initial related information, and sends this information to the server. Since the transmitted information is used as foundational data for the business plan, the input data is structured and processed on the server in JSON format.

[0207] Step 2:

[0208] Based on the information received, the server collects additional information from external databases and the internet. Using web crawling technology, it automatically retrieves highly relevant data and stores it in its internal database. The collected data is then expanded using diverse sources.

[0209] Step 3:

[0210] The server applies natural language processing techniques to the collected data for preprocessing. Using the Python NLTK library, the information is tokenized and stop words are removed, converting it into a format usable by generative AI models. This process also includes data classification and importance analysis, thereby refining the data.

[0211] Step 4:

[0212] The server inputs pre-processed data into a generative AI model built with TensorFlow or PyTorch. The model automatically generates a business plan based on the input data, constructing a concrete business plan by referencing past success stories and market conditions. This output is generated as a business plan in text format.

[0213] Step 5:

[0214] The server uses the generated business plan to create a template-based presentation. The presentation is automatically structured in slide format and laid out in a visually easy-to-understand manner. The materials include images and graphs, and incorporate elements in various formats.

[0215] Step 6:

[0216] The user's device receives and displays the presentation materials in real time. Users can review the materials on their device and provide feedback as needed. The materials are displayed through an intuitive and user-friendly interface.

[0217] Step 7:

[0218] The device uses its camera and microphone to analyze the user's facial expressions and voice, and sends the data to an emotion engine. The device utilizes OpenCV and the Google Cloud Speech-to-Text API to recognize and analyze the user's emotions. Based on this, the user's reaction to specific elements is measured.

[0219] Step 8:

[0220] The server analyzes emotional data and adjusts the content of the presentation materials and business plan. Based on the results from the emotional engine, it generates emphasis on elements of the materials and additional suggestions, and then sends the optimized information back to the user's terminal.

[0221] 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.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention is a system developed to streamline the automatic generation of new business plans and the creation of presentation materials. In the embodiment, the user, server, and terminal collaborate to smoothly execute processes from information gathering to document creation and review.

[0238] First, users input the business theme and related initial information through their terminal. This input information is sent to the server as the basis for the business plan. The server has means to collect necessary external information from the web and internal databases, and automatically performs large-scale data collection.

[0239] Next, the server formats the collected information and preprocesses the data using natural language processing techniques. This transforms the information into a format usable by the generative model. The generative model is trained on a dataset of past success stories and common business plans, and the server uses this model to generate new business plans.

[0240] Based on the generated business plan, the server provides tools for automatically creating presentation materials. Specifically, it automatically arranges slides based on templates and organizes key points to emphasize within the document. The completed presentation materials are displayed to the user in real time via their terminal, allowing them to review the content and make adjustments as needed.

[0241] Subsequently, the server runs an automated review function on the created document, analyzing its content and providing feedback indicating areas for improvement and additional information. This review function objectively evaluates the quality of the document.

[0242] As a concrete example, consider a scenario where a user develops a business plan for "sustainable energy solutions." The user inputs relevant keywords and ideas into a terminal, and the server gathers the latest market data and success stories related to environmental technologies based on this input. The generative model utilizes this information to generate a business plan proposing a new business model using renewable energy. Subsequently, the server automatically generates persuasive presentation materials for investors based on the proposed plan and presents them to the user via the terminal.

[0243] This system helps users create high-quality business plans and presentation materials in a short period of time, streamlining the startup process.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user enters the business theme and initial information into the terminal. This information includes keywords and ideas related to the business, as well as data on the target market. This information is sent to the server.

[0247] Step 2:

[0248] The server collects relevant data based on the information it receives. It obtains necessary external information through web scraping and access to internal databases. The collected data is used to generate business plans.

[0249] Step 3:

[0250] The server preprocesses the acquired data. Specifically, it performs tokenization and text cleaning, and formats the information using natural language processing techniques. The preprocessed data is then used as input to a generative model.

[0251] Step 4:

[0252] The server inputs pre-processed data into an AI generative model. This generative model generates an optimal business plan based on its knowledge base. The generated plan is saved on the server as the initial draft of the business plan.

[0253] Step 5:

[0254] The server automatically creates presentation materials based on the generated business plan. It generates the materials while considering visual aspects such as slide layout and highlighting of key points.

[0255] Step 6:

[0256] The terminal displays the completed presentation materials to the user in real time. The user can review the content of the materials and make any necessary adjustments.

[0257] Step 7:

[0258] The server activates an automated review function to quantitatively and qualitatively evaluate the content of the presentation materials. The algorithm scrutinizes the materials and suggests areas for improvement and additional information to the user.

[0259] Step 8:

[0260] Users revise documents via their devices based on feedback provided by the server. The finalized documents are saved and used in preparation for presentations and proposals.

[0261] (Example 1)

[0262] 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".

[0263] In today's business environment, creating new business plans and compelling presentation materials to effectively communicate those plans are essential. However, these processes are time-consuming and labor-intensive, requiring specialized knowledge and experience to achieve high-quality results. Furthermore, the inefficiency of gathering necessary information and reviewing relevant materials remains a challenge.

[0264] 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.

[0265] In this invention, the server includes means for collecting information, means for preprocessing the collected information, and means for generating a plan using a generative model. This makes it possible to efficiently and automatically perform the entire process from information collection to planning using a generative model, document creation, and automated review and feedback of the documents.

[0266] "Means of collecting information" refers to functions for collecting relevant information from external data sources or internal databases.

[0267] "Preprocessing methods" refer to functions that reshape collected raw data and convert it into a format that can be used for analysis and generative models.

[0268] A "generative model" is an algorithm that learns from past data and is used to automatically generate new business plans.

[0269] "Means for creating documents" refers to a function that automatically generates visual documents based on the generated business plan.

[0270] "Means for automatically reviewing and providing feedback" refers to a function that analyzes the content of created documents and indicates areas for improvement or information that should be added.

[0271] "Means for making adjustments" refers to functions that allow users to review generated materials and make changes or additions as needed.

[0272] "Referencing successful case studies" refers to a function that improves the quality and success rate of new plans by referring to data from past successful business plans.

[0273] This system aims to automatically generate new business plans and presentation materials. The implementation of this invention involves collaboration between the user, server, and terminal. First, the user uses a terminal to input business themes and related initial information. This data is then sent from the terminal to the server. The server collects external and internal company data using web scraping and database search techniques. The collected information is preprocessed using natural language processing libraries (e.g., NLTK and spaCy) with programming languages ​​such as Python and Java.

[0274] This preprocessing converts the information into a format usable by the generative AI model. The server leverages a generative AI model, trained on past success stories and business plan datasets, to generate innovative business plans. Based on the generated plans, presentation materials are automatically constructed using PowerPoint templates. This step utilizes Microsoft Office APIs, among others.

[0275] The terminal displays the generated presentation materials to the user in real time, and the user can review and adjust the materials through a GUI-based user interface. The created materials are automatically reviewed by the server, and feedback is provided to objectively evaluate the quality of the materials. Grammar checks and consistency analysis functions are based on machine learning algorithms.

[0276] As a concrete example, when developing a business plan for "sustainable energy solutions," users can input keywords such as "sustainable energy," "renewable resources," and "environmental technology." The server then collects relevant market data and generates a business plan proposing a new business model utilizing renewable energy. Furthermore, presentation materials suitable for investors are automatically created, which the user can review. This system supports the efficient creation of business plans.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] Users input business themes and related initial information using a terminal. This includes business objectives, target markets, and competitor information. The entered data is sent to the server. The information entered here forms the basis of the new business plan.

[0280] Step 2:

[0281] Based on the received data, the server collects information using web scraping technology and in-house database searches. In this process, a large amount of data including relevant market data and industry trends is obtained. As output, a variety of information is stored on the server in an untreated state.

[0282] Step 3:

[0283] The server preprocesses the collected raw data using natural language processing technology. At this stage, text cleansing, extraction of important keywords, text summarization, etc. are performed using Python. The input is the untreated data collected in the previous step, and the output is data in a format suitable for use in the generative AI model.

[0284] Step 4:

[0285] The server feeds the preprocessed data into the generative AI model. This model has learned from past success stories and business data and generates a new business plan. The input is the formatted data, and the output is a draft of a specific new business plan.

[0286] Step 5:

[0287] The server automatically creates presentation materials based on the generated business plan. Here, the Microsoft Office API is used to compose the slides and design visually appealing materials. The input is the business plan, and the output is high-quality presentation materials.

[0288] Step 6:

[0289] The terminal displays the completed presentation materials to the user in real time. The user can view the materials and make adjustments or additions as needed using the GUI. The input is the automatically created presentation materials, and the output is the final materials adjusted by the user.

[0290] Step 7:

[0291] The server performs an automated review function on the completed document. It evaluates grammar, content consistency, and structural clarity, and provides feedback. The input is the finalized presentation material, and the output is specific improvement suggestions and feedback.

[0292] (Application Example 1)

[0293] 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."

[0294] Developing new business plans and creating effective presentation materials are tasks that require a tremendous amount of time and effort. Furthermore, in today's rapidly changing business environment, building business models that adapt quickly and accurately to the market is difficult. In particular, there is a lack of means to efficiently collect and analyze large amounts of information and then make persuasive proposals. This invention aims to provide a system to solve these problems.

[0295] 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.

[0296] In this invention, the server includes means for collecting information, means for pre-processing the collected information, means for generating a business plan using a data model based on the pre-processed information, means for creating commercial materials based on the generated business plan, means for automatically verifying the created commercial materials and providing improvement information, means for receiving input of business ideas, analyzing data, and extracting relevant information, means for aggregating market information and competitor information and presenting a business model including proposals, and means for automatically creating exhibition materials based on the above proposals. This makes it possible to quickly and efficiently generate high-quality business plans and presentation materials, and to smoothly advance proposals for new businesses.

[0297] "Means of collecting information" refers to functions for systematically collecting user input and related external data.

[0298] "Means of pre-processing collected information" refers to the process of preparing collected information into a format that can be analyzed.

[0299] A "data model" is a set of algorithms that have been trained to rationally generate business plans from information.

[0300] "Means of creating commercial materials" refers to the function of constructing visually clear and easy-to-understand materials based on the generated business plan.

