system

Abstract

Provide a system. A means for receiving business ideas from users; A means of analyzing received business ideas and extracting keywords; A method for searching a database for similar past services based on the extracted keywords, and A means for analyzing the data of the searched similar services using the generated artificial intelligence to identify success factors and failure factors; A means for generating recommendations applicable to the user's business idea based on the analysis results; a means for feeding back the generated recommendations to the user; A system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Companies and entrepreneurs with business ideas can increase the chances of success by learning from past successes and failures of similar services. However, collecting and analyzing vast amounts of past data to obtain useful information requires a great deal of time and effort. Furthermore, identifying factors behind success and failure from past cases requires advanced expertise and analytical skills. Therefore, there is a need for a system that can efficiently carry out these processes and provide specific recommendations that can be applied to users' business ideas. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. Specifically, the present invention provides a system including: means for receiving a business idea from a user; means for analyzing the received business idea and extracting keywords; means for searching a database of similar past services based on the extracted keywords; means for analyzing the data of the searched similar services using generated artificial intelligence to identify factors for success and failure; means for generating recommendations applicable to the user's business idea based on the analysis results; and means for providing the generated recommendations as feedback to the user. Furthermore, the present invention makes it possible to provide specific recommendations for improving the user's business idea efficiently and effectively by including a configuration including a means for generating feedback in the form of a report and a means for organizing data of similar services through a standardization process.

[0006] "Users" are entities that provide business ideas, such as companies and entrepreneurs who use the system.

[0007] A "business idea" is a plan, concept, or concept for a new business that a user intends to implement.

[0008] A "means" is a technical component or method used by a system to achieve a given function.

[0009] "Generative AI" is an artificial intelligence technology that uses generative models to analyze data and generate specific outputs.

[0010] "Recommendations" are specific advice and improvement ideas that the system provides based on the user's business ideas.

[0011] A "database" is a collection of structured data that stores information on similar past services.

[0012] "Keywords" are important words or phrases extracted in the analysis of a business idea.

[0013] "Analysis" is the process of breaking down business ideas and data to understand their meaning and extract important elements.

[0014] "Feedback" is the act or process of returning analysis results and recommendations to the user.

[0015] The "standardization process" is the process of formatting acquired data to conform to certain standards, making it easier to analyze and compare. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users, and provides specific recommendations. This system is mainly composed of a server, terminals, and generation AI.

[0038] System configuration

[0039] 1. User Device

[0040] Users input their business ideas through the terminal interface, providing detailed information about the idea (target user demographics, characteristics, goals, etc.) through an input form.

[0041] The terminal generates and sends an API request to send the input data to the server.

[0042] 2. Server

[0043] The server analyzes the business ideas received from the device, extracting important keywords using text analysis libraries and natural language processing technology.

[0044] The server generates a database query based on the extracted keywords and searches the database for past information on similar services.

[0045] The server formats the data and converts it into a format that can be input into the generative AI through a standardization process.

[0046] 3. Generation AI

[0047] Generative AI analyzes past data on similar services to identify factors that led to success and failure. For example, it uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[0048] Based on the factors identified by the generative AI, it generates recommendations that can be applied to the user's business idea.

[0049] Providing feedback

[0050] The server generates a report based on the analysis results and recommendations received from the generative AI, which includes specific improvements and recommendations.

[0051] The server transmits the generated report to the terminal.

[0052] The terminal displays the report in a user interface so that the user can easily view and understand it.

[0053] Specific examples

[0054] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0055] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0056] 2. The device converts the input idea information into JSON format and sends it to the server.

[0057] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0058] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[0059] 5. The server standardizes the acquired data and passes it to the generation AI.

[0060] 6. Generative AI analyzes and identifies the factors that made similar services successful (e.g., "improved user engagement") and the factors that made them unsuccessful (e.g., "privacy issues") in the past.

[0061] 7. Based on the analysis results, the server generates recommendations such as "strengthen privacy protection measures and add specific interactive features to increase user engagement" and compiles them in the form of a report.

[0062] 8. The server sends the generated report to the terminal.

[0063] 9. The terminal displays the report on the user interface, allowing the user to easily view the feedback.

[0064] In this way, the system of the present invention leverages past lessons learned from the user's business ideas and provides specific and useful feedback.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] Users input their business ideas through the terminal interface, providing specific information such as the business overview, target users, unique features, and the problems they are trying to solve.

[0068] Step 2:

[0069] The device organizes the input business idea, converts it into JSON format data, generates an API request, and sends the business idea data to the server.

[0070] Step 3:

[0071] The server analyzes the business idea data received from the API request. It uses text analysis libraries and natural language processing techniques to extract important keywords from the business idea. For example, keywords such as "young people" and "interactive features" are extracted.

[0072] Step 4:

[0073] The server generates a database query based on the extracted keywords, which includes conditions for searching for similar past services.

[0074] Step 5:

[0075] The server executes the generated query against a database to retrieve data on similar services from the past. The data retrieved from the database includes success stories and failure stories for each service.

[0076] Step 6:

[0077] The server puts the acquired data through a standardization process, which puts the data into a consistent format and makes it suitable for AI analysis.

[0078] Step 7:

[0079] The server passes the standardized data to the generation AI, which analyzes the data and identifies the success and failure factors of similar services in the past.

[0080] Step 8:

[0081] Generative AI uses natural language processing technology to analyze reviews and rating data and extract specific success factors (e.g., "high user engagement") and failure factors (e.g., "privacy issues").

[0082] Step 9:

[0083] Based on the analysis results returned by the generative AI, the server generates specific recommendations that can be applied to the user's business idea, including how to incorporate success factors and how to avoid failure factors.

[0084] Step 10:

[0085] The server documents the generated recommendations in a report format and presents them in a form that is easily understandable to the user.

[0086] Step 11:

[0087] The server sends the generated report to the terminal.

[0088] Step 12:

[0089] The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[0090] In this way, the system of the present invention can analyze the business ideas provided by the user and provide specific feedback based on past lessons learned, thereby increasing the chances of business success.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Conventional business idea evaluation systems have difficulty analyzing user-provided ideas while effectively utilizing similar past cases. This makes it difficult to provide specific and useful feedback to users. Furthermore, the process of providing analysis results to users is inefficient, making it difficult to provide useful information in a timely manner. This results in a lack of concrete guidelines for increasing the success rate of business ideas, and a lack of information for users to refine their ideas.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, means for searching a database of similar past services based on the extracted keywords, means for analyzing the data of the searched similar services using generated artificial intelligence to identify factors for success and failure, means for generating recommendations applicable to the user's business idea based on the analysis results using numerical analysis techniques and the generated artificial intelligence, and means for feeding back the generated recommendations to the user in the form of a report. This makes it possible to provide specific and useful feedback based on similar past cases in a timely manner.

[0096] "User" refers to a user who inputs a business idea into the system.

[0097] "Terminal" refers to a communication device used by a user to input a business idea and receive feedback from the server.

[0098] "Server" refers to the core part of the system that analyzes data sent by users, generates recommendations using the generated artificial intelligence, and provides feedback to users.

[0099] "Business idea" refers to a business concept or plan newly devised by a user.

[0100] "Keywords" refer to important words and phrases extracted through analysis from the text of a business idea.

[0101] "Information repository" refers to a database that stores data on similar past services.

[0102] "Generated AI" refers to the AI ​​technology used to analyze data from similar past services, identify factors that led to success and failure, and generate recommendations.

[0103] "Success factors" refer to the elements and conditions that made similar services successful in the past.

[0104] "Failure factors" refer to the elements and conditions that caused similar services to fail in the past.

[0105] "Numerical analysis technology" refers to the technology used to generate specific recommendations based on data analyzed by the generated artificial intelligence.

[0106] The "report format" refers to the format of a document compiled to convey the generated recommendations to the user in an easy-to-understand manner.

[0107] "Feedback" refers to information provided to users that has been analyzed and recommended by the generated artificial intelligence.

[0108] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users and provides specific recommendations. This system is mainly composed of a server, terminals, and generation AI. The following specific hardware and software are used for the processing:

[0109] 1. User Device

[0110] Users input their business ideas through the terminal interface, providing detailed information about the idea (target user demographics, characteristics, goals, etc.) through an input form.

[0111] The terminal generates and sends an API request to send the input data to the server.

[0112] 2. Server

[0113] The server analyzes the business ideas received from the device and extracts important keywords using text analysis libraries (e.g., NLTK or spaCy) and natural language processing techniques (e.g., BERT or GPT-4 (registered trademark)).

[0114] The server generates a database query based on the extracted keywords and searches for past information on similar services from a database (e.g., MySQL (registered trademark), PostgreSQL).

[0115] The server formats the acquired data and converts it into a format that can be input into the generation AI through a standardization process.

[0116] 3. Generation AI

[0117] Generative AI analyzes past data on similar services to identify factors that led to success and failure. For example, it uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[0118] Based on the factors identified by the generative AI, it generates recommendations that can be applied to the user's business idea.

[0119] 4. Providing Feedback

[0120] The server generates a report based on the analysis results and recommendations received from the generative AI, which includes specific improvements and recommendations.

[0121] The server transmits the generated report to the terminal.

[0122] The terminal displays the report in a user interface so that the user can easily view and understand it.

[0123] Specific examples

[0124] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0125] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0126] 2. The device converts the input idea information into JSON format and sends it to the server.

[0127] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0128] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[0129] 5. The server standardizes the acquired data and passes it to the generation AI.

[0130] 6. Generative AI analyzes and identifies the factors that made similar services successful (e.g., "improved user engagement") and the factors that made them unsuccessful (e.g., "privacy issues") in the past.

[0131] 7. Based on the analysis results, the server generates recommendations such as "strengthen privacy protection measures and add specific interactive features to increase user engagement" and compiles them in the form of a report.

[0132] 8. The server sends the generated report to the terminal.

[0133] 9. The terminal displays the report on the user interface, allowing the user to easily view the feedback.

[0134] In this way, the system of the present invention leverages past lessons learned from the user's business ideas and provides specific and useful feedback.

[0135] Prompt Sentence Examples

[0136] The following is a specific example of a prompt sentence to be input to the generation AI of this system:

[0137] Analyze a business idea for a "new social media app." The target user demographic is young people, and interactive features are planned. Provide specific recommendations for this idea based on the success and failure factors of similar services in the past.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Step 1:

[0140] Users input their business idea on their device, for example, "Add interactive features to a new social media app targeting a younger demographic," and this input data is collected through a front-end input form.

[0141] Step 2:

[0142] The terminal receives the business idea entered by the user and converts it into JSON format. Specifically, it generates the following JSON data:

[0143] json

[0144] {

[0145] "idea": "New social media app targets a younger demographic and adds interactive features"

[0146] }

[0147] This converted data is sent to the server as an API request.

[0148] Step 3:

[0149] The server parses the received JSON data and extracts the text of the business idea. It then uses a text analysis library (e.g., NLTK or spaCy) to extract important keywords. For example, keywords such as "young people" and "interactive features" are extracted. The input of this keyword extraction process is the text of the business idea, and the output is a list of extracted keywords.

[0150] Step 4:

[0151] The server generates a database query based on the keyword list. The generated query is used to search and retrieve related past similar service information from a database (e.g., MySQL, PostgreSQL). The input of this database search is the keyword list, and the output is a set of past similar service information.

[0152] Step 5:

[0153] The server standardizes the acquired data into a format that can be input into the generation AI. Specifically, it formats the data and deletes unnecessary information. The input for this standardization process is the acquired information on similar services from the past, and the output is the standardized data.

[0154] Step 6:

[0155] The server passes the standardized data to a generation AI (e.g., GPT-4) to analyze the success and failure factors of similar services in the past. The generation AI analyzes reviews and evaluation data and extracts important factors. The input of this analysis process is the standardized data, and the output is a list of success factors and a list of failure factors.

[0156] Step 7:

[0157] Based on the analysis results, the generative AI generates specific recommendations that can be applied to the user's business idea. For example, a recommendation might be generated such as "strengthen privacy protection measures and add specific interactive features to increase user engagement." The input to this recommendation generation process is a list of success factors and a list of failure factors, and the output is a specific recommendation.

[0158] Step 8:

[0159] The server generates a detailed report based on the recommendations received from the generation AI. This report includes the recommendations and an analysis of the success and failure factors. The input to this report generation process is the specific recommendations, and the output is the report.

[0160] Step 9:

[0161] The server sends the generated report to the user's device. Specifically, it sends the report data as an API response. The input of this sending process is the report, and the output is a transmission to the user's device.

[0162] Step 10:

[0163] The terminal displays the received report on a user interface so that the user can easily check it. For example, the report is displayed in a text area and formatted so that each recommendation and analysis result is clearly visible. The input of this display process is the report data, and the output is the display on the user interface.

[0164] (Application example 1)

[0165] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0166] In today's world, it is extremely important to design new services using autonomous vehicles and obtain specific recommendations. However, there is a lack of systems that can effectively utilize the success and failure factors of past similar services and obtain appropriate feedback. As a result, entrepreneurs and companies face many challenges in planning and executing new businesses, making it difficult to design effective services. In particular, the lack of a means to obtain recommendations specific to services using autonomous vehicles makes it difficult to provide efficient and safe services. Furthermore, there is a need to automatically generate specific recommendations based on success and failure factors and provide them to users.

[0167] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0168] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, and means for searching a database for similar past services based on the extracted keywords. This makes it possible to analyze the success and failure factors of similar past services, automatically generate specific recommendations to support the service design for autonomous vehicles, and provide them to the user.

[0169] A "business idea" is a new business concept or service design proposal devised by a user.

[0170] "Analysis" is the act of examining received data in detail and extracting meaningful information from it.

[0171] "Keywords" are important words or phrases that represent a particular theme or content and are extracted through data analysis.

[0172] "Similar services" refer to businesses or services that have been developed in the past that share characteristics and objectives with the user's business idea.

[0173] A "database" is a system that can store, manage, and search large amounts of information in an organized manner.

[0174] "Generative artificial intelligence" is a collection of machine learning models and algorithms designed and trained to perform specific tasks.

[0175] "Success factors" refer to the elements and conditions that have brought about positive results in similar services in the past.

[0176] "Failure factors" refer to elements or conditions that have led to negative results in similar services in the past.

[0177] A "Recommendation" is specific advice or recommendation based on the analysis results to improve or optimize the user's business idea.

[0178] The "report format" is a format in which the analysis results and recommendations are organized and formalized into a single document.

[0179] A "standardization process" is a procedure for converting diverse data into a consistent format to facilitate subsequent analysis and processing.

[0180] An "autonomous vehicle" is a vehicle that can drive autonomously without the need for driver intervention.

[0181] "Feedback" refers to the act of returning the analysis results and recommendations generated by the system to the user, providing advice and evaluation.

[0182] An embodiment of the present invention will now be described in detail.

[0183] System configuration

[0184] A system is constructed that uses a server, user terminals, and a generative AI model. Users input business ideas using user terminals such as smartphones or computers. The server analyzes the received business ideas and extracts keywords. Based on the extracted keywords, it searches and retrieves data on similar past services from a database and converts it into a format that can be input into the generative AI model. The generative AI model analyzes data on similar past services, identifies factors that led to success and failure, and generates specific recommendations to be provided to the user. The server sends the generated recommendations to the user terminal as feedback.

[0185] Hardware and software used

[0186] Hardware: Smartphones (iPhone(R), ANDROID(R)), Computers (PC, Mac)

[0187] software:

[0188] Flask: Used as a web server to manage API requests and responses.

[0189] SQLite: Used as a database to store and search historical data on similar services.

[0190] transformers: Libraries for using generative AI models, especially for text analysis and natural language processing.

[0191] Data processing and calculation

[0192] User Interface

[0193] Users enter their business ideas for autonomous vehicles through a user interface. The interface provides an easy-to-use input form to collect details about the business idea (e.g., target customer demographic, characteristics, goals, etc.). For example, a user may enter an idea for adding interactive features to a "transportation service using autonomous vehicles" targeted at young people.

[0194] Server-side processing

[0195] 1. Receiving and analyzing: The server receives the business idea sent from the user's device and uses a text analysis library to extract important keywords.

[0196] 2. Database search: Search the SQLite database based on the extracted keywords to obtain past data on similar services.

[0197] 3. Data conversion and input: The acquired data is processed through a standardization process and converted into a format that can be processed by the generative AI model.

[0198] Analysis and recommendation generation by generative AI

[0199] The generative AI model analyzes the success and failure factors of similar services in the past and generates specific recommendations for improving the business idea, such as "strengthening privacy protection measures and adding specific interactive features to increase user engagement."

[0200] Providing feedback

[0201] The server generates a report based on the generated recommendations and sends it to the user interface for display, allowing the user to read the report and get specific feedback on their business idea.

[0202] Examples of prompt statements

[0203] An example of a prompt is as follows:

[0204] Analyze the following data and provide business optimization suggestions: [{"idea": "A shuttle service using autonomous vehicles. The main target is urban businessmen. It should help them reach their destinations quickly and efficiently.", "keywords": ["autonomous vehicle", "shuttle service", "urban area", "businessmen"], "similar_services": [{"name": "Service A", "success_factors": ["route optimization"], "failure_factors": ["security concerns"]}, {"name": "Service B", "success_factors": ["user engagement"], "failure_factors": ["high cost"]}]}]

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] Users input their business ideas using the user interface of their smartphone or computer. In the input form, they can enter detailed information about the business idea (target user demographics, characteristics, goals, etc.). For example, they can enter an idea for a "transportation service using self-driving cars" targeting "urban businessmen." The entered business idea is sent to the server.

[0208] Step 2:

[0209] The server receives business ideas submitted by users. It analyzes the received data using a text analysis library (e.g., NLTK or SpaCy) to extract important keywords. The input for the analysis is the text of the business idea, and the output is the extracted keywords (e.g., "self-driving car," "transportation," "urban area," "businessman").

[0210] Step 3:

[0211] The server searches for data on similar services from an SQLite database based on the extracted keywords. The search query includes the extracted keywords and retrieves past information on similar services from the database. The input is the extracted keywords, and the output is similar service data (e.g., "Service A" and "Service B") as search results.

[0212] Step 4:

[0213] The server organizes the acquired similar service data through a standardization process and converts it into a format that can be input into the generative AI model. The input is the similar service data as a search result, and the output is formatted data that is easy for the generative AI model to analyze.

[0214] Step 5:

[0215] The server inputs the formatted data into a generative AI model to identify the success and failure factors of similar past services. The input is standardized data on similar services, and the output is the results of identifying the success and failure factors. The identified success and failure factors are obtained by analyzing review and evaluation data using natural language processing technology.

[0216] Step 6:

[0217] The generative AI model generates specific recommendations that can be applied to the user's business idea based on the identified success and failure factors. The input is the analysis results of the success and failure factors, and the output is the generated recommendation (e.g., "strengthen privacy protection measures and add specific interactive features to increase user engagement").

[0218] Step 7:

[0219] The server generates a report based on the generated recommendations, which includes improvements and recommendations. The input is the generated recommendations, and the output is feedback in the form of a report.

[0220] Step 8:

[0221] The server sends the generated report to the user interface and displays it for the user to review. The user can use the provided feedback to improve or optimize their business idea. The input is the feedback in the form of a report, and the output is the feedback information reviewed by the user.

[0222] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0223] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users and provides specific recommendations. The system is characterized by the addition of an emotion engine, which recognizes the user's emotions and reflects that emotion data in the feedback content. The system is primarily composed of a server, a terminal, a generation AI, and an emotion engine.

[0224] System configuration

[0225] User terminal

[0226] 1. The user enters their business idea through the terminal interface, providing detailed information about the idea (target user demographic, characteristics, goals, etc.) through the input form.

[0227] 2. The device generates and sends an API request to send the input data to the server.

[0228] server

[0229] 3. The server analyzes the business idea received from the device, using text analysis libraries and natural language processing techniques to extract important keywords.

[0230] 4. The server generates a database query based on the extracted keywords and searches for past information on similar services.