[0301] "Means for automatically verifying and providing improvement information" refers to a process that objectively evaluates the created materials and indicates necessary improvements or additional information.

[0302] A "means for receiving business idea input" refers to an interface for users to input the basic concept or theme of a business they are considering.

[0303] "Methods for analyzing data and extracting relevant information" refers to the process of deriving relevant market and technological information based on the input business idea.

[0304] "A means of aggregating market and competitor information and presenting business models, including proposals" refers to a function that comprehensively analyzes collected data and proposes new business possibilities.

[0305] "Methods for automatically creating exhibition materials" refer to a process that visualizes generated proposals and automatically compiles them into a format suitable for presentations.

[0306] The present invention provides a system for efficiently generating a new business plan and creating presentation materials. Through input from a user's terminal, the server automatically collects, analyzes, and creates materials. Specifically, the user inputs basic ideas or themes related to the business into the terminal. The input information is sent to the server, which then starts a process of collecting information based on this.

[0307] The server receives data through a front - end developed using React Native and processes it in a back - end constructed with Flask. The collected information is analyzed by an AI model using libraries such as TensorFlow or PyTorch, and a business plan is generated. For information handling, natural language processing technology is utilized to format the information and convert it into a form suitable for a business - related data model.

[0308] Based on the generated business plan, the server automatically creates commercial materials. In this process, the materials are generated in LaTeX or PPTX format and provided in a visualized form as reports or presentations. Also, the created materials are automatically verified, and points for improvement are fed back. This evaluation is for enhancing the objectivity and effectiveness of the materials.

[0309] <小 As a specific example, when the user proposes "environment - friendly payment solutions", the server can immediately analyze relevant market data and technology trends through the configured program and construct a new business model. The generated business plan is simultaneously formalized as appropriate presentation materials. The following shows a specific prompt sentence for the generation AI model: "Based on the following information, generate a business plan for the new payment technology market. Market target: young people, Competitors: general enterprises, Feature: biometric authentication technology"

[0310] The flow of a specific process in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The user inputs a basic business idea or theme through a terminal. The input information is sent from the terminal to the server. At this stage, text input data is used. Based on the business idea input, the user selects relevant keywords.

[0313] Step 2:

[0314] The server initiates the information gathering process based on the received data. Market data, technology trends, and competitor information are acquired via external APIs. This information is stored as primary data within the server. This process automatically collects and stores necessary information via the internet.

[0315] Step 3:

[0316] The server preprocesses the collected information. Using natural language processing techniques, it analyzes the data, removing unnecessary data while extracting the information necessary for the business plan. This data processing includes text cleaning and keyword extraction. As a result, preprocessed data is generated.

[0317] Step 4:

[0318] The server generates business plans using an AI model based on pre-processed data. Scenario planning and marketing strategies are then constructed using the data model. Pre-processed data is the input, and the output is a structured business plan.

[0319] Step 5:

[0320] The server automatically generates commercial materials based on the generated business plan. Using templates, it presents the key points of the business plan in slide and document formats. This process converts the plan created by the AI ​​model into visual materials.

[0321] Step 6:

[0322] The server automatically validates commercial materials and generates feedback. This feedback includes suggestions for improvement and additional information. Based on the input commercial materials, improvement suggestions are provided as output.

[0323] Step 7:

[0324] Users fine-tune commercial materials using editing tools provided on their devices. They receive feedback from the server and perform final checks. This results in the completed commercial material.

[0325] 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.

[0326] This invention is a system that integrates information gathering, preprocessing, business plan generation using AI generative models, presentation material creation, automated review, and an emotion engine for recognizing user emotions. The aim of this system is to efficiently support the design and proposal of new businesses.

[0327] First, users input the business theme and initial relevant information through their terminals. This information is sent to the server and used as foundational data for effective business planning. Based on the collected information, the server retrieves relevant external data from the web and internal databases, accumulating knowledge by utilizing a wealth of information sources.

[0328] Next, the server preprocesses the acquired information. By utilizing natural language processing techniques, the data is organized and formatted, and converted into a form that can be used by the AI ​​generative model. The preprocessed data is input into the generative model, which then generates a business plan based on past success stories and existing business plans.

[0329] Next, the server automatically creates presentation materials based on the generated business plan. Slide generation based on templates and the arrangement of visual layouts are automated. The created materials are presented to the user via their terminal, allowing them to review the content in real time and make corrections as needed.

[0330] Furthermore, the server activates an automated review function to review the content of the document and provide feedback on areas for improvement and shortcomings. This helps to improve the quality of the document.

[0331] This is where the emotion engine plays a crucial role. The device recognizes the user's emotions through their facial expressions and tone of voice and sends that data to the server. The server can then analyze the user's feedback through the emotion engine and use it to improve the materials. For example, if a user expresses concern or dissatisfaction, the emotion engine identifies the contributing factors and suggests revisions to the materials. It also adjusts the interface and guides based on the user's emotional state to assist the user.

[0332] For example, when a user is developing a business plan regarding the sustainability of a new product, the emotion engine recognizes that the user has a positive reaction to "environmentally friendly" options, and the server recommends presentation materials that highlight relevant information based on that. In this way, the system can understand the complex information the user is faced with and provide appropriate advice.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] Users input new business themes, related keywords, and initial data through their terminals. This provides the server with the information that forms the basis of the business plan.

[0336] Step 2:

[0337] The server collects relevant information from external websites and internal databases based on the input data. This collection process is automated using APIs and web scraping techniques. The collected information is stored in a database.

[0338] Step 3:

[0339] The server preprocesses the collected data. Specifically, this involves text cleaning, extraction of important keywords, and data organization using natural language processing. The data prepared in this preprocessing step is then supplied to the AI ​​generative model.

[0340] Step 4:

[0341] The server inputs pre-processed data into an AI generative model. Based on the trained dataset, the generative model generates a new business plan. The generated plan is recorded in the database as an initial business plan.

[0342] Step 5:

[0343] The server creates presentation materials based on the generated business plan. Following the appropriate template, visually organized materials, including slide structure and graph placement, are automatically generated.

[0344] Step 6:

[0345] The terminal displays the generated presentation materials to the user in real time. The user can review the content of the materials and make adjustments as needed.

[0346] Step 7:

[0347] The server runs an automated review function to evaluate presentation materials. It analyzes the materials in detail, generates feedback with necessary corrections and improvement ideas, and provides them to the user.

[0348] Step 8:

[0349] The emotion engine built into the device analyzes the user's facial expressions and tone of voice to collect emotional data. This emotional data is sent to a server, providing clues to understanding the user's response.

[0350] Step 9:

[0351] The server evaluates which parts of the material are influencing the user based on user sentiment data obtained from the sentiment engine. Based on the identified points, it suggests adjustments to the material.

[0352] Step 10:

[0353] Users receive feedback provided by the emotion engine and finalize the document on their device. The finalized document is saved for use in proposals and presentations and can be printed or shared electronically as needed.

[0354] (Example 2)

[0355] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0356] Traditional business planning systems often handled information gathering, plan generation, document creation, and evaluation processes separately, resulting in decreased overall work efficiency. Furthermore, it was difficult to immediately reflect users' emotions and reactions, making it challenging to create flexible plans and documents that reflected user intentions.

[0357] 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.

[0358] In this invention, the server includes means having a function for collecting information, means having a function for preprocessing the collected information, and means utilizing a generative model that generates a plan based on the preprocessed information. This makes it possible to perform the entire process from information collection to plan generation, document creation, and feedback provision in an integrated manner, enabling the immediate reflection of the user's feelings and the provision of optimized business plans and documents.

[0359] "Means that have the function of collecting information" refers to a mechanism for gathering relevant information from user input or external databases.

[0360] "Means having the function of pre-processing collected information" refers to technologies that organize and process acquired information using natural language processing techniques, etc., and convert it into a form suitable for subsequent processing.

[0361] "Using generative models to generate plans" refers to the process of creating business plans using AI models, based on past success stories and data.

[0362] "Means of creating materials" refers to a function that automatically creates presentation materials based on the generated plan, providing visually organized slides.

[0363] "Methods for automatically reviewing documents and providing feedback" refers to a process that analyzes generated documents and automatically provides feedback on areas for improvement and shortcomings in their content.

[0364] An "emotion engine that recognizes emotions and uses feedback to improve materials" is a technology that senses the user's facial expressions and tone of voice, evaluates their emotional state, and uses that feedback to improve materials and optimize the user interface.

[0365] This invention is an integrated system for efficiently creating and proposing business plans. Specifically, it incorporates technologies that automate information gathering, preprocessing, plan generation, document creation, and review. Furthermore, it includes an emotion engine that recognizes user emotions and uses them to improve the documents.

[0366] The system is structured as follows: First, users input business themes and related information via a terminal. This information is entered using an internet-connected terminal device and transmitted to the server in real time. The server has the function of collecting information, and strengthens the information base by obtaining additional data from external databases and web APIs.

[0367] The server then preprocesses the collected information. The primary software technology used here is natural language processing (NLP), through which the information is organized and formatted into a predetermined format.

[0368] The processed information is passed to a generating AI model. The server can then use this AI model to automatically generate a business plan based on historical data. The generated plan includes business objectives, strategies, and risk assessments.

[0369] Based on the generated plan, the server automatically creates presentation materials. The materials are provided in slide format with a visual layout based on a template. This completed material is presented to the user via a terminal, allowing the user to review the content and make corrections as needed.

[0370] Furthermore, the server automatically reviews the materials and provides feedback on areas for improvement. In this process, the emotion engine analyzes emotional data based on the user's facial expressions and tone of voice captured by the terminal, which can then be used to improve the materials. Based on what the emotion engine recognizes, the system makes adjustments to the user interface to improve user convenience.

[0371] For example, when a user is developing a sustainability business plan for a new product, the emotion engine can capture a positive response to "environmentally friendly" options. Based on this data, the server creates materials that emphasize that element. An example of a prompt might be, "Please create a business plan for a new product that takes sustainability into consideration. I would especially like to emphasize environmentally friendly options."

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user uses a terminal to input themes and initial information related to a new business. This input information serves as the basis for creating a business plan. The terminal packets this information and sends it to the server via a secure communication path.

[0375] Step 2:

[0376] The server collects relevant information from external databases and web APIs based on the user information it receives. During this process, the server generates queries and retrieves the necessary data from the databases based on those queries. The collected data is stored as additional foundational data.

[0377] Step 3:

[0378] The server preprocesses the collected data. This process utilizes natural language processing techniques to normalize, classify, and summarize the text data. As a result of the preprocessing, the information is converted into a format usable by generative AI models, and only the most relevant parts are extracted.

[0379] Step 4:

[0380] The server inputs pre-processed information into a generating AI model to create a business plan. The AI ​​model automatically creates the plan based on prompts and historical data. The generated business plan includes detailed information such as business goals, strategies, and anticipated challenges.

[0381] Step 5:

[0382] The server automatically creates presentation materials based on the generated business plan. It applies templates and generates visually consistent slides. This provides an easy-to-understand output for the user, and the materials are sent to the terminal.

[0383] Step 6:

[0384] The terminal provides an environment where users can view the presented presentation materials and provide real-time feedback. If a user makes corrections to the materials, those corrections are sent from the terminal to the server.

[0385] Step 7:

[0386] The server activates an automated review function to analyze the content of the presentation materials, identify areas for improvement, and provide feedback. Advice on what needs to be corrected and strengthened is generated and presented to the user.

[0387] Step 8:

[0388] The device recognizes the user's emotions from their facial expressions and voice and sends this information to the server. The server utilizes an emotion engine to analyze the user's feedback. Specifically, the emotion data is used to improve the materials, enabling the server to suggest the most suitable materials that match the user's intentions and feelings. Through this process, the interface is also adjusted based on user feedback.

[0389] (Application Example 2)

[0390] 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."

[0391] In today's world, the process of planning and proposing new businesses is fraught with challenges, as it requires extensive information gathering and document creation, making it difficult to conduct efficiently. Furthermore, proposals that do not utilize users' past activity data fail to provide information tailored to their needs. A system is needed to address these challenges and efficiently and effectively support the design and proposal of new businesses.

[0392] 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.

[0393] In this invention, the server includes means for collecting information, means for preprocessing the collected information, and means for utilizing a generative model that generates a business plan based on the preprocessed information. This enables efficient aggregation and processing of information. Furthermore, it includes means for recognizing the user's emotions and adjusting the system's output based on that data, and means for analyzing the user's past activity data and providing optimized suggestions, thereby realizing the provision of optimal information and suggestions to the user.

[0394] "Means of collecting information" refers to the process of gathering necessary data from initial information provided by the user and from external databases.

[0395] "Preprocessing" refers to the process of organizing collected information using natural language processing techniques or similar methods in order to convert it into a format suitable for use in generative models.

[0396] "Methods of using generative models to generate business plans" refers to technologies that utilize AI technology and pre-processed information to generate new business plans based on past success stories and other factors.

[0397] "Methods for creating presentation materials" refers to the process of automatically generating visual materials for presentations based on the generated business plan.

[0398] "Means of reviewing and providing feedback" refers to an automated diagnostic process that evaluates the content of created materials and identifies areas for improvement.

[0399] "Means of recognizing emotions" refers to technologies that analyze a user's facial expressions and voice to identify their emotional state.

[0400] "A means of analyzing past activity data and providing optimized suggestions" refers to a process that analyzes a user's past data and automatically provides suggestions that are best suited to the user's interests and needs.

[0401] This invention is a system that effectively collects various types of information and generates an optimal business plan for the user. This system operates through the collaborative efforts of a server and a terminal. The user inputs the business theme and related information via their terminal. This information is sent to the server, which collects additional information from external databases and the web to build a comprehensive dataset.

[0402] The server then performs preprocessing using natural language processing techniques. In this process, the Python programming language and its libraries, NLTK and spaCy, are used to organize and format the unstructured data, converting it into a form that can be easily processed as input by the generative AI model. In addition, data on the user's past activity is also collected and analyzed by the generative model.

[0403] The generative model is implemented using machine learning frameworks such as TensorFlow and PyTorch, and automatically generates a business plan optimized for the user. The server then creates presentation materials based on this, arranging them into templated slides. These materials are sent to the user's device, where the user can view them in real time and provide feedback as needed.

[0404] Furthermore, the device's built-in camera and microphone are used to analyze the user's facial expressions and voice, and the emotion engine sends this data to the server. OpenCV and the Google Cloud Speech-to-Text API are used to analyze emotions, and the server adjusts the presentation content based on the results. The content of the proposal is optimized according to the user's reactions to the materials.

[0405] Let's look at a concrete example. For instance, when a user starts a new restaurant business plan, the system can generate a "promotion that will pique their interest in recipes using local ingredients" based on the user's preferences and past payment history. An example of an input prompt for the AI ​​model during this generation process would be: "Generate the most suitable promotional offer based on the user's recent payment history. The user particularly likes to pay for restaurants."

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The user's terminal inputs the business theme and initial related information, and sends this information to the server. Since the transmitted information is used as foundational data for the business plan, the input data is structured and processed on the server in JSON format.

[0409] Step 2:

[0410] Based on the information received, the server collects additional information from external databases and the internet. Using web crawling technology, it automatically retrieves highly relevant data and stores it in its internal database. The collected data is then expanded using diverse sources.

[0411] Step 3:

[0412] The server applies natural language processing techniques to the collected data for preprocessing. Using the Python NLTK library, the information is tokenized and stop words are removed, converting it into a format usable by generative AI models. This process also includes data classification and importance analysis, thereby refining the data.

[0413] Step 4:

[0414] The server inputs pre-processed data into a generative AI model built with TensorFlow or PyTorch. The model automatically generates a business plan based on the input data, constructing a concrete business plan by referencing past success stories and market conditions. This output is generated as a business plan in text format.

[0415] Step 5:

[0416] The server uses the generated business plan to create a template-based presentation. The presentation is automatically structured in slide format and laid out in a visually easy-to-understand manner. The materials include images and graphs, and incorporate elements in various formats.

[0417] Step 6:

[0418] The user's device receives and displays the presentation materials in real time. Users can review the materials on their device and provide feedback as needed. The materials are displayed through an intuitive and user-friendly interface.

[0419] Step 7:

[0420] The device uses its camera and microphone to analyze the user's facial expressions and voice, and sends the data to an emotion engine. The device utilizes OpenCV and the Google Cloud Speech-to-Text API to recognize and analyze the user's emotions. Based on this, the user's reaction to specific elements is measured.

[0421] Step 8:

[0422] The server analyzes emotional data and adjusts the content of the presentation materials and business plan. Based on the results from the emotional engine, it generates emphasis on elements of the materials and additional suggestions, and then sends the optimized information back to the user's terminal.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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".

[0439] This invention is a system developed to streamline the automatic generation of new business plans and the creation of presentation materials. In the embodiment, the user, server, and terminal collaborate to smoothly execute processes from information gathering to document creation and review.

[0440] First, users input the business theme and related initial information through their terminal. This input information is sent to the server as the basis for the business plan. The server has means to collect necessary external information from the web and internal databases, and automatically performs large-scale data collection.

[0441] Next, the server formats the collected information and preprocesses the data using natural language processing techniques. This transforms the information into a format usable by the generative model. The generative model is trained on a dataset of past success stories and common business plans, and the server uses this model to generate new business plans.

[0442] Based on the generated business plan, the server provides tools for automatically creating presentation materials. Specifically, it automatically arranges slides based on templates and organizes key points to emphasize within the document. The completed presentation materials are displayed to the user in real time via their terminal, allowing them to review the content and make adjustments as needed.

[0443] Subsequently, the server runs an automated review function on the created document, analyzing its content and providing feedback indicating areas for improvement and additional information. This review function objectively evaluates the quality of the document.

[0444] As a concrete example, consider a scenario where a user develops a business plan for "sustainable energy solutions." The user inputs relevant keywords and ideas into a terminal, and the server gathers the latest market data and success stories related to environmental technologies based on this input. The generative model utilizes this information to generate a business plan proposing a new business model using renewable energy. Subsequently, the server automatically generates persuasive presentation materials for investors based on the proposed plan and presents them to the user via the terminal.

[0445] This system helps users create high-quality business plans and presentation materials in a short period of time, streamlining the startup process.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] The user enters the business theme and initial information into the terminal. This information includes keywords and ideas related to the business, as well as data on the target market. This information is sent to the server.

[0449] Step 2:

[0450] The server collects relevant data based on the information it receives. It obtains necessary external information through web scraping and access to internal databases. The collected data is used to generate business plans.

[0451] Step 3:

[0452] The server preprocesses the acquired data. Specifically, it performs tokenization and text cleaning, and formats the information using natural language processing techniques. The preprocessed data is then used as input to a generative model.

[0453] Step 4:

[0454] The server inputs pre-processed data into an AI generative model. This generative model generates an optimal business plan based on its knowledge base. The generated plan is saved on the server as the initial draft of the business plan.

[0455] Step 5:

[0456] The server automatically creates presentation materials based on the generated business plan. It generates the materials while considering visual aspects such as slide layout and highlighting of key points.

[0457] Step 6:

[0458] The terminal displays the completed presentation materials to the user in real time. The user can review the content of the materials and make any necessary adjustments.

[0459] Step 7:

[0460] The server activates an automated review function to quantitatively and qualitatively evaluate the content of the presentation materials. The algorithm scrutinizes the materials and suggests areas for improvement and additional information to the user.

[0461] Step 8:

[0462] Users revise documents via their devices based on feedback provided by the server. The finalized documents are saved and used in preparation for presentations and proposals.

[0463] (Example 1)

[0464] 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."

[0465] In today's business environment, creating new business plans and compelling presentation materials to effectively communicate those plans are essential. However, these processes are time-consuming and labor-intensive, requiring specialized knowledge and experience to achieve high-quality results. Furthermore, the inefficiency of gathering necessary information and reviewing relevant materials remains a challenge.

[0466] 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.

[0467] In this invention, the server includes means for collecting information, means for preprocessing the collected information, and means for generating a plan using a generative model. This makes it possible to efficiently and automatically perform the entire process from information collection to planning using a generative model, document creation, and automated review and feedback of the documents.

[0468] "Means of collecting information" refers to functions for collecting relevant information from external data sources or internal databases.

[0469] "Preprocessing methods" refer to functions that reshape collected raw data and convert it into a format that can be used for analysis and generative models.