[0231] 5. The server standardizes the data and converts it into a format that can be input into the generative AI.

[0232] 6. The server runs an emotion engine to identify emotions from the user's input data. The emotion engine recognizes emotions (e.g., positive, negative, doubt, etc.) from the user's input text and voice data.

[0233] Generative AI and Emotion Engine

[0234] 7. Generative AI analyzes past data on similar services to identify factors that led to success and failure. It uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[0235] 8. The emotion engine analyzes the user's emotion data and determines the priority of suggesting improvements to the business idea. For example, if the user is expressing positive emotions, it will prioritize aggressive suggestions, and if the user is expressing negative emotions, it will prioritize risk-avoidance suggestions.

[0236] Providing feedback

[0237] 9. The server combines the analysis results received from the generative AI with the results of the emotion engine to generate specific recommendations that can be applied to each individual. These recommendations include how to incorporate success factors and how to avoid failure factors.

[0238] 10. The server documents the generated recommendations in a report format, making them easy for the user to understand. At this time, it also adjusts the way feedback is presented based on the emotional data.

[0239] 11. The server sends the generated report to the terminal.

[0240] 12. The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[0241] Specific examples

[0242] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0243] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0244] 2. The device converts the input idea information into JSON format and sends it to the server.

[0245] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0246] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[0247] 5. The server standardizes the acquired data and passes it to the generation AI.

[0248] 6. At the same time, the server uses an emotion engine to recognize emotions from the user's input text. For example, if the user includes many positive comments, it will recognize the emotion as positive.

[0249] 7. Generative AI analyzes and identifies the success factors (e.g., "improved user engagement") and failure factors (e.g., "privacy issues") of similar services in the past.

[0250] 8. The emotion engine suggests improvements based on positive emotions, prioritizing risk-taking and proactive proposals.

[0251] 9. The server integrates the analysis results with the emotional data and generates recommendations such as "strengthening privacy protection measures while adding interactive features to increase user engagement."

[0252] 10. The server compiles the generated recommendations into a report and sends it to the device.

[0253] 11. The terminal displays the report for the user to easily review, which includes feedback reflecting the user's positive feelings.

[0254] The processing flow will be explained below.

[0255] Step 1:

[0256] Users input their business ideas through the device interface, providing specific information such as a business overview, target user demographics, unique features, and the problems they aim to solve.

[0257] Step 2:

[0258] The device organizes the input business idea and converts it into JSON format data, which is then sent to the server as an API request.

[0259] Step 3:

[0260] The server analyzes the business idea data received from the API request. It uses text analysis libraries and natural language processing techniques to extract important keywords from the business idea. For example, "younger demographic" and "interactive features" are extracted.

[0261] Step 4:

[0262] The server generates a database query based on the extracted keywords, the query including conditions for searching for similar past services.

[0263] Step 5:

[0264] The server uses the generated query to retrieve data on similar services from a database, including success stories and failure stories for each service.

[0265] Step 6:

[0266] The server puts the acquired data through a standardization process, which puts the data into a consistent format and makes it suitable for AI analysis.

[0267] Step 7:

[0268] The server passes the standardized data to the Generator AI, which analyzes the data and identifies factors that contributed to the success and failure of similar services in the past. For example, it identifies factors such as "high user engagement" and "privacy issues."

[0269] Step 8:

[0270] At the same time, the server passes the user's input data to the emotion engine, which recognizes the user's emotions from the text and feedback content. For example, it analyzes emotions such as "positive," "negative," and "doubtful."

[0271] Step 9:

[0272] The server generates feedback adapted to the user's emotions based on the emotion data received from the emotion engine. If the emotion is positive, it prioritizes aggressive suggestions, and if the emotion is negative, it prioritizes risk-averse suggestions.

[0273] Step 10:

[0274] The server combines the analysis results of the generative AI with data from the emotion engine to generate specific recommendations for the user's business idea, including how to incorporate success factors and how to avoid failure factors.

[0275] Step 11:

[0276] The server documents the generated recommendations in a report format, making them easy for users to understand, and adjusts the way feedback is presented based on emotional data.

[0277] Step 12:

[0278] The server sends the generated report to the terminal.

[0279] Step 13:

[0280] The terminal displays the report in a user interface, allowing the user to review the report and revise or improve their business idea based on the suggested improvements and recommendations.

[0281] Specific examples

[0282] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0283] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0284] 2. The device converts the input idea information into JSON format and sends it to the server.

[0285] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0286] 4. The server searches the database using the extracted keywords to obtain data on similar past services.

[0287] 5. The server standardizes the acquired data and inputs it into the generation AI.

[0288] 6. The server simultaneously runs an emotion engine to analyze the user's emotions. For example, if there are many positive comments, it will recognize the emotion as positive.

[0289] 7. Generative AI identifies the success and failure factors of similar services in the past, such as "high user engagement" and "privacy issues."

[0290] 8. The server generates feedback that reflects the user's positive emotions based on the analysis results of the emotion engine.

[0291] 9. The server integrates the analysis results of the generative AI with the data from the emotion engine and generates recommendations such as "strengthening privacy protection measures while adding interactive features to increase user engagement."

[0292] 10. The server compiles the generated recommendations into a report and sends it to the terminal.

[0293] 11. The device displays the report and allows the user to review the recommendations. The report reflects feedback based on the user's positive sentiment.

[0294] In this way, the system of the present invention analyzes the user's business idea and adjusts the feedback according to the user's emotions, thereby providing more effective and personalized recommendations.

[0295] Example 2

[0296] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0297] While conventional business idea evaluation systems analyze ideas submitted by users and generate recommendations, they have issues with providing feedback that takes users' emotions into consideration. Furthermore, the process of effectively standardizing data from similar services and using artificial intelligence to identify factors behind success and failure remains immature. As a result, recommendations provided to users are uniform and lack specificity tailored to individual needs.

[0298] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0299] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, means for searching a database for similar past services based on the extracted keywords, means for organizing the data of the searched similar services through a standardization process, means for identifying success factors and failure factors using generated artificial intelligence based on the received business idea and data on similar past services, means for recognizing the user's feelings about the user's business idea and adjusting the content of recommendations based on the emotion data, means for generating recommendations applicable to the user's business idea based on the analysis results and the emotion data, and means for feeding back the generated recommendations to the user. This makes it possible to provide personalized recommendations that take the user's emotions into consideration, enabling feedback that is specific and effective.

[0300] "User" means an individual or legal entity that utilizes the system to provide business ideas and receive feedback.

[0301] A "business idea" is a new business concept or plan provided by a user.

[0302] A "terminal" is a hardware or software device through which a user inputs a business idea and communicates with a server.

[0303] The "server" is a central control device that analyzes business ideas received from users and performs database searches and executes generative AI.

[0304] "Generative AI" is a machine learning model or artificial intelligence program that analyzes past data on similar services to identify factors that contribute to success or failure.

[0305] An "emotion engine" is software or algorithms for identifying emotions from user input text or voice data.

[0306] "Keywords" are important words or phrases extracted through analysis of a business idea.

[0307] A "database" is a digital storage system for storing information on similar past services.

[0308] The "standardization process" is a series of procedures for converting acquired data into a format suitable for analysis and AI input.

[0309] "Success factors" are elements derived from past successful service cases.

[0310] "Failure factors" are elements derived from past cases of service failure.

[0311] "Recommendations" are advice and recommendations created based on the analysis results of the generative AI and emotion engine, based on the user's business idea.

[0312] "Feedback" refers to the process and output of returning recommendations to the user.

[0313] A "report" is a documented summary of the generated recommendations that is provided to the user.

[0314] This system analyzes business ideas submitted by users and provides specific recommendations based on the factors behind the success and failure of similar services in the past. This system is primarily composed of a server, a terminal, a generative AI, and an emotion engine.

[0315] User terminal

[0316] 1. Users input their business idea through the device interface. For example, they could input an idea such as "Add interactive features to a new social media app targeted at young people."

[0317] 2. The device converts the input business idea into JSON format and generates an API request to send to the server. The device sends this data to the server via an HTTP POST request.

[0318] server

[0319] 3. The server analyzes the business ideas received from the device. Specifically, it uses a text analysis library (e.g., SpaCy or NLTK) to extract important keywords. Keywords such as "young people" and "interactive features" are extracted from the received data.

[0320] 4. The server queries a database (e.g., MySQL or MongoDB) based on the extracted keywords to search for past information on similar services. This involves retrieving data on similar services from the database and preparing the settings for analysis.

[0321] 5. The server standardizes the acquired data and converts it into a format that can be input to the generative AI (e.g., CSV or JSON). It formats the acquired data to remove unnecessary information and prepares the standardized data for passing to the generative AI model.

[0322] 6. The server runs an emotion engine (e.g., IBM Watson® or Microsoft® Azure® emotion analysis API) to identify emotions from the user's input text. The emotion engine analyzes emotions (positive, negative, suspicious, etc.) and generates emotion data for further processing.

[0323] Generative AI and Emotion Engine

[0324] 7. Generative AI analyzes past data from similar services to identify factors that led to success and failure. Specifically, it uses natural language processing technology (e.g., BERT or GPT-3 (registered trademark)) to analyze reviews and evaluation data and extract important factors. For example, it identifies factors that led to success and failure, such as "improved user engagement" and "privacy issues."

[0325] 8. The emotion engine prioritizes suggestions for improving business ideas based on the user's emotion data. If the emotion is positive, it prioritizes proactive suggestions (e.g., "rapid rollout of new features"), and if the emotion is negative, it prioritizes risk-avoidance suggestions (e.g., "trial run").

[0326] Providing feedback

[0327] 9. The server combines the results of the generative AI analysis with those of the emotion engine to generate specific recommendations that can be applied individually. An example of a recommendation would be to "strengthen privacy protection measures while adding interactive features to increase user engagement."

[0328] 10. The server documents the generated recommendations in a report format (e.g., PDF or HTML) and organizes it in a format that is easy for the user to understand. The report also adjusts the presentation of feedback based on the user's emotional data.

[0329] 11. The server sends the generated report to the terminal.

[0330] 12. The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[0331] Specific examples

[0332] For example, if a user provides an idea for a "new social media app," the system might:

[0333] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0334] 2. The device converts this information into JSON format and sends it to the server as an API request.

[0335] 3. The server extracts keywords such as "young people" and "interactive features" from the input data and retrieves information on past similar services from a database.

[0336] 4. The server normalizes the acquired data and prepares it for passing to the generation AI.

[0337] 5. The server uses an emotion engine to recognize emotions from the user's input text, for example, identifying positive emotions.

[0338] 6. Generative AI identifies the success and failure factors of similar services and suggests areas for improvement.

[0339] 7. The emotion engine prioritizes proactive suggestions based on positive emotions.

[0340] 8. The server integrates these findings and generates specific recommendations, such as "strengthen privacy protection measures while adding interactive features to increase user engagement."

[0341] 9. The server compiles the recommendations into a report and sends it to the device.

[0342] 10. The terminal displays the report so that the user can easily check it and use it to improve their idea.

[0343] This system provides personalized recommendations that reflect the user's feelings, effectively supporting the process of improving business ideas.

[0344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0345] Step 1:

[0346] The user enters a new business idea into the user interface. Specifically, the user enters text such as "Add interactive features to a new social media app targeted at young people" into a dedicated form on the user interface. This is the input data for step 1.

[0347] Step 2:

[0348] The terminal converts the input business idea into JSON format. Specifically, it parses the input text data, organizes it into appropriate key-value pairs, and generates a JSON object. The resulting JSON format data is then output.

[0349] Step 3:

[0350] The device sends the generated JSON data to the server as an API request. Specifically, it generates an HTTP POST request and sends the JSON data in the request body to the server. If the transmission is successful, the server receives the data.

[0351] Step 4:

[0352] The server analyzes the business ideas received from the device. Specifically, it uses a text analysis library (e.g., SpaCy or NLTK) to extract text from the JSON data and analyze important keywords. The input is the JSON data received from the device, and the output is a list of extracted keywords.

[0353] Step 5:

[0354] The server performs a database search based on the extracted keywords. Specifically, it generates an SQL query and executes it against a database (e.g., MySQL or MongoDB). The input is the extracted keywords, and the output is the search results of similar services.

[0355] Step 6:

[0356] The server standardizes the retrieved data from similar services. Specifically, it performs data cleansing and format conversion to prepare the data in a format suitable for the generative AI model (e.g., CSV or JSON). The input is the data obtained by the search, and the output is the standardized data.

[0357] Step 7:

[0358] The server passes the standardized data to the generative AI model. Specifically, it sends an API request in a format that the AI ​​model can understand and begins analysis. The input is the standardized data, and the output is the analysis results from the generative AI model.

[0359] Step 8:

[0360] The server uses an emotion engine to identify emotions from the user's input data. Specifically, it sends the data to an emotion engine API (e.g., IBM Watson or Microsoft Azure's emotion analysis API) to perform emotion analysis. The input is the user's input text, and the output is the recognized emotion data.

[0361] Step 9:

[0362] The server integrates the analysis results of the generative AI model and the results of the emotion engine. Specifically, it combines these data and generates specific recommendations for the user. The inputs are the analysis results of the generative AI model and the data from the emotion engine, and the output is specific recommendations.

[0363] Step 10:

[0364] The server documents the generated recommendations in a report format. Specifically, it formats the recommendations into PDF or HTML format and arranges them in an easy-to-understand layout. The input is the specific recommendations, and the output is a digital report.

[0365] Step 11:

[0366] The server sends the generated report to the terminal. Specifically, it sends the report as an HTTP response and makes it displayable on the terminal. The input at this time is the digital document of the report, and the output is the data sent to the terminal.

[0367] Step 12:

[0368] The terminal displays the received report on a user interface. Specifically, the terminal receives the report and displays it in a format that can be viewed by the user on a web page or in an app. The input at this time is the received report data, and the output is a display in a format that can be viewed by the user.

[0369] These are the detailed processing steps of the system, which makes it possible to provide personalized recommendations that take into account the user's emotions.

[0370] (Application example 2)

[0371] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0372] In conventional systems, feedback on users' business ideas was based solely on the success and failure factors of similar services in the past, and suggestions and feedback did not take into account the user's emotional state. This made it difficult to provide optimal recommendations based on the user's motivation and emotional state. This led to issues such as reduced user satisfaction and the feasibility of suggestions.

[0373] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions, analyzing the emotional data, and reflecting the emotional data in the feedback content, means for generating feedback in the form of a report and adjusting the expression method based on the emotional data, and means for standardizing data on similar services. This makes it possible to provide specific and actionable recommendations according to the user's emotional state.

[0374] definition statement

[0375] The "means for receiving business ideas from users" refers to an interface or application that allows users to input business ideas.

[0376] The "means for analyzing received business ideas and extracting keywords" refers to software and algorithms for analyzing business idea documents using natural language processing technology and identifying important keywords.

[0377] The "means for searching a database for similar past services based on extracted keywords" is a system for generating a database query and using the query to search for similar past service data.

[0378] "Means for using the searched data of similar services to analyze it using generated artificial intelligence and identify factors for success and failure" refers to a generated AI model and its execution environment for analyzing the identified data of similar services.

[0379] "Means for recognizing the user's emotions, analyzing the emotional data, and reflecting it in the feedback content" refers to the process and technology for analyzing the user's emotions using an emotion engine and applying the results to the feedback content.

[0380] "Means for generating recommendations applicable to users' business ideas based on analysis results and emotion data" refers to a system that integrates data obtained using generative AI and an emotion engine to create appropriate recommendations for users.

[0381] The "means for providing feedback of generated recommendations to the user" is an interface or application that provides the generated recommendations to the user in the form of a report.

[0382] The "means for generating feedback in the form of a report and adjusting the presentation method based on emotional data" refers to the process of converting the generated recommendations into a user-friendly format and providing feedback in a presentation method that corresponds to the user's emotions.

[0383] "Means for preparing data from similar services through a standardization process" refers to data preprocessing algorithms and technologies for converting data into a format that is easy to input into generative AI.

[0384] MODE FOR CARRYING OUT THE INVENTION

[0385] System configuration

[0386] This invention is implemented using a system comprising a user terminal, a server, a generation AI, and an emotion engine.

[0387] User terminal

[0388] The user terminal provides an interface for the user to input a business idea, which is then converted into JSON format and an API request is generated to be sent to the server.

[0389] server

[0390] The server has the following main functions:

[0391] 1. Business idea analysis:

[0392] The server uses natural language processing technology to extract important keywords from the user's business idea.

[0393] Use a text analysis library (e.g., NLTK, spaCy) to identify keywords that are used to generate data queries for further processing.

[0394] 2. Search for similar service data:

[0395] Based on the extracted keywords, the server sends a query to a database to obtain information on similar past services.

[0396] Manage data using a database management system (e.g., MySQL, PostgreSQL).

[0397] 3. Data Standardization:

[0398] The acquired data from similar services is then converted into a format that is easy for the generative AI to handle through a standardization process, which includes data cleaning and data shaping.

[0399] 4. Emotion Recognition:

[0400] The server uses an emotion engine to recognize emotions from the user's input text.

[0401] Use sentiment analysis tools (e.g., IBM Watson, Microsoft Azure Emotion API) to extract sentiment data such as positive, negative, and suspicious.

[0402] 5. Recommendation generation:

[0403] The generative AI analyzes the success and failure factors of similar services in the past and combines them with emotional data to generate recommendations for the user's business idea.

[0404] Generative AI optimizes recommendations based on the user's emotional state and provides actionable feedback.

[0405] 6. Providing Feedback:

[0406] The server compiles the generated recommendations into a report format and transmits it to the user terminal.

[0407] Based on the emotional data, the way the report is presented can be adjusted to make it easier for users to understand and accept.

[0408] Specific examples

[0409] For example, if a user submits a business idea such as:

[0410] User: "I want the layout of the virtual store to be more intuitive. I want to color-code products by category to make it easier to understand visually."

[0411] The server generates the following prompt:

[0412] User Ideas:

[0413] Intuitive virtual store layout.

[0414] Color-code your products by category.

[0415] Visually easy to understand.

[0416] Sentiment: Positive (engaged and motivated)

[0417] Recommendation:

[0418] In a similar success story, color-coded categories increased user satisfaction by 20%.

[0419] To further highlight the visual impact of the category layout, high-resolution images and interactive feedback features should be added.

[0420] It is also important to implement concise design guidelines to avoid visual clutter.

[0421] In this way, the system of the present invention supports the user's business success by providing specific and practical suggestions for the user's business ideas.

[0422] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0423] System processing steps

[0424] Step 1: User enters business idea

[0425] A user inputs a business idea using a terminal, and the input business idea is saved in text format on the terminal.

[0426] Input: Text data of the business idea (e.g., "I want to make the layout of the virtual store more intuitive. I want to color-code products by category.")

[0427] Output: Text data saved on the device

[0428] Step 2: The device sends the business idea to the server

[0429] The terminal converts the input business idea into JSON format and generates an API request to send to the server.

[0430] Input: Text data of business ideas

[0431] Data processing: Convert text data into JSON format

[0432] Output: API request in JSON format

[0433] Step 3: The server analyzes the business idea

[0434] The server analyzes the received business ideas and extracts important keywords using natural language processing technology.

[0435] Input: Business idea in JSON format

[0436] Data processing: Extract keywords using natural language processing techniques (e.g., spaCy)

[0437] Output: Extracted keywords (e.g. "layout", "intuitive", "categorical")

[0438] Step 4: The server searches for similar services

[0439] The server generates a query to the database based on the extracted keywords and searches for similar past services.

[0440] Input: Extracted keywords

[0441] Data retrieval: Search using a database management system (e.g. MySQL)

[0442] Output: Data for similar services

[0443] Step 5: Server standardizes data for similar services

[0444] The server standardizes the data it obtains from similar services and formats it into a format that is easy for the generating AI to process.

[0445] Input: Data from similar services

[0446] Data processing: Data cleaning and standardization process

[0447] Output: Standardized dataset

[0448] Step 6: The server recognizes the emotion

[0449] The server uses an emotion engine to recognize emotions from the user's input text and generate emotion data.

[0450] Input: Text data of business ideas

[0451] Data computation: Recognizing emotions using sentiment analysis tools (e.g., IBM Watson)

[0452] Output: Sentiment data (e.g., positive, negative)

[0453] Step 7: Generative AI generates recommendations

[0454] The server inputs standardized data and emotional data into the generative AI, which generates recommendations based on factors that lead to success and failure.

[0455] Input: Standardized data, emotion data

[0456] Data computation: analysis using generative AI models

[0457] Output: Recommendation (e.g., "Use a categorized layout and add high-resolution images")

[0458] Step 8: The server generates feedback in the form of a report

[0459] The server compiles the generated recommendations into a report format and adjusts the way the feedback is presented based on the emotional data.

[0460] Input: Recommendation, sentiment data

[0461] Data processing: Report generation and presentation adjustment

[0462] Output: Feedback in report format

[0463] Step 9: Your device will display feedback

[0464] The terminal receives the report sent from the server and displays it in a format that is easily understandable to the user.

[0465] Input: Feedback in report format

[0466] Data display: how it appears in the user interface

[0467] Output: On-screen feedback

[0468] Through the above processing steps, specific and practical recommendations for the user's business idea are provided. For example, information such as the following prompt sentence is presented:

[0469] User Ideas:

[0470] Intuitive virtual store layout.

[0471] Color-code your products by category.

[0472] Visually easy to understand.

[0473] Sentiment: Positive (engaged and motivated)

[0474] Recommendation:

[0475] In a similar success story, color-coded categories increased user satisfaction by 20%.

[0476] To further highlight the visual impact of the category layout, high-resolution images and interactive feedback features should be added.

[0477] It is also important to implement concise design guidelines to avoid visual clutter.

[0478] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0479] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0480] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0481] [Second embodiment]

[0482] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0483] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0484] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0485] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0486] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0487] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0488] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0489] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0490] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0491] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0492] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0493] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0494] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users, and provides specific recommendations. This system is mainly composed of a server, terminals, and generation AI.

[0495] System configuration

[0496] 1. User Device

[0497] Users input their business ideas through the terminal interface, providing detailed information about the idea (target user demographics, characteristics, goals, etc.) through an input form.

[0498] The terminal generates and sends an API request to send the input data to the server.

[0499] 2. Server

[0500] The server analyzes the business ideas received from the device, extracting important keywords using text analysis libraries and natural language processing technology.

[0501] The server generates a database query based on the extracted keywords and searches the database for past information on similar services.

[0502] The server formats the data and converts it into a format that can be input into the generative AI through a standardization process.

[0503] 3. Generation AI

[0504] Generative AI analyzes past data on similar services to identify factors that led to success and failure. For example, it uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[0505] Based on the factors identified by the generative AI, it generates recommendations that can be applied to the user's business idea.

[0506] Providing feedback

[0507] The server generates a report based on the analysis results and recommendations received from the generative AI, which includes specific improvements and recommendations.

[0508] The server transmits the generated report to the terminal.

[0509] The terminal displays the report in a user interface so that the user can easily view and understand it.

[0510] Specific examples

[0511] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0512] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0513] 2. The device converts the input idea information into JSON format and sends it to the server.

[0514] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0515] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[0516] 5. The server standardizes the acquired data and passes it to the generation AI.

[0517] 6. Generative AI analyzes and identifies the factors that made similar services successful (e.g., "improved user engagement") and the factors that made them unsuccessful (e.g., "privacy issues") in the past.

[0518] 7. Based on the analysis results, the server generates recommendations such as "strengthen privacy protection measures and add specific interactive features to increase user engagement" and compiles them in the form of a report.

[0519] 8. The server sends the generated report to the terminal.

[0520] 9. The terminal displays the report on the user interface, allowing the user to easily view the feedback.

[0521] In this way, the system of the present invention leverages past lessons learned from the user's business ideas and provides specific and useful feedback.

[0522] The processing flow will be explained below.

[0523] Step 1:

[0524] Users input their business ideas through the terminal interface, providing specific information such as the business overview, target users, unique features, and the problems they are trying to solve.

[0525] Step 2:

[0526] The device organizes the input business idea, converts it into JSON format data, generates an API request, and sends the business idea data to the server.

[0527] Step 3:

[0528] The server analyzes the business idea data received from the API request. It uses text analysis libraries and natural language processing techniques to extract important keywords from the business idea. For example, keywords such as "young people" and "interactive features" are extracted.

[0529] Step 4:

[0530] The server generates a database query based on the extracted keywords, which includes conditions for searching for similar past services.

[0531] Step 5:

[0532] The server executes the generated query against a database to retrieve data on similar services from the past. The data retrieved from the database includes success stories and failure stories for each service.

[0533] Step 6:

[0534] The server puts the acquired data through a standardization process, which puts the data into a consistent format and makes it suitable for AI analysis.

[0535] Step 7:

[0536] The server passes the standardized data to the generation AI, which analyzes the data and identifies the success and failure factors of similar services in the past.

[0537] Step 8:

[0538] Generative AI uses natural language processing technology to analyze reviews and rating data and extract specific success factors (e.g., "high user engagement") and failure factors (e.g., "privacy issues").

[0539] Step 9:

[0540] Based on the analysis results returned by the generative AI, the server generates specific recommendations that can be applied to the user's business idea, including how to incorporate success factors and how to avoid failure factors.

[0541] Step 10:

[0542] The server documents the generated recommendations in a report format and presents them in a form that is easily understandable to the user.

[0543] Step 11:

[0544] The server sends the generated report to the terminal.

[0545] Step 12:

[0546] The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[0547] In this way, the system of the present invention can analyze the business ideas provided by the user and provide specific feedback based on past lessons learned, thereby increasing the chances of business success.

[0548] Example 1

[0549] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0550] Conventional business idea evaluation systems have difficulty analyzing user-provided ideas while effectively utilizing similar past cases. This makes it difficult to provide specific and useful feedback to users. Furthermore, the process of providing analysis results to users is inefficient, making it difficult to provide useful information in a timely manner. This results in a lack of concrete guidelines for increasing the success rate of business ideas, and a lack of information for users to refine their ideas.

[0551] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0552] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, means for searching a database of similar past services based on the extracted keywords, means for analyzing the data of the searched similar services using generated artificial intelligence to identify factors for success and failure, means for generating recommendations applicable to the user's business idea based on the analysis results using numerical analysis techniques and the generated artificial intelligence, and means for feeding back the generated recommendations to the user in the form of a report. This makes it possible to provide specific and useful feedback based on similar past cases in a timely manner.

[0553] "User" refers to a user who inputs a business idea into the system.

[0554] "Terminal" refers to a communication device used by a user to input a business idea and receive feedback from the server.

[0555] "Server" refers to the core part of the system that analyzes data sent by users, generates recommendations using the generated artificial intelligence, and provides feedback to users.

[0556] "Business idea" refers to a business concept or plan newly devised by a user.

[0557] "Keywords" refer to important words and phrases extracted through analysis from the text of a business idea.

[0558] "Information repository" refers to a database that stores data on similar past services.

[0559] "Generated AI" refers to the AI ​​technology used to analyze data from similar past services, identify factors that led to success and failure, and generate recommendations.

[0560] "Success factors" refer to the elements and conditions that made similar services successful in the past.

[0561] "Failure factors" refer to the elements and conditions that caused similar services to fail in the past.

[0562] "Numerical analysis technology" refers to the technology used to generate specific recommendations based on data analyzed by the generated artificial intelligence.

[0563] The "report format" refers to the format of a document compiled to convey the generated recommendations to the user in an easy-to-understand manner.

[0564] "Feedback" refers to information provided to users that has been analyzed and recommended by the generated artificial intelligence.

[0565] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users and provides specific recommendations. This system is mainly composed of a server, terminals, and generation AI. The following specific hardware and software are used for the processing:

[0566] 1. User Device

[0567] Users input their business ideas through the terminal interface, providing detailed information about the idea (target user demographics, characteristics, goals, etc.) through an input form.

[0568] The terminal generates and sends an API request to send the input data to the server.

[0569] 2. Server

[0570] The server analyzes the business ideas received from the device and extracts important keywords using text analysis libraries (e.g., NLTK and spaCy) and natural language processing techniques (e.g., BERT and GPT-4).

[0571] The server generates a database query based on the extracted keywords and searches for past information on similar services from a database (e.g., MySQL, PostgreSQL).

[0572] The server formats the acquired data and converts it into a format that can be input into the generation AI through a standardization process.

[0573] 3. Generation AI

[0574] Generative AI analyzes past data on similar services to identify factors that led to success and failure. For example, it uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[0575] Based on the factors identified by the generative AI, it generates recommendations that can be applied to the user's business idea.

[0576] 4. Providing Feedback

[0577] The server generates a report based on the analysis results and recommendations received from the generative AI, which includes specific improvements and recommendations.

[0578] The server transmits the generated report to the terminal.

[0579] The terminal displays the report in a user interface so that the user can easily view and understand it.

[0580] Specific examples

[0581] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0582] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0583] 2. The device converts the input idea information into JSON format and sends it to the server.

[0584] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0585] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[0586] 5. The server standardizes the acquired data and passes it to the generation AI.

[0587] 6. Generative AI analyzes and identifies the factors that made similar services successful (e.g., "improved user engagement") and the factors that made them unsuccessful (e.g., "privacy issues") in the past.

[0588] 7. Based on the analysis results, the server generates recommendations such as "strengthen privacy protection measures and add specific interactive features to increase user engagement" and compiles them in the form of a report.

[0589] 8. The server sends the generated report to the terminal.

[0590] 9. The terminal displays the report on the user interface, allowing the user to easily view the feedback.

[0591] In this way, the system of the present invention leverages past lessons learned from the user's business ideas and provides specific and useful feedback.

[0592] Prompt Sentence Examples

[0593] The following is a specific example of a prompt sentence to be input to the generation AI of this system:

[0594] Analyze a business idea for a "new social media app." The target user demographic is young people, and interactive features are planned. Provide specific recommendations for this idea based on the success and failure factors of similar services in the past.

[0595] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0596] Step 1:

[0597] Users input their business idea on their device, for example, "Add interactive features to a new social media app targeting a younger demographic," and this input data is collected through a front-end input form.

[0598] Step 2:

[0599] The terminal receives the business idea entered by the user and converts it into JSON format. Specifically, it generates the following JSON data:

[0600] json

[0601] {

[0602] "idea": "New social media app targets a younger demographic and adds interactive features"

[0603] }

[0604] This converted data is sent to the server as an API request.

[0605] Step 3:

[0606] The server parses the received JSON data and extracts the text of the business idea. It then uses a text analysis library (e.g., NLTK or spaCy) to extract important keywords. For example, keywords such as "young people" and "interactive features" are extracted. The input of this keyword extraction process is the text of the business idea, and the output is a list of extracted keywords.

[0607] Step 4:

[0608] The server generates a database query based on the keyword list. The generated query is used to search and retrieve related past similar service information from a database (e.g., MySQL, PostgreSQL). The input of this database search is the keyword list, and the output is a set of past similar service information.

[0609] Step 5:

[0610] The server standardizes the acquired data into a format that can be input into the generation AI. Specifically, it formats the data and deletes unnecessary information. The input for this standardization process is the acquired information on similar services from the past, and the output is the standardized data.

[0611] Step 6:

[0612] The server passes the standardized data to a generation AI (e.g., GPT-4) to analyze the success and failure factors of similar services in the past. The generation AI analyzes reviews and evaluation data and extracts important factors. The input of this analysis process is the standardized data, and the output is a list of success factors and a list of failure factors.

[0613] Step 7:

[0614] Based on the analysis results, the generative AI generates specific recommendations that can be applied to the user's business idea. For example, a recommendation might be generated such as "strengthen privacy protection measures and add specific interactive features to increase user engagement." The input to this recommendation generation process is a list of success factors and a list of failure factors, and the output is a specific recommendation.

[0615] Step 8:

[0616] The server generates a detailed report based on the recommendations received from the generation AI. This report includes the recommendations and an analysis of the success and failure factors. The input to this report generation process is the specific recommendations, and the output is the report.

[0617] Step 9:

[0618] The server sends the generated report to the user's device. Specifically, it sends the report data as an API response. The input of this sending process is the report, and the output is a transmission to the user's device.

[0619] Step 10:

[0620] The terminal displays the received report on a user interface so that the user can easily check it. For example, the report is displayed in a text area and formatted so that each recommendation and analysis result is clearly visible. The input of this display process is the report data, and the output is the display on the user interface.

[0621] (Application example 1)

[0622] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0623] In today's world, it is extremely important to design new services using autonomous vehicles and obtain specific recommendations. However, there is a lack of systems that can effectively utilize the success and failure factors of past similar services and obtain appropriate feedback. As a result, entrepreneurs and companies face many challenges in planning and executing new businesses, making it difficult to design effective services. In particular, the lack of a means to obtain recommendations specific to services using autonomous vehicles makes it difficult to provide efficient and safe services. Furthermore, there is a need to automatically generate specific recommendations based on success and failure factors and provide them to users.

[0624] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0625] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, and means for searching a database for similar past services based on the extracted keywords. This makes it possible to analyze the success and failure factors of similar past services, automatically generate specific recommendations to support the service design for autonomous vehicles, and provide them to the user.

[0626] A "business idea" is a new business concept or service design proposal devised by a user.

[0627] "Analysis" is the act of examining received data in detail and extracting meaningful information from it.

[0628] "Keywords" are important words or phrases that represent a particular theme or content and are extracted through data analysis.

[0629] "Similar services" refer to businesses or services that have been developed in the past that share characteristics and objectives with the user's business idea.

[0630] A "database" is a system that can store, manage, and search large amounts of information in an organized manner.

[0631] "Generative artificial intelligence" is a collection of machine learning models and algorithms designed and trained to perform specific tasks.

[0632] "Success factors" refer to the elements and conditions that have brought about positive results in similar services in the past.

[0633] "Failure factors" refer to elements or conditions that have led to negative results in similar services in the past.

[0634] A "Recommendation" is specific advice or recommendation based on the analysis results to improve or optimize the user's business idea.

[0635] The "report format" is a format in which the analysis results and recommendations are organized and formalized into a single document.

[0636] A "standardization process" is a procedure for converting diverse data into a consistent format to facilitate subsequent analysis and processing.

[0637] An "autonomous vehicle" is a vehicle that can drive autonomously without the need for driver intervention.

[0638] "Feedback" refers to the act of returning the analysis results and recommendations generated by the system to the user, providing advice and evaluation.

[0639] An embodiment of the present invention will now be described in detail.

[0640] System configuration

[0641] A system is constructed that uses a server, user terminals, and a generative AI model. Users input business ideas using user terminals such as smartphones or computers. The server analyzes the received business ideas and extracts keywords. Based on the extracted keywords, it searches and retrieves data on similar past services from a database and converts it into a format that can be input into the generative AI model. The generative AI model analyzes data on similar past services, identifies factors that led to success and failure, and generates specific recommendations to be provided to the user. The server sends the generated recommendations to the user terminal as feedback.

[0642] Hardware and software used

[0643] Hardware: Smartphones (iPhone, Android), Computers (PC, Mac)

[0644] software:

[0645] Flask: Used as a web server to manage API requests and responses.

[0646] SQLite: Used as a database to store and search historical data on similar services.

[0647] transformers: Libraries for using generative AI models, especially for text analysis and natural language processing.

[0648] Data processing and calculation

[0649] User Interface

[0650] Users enter their business ideas for autonomous vehicles through a user interface. The interface provides an easy-to-use input form to collect details about the business idea (e.g., target customer demographic, characteristics, goals, etc.). For example, a user may enter an idea for adding interactive features to a "transportation service using autonomous vehicles" targeted at young people.

[0651] Server-side processing

[0652] 1. Receiving and analyzing: The server receives the business idea sent from the user's device and uses a text analysis library to extract important keywords.

[0653] 2. Database search: Search the SQLite database based on the extracted keywords to obtain past data on similar services.

[0654] 3. Data conversion and input: The acquired data is processed through a standardization process and converted into a format that can be processed by the generative AI model.

[0655] Analysis and recommendation generation by generative AI

[0656] The generative AI model analyzes the success and failure factors of similar services in the past and generates specific recommendations for improving the business idea, such as "strengthening privacy protection measures and adding specific interactive features to increase user engagement."

[0657] Providing feedback

[0658] The server generates a report based on the generated recommendations and sends it to the user interface for display, allowing the user to read the report and get specific feedback on their business idea.

[0659] Examples of prompt statements

[0660] An example of a prompt is as follows:

[0661] Analyze the following data and provide business optimization suggestions: [{"idea": "A shuttle service using autonomous vehicles. The main target is urban businessmen. It should help them reach their destinations quickly and efficiently.", "keywords": ["autonomous vehicle", "shuttle service", "urban area", "businessmen"], "similar_services": [{"name": "Service A", "success_factors": ["route optimization"], "failure_factors": ["security concerns"]}, {"name": "Service B", "success_factors": ["user engagement"], "failure_factors": ["high cost"]}]}]

[0662] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0663] Step 1:

[0664] Users input their business ideas using the user interface of their smartphone or computer. In the input form, they can enter detailed information about the business idea (target user demographics, characteristics, goals, etc.). For example, they can enter an idea for a "transportation service using self-driving cars" targeting "urban businessmen." The entered business idea is sent to the server.

[0665] Step 2:

[0666] The server receives business ideas submitted by users. It analyzes the received data using a text analysis library (e.g., NLTK or SpaCy) to extract important keywords. The input for the analysis is the text of the business idea, and the output is the extracted keywords (e.g., "self-driving car," "transportation," "urban area," "businessman").

[0667] Step 3:

[0668] The server searches for data on similar services from an SQLite database based on the extracted keywords. The search query includes the extracted keywords and retrieves past information on similar services from the database. The input is the extracted keywords, and the output is similar service data (e.g., "Service A" and "Service B") as search results.

[0669] Step 4:

[0670] The server organizes the acquired similar service data through a standardization process and converts it into a format that can be input into the generative AI model. The input is the similar service data as a search result, and the output is formatted data that is easy for the generative AI model to analyze.

[0671] Step 5:

[0672] The server inputs the formatted data into a generative AI model to identify the success and failure factors of similar past services. The input is standardized data on similar services, and the output is the results of identifying the success and failure factors. The identified success and failure factors are obtained by analyzing review and evaluation data using natural language processing technology.

[0673] Step 6:

[0674] The generative AI model generates specific recommendations that can be applied to the user's business idea based on the identified success and failure factors. The input is the analysis results of the success and failure factors, and the output is the generated recommendation (e.g., "strengthen privacy protection measures and add specific interactive features to increase user engagement").

[0675] Step 7:

[0676] The server generates a report based on the generated recommendations, which includes improvements and recommendations. The input is the generated recommendations, and the output is feedback in the form of a report.

[0677] Step 8:

[0678] The server sends the generated report to the user interface and displays it for the user to review. The user can use the provided feedback to improve or optimize their business idea. The input is the feedback in the form of a report, and the output is the feedback information reviewed by the user.

[0679] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0680] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users and provides specific recommendations. The system is characterized by the addition of an emotion engine, which recognizes the user's emotions and reflects that emotion data in the feedback content. The system is primarily composed of a server, a terminal, a generation AI, and an emotion engine.

[0681] System configuration

[0682] User terminal

[0683] 1. The user enters their business idea through the terminal interface, providing detailed information about the idea (target user demographic, characteristics, goals, etc.) through the input form.

[0684] 2. The device generates and sends an API request to send the input data to the server.

[0685] server

[0686] 3. The server analyzes the business idea received from the device, using text analysis libraries and natural language processing techniques to extract important keywords.

[0687] 4. The server generates a database query based on the extracted keywords and searches for past information on similar services.