[0470] A "generative model" is an algorithm that learns from past data and is used to automatically generate new business plans.

[0471] "Means for creating documents" refers to a function that automatically generates visual documents based on the generated business plan.

[0472] "Means for automatically reviewing and providing feedback" refers to a function that analyzes the content of created documents and indicates areas for improvement or information that should be added.

[0473] "Means for making adjustments" refers to functions that allow users to review generated materials and make changes or additions as needed.

[0474] "Referencing successful case studies" refers to a function that improves the quality and success rate of new plans by referring to data from past successful business plans.

[0475] This system aims to automatically generate new business plans and presentation materials. The implementation of this invention involves collaboration between the user, server, and terminal. First, the user uses a terminal to input business themes and related initial information. This data is then sent from the terminal to the server. The server collects external and internal company data using web scraping and database search techniques. The collected information is preprocessed using natural language processing libraries (e.g., NLTK and spaCy) with programming languages ​​such as Python and Java.

[0476] This preprocessing converts the information into a format usable by the generative AI model. The server leverages a generative AI model, trained on past success stories and business plan datasets, to generate innovative business plans. Based on the generated plans, presentation materials are automatically constructed using PowerPoint templates. This step utilizes Microsoft Office APIs, among others.

[0477] The terminal displays the generated presentation materials to the user in real time, and the user can review and adjust the materials through a GUI-based user interface. The created materials are automatically reviewed by the server, and feedback is provided to objectively evaluate the quality of the materials. Grammar checks and consistency analysis functions are based on machine learning algorithms.

[0478] As a concrete example, when developing a business plan for "sustainable energy solutions," users can input keywords such as "sustainable energy," "renewable resources," and "environmental technology." The server then collects relevant market data and generates a business plan proposing a new business model utilizing renewable energy. Furthermore, presentation materials suitable for investors are automatically created, which the user can review. This system supports the efficient creation of business plans.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] Users input business themes and related initial information using a terminal. This includes business objectives, target markets, and competitor information. The entered data is sent to the server. The information entered here forms the basis of the new business plan.

[0482] Step 2:

[0483] Based on the received data, the server collects information using web scraping techniques and internal database searches. This process acquires a large amount of data, including relevant market data and industry trends. As output, a wide variety of information is stored on the server in an unprocessed state.

[0484] Step 3:

[0485] The server preprocesses the collected raw data using natural language processing techniques. This stage involves text cleansing, extraction of important keywords, and text summarization using Python. The input is the unprocessed data collected in the previous step, and the output is data in a format suitable for use in a generative AI model.

[0486] Step 4:

[0487] The server feeds pre-processed data into a generating AI model. This model has already learned from past success stories and business data, and generates new business plans. The input is formatted data, and the output is a draft of a concrete new business plan.

[0488] Step 5:

[0489] The server automatically generates presentation materials based on the generated business plan. Here, Microsoft Office APIs are used to construct the slides and design visually appealing materials. The input is the business plan, and the output is a high-quality presentation document.

[0490] Step 6:

[0491] The terminal displays the completed presentation materials to the user in real time. The user can review the materials and make adjustments or additions as needed using the GUI. The input is the automatically generated presentation material, and the output is the final version adjusted by the user.

[0492] Step 7:

[0493] The server performs an automated review function on the completed document. It evaluates grammar, content consistency, and structural clarity, and provides feedback. The input is the finalized presentation material, and the output is specific improvement suggestions and feedback.

[0494] (Application Example 1)

[0495] 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."

[0496] Developing new business plans and creating effective presentation materials are tasks that require a tremendous amount of time and effort. Furthermore, in today's rapidly changing business environment, building business models that adapt quickly and accurately to the market is difficult. In particular, there is a lack of means to efficiently collect and analyze large amounts of information and then make persuasive proposals. This invention aims to provide a system to solve these problems.

[0497] 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.

[0498] In this invention, the server includes means for collecting information, means for pre-processing the collected information, means for generating a business plan using a data model based on the pre-processed information, means for creating commercial materials based on the generated business plan, means for automatically verifying the created commercial materials and providing improvement information, means for receiving input of business ideas, analyzing data, and extracting relevant information, means for aggregating market information and competitor information and presenting a business model including proposals, and means for automatically creating exhibition materials based on the above proposals. This makes it possible to quickly and efficiently generate high-quality business plans and presentation materials, and to smoothly advance proposals for new businesses.

[0499] "Means of collecting information" refers to functions for systematically collecting user input and related external data.

[0500] "Means of pre-processing collected information" refers to the process of preparing collected information into a format that can be analyzed.

[0501] A "data model" is a set of algorithms that have been trained to rationally generate business plans from information.

[0502] "Means of creating commercial materials" refers to the function of constructing visually clear and easy-to-understand materials based on the generated business plan.

[0503] "Means for automatically verifying and providing improvement information" refers to a process that objectively evaluates the created materials and indicates necessary improvements or additional information.

[0504] A "means for receiving business idea input" refers to an interface for users to input the basic concept or theme of a business they are considering.

[0505] "Methods for analyzing data and extracting relevant information" refers to the process of deriving relevant market and technological information based on the input business idea.

[0506] "A means of aggregating market and competitor information and presenting business models, including proposals" refers to a function that comprehensively analyzes collected data and proposes new business possibilities.

[0507] "Methods for automatically creating exhibition materials" refer to a process that visualizes generated proposals and automatically compiles them into a format suitable for presentations.

[0508] This invention provides a system that streamlines the generation of new business plans and the creation of presentation materials. This system automatically collects, analyzes, and creates materials based on input from a user's terminal. Specifically, the user inputs basic ideas or themes related to the business into the terminal. The input information is sent to the server, which then initiates the process of collecting information based on it.

[0509] The server receives data through a frontend developed using React Native and processes it in a backend built with Flask. The collected information is analyzed by AI models using libraries such as TensorFlow and PyTorch to generate business plans. Natural language processing techniques are used to format the information and transform it into a form suitable for business-related data models.

[0510] Based on the generated business plan, the server automatically creates commercial materials. In this process, the materials are generated in LaTeX or PPTX format and provided in a visualized form as reports or presentations. Furthermore, the generated materials are automatically reviewed, and feedback is provided on areas for improvement. This evaluation aims to increase the objectivity and effectiveness of the materials.

[0511] For example, if a user proposes an "environmentally friendly payment solution," the server can instantly analyze relevant market data and technology trends through its configured program and build a new business model. The generated business plan is simultaneously formatted as appropriate presentation material. Below is a specific prompt for the generating AI model: "Generate a business plan for a new payment technology market based on the following information: Target market: Young people, Competitors: General companies, Features: Biometric authentication technology."

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The user inputs a basic business idea or theme through a terminal. The input information is sent from the terminal to the server. At this stage, text input data is used. Based on the business idea input, the user selects relevant keywords.

[0515] Step 2:

[0516] The server initiates the information gathering process based on the received data. Market data, technology trends, and competitor information are acquired via external APIs. This information is stored as primary data within the server. This process automatically collects and stores necessary information via the internet.

[0517] Step 3:

[0518] The server preprocesses the collected information. Using natural language processing techniques, it analyzes the data, removing unnecessary data while extracting the information necessary for the business plan. This data processing includes text cleaning and keyword extraction. As a result, preprocessed data is generated.

[0519] Step 4:

[0520] The server generates business plans using an AI model based on pre-processed data. Scenario planning and marketing strategies are then constructed using the data model. Pre-processed data is the input, and the output is a structured business plan.

[0521] Step 5:

[0522] The server automatically generates commercial materials based on the generated business plan. Using templates, it presents the key points of the business plan in slide and document formats. This process converts the plan created by the AI ​​model into visual materials.

[0523] Step 6:

[0524] The server automatically validates commercial materials and generates feedback. This feedback includes suggestions for improvement and additional information. Based on the input commercial materials, improvement suggestions are provided as output.

[0525] Step 7:

[0526] Users fine-tune commercial materials using editing tools provided on their devices. They receive feedback from the server and perform final checks. This results in the completed commercial material.

[0527] 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.

[0528] This invention is a system that integrates information gathering, preprocessing, business plan generation using AI generative models, presentation material creation, automated review, and an emotion engine for recognizing user emotions. The aim of this system is to efficiently support the design and proposal of new businesses.

[0529] First, users input the business theme and initial relevant information through their terminals. This information is sent to the server and used as foundational data for effective business planning. Based on the collected information, the server retrieves relevant external data from the web and internal databases, accumulating knowledge by utilizing a wealth of information sources.

[0530] Next, the server preprocesses the acquired information. By utilizing natural language processing techniques, the data is organized and formatted, and converted into a form that can be used by the AI ​​generative model. The preprocessed data is input into the generative model, which then generates a business plan based on past success stories and existing business plans.

[0531] Next, the server automatically creates presentation materials based on the generated business plan. Slide generation based on templates and the arrangement of visual layouts are automated. The created materials are presented to the user via their terminal, allowing them to review the content in real time and make corrections as needed.

[0532] Furthermore, the server activates an automated review function to review the content of the document and provide feedback on areas for improvement and shortcomings. This helps to improve the quality of the document.

[0533] This is where the emotion engine plays a crucial role. The device recognizes the user's emotions through their facial expressions and tone of voice and sends that data to the server. The server can then analyze the user's feedback through the emotion engine and use it to improve the materials. For example, if a user expresses concern or dissatisfaction, the emotion engine identifies the contributing factors and suggests revisions to the materials. It also adjusts the interface and guides based on the user's emotional state to assist the user.

[0534] For example, when a user is developing a business plan regarding the sustainability of a new product, the emotion engine recognizes that the user has a positive reaction to "environmentally friendly" options, and the server recommends presentation materials that highlight relevant information based on that. In this way, the system can understand the complex information the user is faced with and provide appropriate advice.

[0535] The following describes the processing flow.

[0536] Step 1:

[0537] Users input new business themes, related keywords, and initial data through their terminals. This provides the server with the information that forms the basis of the business plan.

[0538] Step 2:

[0539] The server collects relevant information from external websites and internal databases based on the input data. This collection process is automated using APIs and web scraping techniques. The collected information is stored in a database.

[0540] Step 3:

[0541] The server preprocesses the collected data. Specifically, this involves text cleaning, extraction of important keywords, and data organization using natural language processing. The data prepared in this preprocessing step is then supplied to the AI ​​generative model.