[0688] 5. The server standardizes the data and converts it into a format that can be input into the generative AI.

[0689] 6. The server runs an emotion engine to identify emotions from the user's input data. The emotion engine recognizes emotions (e.g., positive, negative, doubt, etc.) from the user's input text and voice data.

[0690] Generative AI and Emotion Engine

[0691] 7. Generative AI analyzes past data on similar services to identify factors that led to success and failure. It uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[0692] 8. The emotion engine analyzes the user's emotion data and determines the priority of suggesting improvements to the business idea. For example, if the user is expressing positive emotions, it will prioritize aggressive suggestions, and if the user is expressing negative emotions, it will prioritize risk-avoidance suggestions.

[0693] Providing feedback

[0694] 9. The server combines the analysis results received from the generative AI with the results of the emotion engine to generate specific recommendations that can be applied to each individual. These recommendations include how to incorporate success factors and how to avoid failure factors.

[0695] 10. The server documents the generated recommendations in a report format, making them easy for the user to understand. At this time, it also adjusts the way feedback is presented based on the emotional data.

[0696] 11. The server sends the generated report to the terminal.

[0697] 12. The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[0698] Specific examples

[0699] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0700] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0701] 2. The device converts the input idea information into JSON format and sends it to the server.

[0702] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0703] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[0704] 5. The server standardizes the acquired data and passes it to the generation AI.

[0705] 6. At the same time, the server uses an emotion engine to recognize emotions from the user's input text. For example, if the user includes many positive comments, it will recognize the emotion as positive.

[0706] 7. Generative AI analyzes and identifies the success factors (e.g., "improved user engagement") and failure factors (e.g., "privacy issues") of similar services in the past.

[0707] 8. The emotion engine suggests improvements based on positive emotions, prioritizing risk-taking and proactive proposals.

[0708] 9. The server integrates the analysis results with the emotional data and generates recommendations such as "strengthening privacy protection measures while adding interactive features to increase user engagement."

[0709] 10. The server compiles the generated recommendations into a report and sends it to the device.

[0710] 11. The terminal displays the report for the user to easily review, which includes feedback reflecting the user's positive feelings.

[0711] The processing flow will be explained below.

[0712] Step 1:

[0713] Users input their business ideas through the device interface, providing specific information such as a business overview, target user demographics, unique features, and the problems they aim to solve.

[0714] Step 2:

[0715] The device organizes the input business idea and converts it into JSON format data, which is then sent to the server as an API request.

[0716] Step 3:

[0717] The server analyzes the business idea data received from the API request. It uses text analysis libraries and natural language processing techniques to extract important keywords from the business idea. For example, "younger demographic" and "interactive features" are extracted.

[0718] Step 4:

[0719] The server generates a database query based on the extracted keywords, the query including conditions for searching for similar past services.

[0720] Step 5:

[0721] The server uses the generated query to retrieve data on similar services from a database, including success stories and failure stories for each service.

[0722] Step 6:

[0723] The server puts the acquired data through a standardization process, which puts the data into a consistent format and makes it suitable for AI analysis.

[0724] Step 7:

[0725] The server passes the standardized data to the Generator AI, which analyzes the data and identifies factors that contributed to the success and failure of similar services in the past. For example, it identifies factors such as "high user engagement" and "privacy issues."

[0726] Step 8:

[0727] At the same time, the server passes the user's input data to the emotion engine, which recognizes the user's emotions from the text and feedback content. For example, it analyzes emotions such as "positive," "negative," and "doubtful."

[0728] Step 9:

[0729] The server generates feedback adapted to the user's emotions based on the emotion data received from the emotion engine. If the emotion is positive, it prioritizes aggressive suggestions, and if the emotion is negative, it prioritizes risk-averse suggestions.

[0730] Step 10:

[0731] The server combines the analysis results of the generative AI with data from the emotion engine to generate specific recommendations for the user's business idea, including how to incorporate success factors and how to avoid failure factors.

[0732] Step 11:

[0733] The server documents the generated recommendations in a report format, making them easy for users to understand, and adjusts the way feedback is presented based on emotional data.

[0734] Step 12:

[0735] The server sends the generated report to the terminal.

[0736] Step 13:

[0737] The terminal displays the report in a user interface, allowing the user to review the report and revise or improve their business idea based on the suggested improvements and recommendations.

[0738] Specific examples

[0739] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0740] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0741] 2. The device converts the input idea information into JSON format and sends it to the server.

[0742] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0743] 4. The server searches the database using the extracted keywords to obtain data on similar past services.

[0744] 5. The server standardizes the acquired data and inputs it into the generation AI.

[0745] 6. The server simultaneously runs an emotion engine to analyze the user's emotions. For example, if there are many positive comments, it will recognize the emotion as positive.

[0746] 7. Generative AI identifies the success and failure factors of similar services in the past, such as "high user engagement" and "privacy issues."

[0747] 8. The server generates feedback that reflects the user's positive emotions based on the analysis results of the emotion engine.

[0748] 9. The server integrates the analysis results of the generative AI with the data from the emotion engine and generates recommendations such as "strengthening privacy protection measures while adding interactive features to increase user engagement."

[0749] 10. The server compiles the generated recommendations into a report and sends it to the terminal.

[0750] 11. The device displays the report and allows the user to review the recommendations. The report reflects feedback based on the user's positive sentiment.

[0751] In this way, the system of the present invention analyzes the user's business idea and adjusts the feedback according to the user's emotions, thereby providing more effective and personalized recommendations.

[0752] Example 2

[0753] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0754] While conventional business idea evaluation systems analyze ideas submitted by users and generate recommendations, they have issues with providing feedback that takes users' emotions into consideration. Furthermore, the process of effectively standardizing data from similar services and using artificial intelligence to identify factors behind success and failure remains immature. As a result, recommendations provided to users are uniform and lack specificity tailored to individual needs.

[0755] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0756] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, means for searching a database for similar past services based on the extracted keywords, means for organizing the data of the searched similar services through a standardization process, means for identifying success factors and failure factors using generated artificial intelligence based on the received business idea and data on similar past services, means for recognizing the user's feelings about the user's business idea and adjusting the content of recommendations based on the emotion data, means for generating recommendations applicable to the user's business idea based on the analysis results and the emotion data, and means for feeding back the generated recommendations to the user. This makes it possible to provide personalized recommendations that take the user's emotions into consideration, enabling feedback that is specific and effective.

[0757] "User" means an individual or legal entity that utilizes the system to provide business ideas and receive feedback.

[0758] A "business idea" is a new business concept or plan provided by a user.

[0759] A "terminal" is a hardware or software device through which a user inputs a business idea and communicates with a server.

[0760] The "server" is a central control device that analyzes business ideas received from users and performs database searches and executes generative AI.

[0761] "Generative AI" is a machine learning model or artificial intelligence program that analyzes past data on similar services to identify factors that contribute to success or failure.

[0762] An "emotion engine" is software or algorithms for identifying emotions from user input text or voice data.

[0763] "Keywords" are important words or phrases extracted through analysis of a business idea.

[0764] A "database" is a digital storage system for storing information on similar past services.

[0765] The "standardization process" is a series of procedures for converting acquired data into a format suitable for analysis and AI input.

[0766] "Success factors" are elements derived from past successful service cases.

[0767] "Failure factors" are elements derived from past cases of service failure.

[0768] "Recommendations" are advice and recommendations created based on the analysis results of the generative AI and emotion engine, based on the user's business idea.

[0769] "Feedback" refers to the process and output of returning recommendations to the user.

[0770] A "report" is a documented summary of the generated recommendations that is provided to the user.

[0771] This system analyzes business ideas submitted by users and provides specific recommendations based on the factors behind the success and failure of similar services in the past. This system is primarily composed of a server, a terminal, a generative AI, and an emotion engine.

[0772] User terminal

[0773] 1. Users input their business idea through the device interface. For example, they could input an idea such as "Add interactive features to a new social media app targeted at young people."

[0774] 2. The device converts the input business idea into JSON format and generates an API request to send to the server. The device sends this data to the server via an HTTP POST request.

[0775] server

[0776] 3. The server analyzes the business ideas received from the device. Specifically, it uses a text analysis library (e.g., SpaCy or NLTK) to extract important keywords. Keywords such as "young people" and "interactive features" are extracted from the received data.

[0777] 4. The server queries a database (e.g., MySQL or MongoDB) based on the extracted keywords to search for past information on similar services. This involves retrieving data on similar services from the database and preparing the settings for analysis.

[0778] 5. The server standardizes the acquired data and converts it into a format that can be input to the generative AI (e.g., CSV or JSON). It formats the acquired data to remove unnecessary information and prepares the standardized data for passing to the generative AI model.

[0779] 6. The server runs an emotion engine (e.g., IBM Watson or Microsoft Azure's emotion analysis API) to identify emotions from the user's input text. The emotion engine analyzes emotions (positive, negative, suspicious, etc.) and generates emotion data for further processing.

[0780] Generative AI and Emotion Engine

[0781] 7. Generative AI analyzes past data from similar services to identify factors that led to success and failure. Specifically, it uses natural language processing technology (e.g., BERT or GPT-3) to analyze reviews and evaluation data and extract important factors. For example, it identifies factors that led to success and failure, such as "improved user engagement" and "privacy issues."

[0782] 8. The emotion engine prioritizes suggestions for improving business ideas based on the user's emotion data. If the emotion is positive, it prioritizes proactive suggestions (e.g., "rapid rollout of new features"), and if the emotion is negative, it prioritizes risk-avoidance suggestions (e.g., "trial run").

[0783] Providing feedback

[0784] 9. The server combines the results of the generative AI analysis with those of the emotion engine to generate specific recommendations that can be applied individually. An example of a recommendation would be to "strengthen privacy protection measures while adding interactive features to increase user engagement."

[0785] 10. The server documents the generated recommendations in a report format (e.g., PDF or HTML) and organizes it in a format that is easy for the user to understand. The report also adjusts the presentation of feedback based on the user's emotional data.

[0786] 11. The server sends the generated report to the terminal.

[0787] 12. The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[0788] Specific examples

[0789] For example, if a user provides an idea for a "new social media app," the system might:

[0790] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0791] 2. The device converts this information into JSON format and sends it to the server as an API request.

[0792] 3. The server extracts keywords such as "young people" and "interactive features" from the input data and retrieves information on past similar services from a database.

[0793] 4. The server normalizes the acquired data and prepares it for passing to the generation AI.

[0794] 5. The server uses an emotion engine to recognize emotions from the user's input text, for example, identifying positive emotions.

[0795] 6. Generative AI identifies the success and failure factors of similar services and suggests areas for improvement.

[0796] 7. The emotion engine prioritizes proactive suggestions based on positive emotions.

[0797] 8. The server integrates these findings and generates specific recommendations, such as "strengthen privacy protection measures while adding interactive features to increase user engagement."

[0798] 9. The server compiles the recommendations into a report and sends it to the device.

[0799] 10. The terminal displays the report so that the user can easily check it and use it to improve their idea.

[0800] This system provides personalized recommendations that reflect the user's feelings, effectively supporting the process of improving business ideas.

[0801] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0802] Step 1:

[0803] The user enters a new business idea into the user interface. Specifically, the user enters text such as "Add interactive features to a new social media app targeted at young people" into a dedicated form on the user interface. This is the input data for step 1.

[0804] Step 2:

[0805] The terminal converts the input business idea into JSON format. Specifically, it parses the input text data, organizes it into appropriate key-value pairs, and generates a JSON object. The resulting JSON format data is then output.

[0806] Step 3:

[0807] The device sends the generated JSON data to the server as an API request. Specifically, it generates an HTTP POST request and sends the JSON data in the request body to the server. If the transmission is successful, the server receives the data.

[0808] Step 4:

[0809] The server analyzes the business ideas received from the device. Specifically, it uses a text analysis library (e.g., SpaCy or NLTK) to extract text from the JSON data and analyze important keywords. The input is the JSON data received from the device, and the output is a list of extracted keywords.

[0810] Step 5:

[0811] The server performs a database search based on the extracted keywords. Specifically, it generates an SQL query and executes it against a database (e.g., MySQL or MongoDB). The input is the extracted keywords, and the output is the search results of similar services.

[0812] Step 6:

[0813] The server standardizes the retrieved data from similar services. Specifically, it performs data cleansing and format conversion to prepare the data in a format suitable for the generative AI model (e.g., CSV or JSON). The input is the data obtained by the search, and the output is the standardized data.

[0814] Step 7:

[0815] The server passes the standardized data to the generative AI model. Specifically, it sends an API request in a format that the AI ​​model can understand and begins analysis. The input is the standardized data, and the output is the analysis results from the generative AI model.

[0816] Step 8:

[0817] The server uses an emotion engine to identify emotions from the user's input data. Specifically, it sends the data to an emotion engine API (e.g., IBM Watson or Microsoft Azure's emotion analysis API) to perform emotion analysis. The input is the user's input text, and the output is the recognized emotion data.

[0818] Step 9:

[0819] The server integrates the analysis results of the generative AI model and the results of the emotion engine. Specifically, it combines these data and generates specific recommendations for the user. The inputs are the analysis results of the generative AI model and the data from the emotion engine, and the output is specific recommendations.

[0820] Step 10:

[0821] The server documents the generated recommendations in a report format. Specifically, it formats the recommendations into PDF or HTML format and arranges them in an easy-to-understand layout. The input is the specific recommendations, and the output is a digital report.

[0822] Step 11:

[0823] The server sends the generated report to the terminal. Specifically, it sends the report as an HTTP response and makes it displayable on the terminal. The input at this time is the digital document of the report, and the output is the data sent to the terminal.

[0824] Step 12:

[0825] The terminal displays the received report on a user interface. Specifically, the terminal receives the report and displays it in a format that can be viewed by the user on a web page or in an app. The input at this time is the received report data, and the output is a display in a format that can be viewed by the user.

[0826] These are the detailed processing steps of the system, which makes it possible to provide personalized recommendations that take into account the user's emotions.

[0827] (Application example 2)

[0828] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0829] In conventional systems, feedback on users' business ideas was based solely on the success and failure factors of similar services in the past, and suggestions and feedback did not take into account the user's emotional state. This made it difficult to provide optimal recommendations based on the user's motivation and emotional state. This led to issues such as reduced user satisfaction and the feasibility of suggestions.

[0830] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions, analyzing the emotional data, and reflecting the emotional data in the feedback content, means for generating feedback in the form of a report and adjusting the expression method based on the emotional data, and means for standardizing data on similar services. This makes it possible to provide specific and actionable recommendations according to the user's emotional state.

[0831] definition statement

[0832] The "means for receiving business ideas from users" refers to an interface or application that allows users to input business ideas.

[0833] The "means for analyzing received business ideas and extracting keywords" refers to software and algorithms for analyzing business idea documents using natural language processing technology and identifying important keywords.

[0834] The "means for searching a database for similar past services based on extracted keywords" is a system for generating a database query and using the query to search for similar past service data.

[0835] "Means for using the searched data of similar services to analyze it using generated artificial intelligence and identify factors for success and failure" refers to a generated AI model and its execution environment for analyzing the identified data of similar services.

[0836] "Means for recognizing the user's emotions, analyzing the emotional data, and reflecting it in the feedback content" refers to the process and technology for analyzing the user's emotions using an emotion engine and applying the results to the feedback content.

[0837] "Means for generating recommendations applicable to users' business ideas based on analysis results and emotion data" refers to a system that integrates data obtained using generative AI and an emotion engine to create appropriate recommendations for users.

[0838] The "means for providing feedback of generated recommendations to the user" is an interface or application that provides the generated recommendations to the user in the form of a report.

[0839] The "means for generating feedback in the form of a report and adjusting the presentation method based on emotional data" refers to the process of converting the generated recommendations into a user-friendly format and providing feedback in a presentation method that corresponds to the user's emotions.

[0840] "Means for preparing data from similar services through a standardization process" refers to data preprocessing algorithms and technologies for converting data into a format that is easy to input into generative AI.

[0841] MODE FOR CARRYING OUT THE INVENTION

[0842] System configuration

[0843] This invention is implemented using a system comprising a user terminal, a server, a generation AI, and an emotion engine.

[0844] User terminal

[0845] The user terminal provides an interface for the user to input a business idea, which is then converted into JSON format and an API request is generated to be sent to the server.

[0846] server

[0847] The server has the following main functions:

[0848] 1. Business idea analysis:

[0849] The server uses natural language processing technology to extract important keywords from the user's business idea.

[0850] Use a text analysis library (e.g., NLTK, spaCy) to identify keywords that are used to generate data queries for further processing.

[0851] 2. Search for similar service data:

[0852] Based on the extracted keywords, the server sends a query to a database to obtain information on similar past services.

[0853] Manage data using a database management system (e.g., MySQL, PostgreSQL).

[0854] 3. Data Standardization:

[0855] The acquired data from similar services is then converted into a format that is easy for the generative AI to handle through a standardization process, which includes data cleaning and data shaping.

[0856] 4. Emotion Recognition:

[0857] The server uses an emotion engine to recognize emotions from the user's input text.

[0858] Use sentiment analysis tools (e.g., IBM Watson, Microsoft Azure Emotion API) to extract sentiment data such as positive, negative, and suspicious.

[0859] 5. Recommendation generation:

[0860] The generative AI analyzes the success and failure factors of similar services in the past and combines them with emotional data to generate recommendations for the user's business idea.

[0861] Generative AI optimizes recommendations based on the user's emotional state and provides actionable feedback.

[0862] 6. Providing Feedback:

[0863] The server compiles the generated recommendations into a report format and transmits it to the user terminal.

[0864] Based on the emotional data, the way the report is presented can be adjusted to make it easier for users to understand and accept.

[0865] Specific examples

[0866] For example, if a user submits a business idea such as:

[0867] User: "I want the layout of the virtual store to be more intuitive. I want to color-code products by category to make it easier to understand visually."

[0868] The server generates the following prompt:

[0869] User Ideas:

[0870] Intuitive virtual store layout.

[0871] Color-code your products by category.

[0872] Visually easy to understand.

[0873] Sentiment: Positive (engaged and motivated)

[0874] Recommendation:

[0875] In a similar success story, color-coded categories increased user satisfaction by 20%.

[0876] To further highlight the visual impact of the category layout, high-resolution images and interactive feedback features should be added.

[0877] It is also important to implement concise design guidelines to avoid visual clutter.

[0878] In this way, the system of the present invention supports the user's business success by providing specific and practical suggestions for the user's business ideas.

[0879] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0880] System processing steps

[0881] Step 1: User enters business idea

[0882] A user inputs a business idea using a terminal, and the input business idea is saved in text format on the terminal.

[0883] Input: Text data of the business idea (e.g., "I want to make the layout of the virtual store more intuitive. I want to color-code products by category.")

[0884] Output: Text data saved on the device

[0885] Step 2: The device sends the business idea to the server

[0886] The terminal converts the input business idea into JSON format and generates an API request to send to the server.

[0887] Input: Text data of business ideas

[0888] Data processing: Convert text data into JSON format

[0889] Output: API request in JSON format

[0890] Step 3: The server analyzes the business idea

[0891] The server analyzes the received business ideas and extracts important keywords using natural language processing technology.

[0892] Input: Business idea in JSON format

[0893] Data processing: Extract keywords using natural language processing techniques (e.g., spaCy)

[0894] Output: Extracted keywords (e.g. "layout", "intuitive", "categorical")

[0895] Step 4: The server searches for similar services

[0896] The server generates a query to the database based on the extracted keywords and searches for similar past services.

[0897] Input: Extracted keywords

[0898] Data retrieval: Search using a database management system (e.g. MySQL)

[0899] Output: Data for similar services

[0900] Step 5: Server standardizes data for similar services

[0901] The server standardizes the data it obtains from similar services and formats it into a format that is easy for the generating AI to process.

[0902] Input: Data from similar services

[0903] Data processing: Data cleaning and standardization process

[0904] Output: Standardized dataset

[0905] Step 6: The server recognizes the emotion

[0906] The server uses an emotion engine to recognize emotions from the user's input text and generate emotion data.