[0542] Step 4:

[0543] The server inputs pre-processed data into an AI generative model. Based on the trained dataset, the generative model generates a new business plan. The generated plan is recorded in the database as an initial business plan.

[0544] Step 5:

[0545] The server creates presentation materials based on the generated business plan. Following the appropriate template, visually organized materials, including slide structure and graph placement, are automatically generated.

[0546] Step 6:

[0547] The terminal displays the generated presentation materials to the user in real time. The user can review the content of the materials and make adjustments as needed.

[0548] Step 7:

[0549] The server runs an automated review function to evaluate presentation materials. It analyzes the materials in detail, generates feedback with necessary corrections and improvement ideas, and provides them to the user.

[0550] Step 8:

[0551] The emotion engine built into the device analyzes the user's facial expressions and tone of voice to collect emotional data. This emotional data is sent to a server, providing clues to understanding the user's response.

[0552] Step 9:

[0553] The server evaluates which parts of the material are influencing the user based on user sentiment data obtained from the sentiment engine. Based on the identified points, it suggests adjustments to the material.

[0554] Step 10:

[0555] Users receive feedback provided by the emotion engine and finalize the document on their device. The finalized document is saved for use in proposals and presentations and can be printed or shared electronically as needed.

[0556] (Example 2)

[0557] 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."

[0558] Traditional business planning systems often handled information gathering, plan generation, document creation, and evaluation processes separately, resulting in decreased overall work efficiency. Furthermore, it was difficult to immediately reflect users' emotions and reactions, making it challenging to create flexible plans and documents that reflected user intentions.

[0559] 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.

[0560] In this invention, the server includes means having a function for collecting information, means having a function for preprocessing the collected information, and means utilizing a generative model that generates a plan based on the preprocessed information. This makes it possible to perform the entire process from information collection to plan generation, document creation, and feedback provision in an integrated manner, enabling the immediate reflection of the user's feelings and the provision of optimized business plans and documents.

[0561] "Means that have the function of collecting information" refers to a mechanism for gathering relevant information from user input or external databases.

[0562] "Means having the function of pre-processing collected information" refers to technologies that organize and process acquired information using natural language processing techniques, etc., and convert it into a form suitable for subsequent processing.

[0563] "Using generative models to generate plans" refers to the process of creating business plans using AI models, based on past success stories and data.

[0564] "Means of creating materials" refers to a function that automatically creates presentation materials based on the generated plan, providing visually organized slides.

[0565] "Methods for automatically reviewing documents and providing feedback" refers to a process that analyzes generated documents and automatically provides feedback on areas for improvement and shortcomings in their content.

[0566] An "emotion engine that recognizes emotions and uses feedback to improve materials" is a technology that senses the user's facial expressions and tone of voice, evaluates their emotional state, and uses that feedback to improve materials and optimize the user interface.

[0567] This invention is an integrated system for efficiently creating and proposing business plans. Specifically, it incorporates technologies that automate information gathering, preprocessing, plan generation, document creation, and review. Furthermore, it includes an emotion engine that recognizes user emotions and uses them to improve the documents.

[0568] The system is structured as follows: First, users input business themes and related information via a terminal. This information is entered using an internet-connected terminal device and transmitted to the server in real time. The server has the function of collecting information, and strengthens the information base by obtaining additional data from external databases and web APIs.

[0569] The server then preprocesses the collected information. The primary software technology used here is natural language processing (NLP), through which the information is organized and formatted into a predetermined format.

[0570] The processed information is passed to a generating AI model. The server can then use this AI model to automatically generate a business plan based on historical data. The generated plan includes business objectives, strategies, and risk assessments.

[0571] Based on the generated plan, the server automatically creates presentation materials. The materials are provided in slide format with a visual layout based on a template. This completed material is presented to the user via a terminal, allowing the user to review the content and make corrections as needed.

[0572] Furthermore, the server automatically reviews the materials and provides feedback on areas for improvement. In this process, the emotion engine analyzes emotional data based on the user's facial expressions and tone of voice captured by the terminal, which can then be used to improve the materials. Based on what the emotion engine recognizes, the system makes adjustments to the user interface to improve user convenience.

[0573] For example, when a user is developing a sustainability business plan for a new product, the emotion engine can capture a positive response to "environmentally friendly" options. Based on this data, the server creates materials that emphasize that element. An example of a prompt might be, "Please create a business plan for a new product that takes sustainability into consideration. I would especially like to emphasize environmentally friendly options."

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] The user uses a terminal to input themes and initial information related to a new business. This input information serves as the basis for creating a business plan. The terminal packets this information and sends it to the server via a secure communication path.

[0577] Step 2:

[0578] The server collects relevant information from external databases and web APIs based on the user information it receives. During this process, the server generates queries and retrieves the necessary data from the databases based on those queries. The collected data is stored as additional foundational data.

[0579] Step 3:

[0580] The server preprocesses the collected data. This process utilizes natural language processing techniques to normalize, classify, and summarize the text data. As a result of the preprocessing, the information is converted into a format usable by generative AI models, and only the most relevant parts are extracted.

[0581] Step 4:

[0582] The server inputs pre-processed information into a generating AI model to create a business plan. The AI ​​model automatically creates the plan based on prompts and historical data. The generated business plan includes detailed information such as business goals, strategies, and anticipated challenges.

[0583] Step 5:

[0584] The server automatically creates presentation materials based on the generated business plan. It applies templates and generates visually consistent slides. This provides an easy-to-understand output for the user, and the materials are sent to the terminal.

[0585] Step 6:

[0586] The terminal provides an environment where users can view the presented presentation materials and provide real-time feedback. If a user makes corrections to the materials, those corrections are sent from the terminal to the server.

[0587] Step 7:

[0588] The server activates an automated review function to analyze the content of the presentation materials, identify areas for improvement, and provide feedback. Advice on what needs to be corrected and strengthened is generated and presented to the user.

[0589] Step 8:

[0590] The device recognizes the user's emotions from their facial expressions and voice and sends this information to the server. The server utilizes an emotion engine to analyze the user's feedback. Specifically, the emotion data is used to improve the materials, enabling the server to suggest the most suitable materials that match the user's intentions and feelings. Through this process, the interface is also adjusted based on user feedback.

[0591] (Application Example 2)

[0592] 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."

[0593] In today's world, the process of planning and proposing new businesses is fraught with challenges, as it requires extensive information gathering and document creation, making it difficult to conduct efficiently. Furthermore, proposals that do not utilize users' past activity data fail to provide information tailored to their needs. A system is needed to address these challenges and efficiently and effectively support the design and proposal of new businesses.

[0594] 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.

[0595] In this invention, the server includes means for collecting information, means for preprocessing the collected information, and means for utilizing a generative model that generates a business plan based on the preprocessed information. This enables efficient aggregation and processing of information. Furthermore, it includes means for recognizing the user's emotions and adjusting the system's output based on that data, and means for analyzing the user's past activity data and providing optimized suggestions, thereby realizing the provision of optimal information and suggestions to the user.

[0596] "Means of collecting information" refers to the process of gathering necessary data from initial information provided by the user and from external databases.

[0597] "Preprocessing" refers to the process of organizing collected information using natural language processing techniques or similar methods in order to convert it into a format suitable for use in generative models.

[0598] "Methods of using generative models to generate business plans" refers to technologies that utilize AI technology and pre-processed information to generate new business plans based on past success stories and other factors.

[0599] "Methods for creating presentation materials" refers to the process of automatically generating visual materials for presentations based on the generated business plan.

[0600] "Means of reviewing and providing feedback" refers to an automated diagnostic process that evaluates the content of created materials and identifies areas for improvement.

[0601] "Means of recognizing emotions" refers to technologies that analyze a user's facial expressions and voice to identify their emotional state.

[0602] "A means of analyzing past activity data and providing optimized suggestions" refers to a process that analyzes a user's past data and automatically provides suggestions that are best suited to the user's interests and needs.

[0603] This invention is a system that effectively collects various types of information and generates an optimal business plan for the user. This system operates through the collaborative efforts of a server and a terminal. The user inputs the business theme and related information via their terminal. This information is sent to the server, which collects additional information from external databases and the web to build a comprehensive dataset.

[0604] The server then performs preprocessing using natural language processing techniques. In this process, the Python programming language and its libraries, NLTK and spaCy, are used to organize and format the unstructured data, converting it into a form that can be easily processed as input by the generative AI model. In addition, data on the user's past activity is also collected and analyzed by the generative model.

[0605] The generative model is implemented using machine learning frameworks such as TensorFlow and PyTorch, and automatically generates a business plan optimized for the user. The server then creates presentation materials based on this, arranging them into templated slides. These materials are sent to the user's device, where the user can view them in real time and provide feedback as needed.

[0606] Furthermore, the device's built-in camera and microphone are used to analyze the user's facial expressions and voice, and the emotion engine sends this data to the server. OpenCV and the Google Cloud Speech-to-Text API are used to analyze emotions, and the server adjusts the presentation content based on the results. The content of the proposal is optimized according to the user's reactions to the materials.

[0607] Let's look at a concrete example. For instance, when a user starts a new restaurant business plan, the system can generate a "promotion that will pique their interest in recipes using local ingredients" based on the user's preferences and past payment history. An example of an input prompt for the AI ​​model during this generation process would be: "Generate the most suitable promotional offer based on the user's recent payment history. The user particularly likes to pay for restaurants."

[0608] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0609] Step 1:

[0610] The user's terminal inputs the business theme and initial related information, and sends this information to the server. Since the transmitted information is used as foundational data for the business plan, the input data is structured and processed on the server in JSON format.

[0611] Step 2:

[0612] Based on the information received, the server collects additional information from external databases and the internet. Using web crawling technology, it automatically retrieves highly relevant data and stores it in its internal database. The collected data is then expanded using diverse sources.

[0613] Step 3:

[0614] The server applies natural language processing techniques to the collected data for preprocessing. Using the Python NLTK library, the information is tokenized and stop words are removed, converting it into a format usable by generative AI models. This process also includes data classification and importance analysis, thereby refining the data.

[0615] Step 4:

[0616] The server inputs pre-processed data into a generative AI model built with TensorFlow or PyTorch. The model automatically generates a business plan based on the input data, constructing a concrete business plan by referencing past success stories and market conditions. This output is generated as a business plan in text format.

[0617] Step 5:

[0618] The server uses the generated business plan to create a template-based presentation. The presentation is automatically structured in slide format and laid out in a visually easy-to-understand manner. The materials include images and graphs, and incorporate elements in various formats.

[0619] Step 6:

[0620] The user's device receives and displays the presentation materials in real time. Users can review the materials on their device and provide feedback as needed. The materials are displayed through an intuitive and user-friendly interface.

[0621] Step 7:

[0622] The device uses its camera and microphone to analyze the user's facial expressions and voice, and sends the data to an emotion engine. The device utilizes OpenCV and the Google Cloud Speech-to-Text API to recognize and analyze the user's emotions. Based on this, the user's reaction to specific elements is measured.

[0623] Step 8:

[0624] The server analyzes emotional data and adjusts the content of the presentation materials and business plan. Based on the results from the emotional engine, it generates emphasis on elements of the materials and additional suggestions, and then sends the optimized information back to the user's terminal.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] [Fourth Embodiment]

[0629] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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".

[0642] This invention is a system developed to streamline the automatic generation of new business plans and the creation of presentation materials. In the embodiment, the user, server, and terminal collaborate to smoothly execute processes from information gathering to document creation and review.

[0643] First, users input the business theme and related initial information through their terminal. This input information is sent to the server as the basis for the business plan. The server has means to collect necessary external information from the web and internal databases, and automatically performs large-scale data collection.

[0644] Next, the server formats the collected information and preprocesses the data using natural language processing techniques. This transforms the information into a format usable by the generative model. The generative model is trained on a dataset of past success stories and common business plans, and the server uses this model to generate new business plans.

[0645] Based on the generated business plan, the server provides tools for automatically creating presentation materials. Specifically, it automatically arranges slides based on templates and organizes key points to emphasize within the document. The completed presentation materials are displayed to the user in real time via their terminal, allowing them to review the content and make adjustments as needed.

[0646] Subsequently, the server runs an automated review function on the created document, analyzing its content and providing feedback indicating areas for improvement and additional information. This review function objectively evaluates the quality of the document.

[0647] As a concrete example, consider a scenario where a user develops a business plan for "sustainable energy solutions." The user inputs relevant keywords and ideas into a terminal, and the server gathers the latest market data and success stories related to environmental technologies based on this input. The generative model utilizes this information to generate a business plan proposing a new business model using renewable energy. Subsequently, the server automatically generates persuasive presentation materials for investors based on the proposed plan and presents them to the user via the terminal.

[0648] This system helps users create high-quality business plans and presentation materials in a short period of time, streamlining the startup process.

[0649] The following describes the processing flow.

[0650] Step 1:

[0651] The user enters the business theme and initial information into the terminal. This information includes keywords and ideas related to the business, as well as data on the target market. This information is sent to the server.

[0652] Step 2:

[0653] The server collects relevant data based on the information it receives. It obtains necessary external information through web scraping and access to internal databases. The collected data is used to generate business plans.

[0654] Step 3:

[0655] The server preprocesses the acquired data. Specifically, it performs tokenization and text cleaning, and formats the information using natural language processing techniques. The preprocessed data is then used as input to a generative model.

[0656] Step 4:

[0657] The server inputs pre-processed data into an AI generative model. This generative model generates an optimal business plan based on its knowledge base. The generated plan is saved on the server as the initial draft of the business plan.

[0658] Step 5:

[0659] The server automatically creates presentation materials based on the generated business plan. It generates the materials while considering visual aspects such as slide layout and highlighting of key points.

[0660] Step 6:

[0661] The terminal displays the completed presentation materials to the user in real time. The user can review the content of the materials and make any necessary adjustments.

[0662] Step 7:

[0663] The server activates an automated review function to quantitatively and qualitatively evaluate the content of the presentation materials. The algorithm scrutinizes the materials and suggests areas for improvement and additional information to the user.

[0664] Step 8:

[0665] Users revise documents via their devices based on feedback provided by the server. The finalized documents are saved and used in preparation for presentations and proposals.

[0666] (Example 1)

[0667] 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".

[0668] In today's business environment, creating new business plans and compelling presentation materials to effectively communicate those plans are essential. However, these processes are time-consuming and labor-intensive, requiring specialized knowledge and experience to achieve high-quality results. Furthermore, the inefficiency of gathering necessary information and reviewing relevant materials remains a challenge.

[0669] 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.

[0670] In this invention, the server includes means for collecting information, means for preprocessing the collected information, and means for generating a plan using a generative model. This makes it possible to efficiently and automatically perform the entire process from information collection to planning using a generative model, document creation, and automated review and feedback of the documents.

[0671] "Means of collecting information" refers to functions for collecting relevant information from external data sources or internal databases.

[0672] "Preprocessing methods" refer to functions that reshape collected raw data and convert it into a format that can be used for analysis and generative models.

[0673] A "generative model" is an algorithm that learns from past data and is used to automatically generate new business plans.

[0674] "Means for creating documents" refers to a function that automatically generates visual documents based on the generated business plan.

[0675] "Means for automatically reviewing and providing feedback" refers to a function that analyzes the content of created documents and indicates areas for improvement or information that should be added.

[0676] "Means for making adjustments" refers to functions that allow users to review generated materials and make changes or additions as needed.

[0677] "Referencing successful case studies" refers to a function that improves the quality and success rate of new plans by referring to data from past successful business plans.

[0678] This system aims to automatically generate new business plans and presentation materials. The implementation of this invention involves collaboration between the user, server, and terminal. First, the user uses a terminal to input business themes and related initial information. This data is then sent from the terminal to the server. The server collects external and internal company data using web scraping and database search techniques. The collected information is preprocessed using natural language processing libraries (e.g., NLTK and spaCy) with programming languages ​​such as Python and Java.

[0679] This preprocessing converts the information into a format usable by the generative AI model. The server leverages a generative AI model, trained on past success stories and business plan datasets, to generate innovative business plans. Based on the generated plans, presentation materials are automatically constructed using PowerPoint templates. This step utilizes Microsoft Office APIs, among others.

[0680] The terminal displays the generated presentation materials to the user in real time, and the user can review and adjust the materials through a GUI-based user interface. The created materials are automatically reviewed by the server, and feedback is provided to objectively evaluate the quality of the materials. Grammar checks and consistency analysis functions are based on machine learning algorithms.

[0681] As a concrete example, when developing a business plan for "sustainable energy solutions," users can input keywords such as "sustainable energy," "renewable resources," and "environmental technology." The server then collects relevant market data and generates a business plan proposing a new business model utilizing renewable energy. Furthermore, presentation materials suitable for investors are automatically created, which the user can review. This system supports the efficient creation of business plans.

[0682] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0683] Step 1:

[0684] Users input business themes and related initial information using a terminal. This includes business objectives, target markets, and competitor information. The entered data is sent to the server. The information entered here forms the basis of the new business plan.

[0685] Step 2:

[0686] Based on the received data, the server collects information using web scraping techniques and internal database searches. This process acquires a large amount of data, including relevant market data and industry trends. As output, a wide variety of information is stored on the server in an unprocessed state.

[0687] Step 3:

[0688] The server preprocesses the collected raw data using natural language processing techniques. This stage involves text cleansing, extraction of important keywords, and text summarization using Python. The input is the unprocessed data collected in the previous step, and the output is data in a format suitable for use in a generative AI model.

[0689] Step 4:

[0690] The server feeds pre-processed data into a generating AI model. This model has already learned from past success stories and business data, and generates new business plans. The input is formatted data, and the output is a draft of a concrete new business plan.

[0691] Step 5:

[0692] The server automatically generates presentation materials based on the generated business plan. Here, Microsoft Office APIs are used to construct the slides and design visually appealing materials. The input is the business plan, and the output is a high-quality presentation document.

[0693] Step 6:

[0694] The terminal displays the completed presentation materials to the user in real time. The user can review the materials and make adjustments or additions as needed using the GUI. The input is the automatically generated presentation material, and the output is the final version adjusted by the user.

[0695] Step 7:

[0696] The server performs an automated review function on the completed document. It evaluates grammar, content consistency, and structural clarity, and provides feedback. The input is the finalized presentation material, and the output is specific improvement suggestions and feedback.

[0697] (Application Example 1)

[0698] 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".

[0699] Developing new business plans and creating effective presentation materials are tasks that require a tremendous amount of time and effort. Furthermore, in today's rapidly changing business environment, building business models that adapt quickly and accurately to the market is difficult. In particular, there is a lack of means to efficiently collect and analyze large amounts of information and then make persuasive proposals. This invention aims to provide a system to solve these problems.

[0700] 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.

[0701] In this invention, the server includes means for collecting information, means for pre-processing the collected information, means for generating a business plan using a data model based on the pre-processed information, means for creating commercial materials based on the generated business plan, means for automatically verifying the created commercial materials and providing improvement information, means for receiving input of business ideas, analyzing data, and extracting relevant information, means for aggregating market information and competitor information and presenting a business model including proposals, and means for automatically creating exhibition materials based on the above proposals. This makes it possible to quickly and efficiently generate high-quality business plans and presentation materials, and to smoothly advance proposals for new businesses.

[0702] "Means of collecting information" refers to functions for systematically collecting user input and related external data.

[0703] "Means of pre-processing collected information" refers to the process of preparing collected information into a format that can be analyzed.

[0704] A "data model" is a set of algorithms that have been trained to rationally generate business plans from information.

[0705] "Means of creating commercial materials" refers to the function of constructing visually clear and easy-to-understand materials based on the generated business plan.

[0706] "Means for automatically verifying and providing improvement information" refers to a process that objectively evaluates the created materials and indicates necessary improvements or additional information.

[0707] A "means for receiving business idea input" refers to an interface for users to input the basic concept or theme of a business they are considering.

[0708] "Methods for analyzing data and extracting relevant information" refers to the process of deriving relevant market and technological information based on the input business idea.

[0709] "A means of aggregating market and competitor information and presenting business models, including proposals" refers to a function that comprehensively analyzes collected data and proposes new business possibilities.

[0710] "Methods for automatically creating exhibition materials" refer to a process that visualizes generated proposals and automatically compiles them into a format suitable for presentations.