[0907] Input: Text data of business ideas

[0908] Data computation: Recognizing emotions using sentiment analysis tools (e.g., IBM Watson)

[0909] Output: Sentiment data (e.g., positive, negative)

[0910] Step 7: Generative AI generates recommendations

[0911] The server inputs standardized data and emotional data into the generative AI, which generates recommendations based on factors that lead to success and failure.

[0912] Input: Standardized data, emotion data

[0913] Data computation: analysis using generative AI models

[0914] Output: Recommendation (e.g., "Use a categorized layout and add high-resolution images")

[0915] Step 8: The server generates feedback in the form of a report

[0916] The server compiles the generated recommendations into a report format and adjusts the way the feedback is presented based on the emotional data.

[0917] Input: Recommendation, sentiment data

[0918] Data processing: Report generation and presentation adjustment

[0919] Output: Feedback in report format

[0920] Step 9: Your device will display feedback

[0921] The terminal receives the report sent from the server and displays it in a format that is easily understandable to the user.

[0922] Input: Feedback in report format

[0923] Data display: how it appears in the user interface

[0924] Output: On-screen feedback

[0925] Through the above processing steps, specific and practical recommendations for the user's business idea are provided. For example, information such as the following prompt sentence is presented:

[0926] User Ideas:

[0927] Intuitive virtual store layout.

[0928] Color-code your products by category.

[0929] Visually easy to understand.

[0930] Sentiment: Positive (engaged and motivated)

[0931] Recommendation:

[0932] In a similar success story, color-coded categories increased user satisfaction by 20%.

[0933] To further highlight the visual impact of the category layout, high-resolution images and interactive feedback features should be added.

[0934] It is also important to implement concise design guidelines to avoid visual clutter.

[0935] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0936] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0937] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0938] [Third embodiment]

[0939] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0940] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0941] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0942] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0943] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0944] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0945] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0946] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0947] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0948] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0949] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0950] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0951] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users, and provides specific recommendations. This system is mainly composed of a server, terminals, and generation AI.

[0952] System configuration

[0953] 1. User Device

[0954] Users input their business ideas through the terminal interface, providing detailed information about the idea (target user demographics, characteristics, goals, etc.) through an input form.

[0955] The terminal generates and sends an API request to send the input data to the server.

[0956] 2. Server

[0957] The server analyzes the business ideas received from the device, extracting important keywords using text analysis libraries and natural language processing technology.

[0958] The server generates a database query based on the extracted keywords and searches the database for past information on similar services.

[0959] The server formats the data and converts it into a format that can be input into the generative AI through a standardization process.

[0960] 3. Generation AI

[0961] Generative AI analyzes past data on similar services to identify factors that led to success and failure. For example, it uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[0962] Based on the factors identified by the generative AI, it generates recommendations that can be applied to the user's business idea.

[0963] Providing feedback

[0964] The server generates a report based on the analysis results and recommendations received from the generative AI, which includes specific improvements and recommendations.

[0965] The server transmits the generated report to the terminal.

[0966] The terminal displays the report in a user interface so that the user can easily view and understand it.

[0967] Specific examples

[0968] For example, if a user provides an idea for a "new social media app," the system works as follows:

[0969] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[0970] 2. The device converts the input idea information into JSON format and sends it to the server.

[0971] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[0972] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[0973] 5. The server standardizes the acquired data and passes it to the generation AI.

[0974] 6. Generative AI analyzes and identifies the factors that made similar services successful (e.g., "improved user engagement") and the factors that made them unsuccessful (e.g., "privacy issues") in the past.

[0975] 7. Based on the analysis results, the server generates recommendations such as "strengthen privacy protection measures and add specific interactive features to increase user engagement" and compiles them in the form of a report.

[0976] 8. The server sends the generated report to the terminal.

[0977] 9. The terminal displays the report on the user interface, allowing the user to easily view the feedback.

[0978] In this way, the system of the present invention leverages past lessons learned from the user's business ideas and provides specific and useful feedback.

[0979] The processing flow will be explained below.

[0980] Step 1:

[0981] Users input their business ideas through the terminal interface, providing specific information such as the business overview, target users, unique features, and the problems they are trying to solve.

[0982] Step 2:

[0983] The device organizes the input business idea, converts it into JSON format data, generates an API request, and sends the business idea data to the server.

[0984] Step 3:

[0985] The server analyzes the business idea data received from the API request. It uses text analysis libraries and natural language processing techniques to extract important keywords from the business idea. For example, keywords such as "young people" and "interactive features" are extracted.

[0986] Step 4:

[0987] The server generates a database query based on the extracted keywords, which includes conditions for searching for similar past services.

[0988] Step 5:

[0989] The server executes the generated query against a database to retrieve data on similar services from the past. The data retrieved from the database includes success stories and failure stories for each service.

[0990] Step 6:

[0991] The server puts the acquired data through a standardization process, which puts the data into a consistent format and makes it suitable for AI analysis.

[0992] Step 7:

[0993] The server passes the standardized data to the generation AI, which analyzes the data and identifies the success and failure factors of similar services in the past.

[0994] Step 8:

[0995] Generative AI uses natural language processing technology to analyze reviews and rating data and extract specific success factors (e.g., "high user engagement") and failure factors (e.g., "privacy issues").

[0996] Step 9:

[0997] Based on the analysis results returned by the generative AI, the server generates specific recommendations that can be applied to the user's business idea, including how to incorporate success factors and how to avoid failure factors.

[0998] Step 10:

[0999] The server documents the generated recommendations in a report format and presents them in a form that is easily understandable to the user.

[1000] Step 11:

[1001] The server sends the generated report to the terminal.

[1002] Step 12:

[1003] The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[1004] In this way, the system of the present invention can analyze the business ideas provided by the user and provide specific feedback based on past lessons learned, thereby increasing the chances of business success.

[1005] Example 1

[1006] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1007] Conventional business idea evaluation systems have difficulty analyzing user-provided ideas while effectively utilizing similar past cases. This makes it difficult to provide specific and useful feedback to users. Furthermore, the process of providing analysis results to users is inefficient, making it difficult to provide useful information in a timely manner. This results in a lack of concrete guidelines for increasing the success rate of business ideas, and a lack of information for users to refine their ideas.

[1008] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1009] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, means for searching a database of similar past services based on the extracted keywords, means for analyzing the data of the searched similar services using generated artificial intelligence to identify factors for success and failure, means for generating recommendations applicable to the user's business idea based on the analysis results using numerical analysis techniques and the generated artificial intelligence, and means for feeding back the generated recommendations to the user in the form of a report. This makes it possible to provide specific and useful feedback based on similar past cases in a timely manner.

[1010] "User" refers to a user who inputs a business idea into the system.

[1011] "Terminal" refers to a communication device used by a user to input a business idea and receive feedback from the server.

[1012] "Server" refers to the core part of the system that analyzes data sent by users, generates recommendations using the generated artificial intelligence, and provides feedback to users.

[1013] "Business idea" refers to a business concept or plan newly devised by a user.

[1014] "Keywords" refer to important words and phrases extracted through analysis from the text of a business idea.

[1015] "Information repository" refers to a database that stores data on similar past services.

[1016] "Generated AI" refers to the AI ​​technology used to analyze data from similar past services, identify factors that led to success and failure, and generate recommendations.

[1017] "Success factors" refer to the elements and conditions that made similar services successful in the past.

[1018] "Failure factors" refer to the elements and conditions that caused similar services to fail in the past.

[1019] "Numerical analysis technology" refers to the technology used to generate specific recommendations based on data analyzed by the generated artificial intelligence.

[1020] The "report format" refers to the format of a document compiled to convey the generated recommendations to the user in an easy-to-understand manner.

[1021] "Feedback" refers to information provided to users that has been analyzed and recommended by the generated artificial intelligence.

[1022] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users and provides specific recommendations. This system is mainly composed of a server, terminals, and generation AI. The following specific hardware and software are used for the processing:

[1023] 1. User Device

[1024] Users input their business ideas through the terminal interface, providing detailed information about the idea (target user demographics, characteristics, goals, etc.) through an input form.

[1025] The terminal generates and sends an API request to send the input data to the server.

[1026] 2. Server

[1027] The server analyzes the business ideas received from the device and extracts important keywords using text analysis libraries (e.g., NLTK and spaCy) and natural language processing techniques (e.g., BERT and GPT-4).

[1028] The server generates a database query based on the extracted keywords and searches for past information on similar services from a database (e.g., MySQL, PostgreSQL).

[1029] The server formats the acquired data and converts it into a format that can be input into the generation AI through a standardization process.

[1030] 3. Generation AI

[1031] Generative AI analyzes past data on similar services to identify factors that led to success and failure. For example, it uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[1032] Based on the factors identified by the generative AI, it generates recommendations that can be applied to the user's business idea.

[1033] 4. Providing Feedback

[1034] The server generates a report based on the analysis results and recommendations received from the generative AI, which includes specific improvements and recommendations.

[1035] The server transmits the generated report to the terminal.

[1036] The terminal displays the report in a user interface so that the user can easily view and understand it.

[1037] Specific examples

[1038] For example, if a user provides an idea for a "new social media app," the system works as follows:

[1039] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1040] 2. The device converts the input idea information into JSON format and sends it to the server.

[1041] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[1042] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[1043] 5. The server standardizes the acquired data and passes it to the generation AI.

[1044] 6. Generative AI analyzes and identifies the factors that made similar services successful (e.g., "improved user engagement") and the factors that made them unsuccessful (e.g., "privacy issues") in the past.

[1045] 7. Based on the analysis results, the server generates recommendations such as "strengthen privacy protection measures and add specific interactive features to increase user engagement" and compiles them in the form of a report.

[1046] 8. The server sends the generated report to the terminal.

[1047] 9. The terminal displays the report on the user interface, allowing the user to easily view the feedback.

[1048] In this way, the system of the present invention leverages past lessons learned from the user's business ideas and provides specific and useful feedback.

[1049] Prompt Sentence Examples

[1050] The following is a specific example of a prompt sentence to be input to the generation AI of this system:

[1051] Analyze a business idea for a "new social media app." The target user demographic is young people, and interactive features are planned. Provide specific recommendations for this idea based on the success and failure factors of similar services in the past.

[1052] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1053] Step 1:

[1054] Users input their business idea on their device, for example, "Add interactive features to a new social media app targeting a younger demographic," and this input data is collected through a front-end input form.

[1055] Step 2:

[1056] The terminal receives the business idea entered by the user and converts it into JSON format. Specifically, it generates the following JSON data:

[1057] json

[1058] {

[1059] "idea": "New social media app targets a younger demographic and adds interactive features"

[1060] }

[1061] This converted data is sent to the server as an API request.

[1062] Step 3:

[1063] The server parses the received JSON data and extracts the text of the business idea. It then uses a text analysis library (e.g., NLTK or spaCy) to extract important keywords. For example, keywords such as "young people" and "interactive features" are extracted. The input of this keyword extraction process is the text of the business idea, and the output is a list of extracted keywords.

[1064] Step 4:

[1065] The server generates a database query based on the keyword list. The generated query is used to search and retrieve related past similar service information from a database (e.g., MySQL, PostgreSQL). The input of this database search is the keyword list, and the output is a set of past similar service information.

[1066] Step 5:

[1067] The server standardizes the acquired data into a format that can be input into the generation AI. Specifically, it formats the data and deletes unnecessary information. The input for this standardization process is the acquired information on similar services from the past, and the output is the standardized data.

[1068] Step 6:

[1069] The server passes the standardized data to a generation AI (e.g., GPT-4) to analyze the success and failure factors of similar services in the past. The generation AI analyzes reviews and evaluation data and extracts important factors. The input of this analysis process is the standardized data, and the output is a list of success factors and a list of failure factors.

[1070] Step 7:

[1071] Based on the analysis results, the generative AI generates specific recommendations that can be applied to the user's business idea. For example, a recommendation might be generated such as "strengthen privacy protection measures and add specific interactive features to increase user engagement." The input to this recommendation generation process is a list of success factors and a list of failure factors, and the output is a specific recommendation.

[1072] Step 8:

[1073] The server generates a detailed report based on the recommendations received from the generation AI. This report includes the recommendations and an analysis of the success and failure factors. The input to this report generation process is the specific recommendations, and the output is the report.

[1074] Step 9:

[1075] The server sends the generated report to the user's device. Specifically, it sends the report data as an API response. The input of this sending process is the report, and the output is a transmission to the user's device.

[1076] Step 10:

[1077] The terminal displays the received report on a user interface so that the user can easily check it. For example, the report is displayed in a text area and formatted so that each recommendation and analysis result is clearly visible. The input of this display process is the report data, and the output is the display on the user interface.

[1078] (Application example 1)

[1079] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1080] In today's world, it is extremely important to design new services using autonomous vehicles and obtain specific recommendations. However, there is a lack of systems that can effectively utilize the success and failure factors of past similar services and obtain appropriate feedback. As a result, entrepreneurs and companies face many challenges in planning and executing new businesses, making it difficult to design effective services. In particular, the lack of a means to obtain recommendations specific to services using autonomous vehicles makes it difficult to provide efficient and safe services. Furthermore, there is a need to automatically generate specific recommendations based on success and failure factors and provide them to users.

[1081] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1082] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, and means for searching a database for similar past services based on the extracted keywords. This makes it possible to analyze the success and failure factors of similar past services, automatically generate specific recommendations to support the service design for autonomous vehicles, and provide them to the user.

[1083] A "business idea" is a new business concept or service design proposal devised by a user.

[1084] "Analysis" is the act of examining received data in detail and extracting meaningful information from it.

[1085] "Keywords" are important words or phrases that represent a particular theme or content and are extracted through data analysis.

[1086] "Similar services" refer to businesses or services that have been developed in the past that share characteristics and objectives with the user's business idea.

[1087] A "database" is a system that can store, manage, and search large amounts of information in an organized manner.

[1088] "Generative artificial intelligence" is a collection of machine learning models and algorithms designed and trained to perform specific tasks.

[1089] "Success factors" refer to the elements and conditions that have brought about positive results in similar services in the past.

[1090] "Failure factors" refer to elements or conditions that have led to negative results in similar services in the past.

[1091] A "Recommendation" is specific advice or recommendation based on the analysis results to improve or optimize the user's business idea.

[1092] The "report format" is a format in which the analysis results and recommendations are organized and formalized into a single document.

[1093] A "standardization process" is a procedure for converting diverse data into a consistent format to facilitate subsequent analysis and processing.

[1094] An "autonomous vehicle" is a vehicle that can drive autonomously without the need for driver intervention.

[1095] "Feedback" refers to the act of returning the analysis results and recommendations generated by the system to the user, providing advice and evaluation.

[1096] An embodiment of the present invention will now be described in detail.

[1097] System configuration

[1098] A system is constructed that uses a server, user terminals, and a generative AI model. Users input business ideas using user terminals such as smartphones or computers. The server analyzes the received business ideas and extracts keywords. Based on the extracted keywords, it searches and retrieves data on similar past services from a database and converts it into a format that can be input into the generative AI model. The generative AI model analyzes data on similar past services, identifies factors that led to success and failure, and generates specific recommendations to be provided to the user. The server sends the generated recommendations to the user terminal as feedback.

[1099] Hardware and software used

[1100] Hardware: Smartphones (iPhone, Android), Computers (PC, Mac)

[1101] software:

[1102] Flask: Used as a web server to manage API requests and responses.

[1103] SQLite: Used as a database to store and search historical data on similar services.

[1104] transformers: Libraries for using generative AI models, especially for text analysis and natural language processing.

[1105] Data processing and calculation

[1106] User Interface

[1107] Users enter their business ideas for autonomous vehicles through a user interface. The interface provides an easy-to-use input form to collect details about the business idea (e.g., target customer demographic, characteristics, goals, etc.). For example, a user may enter an idea for adding interactive features to a "transportation service using autonomous vehicles" targeted at young people.

[1108] Server-side processing

[1109] 1. Receiving and analyzing: The server receives the business idea sent from the user's device and uses a text analysis library to extract important keywords.

[1110] 2. Database search: Search the SQLite database based on the extracted keywords to obtain past data on similar services.

[1111] 3. Data conversion and input: The acquired data is processed through a standardization process and converted into a format that can be processed by the generative AI model.

[1112] Analysis and recommendation generation by generative AI

[1113] The generative AI model analyzes the success and failure factors of similar services in the past and generates specific recommendations for improving the business idea, such as "strengthening privacy protection measures and adding specific interactive features to increase user engagement."

[1114] Providing feedback

[1115] The server generates a report based on the generated recommendations and sends it to the user interface for display, allowing the user to read the report and get specific feedback on their business idea.

[1116] Examples of prompt statements

[1117] An example of a prompt is as follows:

[1118] Analyze the following data and provide business optimization suggestions: [{"idea": "A shuttle service using autonomous vehicles. The main target is urban businessmen. It should help them reach their destinations quickly and efficiently.", "keywords": ["autonomous vehicle", "shuttle service", "urban area", "businessmen"], "similar_services": [{"name": "Service A", "success_factors": ["route optimization"], "failure_factors": ["security concerns"]}, {"name": "Service B", "success_factors": ["user engagement"], "failure_factors": ["high cost"]}]}]

[1119] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1120] Step 1:

[1121] Users input their business ideas using the user interface of their smartphone or computer. In the input form, they can enter detailed information about the business idea (target user demographics, characteristics, goals, etc.). For example, they can enter an idea for a "transportation service using self-driving cars" targeting "urban businessmen." The entered business idea is sent to the server.

[1122] Step 2:

[1123] The server receives business ideas submitted by users. It analyzes the received data using a text analysis library (e.g., NLTK or SpaCy) to extract important keywords. The input for the analysis is the text of the business idea, and the output is the extracted keywords (e.g., "self-driving car," "transportation," "urban area," "businessman").

[1124] Step 3:

[1125] The server searches for data on similar services from an SQLite database based on the extracted keywords. The search query includes the extracted keywords and retrieves past information on similar services from the database. The input is the extracted keywords, and the output is similar service data (e.g., "Service A" and "Service B") as search results.

[1126] Step 4:

[1127] The server organizes the acquired similar service data through a standardization process and converts it into a format that can be input into the generative AI model. The input is the similar service data as a search result, and the output is formatted data that is easy for the generative AI model to analyze.

[1128] Step 5:

[1129] The server inputs the formatted data into a generative AI model to identify the success and failure factors of similar past services. The input is standardized data on similar services, and the output is the results of identifying the success and failure factors. The identified success and failure factors are obtained by analyzing review and evaluation data using natural language processing technology.

[1130] Step 6:

[1131] The generative AI model generates specific recommendations that can be applied to the user's business idea based on the identified success and failure factors. The input is the analysis results of the success and failure factors, and the output is the generated recommendation (e.g., "strengthen privacy protection measures and add specific interactive features to increase user engagement").

[1132] Step 7:

[1133] The server generates a report based on the generated recommendations, which includes improvements and recommendations. The input is the generated recommendations, and the output is feedback in the form of a report.

[1134] Step 8:

[1135] The server sends the generated report to the user interface and displays it for the user to review. The user can use the provided feedback to improve or optimize their business idea. The input is the feedback in the form of a report, and the output is the feedback information reviewed by the user.

[1136] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1137] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users and provides specific recommendations. The system is characterized by the addition of an emotion engine, which recognizes the user's emotions and reflects that emotion data in the feedback content. The system is primarily composed of a server, a terminal, a generation AI, and an emotion engine.

[1138] System configuration

[1139] User terminal

[1140] 1. The user enters their business idea through the terminal interface, providing detailed information about the idea (target user demographic, characteristics, goals, etc.) through the input form.

[1141] 2. The device generates and sends an API request to send the input data to the server.

[1142] server

[1143] 3. The server analyzes the business idea received from the device, using text analysis libraries and natural language processing techniques to extract important keywords.