[0711] This invention provides a system that streamlines the generation of new business plans and the creation of presentation materials. This system automatically collects, analyzes, and creates materials based on input from a user's terminal. Specifically, the user inputs basic ideas or themes related to the business into the terminal. The input information is sent to the server, which then initiates the process of collecting information based on it.

[0712] The server receives data through a frontend developed using React Native and processes it in a backend built with Flask. The collected information is analyzed by AI models using libraries such as TensorFlow and PyTorch to generate business plans. Natural language processing techniques are used to format the information and transform it into a form suitable for business-related data models.

[0713] Based on the generated business plan, the server automatically creates commercial materials. In this process, the materials are generated in LaTeX or PPTX format and provided in a visualized form as reports or presentations. Furthermore, the generated materials are automatically reviewed, and feedback is provided on areas for improvement. This evaluation aims to increase the objectivity and effectiveness of the materials.

[0714] For example, if a user proposes an "environmentally friendly payment solution," the server can instantly analyze relevant market data and technology trends through its configured program and build a new business model. The generated business plan is simultaneously formatted as appropriate presentation material. Below is a specific prompt for the generating AI model: "Generate a business plan for a new payment technology market based on the following information: Target market: Young people, Competitors: General companies, Features: Biometric authentication technology."

[0715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0716] Step 1:

[0717] The user inputs a basic business idea or theme through a terminal. The input information is sent from the terminal to the server. At this stage, text input data is used. Based on the business idea input, the user selects relevant keywords.

[0718] Step 2:

[0719] The server initiates the information gathering process based on the received data. Market data, technology trends, and competitor information are acquired via external APIs. This information is stored as primary data within the server. This process automatically collects and stores necessary information via the internet.

[0720] Step 3:

[0721] The server preprocesses the collected information. Using natural language processing techniques, it analyzes the data, removing unnecessary data while extracting the information necessary for the business plan. This data processing includes text cleaning and keyword extraction. As a result, preprocessed data is generated.

[0722] Step 4:

[0723] The server generates business plans using an AI model based on pre-processed data. Scenario planning and marketing strategies are then constructed using the data model. Pre-processed data is the input, and the output is a structured business plan.

[0724] Step 5:

[0725] The server automatically generates commercial materials based on the generated business plan. Using templates, it presents the key points of the business plan in slide and document formats. This process converts the plan created by the AI ​​model into visual materials.

[0726] Step 6:

[0727] The server automatically validates commercial materials and generates feedback. This feedback includes suggestions for improvement and additional information. Based on the input commercial materials, improvement suggestions are provided as output.

[0728] Step 7:

[0729] Users fine-tune commercial materials using editing tools provided on their devices. They receive feedback from the server and perform final checks. This results in the completed commercial material.

[0730] 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.

[0731] This invention is a system that integrates information gathering, preprocessing, business plan generation using AI generative models, presentation material creation, automated review, and an emotion engine for recognizing user emotions. The aim of this system is to efficiently support the design and proposal of new businesses.

[0732] First, users input the business theme and initial relevant information through their terminals. This information is sent to the server and used as foundational data for effective business planning. Based on the collected information, the server retrieves relevant external data from the web and internal databases, accumulating knowledge by utilizing a wealth of information sources.

[0733] Next, the server preprocesses the acquired information. By utilizing natural language processing techniques, the data is organized and formatted, and converted into a form that can be used by the AI ​​generative model. The preprocessed data is input into the generative model, which then generates a business plan based on past success stories and existing business plans.

[0734] Next, the server automatically creates presentation materials based on the generated business plan. Slide generation based on templates and the arrangement of visual layouts are automated. The created materials are presented to the user via their terminal, allowing them to review the content in real time and make corrections as needed.

[0735] Furthermore, the server activates an automated review function to review the content of the document and provide feedback on areas for improvement and shortcomings. This helps to improve the quality of the document.

[0736] This is where the emotion engine plays a crucial role. The device recognizes the user's emotions through their facial expressions and tone of voice and sends that data to the server. The server can then analyze the user's feedback through the emotion engine and use it to improve the materials. For example, if a user expresses concern or dissatisfaction, the emotion engine identifies the contributing factors and suggests revisions to the materials. It also adjusts the interface and guides based on the user's emotional state to assist the user.

[0737] For example, when a user is developing a business plan regarding the sustainability of a new product, the emotion engine recognizes that the user has a positive reaction to "environmentally friendly" options, and the server recommends presentation materials that highlight relevant information based on that. In this way, the system can understand the complex information the user is faced with and provide appropriate advice.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] Users input new business themes, related keywords, and initial data through their terminals. This provides the server with the information that forms the basis of the business plan.

[0741] Step 2:

[0742] The server collects relevant information from external websites and internal databases based on the input data. This collection process is automated using APIs and web scraping techniques. The collected information is stored in a database.

[0743] Step 3:

[0744] The server preprocesses the collected data. Specifically, this involves text cleaning, extraction of important keywords, and data organization using natural language processing. The data prepared in this preprocessing step is then supplied to the AI ​​generative model.

[0745] Step 4:

[0746] The server inputs pre-processed data into an AI generative model. Based on the trained dataset, the generative model generates a new business plan. The generated plan is recorded in the database as an initial business plan.

[0747] Step 5:

[0748] The server creates presentation materials based on the generated business plan. Following the appropriate template, visually organized materials, including slide structure and graph placement, are automatically generated.

[0749] Step 6:

[0750] The terminal displays the generated presentation materials to the user in real time. The user can review the content of the materials and make adjustments as needed.

[0751] Step 7:

[0752] The server runs an automated review function to evaluate presentation materials. It analyzes the materials in detail, generates feedback with necessary corrections and improvement ideas, and provides them to the user.

[0753] Step 8:

[0754] The emotion engine built into the device analyzes the user's facial expressions and tone of voice to collect emotional data. This emotional data is sent to a server, providing clues to understanding the user's response.

[0755] Step 9:

[0756] The server evaluates which parts of the material are influencing the user based on user sentiment data obtained from the sentiment engine. Based on the identified points, it suggests adjustments to the material.

[0757] Step 10:

[0758] Users receive feedback provided by the emotion engine and finalize the document on their device. The finalized document is saved for use in proposals and presentations and can be printed or shared electronically as needed.

[0759] (Example 2)

[0760] 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".

[0761] Traditional business planning systems often handled information gathering, plan generation, document creation, and evaluation processes separately, resulting in decreased overall work efficiency. Furthermore, it was difficult to immediately reflect users' emotions and reactions, making it challenging to create flexible plans and documents that reflected user intentions.

[0762] 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.

[0763] In this invention, the server includes means having a function for collecting information, means having a function for preprocessing the collected information, and means utilizing a generative model that generates a plan based on the preprocessed information. This makes it possible to perform the entire process from information collection to plan generation, document creation, and feedback provision in an integrated manner, enabling the immediate reflection of the user's feelings and the provision of optimized business plans and documents.

[0764] "Means that have the function of collecting information" refers to a mechanism for gathering relevant information from user input or external databases.

[0765] "Means having the function of pre-processing collected information" refers to technologies that organize and process acquired information using natural language processing techniques, etc., and convert it into a form suitable for subsequent processing.

[0766] "Using generative models to generate plans" refers to the process of creating business plans using AI models, based on past success stories and data.

[0767] "Means of creating materials" refers to a function that automatically creates presentation materials based on the generated plan, providing visually organized slides.

[0768] "Methods for automatically reviewing documents and providing feedback" refers to a process that analyzes generated documents and automatically provides feedback on areas for improvement and shortcomings in their content.

[0769] An "emotion engine that recognizes emotions and uses feedback to improve materials" is a technology that senses the user's facial expressions and tone of voice, evaluates their emotional state, and uses that feedback to improve materials and optimize the user interface.

[0770] This invention is an integrated system for efficiently creating and proposing business plans. Specifically, it incorporates technologies that automate information gathering, preprocessing, plan generation, document creation, and review. Furthermore, it includes an emotion engine that recognizes user emotions and uses them to improve the documents.

[0771] The system is structured as follows: First, users input business themes and related information via a terminal. This information is entered using an internet-connected terminal device and transmitted to the server in real time. The server has the function of collecting information, and strengthens the information base by obtaining additional data from external databases and web APIs.

[0772] The server then preprocesses the collected information. The primary software technology used here is natural language processing (NLP), through which the information is organized and formatted into a predetermined format.

[0773] The processed information is passed to a generating AI model. The server can then use this AI model to automatically generate a business plan based on historical data. The generated plan includes business objectives, strategies, and risk assessments.

[0774] Based on the generated plan, the server automatically creates presentation materials. The materials are provided in slide format with a visual layout based on a template. This completed material is presented to the user via a terminal, allowing the user to review the content and make corrections as needed.

[0775] Furthermore, the server automatically reviews the materials and provides feedback on areas for improvement. In this process, the emotion engine analyzes emotional data based on the user's facial expressions and tone of voice captured by the terminal, which can then be used to improve the materials. Based on what the emotion engine recognizes, the system makes adjustments to the user interface to improve user convenience.

[0776] For example, when a user is developing a sustainability business plan for a new product, the emotion engine can capture a positive response to "environmentally friendly" options. Based on this data, the server creates materials that emphasize that element. An example of a prompt might be, "Please create a business plan for a new product that takes sustainability into consideration. I would especially like to emphasize environmentally friendly options."

[0777] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0778] Step 1:

[0779] The user uses a terminal to input themes and initial information related to a new business. This input information serves as the basis for creating a business plan. The terminal packets this information and sends it to the server via a secure communication path.

[0780] Step 2:

[0781] The server collects relevant information from external databases and web APIs based on the user information it receives. During this process, the server generates queries and retrieves the necessary data from the databases based on those queries. The collected data is stored as additional foundational data.

[0782] Step 3:

[0783] The server preprocesses the collected data. This process utilizes natural language processing techniques to normalize, classify, and summarize the text data. As a result of the preprocessing, the information is converted into a format usable by generative AI models, and only the most relevant parts are extracted.

[0784] Step 4:

[0785] The server inputs pre-processed information into a generating AI model to create a business plan. The AI ​​model automatically creates the plan based on prompts and historical data. The generated business plan includes detailed information such as business goals, strategies, and anticipated challenges.