[1144] 4. The server generates a database query based on the extracted keywords and searches for past information on similar services.

[1145] 5. The server standardizes the data and converts it into a format that can be input into the generative AI.

[1146] 6. The server runs an emotion engine to identify emotions from the user's input data. The emotion engine recognizes emotions (e.g., positive, negative, doubt, etc.) from the user's input text and voice data.

[1147] Generative AI and Emotion Engine

[1148] 7. Generative AI analyzes past data on similar services to identify factors that led to success and failure. It uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[1149] 8. The emotion engine analyzes the user's emotion data and determines the priority of suggesting improvements to the business idea. For example, if the user is expressing positive emotions, it will prioritize aggressive suggestions, and if the user is expressing negative emotions, it will prioritize risk-avoidance suggestions.

[1150] Providing feedback

[1151] 9. The server combines the analysis results received from the generative AI with the results of the emotion engine to generate specific recommendations that can be applied to each individual. These recommendations include how to incorporate success factors and how to avoid failure factors.

[1152] 10. The server documents the generated recommendations in a report format, making them easy for the user to understand. At this time, it also adjusts the way feedback is presented based on the emotional data.

[1153] 11. The server sends the generated report to the terminal.

[1154] 12. The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[1155] Specific examples

[1156] For example, if a user provides an idea for a "new social media app," the system works as follows:

[1157] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1158] 2. The device converts the input idea information into JSON format and sends it to the server.

[1159] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[1160] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[1161] 5. The server standardizes the acquired data and passes it to the generation AI.

[1162] 6. At the same time, the server uses an emotion engine to recognize emotions from the user's input text. For example, if the user includes many positive comments, it will recognize the emotion as positive.

[1163] 7. Generative AI analyzes and identifies the success factors (e.g., "improved user engagement") and failure factors (e.g., "privacy issues") of similar services in the past.

[1164] 8. The emotion engine suggests improvements based on positive emotions, prioritizing risk-taking and proactive proposals.

[1165] 9. The server integrates the analysis results with the emotional data and generates recommendations such as "strengthening privacy protection measures while adding interactive features to increase user engagement."

[1166] 10. The server compiles the generated recommendations into a report and sends it to the device.

[1167] 11. The terminal displays the report for the user to easily review, which includes feedback reflecting the user's positive feelings.

[1168] The processing flow will be explained below.

[1169] Step 1:

[1170] Users input their business ideas through the device interface, providing specific information such as a business overview, target user demographics, unique features, and the problems they aim to solve.

[1171] Step 2:

[1172] The device organizes the input business idea and converts it into JSON format data, which is then sent to the server as an API request.

[1173] Step 3:

[1174] The server analyzes the business idea data received from the API request. It uses text analysis libraries and natural language processing techniques to extract important keywords from the business idea. For example, "younger demographic" and "interactive features" are extracted.

[1175] Step 4:

[1176] The server generates a database query based on the extracted keywords, the query including conditions for searching for similar past services.

[1177] Step 5:

[1178] The server uses the generated query to retrieve data on similar services from a database, including success stories and failure stories for each service.

[1179] Step 6:

[1180] The server puts the acquired data through a standardization process, which puts the data into a consistent format and makes it suitable for AI analysis.

[1181] Step 7:

[1182] The server passes the standardized data to the Generator AI, which analyzes the data and identifies factors that contributed to the success and failure of similar services in the past. For example, it identifies factors such as "high user engagement" and "privacy issues."

[1183] Step 8:

[1184] At the same time, the server passes the user's input data to the emotion engine, which recognizes the user's emotions from the text and feedback content. For example, it analyzes emotions such as "positive," "negative," and "doubtful."

[1185] Step 9:

[1186] The server generates feedback adapted to the user's emotions based on the emotion data received from the emotion engine. If the emotion is positive, it prioritizes aggressive suggestions, and if the emotion is negative, it prioritizes risk-averse suggestions.

[1187] Step 10:

[1188] The server combines the analysis results of the generative AI with data from the emotion engine to generate specific recommendations for the user's business idea, including how to incorporate success factors and how to avoid failure factors.

[1189] Step 11:

[1190] The server documents the generated recommendations in a report format, making them easy for users to understand, and adjusts the way feedback is presented based on emotional data.

[1191] Step 12:

[1192] The server sends the generated report to the terminal.

[1193] Step 13:

[1194] The terminal displays the report in a user interface, allowing the user to review the report and revise or improve their business idea based on the suggested improvements and recommendations.

[1195] Specific examples

[1196] For example, if a user provides an idea for a "new social media app," the system works as follows:

[1197] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1198] 2. The device converts the input idea information into JSON format and sends it to the server.

[1199] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[1200] 4. The server searches the database using the extracted keywords to obtain data on similar past services.

[1201] 5. The server standardizes the acquired data and inputs it into the generation AI.

[1202] 6. The server simultaneously runs an emotion engine to analyze the user's emotions. For example, if there are many positive comments, it will recognize the emotion as positive.

[1203] 7. Generative AI identifies the success and failure factors of similar services in the past, such as "high user engagement" and "privacy issues."

[1204] 8. The server generates feedback that reflects the user's positive emotions based on the analysis results of the emotion engine.

[1205] 9. The server integrates the analysis results of the generative AI with the data from the emotion engine and generates recommendations such as "strengthening privacy protection measures while adding interactive features to increase user engagement."

[1206] 10. The server compiles the generated recommendations into a report and sends it to the terminal.

[1207] 11. The device displays the report and allows the user to review the recommendations. The report reflects feedback based on the user's positive sentiment.

[1208] In this way, the system of the present invention analyzes the user's business idea and adjusts the feedback according to the user's emotions, thereby providing more effective and personalized recommendations.

[1209] Example 2

[1210] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1211] While conventional business idea evaluation systems analyze ideas submitted by users and generate recommendations, they have issues with providing feedback that takes users' emotions into consideration. Furthermore, the process of effectively standardizing data from similar services and using artificial intelligence to identify factors behind success and failure remains immature. As a result, recommendations provided to users are uniform and lack specificity tailored to individual needs.

[1212] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1213] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, means for searching a database for similar past services based on the extracted keywords, means for organizing the data of the searched similar services through a standardization process, means for identifying success factors and failure factors using generated artificial intelligence based on the received business idea and data on similar past services, means for recognizing the user's feelings about the user's business idea and adjusting the content of recommendations based on the emotion data, means for generating recommendations applicable to the user's business idea based on the analysis results and the emotion data, and means for feeding back the generated recommendations to the user. This makes it possible to provide personalized recommendations that take the user's emotions into consideration, enabling feedback that is specific and effective.

[1214] "User" means an individual or legal entity that utilizes the system to provide business ideas and receive feedback.

[1215] A "business idea" is a new business concept or plan provided by a user.

[1216] A "terminal" is a hardware or software device through which a user inputs a business idea and communicates with a server.

[1217] The "server" is a central control device that analyzes business ideas received from users and performs database searches and executes generative AI.

[1218] "Generative AI" is a machine learning model or artificial intelligence program that analyzes past data on similar services to identify factors that contribute to success or failure.

[1219] An "emotion engine" is software or algorithms for identifying emotions from user input text or voice data.

[1220] "Keywords" are important words or phrases extracted through analysis of a business idea.

[1221] A "database" is a digital storage system for storing information on similar past services.

[1222] The "standardization process" is a series of procedures for converting acquired data into a format suitable for analysis and AI input.

[1223] "Success factors" are elements derived from past successful service cases.

[1224] "Failure factors" are elements derived from past cases of service failure.

[1225] "Recommendations" are advice and recommendations created based on the analysis results of the generative AI and emotion engine, based on the user's business idea.

[1226] "Feedback" refers to the process and output of returning recommendations to the user.

[1227] A "report" is a documented summary of the generated recommendations that is provided to the user.

[1228] This system analyzes business ideas submitted by users and provides specific recommendations based on the factors behind the success and failure of similar services in the past. This system is primarily composed of a server, a terminal, a generative AI, and an emotion engine.

[1229] User terminal

[1230] 1. Users input their business idea through the device interface. For example, they could input an idea such as "Add interactive features to a new social media app targeted at young people."

[1231] 2. The device converts the input business idea into JSON format and generates an API request to send to the server. The device sends this data to the server via an HTTP POST request.

[1232] server

[1233] 3. The server analyzes the business ideas received from the device. Specifically, it uses a text analysis library (e.g., SpaCy or NLTK) to extract important keywords. Keywords such as "young people" and "interactive features" are extracted from the received data.

[1234] 4. The server queries a database (e.g., MySQL or MongoDB) based on the extracted keywords to search for past information on similar services. This involves retrieving data on similar services from the database and preparing the settings for analysis.

[1235] 5. The server standardizes the acquired data and converts it into a format that can be input to the generative AI (e.g., CSV or JSON). It formats the acquired data to remove unnecessary information and prepares the standardized data for passing to the generative AI model.

[1236] 6. The server runs an emotion engine (e.g., IBM Watson or Microsoft Azure's emotion analysis API) to identify emotions from the user's input text. The emotion engine analyzes emotions (positive, negative, suspicious, etc.) and generates emotion data for further processing.

[1237] Generative AI and Emotion Engine

[1238] 7. Generative AI analyzes past data from similar services to identify factors that led to success and failure. Specifically, it uses natural language processing technology (e.g., BERT or GPT-3) to analyze reviews and evaluation data and extract important factors. For example, it identifies factors that led to success and failure, such as "improved user engagement" and "privacy issues."

[1239] 8. The emotion engine prioritizes suggestions for improving business ideas based on the user's emotion data. If the emotion is positive, it prioritizes proactive suggestions (e.g., "rapid rollout of new features"), and if the emotion is negative, it prioritizes risk-avoidance suggestions (e.g., "trial run").

[1240] Providing feedback

[1241] 9. The server combines the results of the generative AI analysis with those of the emotion engine to generate specific recommendations that can be applied individually. An example of a recommendation would be to "strengthen privacy protection measures while adding interactive features to increase user engagement."

[1242] 10. The server documents the generated recommendations in a report format (e.g., PDF or HTML) and organizes it in a format that is easy for the user to understand. The report also adjusts the presentation of feedback based on the user's emotional data.

[1243] 11. The server sends the generated report to the terminal.

[1244] 12. The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[1245] Specific examples

[1246] For example, if a user provides an idea for a "new social media app," the system might:

[1247] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1248] 2. The device converts this information into JSON format and sends it to the server as an API request.

[1249] 3. The server extracts keywords such as "young people" and "interactive features" from the input data and retrieves information on past similar services from a database.

[1250] 4. The server normalizes the acquired data and prepares it for passing to the generation AI.

[1251] 5. The server uses an emotion engine to recognize emotions from the user's input text, for example, identifying positive emotions.

[1252] 6. Generative AI identifies the success and failure factors of similar services and suggests areas for improvement.

[1253] 7. The emotion engine prioritizes proactive suggestions based on positive emotions.

[1254] 8. The server integrates these findings and generates specific recommendations, such as "strengthen privacy protection measures while adding interactive features to increase user engagement."

[1255] 9. The server compiles the recommendations into a report and sends it to the device.

[1256] 10. The terminal displays the report so that the user can easily check it and use it to improve their idea.

[1257] This system provides personalized recommendations that reflect the user's feelings, effectively supporting the process of improving business ideas.

[1258] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1259] Step 1:

[1260] The user enters a new business idea into the user interface. Specifically, the user enters text such as "Add interactive features to a new social media app targeted at young people" into a dedicated form on the user interface. This is the input data for step 1.

[1261] Step 2:

[1262] The terminal converts the input business idea into JSON format. Specifically, it parses the input text data, organizes it into appropriate key-value pairs, and generates a JSON object. The resulting JSON format data is then output.

[1263] Step 3:

[1264] The device sends the generated JSON data to the server as an API request. Specifically, it generates an HTTP POST request and sends the JSON data in the request body to the server. If the transmission is successful, the server receives the data.

[1265] Step 4:

[1266] The server analyzes the business ideas received from the device. Specifically, it uses a text analysis library (e.g., SpaCy or NLTK) to extract text from the JSON data and analyze important keywords. The input is the JSON data received from the device, and the output is a list of extracted keywords.

[1267] Step 5:

[1268] The server performs a database search based on the extracted keywords. Specifically, it generates an SQL query and executes it against a database (e.g., MySQL or MongoDB). The input is the extracted keywords, and the output is the search results of similar services.

[1269] Step 6:

[1270] The server standardizes the retrieved data from similar services. Specifically, it performs data cleansing and format conversion to prepare the data in a format suitable for the generative AI model (e.g., CSV or JSON). The input is the data obtained by the search, and the output is the standardized data.

[1271] Step 7:

[1272] The server passes the standardized data to the generative AI model. Specifically, it sends an API request in a format that the AI ​​model can understand and begins analysis. The input is the standardized data, and the output is the analysis results from the generative AI model.

[1273] Step 8:

[1274] The server uses an emotion engine to identify emotions from the user's input data. Specifically, it sends the data to an emotion engine API (e.g., IBM Watson or Microsoft Azure's emotion analysis API) to perform emotion analysis. The input is the user's input text, and the output is the recognized emotion data.

[1275] Step 9:

[1276] The server integrates the analysis results of the generative AI model and the results of the emotion engine. Specifically, it combines these data and generates specific recommendations for the user. The inputs are the analysis results of the generative AI model and the data from the emotion engine, and the output is specific recommendations.

[1277] Step 10:

[1278] The server documents the generated recommendations in a report format. Specifically, it formats the recommendations into PDF or HTML format and arranges them in an easy-to-understand layout. The input is the specific recommendations, and the output is a digital report.

[1279] Step 11:

[1280] The server sends the generated report to the terminal. Specifically, it sends the report as an HTTP response and makes it displayable on the terminal. The input at this time is the digital document of the report, and the output is the data sent to the terminal.

[1281] Step 12:

[1282] The terminal displays the received report on a user interface. Specifically, the terminal receives the report and displays it in a format that can be viewed by the user on a web page or in an app. The input at this time is the received report data, and the output is a display in a format that can be viewed by the user.

[1283] These are the detailed processing steps of the system, which makes it possible to provide personalized recommendations that take into account the user's emotions.

[1284] (Application example 2)

[1285] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1286] In conventional systems, feedback on users' business ideas was based solely on the success and failure factors of similar services in the past, and suggestions and feedback did not take into account the user's emotional state. This made it difficult to provide optimal recommendations based on the user's motivation and emotional state. This led to issues such as reduced user satisfaction and the feasibility of suggestions.

[1287] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions, analyzing the emotional data, and reflecting the emotional data in the feedback content, means for generating feedback in the form of a report and adjusting the expression method based on the emotional data, and means for standardizing data on similar services. This makes it possible to provide specific and actionable recommendations according to the user's emotional state.

[1288] definition statement

[1289] The "means for receiving business ideas from users" refers to an interface or application that allows users to input business ideas.

[1290] The "means for analyzing received business ideas and extracting keywords" refers to software and algorithms for analyzing business idea documents using natural language processing technology and identifying important keywords.

[1291] The "means for searching a database for similar past services based on extracted keywords" is a system for generating a database query and using the query to search for similar past service data.

[1292] "Means for using the searched data of similar services to analyze it using generated artificial intelligence and identify factors for success and failure" refers to a generated AI model and its execution environment for analyzing the identified data of similar services.

[1293] "Means for recognizing the user's emotions, analyzing the emotional data, and reflecting it in the feedback content" refers to the process and technology for analyzing the user's emotions using an emotion engine and applying the results to the feedback content.

[1294] "Means for generating recommendations applicable to users' business ideas based on analysis results and emotion data" refers to a system that integrates data obtained using generative AI and an emotion engine to create appropriate recommendations for users.

[1295] The "means for providing feedback of generated recommendations to the user" is an interface or application that provides the generated recommendations to the user in the form of a report.

[1296] The "means for generating feedback in the form of a report and adjusting the presentation method based on emotional data" refers to the process of converting the generated recommendations into a user-friendly format and providing feedback in a presentation method that corresponds to the user's emotions.

[1297] "Means for preparing data from similar services through a standardization process" refers to data preprocessing algorithms and technologies for converting data into a format that is easy to input into generative AI.

[1298] MODE FOR CARRYING OUT THE INVENTION

[1299] System configuration

[1300] This invention is implemented using a system comprising a user terminal, a server, a generation AI, and an emotion engine.

[1301] User terminal

[1302] The user terminal provides an interface for the user to input a business idea, which is then converted into JSON format and an API request is generated to be sent to the server.

[1303] server

[1304] The server has the following main functions:

[1305] 1. Business idea analysis:

[1306] The server uses natural language processing technology to extract important keywords from the user's business idea.

[1307] Use a text analysis library (e.g., NLTK, spaCy) to identify keywords that are used to generate data queries for further processing.

[1308] 2. Search for similar service data:

[1309] Based on the extracted keywords, the server sends a query to a database to obtain information on similar past services.

[1310] Manage data using a database management system (e.g., MySQL, PostgreSQL).

[1311] 3. Data Standardization:

[1312] The acquired data from similar services is then converted into a format that is easy for the generative AI to handle through a standardization process, which includes data cleaning and data shaping.

[1313] 4. Emotion Recognition:

[1314] The server uses an emotion engine to recognize emotions from the user's input text.

[1315] Use sentiment analysis tools (e.g., IBM Watson, Microsoft Azure Emotion API) to extract sentiment data such as positive, negative, and suspicious.

[1316] 5. Recommendation generation:

[1317] The generative AI analyzes the success and failure factors of similar services in the past and combines them with emotional data to generate recommendations for the user's business idea.

[1318] Generative AI optimizes recommendations based on the user's emotional state and provides actionable feedback.

[1319] 6. Providing Feedback:

[1320] The server compiles the generated recommendations into a report format and transmits it to the user terminal.

[1321] Based on the emotional data, the way the report is presented can be adjusted to make it easier for users to understand and accept.

[1322] Specific examples

[1323] For example, if a user submits a business idea such as:

[1324] User: "I want the layout of the virtual store to be more intuitive. I want to color-code products by category to make it easier to understand visually."

[1325] The server generates the following prompt:

[1326] User Ideas:

[1327] Intuitive virtual store layout.

[1328] Color-code your products by category.

[1329] Visually easy to understand.

[1330] Sentiment: Positive (engaged and motivated)

[1331] Recommendation:

[1332] In a similar success story, color-coded categories increased user satisfaction by 20%.

[1333] To further highlight the visual impact of the category layout, high-resolution images and interactive feedback features should be added.

[1334] It is also important to implement concise design guidelines to avoid visual clutter.

[1335] In this way, the system of the present invention supports the user's business success by providing specific and practical suggestions for the user's business ideas.

[1336] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1337] System processing steps

[1338] Step 1: User enters business idea

[1339] A user inputs a business idea using a terminal, and the input business idea is saved in text format on the terminal.

[1340] Input: Text data of the business idea (e.g., "I want to make the layout of the virtual store more intuitive. I want to color-code products by category.")

[1341] Output: Text data saved on the device

[1342] Step 2: The device sends the business idea to the server

[1343] The terminal converts the input business idea into JSON format and generates an API request to send to the server.

[1344] Input: Text data of business ideas

[1345] Data processing: Convert text data into JSON format

[1346] Output: API request in JSON format

[1347] Step 3: The server analyzes the business idea

[1348] The server analyzes the received business ideas and extracts important keywords using natural language processing technology.

[1349] Input: Business idea in JSON format

[1350] Data processing: Extract keywords using natural language processing techniques (e.g., spaCy)

[1351] Output: Extracted keywords (e.g. "layout", "intuitive", "categorical")

[1352] Step 4: The server searches for similar services

[1353] The server generates a query to the database based on the extracted keywords and searches for similar past services.

[1354] Input: Extracted keywords

[1355] Data retrieval: Search using a database management system (e.g. MySQL)

[1356] Output: Data for similar services

[1357] Step 5: Server standardizes data for similar services

[1358] The server standardizes the data it obtains from similar services and formats it into a format that is easy for the generating AI to process.