[0786] Step 5:

[0787] The server automatically creates presentation materials based on the generated business plan. It applies templates and generates visually consistent slides. This provides an easy-to-understand output for the user, and the materials are sent to the terminal.

[0788] Step 6:

[0789] The terminal provides an environment where users can view the presented presentation materials and provide real-time feedback. If a user makes corrections to the materials, those corrections are sent from the terminal to the server.

[0790] Step 7:

[0791] The server activates an automated review function to analyze the content of the presentation materials, identify areas for improvement, and provide feedback. Advice on what needs to be corrected and strengthened is generated and presented to the user.

[0792] Step 8:

[0793] The device recognizes the user's emotions from their facial expressions and voice and sends this information to the server. The server utilizes an emotion engine to analyze the user's feedback. Specifically, the emotion data is used to improve the materials, enabling the server to suggest the most suitable materials that match the user's intentions and feelings. Through this process, the interface is also adjusted based on user feedback.

[0794] (Application Example 2)

[0795] 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".

[0796] In today's world, the process of planning and proposing new businesses is fraught with challenges, as it requires extensive information gathering and document creation, making it difficult to conduct efficiently. Furthermore, proposals that do not utilize users' past activity data fail to provide information tailored to their needs. A system is needed to address these challenges and efficiently and effectively support the design and proposal of new businesses.

[0797] 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.

[0798] In this invention, the server includes means for collecting information, means for preprocessing the collected information, and means for utilizing a generative model that generates a business plan based on the preprocessed information. This enables efficient aggregation and processing of information. Furthermore, it includes means for recognizing the user's emotions and adjusting the system's output based on that data, and means for analyzing the user's past activity data and providing optimized suggestions, thereby realizing the provision of optimal information and suggestions to the user.

[0799] "Means of collecting information" refers to the process of gathering necessary data from initial information provided by the user and from external databases.

[0800] "Preprocessing" refers to the process of organizing collected information using natural language processing techniques or similar methods in order to convert it into a format suitable for use in generative models.

[0801] "Methods of using generative models to generate business plans" refers to technologies that utilize AI technology and pre-processed information to generate new business plans based on past success stories and other factors.

[0802] "Methods for creating presentation materials" refers to the process of automatically generating visual materials for presentations based on the generated business plan.

[0803] "Means of reviewing and providing feedback" refers to an automated diagnostic process that evaluates the content of created materials and identifies areas for improvement.

[0804] "Means of recognizing emotions" refers to technologies that analyze a user's facial expressions and voice to identify their emotional state.

[0805] "A means of analyzing past activity data and providing optimized suggestions" refers to a process that analyzes a user's past data and automatically provides suggestions that are best suited to the user's interests and needs.

[0806] This invention is a system that effectively collects various types of information and generates an optimal business plan for the user. This system operates through the collaborative efforts of a server and a terminal. The user inputs the business theme and related information via their terminal. This information is sent to the server, which collects additional information from external databases and the web to build a comprehensive dataset.

[0807] The server then performs preprocessing using natural language processing techniques. In this process, the Python programming language and its libraries, NLTK and spaCy, are used to organize and format the unstructured data, converting it into a form that can be easily processed as input by the generative AI model. In addition, data on the user's past activity is also collected and analyzed by the generative model.

[0808] The generative model is implemented using machine learning frameworks such as TensorFlow and PyTorch, and automatically generates a business plan optimized for the user. The server then creates presentation materials based on this, arranging them into templated slides. These materials are sent to the user's device, where the user can view them in real time and provide feedback as needed.

[0809] Furthermore, the device's built-in camera and microphone are used to analyze the user's facial expressions and voice, and the emotion engine sends this data to the server. OpenCV and the Google Cloud Speech-to-Text API are used to analyze emotions, and the server adjusts the presentation content based on the results. The content of the proposal is optimized according to the user's reactions to the materials.

[0810] Let's look at a concrete example. For instance, when a user starts a new restaurant business plan, the system can generate a "promotion that will pique their interest in recipes using local ingredients" based on the user's preferences and past payment history. An example of an input prompt for the AI ​​model during this generation process would be: "Generate the most suitable promotional offer based on the user's recent payment history. The user particularly likes to pay for restaurants."

[0811] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0812] Step 1:

[0813] The user's terminal inputs the business theme and initial related information, and sends this information to the server. Since the transmitted information is used as foundational data for the business plan, the input data is structured and processed on the server in JSON format.

[0814] Step 2:

[0815] Based on the information received, the server collects additional information from external databases and the internet. Using web crawling technology, it automatically retrieves highly relevant data and stores it in its internal database. The collected data is then expanded using diverse sources.

[0816] Step 3:

[0817] The server applies natural language processing techniques to the collected data for preprocessing. Using the Python NLTK library, the information is tokenized and stop words are removed, converting it into a format usable by generative AI models. This process also includes data classification and importance analysis, thereby refining the data.

[0818] Step 4:

[0819] The server inputs pre-processed data into a generative AI model built with TensorFlow or PyTorch. The model automatically generates a business plan based on the input data, constructing a concrete business plan by referencing past success stories and market conditions. This output is generated as a business plan in text format.

[0820] Step 5:

[0821] The server uses the generated business plan to create a template-based presentation. The presentation is automatically structured in slide format and laid out in a visually easy-to-understand manner. The materials include images and graphs, and incorporate elements in various formats.

[0822] Step 6:

[0823] The user's device receives and displays the presentation materials in real time. Users can review the materials on their device and provide feedback as needed. The materials are displayed through an intuitive and user-friendly interface.

[0824] Step 7:

[0825] The device uses its camera and microphone to analyze the user's facial expressions and voice, and sends the data to an emotion engine. The device utilizes OpenCV and the Google Cloud Speech-to-Text API to recognize and analyze the user's emotions. Based on this, the user's reaction to specific elements is measured.

[0826] Step 8:

[0827] The server analyzes emotional data and adjusts the content of the presentation materials and business plan. Based on the results from the emotional engine, it generates emphasis on elements of the materials and additional suggestions, and then sends the optimized information back to the user's terminal.

[0828] 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.

[0829] 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.

[0830] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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."

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] The following is further disclosed regarding the embodiments described above.

[0850] (Claim 1)

[0851] Means of collecting information,

[0852] A means for preprocessing the collected information,

[0853] A means of using a generative model that generates a business plan based on pre-processed information,

[0854] A means of creating presentation materials based on the generated business plan,

[0855] A means to automatically review the created presentation materials and provide feedback,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, comprising means for feeding pre-processed information to a generation model, and referencing successful cases in the generated business plan.

[0859] (Claim 3)

[0860] The system according to claim 1, which provides a user interface for revising and finalizing presentation materials.

[0861] "Example 1"

[0862] (Claim 1)

[0863] Means of collecting information,

[0864] A means for preprocessing the collected information,

[0865] A means of using a generative model that generates a plan based on preprocessed information,

[0866] Means for creating documents based on the generated plan,

[0867] A means to automatically review the created documents and provide feedback,

[0868] A means of receiving input information and starting processing,

[0869] A means of applying natural language processing to the collected information,

[0870] One method is to use generative models to refer to past success stories,

[0871] A means of displaying and adjusting the generated materials on a terminal,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, comprising means for feeding pre-processed information to a generative model and referencing successful cases in the generated plan.

[0875] (Claim 3)

[0876] The system according to claim 1, which provides a user interface for modifying and finalizing documents.

[0877] "Application Example 1"

[0878] (Claim 1)

[0879] Means of collecting information,

[0880] A means for preprocessing the collected information,

[0881] A means of generating a business plan using a data model based on pre-processed information,

[0882] Means for creating commercial materials based on the generated business plan,

[0883] A means to automatically verify the created commercial materials and provide improvement information,

[0884] A means of receiving business idea input, analyzing the data, and extracting relevant information,

[0885] A means of aggregating market and competitor information and presenting business models, including proposals,

[0886] Based on the above proposal, a method for automatically creating exhibition materials,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, comprising means for feeding pre-processed information into a data model, and referencing achievement examples in the generated business plan.

[0890] (Claim 3)

[0891] The system according to claim 1, which provides an operation screen for modifying and finalizing exhibition materials.

[0892] "Example 2 of combining an emotion engine"

[0893] (Claim 1)

[0894] A means having the function of collecting information,

[0895] A means having a function for preprocessing the collected information,

[0896] A means of using a generative model that generates a plan based on preprocessed information,

[0897] Means for creating documents based on the generated plan,

[0898] A means to automatically review the created documents and provide feedback,

[0899] A means including an emotion engine that recognizes the user's emotions and uses the feedback to improve the material,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, comprising means for inputting preprocessed information into a generation model, and referencing examples in the generated plan.

[0903] (Claim 3)

[0904] The system according to claim 1, which provides an operating interface for modifying and making final adjustments to documents.

[0905] "Application example 2 when combining with an emotional engine"

[0906] (Claim 1)

[0907] Means of collecting information,

[0908] A means for preprocessing the collected information,

[0909] A means of using a generative model that generates a business plan based on pre-processed information,

[0910] A means of creating presentation materials based on the generated business plan,

[0911] A means to automatically review the created presentation materials and provide feedback,

[0912] A means of recognizing the user's emotions and adjusting the system's output based on that data,

[0913] A means of analyzing users' past activity data and providing optimized suggestions,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, comprising means for feeding pre-processed information to a generation model, and referencing successful cases in the generated business plan.

[0917] (Claim 3)

[0918] The system according to claim 1, which provides a user interface for revising and finalizing presentation materials and suggests information tailored to the user's interests. [Explanation of Symbols]

[0919] 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. Means of collecting information, A means for preprocessing the collected information, A means of generating a business plan using a data model based on pre-processed information, Means for creating commercial materials based on the generated business plan, A means to automatically verify the created commercial materials and provide improvement information, A means of receiving business idea input, analyzing the data, and extracting relevant information, A means of aggregating market and competitor information and presenting business models, including proposals, Based on the above proposal, a method for automatically creating exhibition materials, A system that includes this.

2. The system according to claim 1, comprising means for feeding pre-processed information into a data model, and referencing achievement examples in the generated business plan.

3. The system according to claim 1, which provides an operation screen for modifying and finalizing exhibition materials.

Images