[1359] Input: Data from similar services

[1360] Data processing: Data cleaning and standardization process

[1361] Output: Standardized dataset

[1362] Step 6: The server recognizes the emotion

[1363] The server uses an emotion engine to recognize emotions from the user's input text and generate emotion data.

[1364] Input: Text data of business ideas

[1365] Data computation: Recognizing emotions using sentiment analysis tools (e.g., IBM Watson)

[1366] Output: Sentiment data (e.g., positive, negative)

[1367] Step 7: Generative AI generates recommendations

[1368] The server inputs standardized data and emotional data into the generative AI, which generates recommendations based on factors that lead to success and failure.

[1369] Input: Standardized data, emotion data

[1370] Data computation: analysis using generative AI models

[1371] Output: Recommendation (e.g., "Use a categorized layout and add high-resolution images")

[1372] Step 8: The server generates feedback in the form of a report

[1373] The server compiles the generated recommendations into a report format and adjusts the way the feedback is presented based on the emotional data.

[1374] Input: Recommendation, sentiment data

[1375] Data processing: Report generation and presentation adjustment

[1376] Output: Feedback in report format

[1377] Step 9: Your device will display feedback

[1378] The terminal receives the report sent from the server and displays it in a format that is easily understandable to the user.

[1379] Input: Feedback in report format

[1380] Data display: how it appears in the user interface

[1381] Output: On-screen feedback

[1382] Through the above processing steps, specific and practical recommendations for the user's business idea are provided. For example, information such as the following prompt sentence is presented:

[1383] User Ideas:

[1384] Intuitive virtual store layout.

[1385] Color-code your products by category.

[1386] Visually easy to understand.

[1387] Sentiment: Positive (engaged and motivated)

[1388] Recommendation:

[1389] In a similar success story, color-coded categories increased user satisfaction by 20%.

[1390] To further highlight the visual impact of the category layout, high-resolution images and interactive feedback features should be added.

[1391] It is also important to implement concise design guidelines to avoid visual clutter.

[1392] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1393] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1394] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1395] [Fourth embodiment]

[1396] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1397] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1398] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1399] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1400] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1401] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1402] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1403] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1404] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1405] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1406] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1407] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1408] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1409] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users, and provides specific recommendations. This system is mainly composed of a server, terminals, and generation AI.

[1410] System configuration

[1411] 1. User Device

[1412] Users input their business ideas through the terminal interface, providing detailed information about the idea (target user demographics, characteristics, goals, etc.) through an input form.

[1413] The terminal generates and sends an API request to send the input data to the server.

[1414] 2. Server

[1415] The server analyzes the business ideas received from the device, extracting important keywords using text analysis libraries and natural language processing technology.

[1416] The server generates a database query based on the extracted keywords and searches the database for past information on similar services.

[1417] The server formats the data and converts it into a format that can be input into the generative AI through a standardization process.

[1418] 3. Generation AI

[1419] Generative AI analyzes past data on similar services to identify factors that led to success and failure. For example, it uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[1420] Based on the factors identified by the generative AI, it generates recommendations that can be applied to the user's business idea.

[1421] Providing feedback

[1422] The server generates a report based on the analysis results and recommendations received from the generative AI, which includes specific improvements and recommendations.

[1423] The server transmits the generated report to the terminal.

[1424] The terminal displays the report in a user interface so that the user can easily view and understand it.

[1425] Specific examples

[1426] For example, if a user provides an idea for a "new social media app," the system works as follows:

[1427] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1428] 2. The device converts the input idea information into JSON format and sends it to the server.

[1429] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[1430] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[1431] 5. The server standardizes the acquired data and passes it to the generation AI.

[1432] 6. Generative AI analyzes and identifies the factors that made similar services successful (e.g., "improved user engagement") and the factors that made them unsuccessful (e.g., "privacy issues") in the past.

[1433] 7. Based on the analysis results, the server generates recommendations such as "strengthen privacy protection measures and add specific interactive features to increase user engagement" and compiles them in the form of a report.

[1434] 8. The server sends the generated report to the terminal.

[1435] 9. The terminal displays the report on the user interface, allowing the user to easily view the feedback.

[1436] In this way, the system of the present invention leverages past lessons learned from the user's business ideas and provides specific and useful feedback.

[1437] The processing flow will be explained below.

[1438] Step 1:

[1439] Users input their business ideas through the terminal interface, providing specific information such as the business overview, target users, unique features, and the problems they are trying to solve.

[1440] Step 2:

[1441] The device organizes the input business idea, converts it into JSON format data, generates an API request, and sends the business idea data to the server.

[1442] Step 3:

[1443] The server analyzes the business idea data received from the API request. It uses text analysis libraries and natural language processing techniques to extract important keywords from the business idea. For example, keywords such as "young people" and "interactive features" are extracted.

[1444] Step 4:

[1445] The server generates a database query based on the extracted keywords, which includes conditions for searching for similar past services.

[1446] Step 5:

[1447] The server executes the generated query against a database to retrieve data on similar services from the past. The data retrieved from the database includes success stories and failure stories for each service.

[1448] Step 6:

[1449] The server puts the acquired data through a standardization process, which puts the data into a consistent format and makes it suitable for AI analysis.

[1450] Step 7:

[1451] The server passes the standardized data to the generation AI, which analyzes the data and identifies the success and failure factors of similar services in the past.

[1452] Step 8:

[1453] Generative AI uses natural language processing technology to analyze reviews and rating data and extract specific success factors (e.g., "high user engagement") and failure factors (e.g., "privacy issues").

[1454] Step 9:

[1455] Based on the analysis results returned by the generative AI, the server generates specific recommendations that can be applied to the user's business idea, including how to incorporate success factors and how to avoid failure factors.

[1456] Step 10:

[1457] The server documents the generated recommendations in a report format and presents them in a form that is easily understandable to the user.

[1458] Step 11:

[1459] The server sends the generated report to the terminal.

[1460] Step 12:

[1461] The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[1462] In this way, the system of the present invention can analyze the business ideas provided by the user and provide specific feedback based on past lessons learned, thereby increasing the chances of business success.

[1463] Example 1

[1464] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1465] Conventional business idea evaluation systems have difficulty analyzing user-provided ideas while effectively utilizing similar past cases. This makes it difficult to provide specific and useful feedback to users. Furthermore, the process of providing analysis results to users is inefficient, making it difficult to provide useful information in a timely manner. This results in a lack of concrete guidelines for increasing the success rate of business ideas, and a lack of information for users to refine their ideas.

[1466] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1467] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, means for searching a database of similar past services based on the extracted keywords, means for analyzing the data of the searched similar services using generated artificial intelligence to identify factors for success and failure, means for generating recommendations applicable to the user's business idea based on the analysis results using numerical analysis techniques and the generated artificial intelligence, and means for feeding back the generated recommendations to the user in the form of a report. This makes it possible to provide specific and useful feedback based on similar past cases in a timely manner.

[1468] "User" refers to a user who inputs a business idea into the system.

[1469] "Terminal" refers to a communication device used by a user to input a business idea and receive feedback from the server.

[1470] "Server" refers to the core part of the system that analyzes data sent by users, generates recommendations using the generated artificial intelligence, and provides feedback to users.

[1471] "Business idea" refers to a business concept or plan newly devised by a user.

[1472] "Keywords" refer to important words and phrases extracted through analysis from the text of a business idea.

[1473] "Information repository" refers to a database that stores data on similar past services.

[1474] "Generated AI" refers to the AI ​​technology used to analyze data from similar past services, identify factors that led to success and failure, and generate recommendations.

[1475] "Success factors" refer to the elements and conditions that made similar services successful in the past.

[1476] "Failure factors" refer to the elements and conditions that caused similar services to fail in the past.

[1477] "Numerical analysis technology" refers to the technology used to generate specific recommendations based on data analyzed by the generated artificial intelligence.

[1478] The "report format" refers to the format of a document compiled to convey the generated recommendations to the user in an easy-to-understand manner.

[1479] "Feedback" refers to information provided to users that has been analyzed and recommended by the generated artificial intelligence.

[1480] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users and provides specific recommendations. This system is mainly composed of a server, terminals, and generation AI. The following specific hardware and software are used for the processing:

[1481] 1. User Device

[1482] Users input their business ideas through the terminal interface, providing detailed information about the idea (target user demographics, characteristics, goals, etc.) through an input form.

[1483] The terminal generates and sends an API request to send the input data to the server.

[1484] 2. Server

[1485] The server analyzes the business ideas received from the device and extracts important keywords using text analysis libraries (e.g., NLTK and spaCy) and natural language processing techniques (e.g., BERT and GPT-4).

[1486] The server generates a database query based on the extracted keywords and searches for past information on similar services from a database (e.g., MySQL, PostgreSQL).

[1487] The server formats the acquired data and converts it into a format that can be input into the generation AI through a standardization process.

[1488] 3. Generation AI

[1489] Generative AI analyzes past data on similar services to identify factors that led to success and failure. For example, it uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[1490] Based on the factors identified by the generative AI, it generates recommendations that can be applied to the user's business idea.

[1491] 4. Providing Feedback

[1492] The server generates a report based on the analysis results and recommendations received from the generative AI, which includes specific improvements and recommendations.

[1493] The server transmits the generated report to the terminal.

[1494] The terminal displays the report in a user interface so that the user can easily view and understand it.

[1495] Specific examples

[1496] For example, if a user provides an idea for a "new social media app," the system works as follows:

[1497] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1498] 2. The device converts the input idea information into JSON format and sends it to the server.

[1499] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[1500] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[1501] 5. The server standardizes the acquired data and passes it to the generation AI.

[1502] 6. Generative AI analyzes and identifies the factors that made similar services successful (e.g., "improved user engagement") and the factors that made them unsuccessful (e.g., "privacy issues") in the past.

[1503] 7. Based on the analysis results, the server generates recommendations such as "strengthen privacy protection measures and add specific interactive features to increase user engagement" and compiles them in the form of a report.

[1504] 8. The server sends the generated report to the terminal.

[1505] 9. The terminal displays the report on the user interface, allowing the user to easily view the feedback.

[1506] In this way, the system of the present invention leverages past lessons learned from the user's business ideas and provides specific and useful feedback.

[1507] Prompt Sentence Examples

[1508] The following is a specific example of a prompt sentence to be input to the generation AI of this system:

[1509] Analyze a business idea for a "new social media app." The target user demographic is young people, and interactive features are planned. Provide specific recommendations for this idea based on the success and failure factors of similar services in the past.

[1510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1511] Step 1:

[1512] Users input their business idea on their device, for example, "Add interactive features to a new social media app targeting a younger demographic," and this input data is collected through a front-end input form.

[1513] Step 2:

[1514] The terminal receives the business idea entered by the user and converts it into JSON format. Specifically, it generates the following JSON data:

[1515] json

[1516] {

[1517] "idea": "New social media app targets a younger demographic and adds interactive features"

[1518] }

[1519] This converted data is sent to the server as an API request.

[1520] Step 3:

[1521] The server parses the received JSON data and extracts the text of the business idea. It then uses a text analysis library (e.g., NLTK or spaCy) to extract important keywords. For example, keywords such as "young people" and "interactive features" are extracted. The input of this keyword extraction process is the text of the business idea, and the output is a list of extracted keywords.

[1522] Step 4:

[1523] The server generates a database query based on the keyword list. The generated query is used to search and retrieve related past similar service information from a database (e.g., MySQL, PostgreSQL). The input of this database search is the keyword list, and the output is a set of past similar service information.

[1524] Step 5:

[1525] The server standardizes the acquired data into a format that can be input into the generation AI. Specifically, it formats the data and deletes unnecessary information. The input for this standardization process is the acquired information on similar services from the past, and the output is the standardized data.

[1526] Step 6:

[1527] The server passes the standardized data to a generation AI (e.g., GPT-4) to analyze the success and failure factors of similar services in the past. The generation AI analyzes reviews and evaluation data and extracts important factors. The input of this analysis process is the standardized data, and the output is a list of success factors and a list of failure factors.

[1528] Step 7:

[1529] Based on the analysis results, the generative AI generates specific recommendations that can be applied to the user's business idea. For example, a recommendation might be generated such as "strengthen privacy protection measures and add specific interactive features to increase user engagement." The input to this recommendation generation process is a list of success factors and a list of failure factors, and the output is a specific recommendation.

[1530] Step 8:

[1531] The server generates a detailed report based on the recommendations received from the generation AI. This report includes the recommendations and an analysis of the success and failure factors. The input to this report generation process is the specific recommendations, and the output is the report.

[1532] Step 9:

[1533] The server sends the generated report to the user's device. Specifically, it sends the report data as an API response. The input of this sending process is the report, and the output is a transmission to the user's device.

[1534] Step 10:

[1535] The terminal displays the received report on a user interface so that the user can easily check it. For example, the report is displayed in a text area and formatted so that each recommendation and analysis result is clearly visible. The input of this display process is the report data, and the output is the display on the user interface.

[1536] (Application example 1)

[1537] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1538] In today's world, it is extremely important to design new services using autonomous vehicles and obtain specific recommendations. However, there is a lack of systems that can effectively utilize the success and failure factors of past similar services and obtain appropriate feedback. As a result, entrepreneurs and companies face many challenges in planning and executing new businesses, making it difficult to design effective services. In particular, the lack of a means to obtain recommendations specific to services using autonomous vehicles makes it difficult to provide efficient and safe services. Furthermore, there is a need to automatically generate specific recommendations based on success and failure factors and provide them to users.

[1539] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1540] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, and means for searching a database for similar past services based on the extracted keywords. This makes it possible to analyze the success and failure factors of similar past services, automatically generate specific recommendations to support the service design for autonomous vehicles, and provide them to the user.

[1541] A "business idea" is a new business concept or service design proposal devised by a user.

[1542] "Analysis" is the act of examining received data in detail and extracting meaningful information from it.

[1543] "Keywords" are important words or phrases that represent a particular theme or content and are extracted through data analysis.

[1544] "Similar services" refer to businesses or services that have been developed in the past that share characteristics and objectives with the user's business idea.

[1545] A "database" is a system that can store, manage, and search large amounts of information in an organized manner.

[1546] "Generative artificial intelligence" is a collection of machine learning models and algorithms designed and trained to perform specific tasks.

[1547] "Success factors" refer to the elements and conditions that have brought about positive results in similar services in the past.

[1548] "Failure factors" refer to elements or conditions that have led to negative results in similar services in the past.

[1549] A "Recommendation" is specific advice or recommendation based on the analysis results to improve or optimize the user's business idea.

[1550] The "report format" is a format in which the analysis results and recommendations are organized and formalized into a single document.

[1551] A "standardization process" is a procedure for converting diverse data into a consistent format to facilitate subsequent analysis and processing.

[1552] An "autonomous vehicle" is a vehicle that can drive autonomously without the need for driver intervention.

[1553] "Feedback" refers to the act of returning the analysis results and recommendations generated by the system to the user, providing advice and evaluation.

[1554] An embodiment of the present invention will now be described in detail.

[1555] System configuration

[1556] A system is constructed that uses a server, user terminals, and a generative AI model. Users input business ideas using user terminals such as smartphones or computers. The server analyzes the received business ideas and extracts keywords. Based on the extracted keywords, it searches and retrieves data on similar past services from a database and converts it into a format that can be input into the generative AI model. The generative AI model analyzes data on similar past services, identifies factors that led to success and failure, and generates specific recommendations to be provided to the user. The server sends the generated recommendations to the user terminal as feedback.

[1557] Hardware and software used

[1558] Hardware: Smartphones (iPhone, Android), Computers (PC, Mac)

[1559] software:

[1560] Flask: Used as a web server to manage API requests and responses.

[1561] SQLite: Used as a database to store and search historical data on similar services.

[1562] transformers: Libraries for using generative AI models, especially for text analysis and natural language processing.

[1563] Data processing and calculation

[1564] User Interface

[1565] Users enter their business ideas for autonomous vehicles through a user interface. The interface provides an easy-to-use input form to collect details about the business idea (e.g., target customer demographic, characteristics, goals, etc.). For example, a user may enter an idea for adding interactive features to a "transportation service using autonomous vehicles" targeted at young people.

[1566] Server-side processing

[1567] 1. Receiving and analyzing: The server receives the business idea sent from the user's device and uses a text analysis library to extract important keywords.

[1568] 2. Database search: Search the SQLite database based on the extracted keywords to obtain past data on similar services.

[1569] 3. Data conversion and input: The acquired data is processed through a standardization process and converted into a format that can be processed by the generative AI model.

[1570] Analysis and recommendation generation by generative AI

[1571] The generative AI model analyzes the success and failure factors of similar services in the past and generates specific recommendations for improving the business idea, such as "strengthening privacy protection measures and adding specific interactive features to increase user engagement."

[1572] Providing feedback

[1573] The server generates a report based on the generated recommendations and sends it to the user interface for display, allowing the user to read the report and get specific feedback on their business idea.

[1574] Examples of prompt statements

[1575] An example of a prompt is as follows:

[1576] Analyze the following data and provide business optimization suggestions: [{"idea": "A shuttle service using autonomous vehicles. The main target is urban businessmen. It should help them reach their destinations quickly and efficiently.", "keywords": ["autonomous vehicle", "shuttle service", "urban area", "businessmen"], "similar_services": [{"name": "Service A", "success_factors": ["route optimization"], "failure_factors": ["security concerns"]}, {"name": "Service B", "success_factors": ["user engagement"], "failure_factors": ["high cost"]}]}]

[1577] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1578] Step 1:

[1579] Users input their business ideas using the user interface of their smartphone or computer. In the input form, they can enter detailed information about the business idea (target user demographics, characteristics, goals, etc.). For example, they can enter an idea for a "transportation service using self-driving cars" targeting "urban businessmen." The entered business idea is sent to the server.

[1580] Step 2:

[1581] The server receives business ideas submitted by users. It analyzes the received data using a text analysis library (e.g., NLTK or SpaCy) to extract important keywords. The input for the analysis is the text of the business idea, and the output is the extracted keywords (e.g., "self-driving car," "transportation," "urban area," "businessman").

[1582] Step 3:

[1583] The server searches for data on similar services from an SQLite database based on the extracted keywords. The search query includes the extracted keywords and retrieves past information on similar services from the database. The input is the extracted keywords, and the output is similar service data (e.g., "Service A" and "Service B") as search results.

[1584] Step 4:

[1585] The server organizes the acquired similar service data through a standardization process and converts it into a format that can be input into the generative AI model. The input is the similar service data as a search result, and the output is formatted data that is easy for the generative AI model to analyze.

[1586] Step 5:

[1587] The server inputs the formatted data into a generative AI model to identify the success and failure factors of similar past services. The input is standardized data on similar services, and the output is the results of identifying the success and failure factors. The identified success and failure factors are obtained by analyzing review and evaluation data using natural language processing technology.

[1588] Step 6:

[1589] The generative AI model generates specific recommendations that can be applied to the user's business idea based on the identified success and failure factors. The input is the analysis results of the success and failure factors, and the output is the generated recommendation (e.g., "strengthen privacy protection measures and add specific interactive features to increase user engagement").

[1590] Step 7:

[1591] The server generates a report based on the generated recommendations, which includes improvements and recommendations. The input is the generated recommendations, and the output is feedback in the form of a report.

[1592] Step 8:

[1593] The server sends the generated report to the user interface and displays it for the user to review. The user can use the provided feedback to improve or optimize their business idea. The input is the feedback in the form of a report, and the output is the feedback information reviewed by the user.

[1594] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1595] The system of the present invention analyzes the success and failure factors of past similar services based on business ideas provided by users and provides specific recommendations. The system is characterized by the addition of an emotion engine, which recognizes the user's emotions and reflects that emotion data in the feedback content. The system is primarily composed of a server, a terminal, a generation AI, and an emotion engine.

[1596] System configuration

[1597] User terminal

[1598] 1. The user enters their business idea through the terminal interface, providing detailed information about the idea (target user demographic, characteristics, goals, etc.) through the input form.

[1599] 2. The device generates and sends an API request to send the input data to the server.

[1600] server

[1601] 3. The server analyzes the business idea received from the device, using text analysis libraries and natural language processing techniques to extract important keywords.

[1602] 4. The server generates a database query based on the extracted keywords and searches for past information on similar services.

[1603] 5. The server standardizes the data and converts it into a format that can be input into the generative AI.

[1604] 6. The server runs an emotion engine to identify emotions from the user's input data. The emotion engine recognizes emotions (e.g., positive, negative, doubt, etc.) from the user's input text and voice data.

[1605] Generative AI and Emotion Engine

[1606] 7. Generative AI analyzes past data on similar services to identify factors that led to success and failure. It uses natural language processing technology to analyze reviews and evaluation data and extract important factors.

[1607] 8. The emotion engine analyzes the user's emotion data and determines the priority of suggesting improvements to the business idea. For example, if the user is expressing positive emotions, it will prioritize aggressive suggestions, and if the user is expressing negative emotions, it will prioritize risk-avoidance suggestions.

[1608] Providing feedback

[1609] 9. The server combines the analysis results received from the generative AI with the results of the emotion engine to generate specific recommendations that can be applied to each individual. These recommendations include how to incorporate success factors and how to avoid failure factors.

[1610] 10. The server documents the generated recommendations in a report format, making them easy for the user to understand. At this time, it also adjusts the way feedback is presented based on the emotional data.

[1611] 11. The server sends the generated report to the terminal.

[1612] 12. The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[1613] Specific examples

[1614] For example, if a user provides an idea for a "new social media app," the system works as follows:

[1615] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1616] 2. The device converts the input idea information into JSON format and sends it to the server.

[1617] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[1618] 4. The server searches the database based on the extracted keywords to retrieve similar past services.

[1619] 5. The server standardizes the acquired data and passes it to the generation AI.

[1620] 6. At the same time, the server uses an emotion engine to recognize emotions from the user's input text. For example, if the user includes many positive comments, it will recognize the emotion as positive.

[1621] 7. Generative AI analyzes and identifies the success factors (e.g., "improved user engagement") and failure factors (e.g., "privacy issues") of similar services in the past.

[1622] 8. The emotion engine suggests improvements based on positive emotions, prioritizing risk-taking and proactive proposals.

[1623] 9. The server integrates the analysis results with the emotional data and generates recommendations such as "strengthening privacy protection measures while adding interactive features to increase user engagement."

[1624] 10. The server compiles the generated recommendations into a report and sends it to the device.

[1625] 11. The terminal displays the report for the user to easily review, which includes feedback reflecting the user's positive feelings.

[1626] The processing flow will be explained below.

[1627] Step 1:

[1628] Users input their business ideas through the device interface, providing specific information such as a business overview, target user demographics, unique features, and the problems they aim to solve.

[1629] Step 2:

[1630] The device organizes the input business idea and converts it into JSON format data, which is then sent to the server as an API request.

[1631] Step 3:

[1632] The server analyzes the business idea data received from the API request. It uses text analysis libraries and natural language processing techniques to extract important keywords from the business idea. For example, "younger demographic" and "interactive features" are extracted.

[1633] Step 4:

[1634] The server generates a database query based on the extracted keywords, the query including conditions for searching for similar past services.

[1635] Step 5:

[1636] The server uses the generated query to retrieve data on similar services from a database, including success stories and failure stories for each service.

[1637] Step 6:

[1638] The server puts the acquired data through a standardization process, which puts the data into a consistent format and makes it suitable for AI analysis.

[1639] Step 7:

[1640] The server passes the standardized data to the Generator AI, which analyzes the data and identifies factors that contributed to the success and failure of similar services in the past. For example, it identifies factors such as "high user engagement" and "privacy issues."

[1641] Step 8:

[1642] At the same time, the server passes the user's input data to the emotion engine, which recognizes the user's emotions from the text and feedback content. For example, it analyzes emotions such as "positive," "negative," and "doubtful."

[1643] Step 9:

[1644] The server generates feedback adapted to the user's emotions based on the emotion data received from the emotion engine. If the emotion is positive, it prioritizes aggressive suggestions, and if the emotion is negative, it prioritizes risk-averse suggestions.

[1645] Step 10:

[1646] The server combines the analysis results of the generative AI with data from the emotion engine to generate specific recommendations for the user's business idea, including how to incorporate success factors and how to avoid failure factors.

[1647] Step 11:

[1648] The server documents the generated recommendations in a report format, making them easy for users to understand, and adjusts the way feedback is presented based on emotional data.

[1649] Step 12:

[1650] The server sends the generated report to the terminal.

[1651] Step 13:

[1652] The terminal displays the report in a user interface, allowing the user to review the report and revise or improve their business idea based on the suggested improvements and recommendations.

[1653] Specific examples

[1654] For example, if a user provides an idea for a "new social media app," the system works as follows:

[1655] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1656] 2. The device converts the input idea information into JSON format and sends it to the server.

[1657] 3. The server extracts keywords such as "young people" and "interactive features" from the received data.

[1658] 4. The server searches the database using the extracted keywords to obtain data on similar past services.

[1659] 5. The server standardizes the acquired data and inputs it into the generation AI.

[1660] 6. The server simultaneously runs an emotion engine to analyze the user's emotions. For example, if there are many positive comments, it will recognize the emotion as positive.

[1661] 7. Generative AI identifies the success and failure factors of similar services in the past, such as "high user engagement" and "privacy issues."

[1662] 8. The server generates feedback that reflects the user's positive emotions based on the analysis results of the emotion engine.

[1663] 9. The server integrates the analysis results of the generative AI with the data from the emotion engine and generates recommendations such as "strengthening privacy protection measures while adding interactive features to increase user engagement."

[1664] 10. The server compiles the generated recommendations into a report and sends it to the terminal.

[1665] 11. The device displays the report and allows the user to review the recommendations. The report reflects feedback based on the user's positive sentiment.

[1666] In this way, the system of the present invention analyzes the user's business idea and adjusts the feedback according to the user's emotions, thereby providing more effective and personalized recommendations.

[1667] Example 2

[1668] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1669] While conventional business idea evaluation systems analyze ideas submitted by users and generate recommendations, they have issues with providing feedback that takes users' emotions into consideration. Furthermore, the process of effectively standardizing data from similar services and using artificial intelligence to identify factors behind success and failure remains immature. As a result, recommendations provided to users are uniform and lack specificity tailored to individual needs.

[1670] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1671] In this invention, the server includes means for receiving a business idea from a user, means for analyzing the received business idea and extracting keywords, means for searching a database for similar past services based on the extracted keywords, means for organizing the data of the searched similar services through a standardization process, means for identifying success factors and failure factors using generated artificial intelligence based on the received business idea and data on similar past services, means for recognizing the user's feelings about the user's business idea and adjusting the content of recommendations based on the emotion data, means for generating recommendations applicable to the user's business idea based on the analysis results and the emotion data, and means for feeding back the generated recommendations to the user. This makes it possible to provide personalized recommendations that take the user's emotions into consideration, enabling feedback that is specific and effective.

[1672] "User" means an individual or legal entity that utilizes the system to provide business ideas and receive feedback.

[1673] A "business idea" is a new business concept or plan provided by a user.

[1674] A "terminal" is a hardware or software device through which a user inputs a business idea and communicates with a server.

[1675] The "server" is a central control device that analyzes business ideas received from users and performs database searches and executes generative AI.

[1676] "Generative AI" is a machine learning model or artificial intelligence program that analyzes past data on similar services to identify factors that contribute to success or failure.

[1677] An "emotion engine" is software or algorithms for identifying emotions from user input text or voice data.

[1678] "Keywords" are important words or phrases extracted through analysis of a business idea.

[1679] A "database" is a digital storage system for storing information on similar past services.

[1680] The "standardization process" is a series of procedures for converting acquired data into a format suitable for analysis and AI input.

[1681] "Success factors" are elements derived from past successful service cases.

[1682] "Failure factors" are elements derived from past cases of service failure.

[1683] "Recommendations" are advice and recommendations created based on the analysis results of the generative AI and emotion engine, based on the user's business idea.

[1684] "Feedback" refers to the process and output of returning recommendations to the user.

[1685] A "report" is a documented summary of the generated recommendations that is provided to the user.

[1686] This system analyzes business ideas submitted by users and provides specific recommendations based on the factors behind the success and failure of similar services in the past. This system is primarily composed of a server, a terminal, a generative AI, and an emotion engine.

[1687] User terminal

[1688] 1. Users input their business idea through the device interface. For example, they could input an idea such as "Add interactive features to a new social media app targeted at young people."

[1689] 2. The device converts the input business idea into JSON format and generates an API request to send to the server. The device sends this data to the server via an HTTP POST request.

[1690] server

[1691] 3. The server analyzes the business ideas received from the device. Specifically, it uses a text analysis library (e.g., SpaCy or NLTK) to extract important keywords. Keywords such as "young people" and "interactive features" are extracted from the received data.

[1692] 4. The server queries a database (e.g., MySQL or MongoDB) based on the extracted keywords to search for past information on similar services. This involves retrieving data on similar services from the database and preparing the settings for analysis.

[1693] 5. The server standardizes the acquired data and converts it into a format that can be input to the generative AI (e.g., CSV or JSON). It formats the acquired data to remove unnecessary information and prepares the standardized data for passing to the generative AI model.

[1694] 6. The server runs an emotion engine (e.g., IBM Watson or Microsoft Azure's emotion analysis API) to identify emotions from the user's input text. The emotion engine analyzes emotions (positive, negative, suspicious, etc.) and generates emotion data for further processing.

[1695] Generative AI and Emotion Engine

[1696] 7. Generative AI analyzes past data from similar services to identify factors that led to success and failure. Specifically, it uses natural language processing technology (e.g., BERT or GPT-3) to analyze reviews and evaluation data and extract important factors. For example, it identifies factors that led to success and failure, such as "improved user engagement" and "privacy issues."

[1697] 8. The emotion engine prioritizes suggestions for improving business ideas based on the user's emotion data. If the emotion is positive, it prioritizes proactive suggestions (e.g., "rapid rollout of new features"), and if the emotion is negative, it prioritizes risk-avoidance suggestions (e.g., "trial run").

[1698] Providing feedback

[1699] 9. The server combines the results of the generative AI analysis with those of the emotion engine to generate specific recommendations that can be applied individually. An example of a recommendation would be to "strengthen privacy protection measures while adding interactive features to increase user engagement."

[1700] 10. The server documents the generated recommendations in a report format (e.g., PDF or HTML) and organizes it in a format that is easy for the user to understand. The report also adjusts the presentation of feedback based on the user's emotional data.

[1701] 11. The server sends the generated report to the terminal.

[1702] 12. The terminal displays the received report in a user interface, allowing the user to review the report and improve their business idea based on the recommendations provided.

[1703] Specific examples

[1704] For example, if a user provides an idea for a "new social media app," the system might:

[1705] 1. The user inputs their idea into the device: "Create a new social media app that targets young people and adds interactive features."

[1706] 2. The device converts this information into JSON format and sends it to the server as an API request.

[1707] 3. The server extracts keywords such as "young people" and "interactive features" from the input data and retrieves information on past similar services from a database.

[1708] 4. The server normalizes the acquired data and prepares it for passing to the generation AI.

[1709] 5. The server uses an emotion engine to recognize emotions from the user's input text, for example, identifying positive emotions.

[1710] 6. Generative AI identifies the success and failure factors of similar services and suggests areas for improvement.

[1711] 7. The emotion engine prioritizes proactive suggestions based on positive emotions.

[1712] 8. The server integrates these findings and generates specific recommendations, such as "strengthen privacy protection measures while adding interactive features to increase user engagement."

[1713] 9. The server compiles the recommendations into a report and sends it to the device.

[1714] 10. The terminal displays the report so that the user can easily check it and use it to improve their idea.

[1715] This system provides personalized recommendations that reflect the user's feelings, effectively supporting the process of improving business ideas.

[1716] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1717] Step 1:

[1718] The user enters a new business idea into the user interface. Specifically, the user enters text such as "Add interactive features to a new social media app targeted at young people" into a dedicated form on the user interface. This is the input data for step 1.

[1719] Step 2:

[1720] The terminal converts the input business idea into JSON format. Specifically, it parses the input text data, organizes it into appropriate key-value pairs, and generates a JSON object. The resulting JSON format data is then output.

[1721] Step 3:

[1722] The device sends the generated JSON data to the server as an API request. Specifically, it generates an HTTP POST request and sends the JSON data in the request body to the server. If the transmission is successful, the server receives the data.

[1723] Step 4:

[1724] The server analyzes the business ideas received from the device. Specifically, it uses a text analysis library (e.g., SpaCy or NLTK) to extract text from the JSON data and analyze important keywords. The input is the JSON data received from the device, and the output is a list of extracted keywords.

[1725] Step 5:

[1726] The server performs a database search based on the extracted keywords. Specifically, it generates an SQL query and executes it against a database (e.g., MySQL or MongoDB). The input is the extracted keywords, and the output is the search results of similar services.

[1727] Step 6:

[1728] The server standardizes the retrieved data from similar services. Specifically, it performs data cleansing and format conversion to prepare the data in a format suitable for the generative AI model (e.g., CSV or JSON). The input is the data obtained by the search, and the output is the standardized data.

[1729] Step 7:

[1730] The server passes the standardized data to the generative AI model. Specifically, it sends an API request in a format that the AI ​​model can understand and begins analysis. The input is the standardized data, and the output is the analysis results from the generative AI model.

[1731] Step 8:

[1732] The server uses an emotion engine to identify emotions from the user's input data. Specifically, it sends the data to an emotion engine API (e.g., IBM Watson or Microsoft Azure's emotion analysis API) to perform emotion analysis. The input is the user's input text, and the output is the recognized emotion data.

[1733] Step 9:

[1734] The server integrates the analysis results of the generative AI model and the results of the emotion engine. Specifically, it combines these data and generates specific recommendations for the user. The inputs are the analysis results of the generative AI model and the data from the emotion engine, and the output is specific recommendations.

[1735] Step 10:

[1736] The server documents the generated recommendations in a report format. Specifically, it formats the recommendations into PDF or HTML format and arranges them in an easy-to-understand layout. The input is the specific recommendations, and the output is a digital report.

[1737] Step 11:

[1738] The server sends the generated report to the terminal. Specifically, it sends the report as an HTTP response and makes it displayable on the terminal. The input at this time is the digital document of the report, and the output is the data sent to the terminal.

[1739] Step 12:

[1740] The terminal displays the received report on a user interface. Specifically, the terminal receives the report and displays it in a format that can be viewed by the user on a web page or in an app. The input at this time is the received report data, and the output is a display in a format that can be viewed by the user.

[1741] These are the detailed processing steps of the system, which makes it possible to provide personalized recommendations that take into account the user's emotions.

[1742] (Application example 2)

[1743] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1744] In conventional systems, feedback on users' business ideas was based solely on the success and failure factors of similar services in the past, and suggestions and feedback did not take into account the user's emotional state. This made it difficult to provide optimal recommendations based on the user's motivation and emotional state. This led to issues such as reduced user satisfaction and the feasibility of suggestions.

[1745] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions, analyzing the emotional data, and reflecting the emotional data in the feedback content, means for generating feedback in the form of a report and adjusting the expression method based on the emotional data, and means for standardizing data on similar services. This makes it possible to provide specific and actionable recommendations according to the user's emotional state.

[1746] definition statement

[1747] The "means for receiving business ideas from users" refers to an interface or application that allows users to input business ideas.

[1748] The "means for analyzing received business ideas and extracting keywords" refers to software and algorithms for analyzing business idea documents using natural language processing technology and identifying important keywords.

[1749] The "means for searching a database for similar past services based on extracted keywords" is a system for generating a database query and using the query to search for similar past service data.

[1750] "Means for using the searched data of similar services to analyze it using generated artificial intelligence and identify factors for success and failure" refers to a generated AI model and its execution environment for analyzing the identified data of similar services.

[1751] "Means for recognizing the user's emotions, analyzing the emotional data, and reflecting it in the feedback content" refers to the process and technology for analyzing the user's emotions using an emotion engine and applying the results to the feedback content.

[1752] "Means for generating recommendations applicable to users' business ideas based on analysis results and emotion data" refers to a system that integrates data obtained using generative AI and an emotion engine to create appropriate recommendations for users.

[1753] The "means for providing feedback of generated recommendations to the user" is an interface or application that provides the generated recommendations to the user in the form of a report.

[1754] The "means for generating feedback in the form of a report and adjusting the presentation method based on emotional data" refers to the process of converting the generated recommendations into a user-friendly format and providing feedback in a presentation method that corresponds to the user's emotions.

[1755] "Means for preparing data from similar services through a standardization process" refers to data preprocessing algorithms and technologies for converting data into a format that is easy to input into generative AI.

[1756] MODE FOR CARRYING OUT THE INVENTION

[1757] System configuration

[1758] This invention is implemented using a system comprising a user terminal, a server, a generation AI, and an emotion engine.

[1759] User terminal

[1760] The user terminal provides an interface for the user to input a business idea, which is then converted into JSON format and an API request is generated to be sent to the server.

[1761] server

[1762] The server has the following main functions:

[1763] 1. Business idea analysis:

[1764] The server uses natural language processing technology to extract important keywords from the user's business idea.

[1765] Use a text analysis library (e.g., NLTK, spaCy) to identify keywords that are used to generate data queries for further processing.

[1766] 2. Search for similar service data:

[1767] Based on the extracted keywords, the server sends a query to a database to obtain information on similar past services.

[1768] Manage data using a database management system (e.g., MySQL, PostgreSQL).

[1769] 3. Data Standardization:

[1770] The acquired data from similar services is then converted into a format that is easy for the generative AI to handle through a standardization process, which includes data cleaning and data shaping.

[1771] 4. Emotion Recognition:

[1772] The server uses an emotion engine to recognize emotions from the user's input text.

[1773] Use sentiment analysis tools (e.g., IBM Watson, Microsoft Azure Emotion API) to extract sentiment data such as positive, negative, and suspicious.

[1774] 5. Recommendation generation:

[1775] The generative AI analyzes the success and failure factors of similar services in the past and combines them with emotional data to generate recommendations for the user's business idea.

[1776] Generative AI optimizes recommendations based on the user's emotional state and provides actionable feedback.

[1777] 6. Providing Feedback:

[1778] The server compiles the generated recommendations into a report format and transmits it to the user terminal.

[1779] Based on the emotional data, the way the report is presented can be adjusted to make it easier for users to understand and accept.

[1780] Specific examples

[1781] For example, if a user submits a business idea such as:

[1782] User: "I want the layout of the virtual store to be more intuitive. I want to color-code products by category to make it easier to understand visually."

[1783] The server generates the following prompt:

[1784] User Ideas:

[1785] Intuitive virtual store layout.

[1786] Color-code your products by category.

[1787] Visually easy to understand.

[1788] Sentiment: Positive (engaged and motivated)

[1789] Recommendation:

[1790] In a similar success story, color-coded categories increased user satisfaction by 20%.

[1791] To further highlight the visual impact of the category layout, high-resolution images and interactive feedback features should be added.

[1792] It is also important to implement concise design guidelines to avoid visual clutter.

[1793] In this way, the system of the present invention supports the user's business success by providing specific and practical suggestions for the user's business ideas.

[1794] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1795] System processing steps

[1796] Step 1: User enters business idea

[1797] A user inputs a business idea using a terminal, and the input business idea is saved in text format on the terminal.

[1798] Input: Text data of the business idea (e.g., "I want to make the layout of the virtual store more intuitive. I want to color-code products by category.")

[1799] Output: Text data saved on the device

[1800] Step 2: The device sends the business idea to the server

[1801] The terminal converts the input business idea into JSON format and generates an API request to send to the server.

[1802] Input: Text data of business ideas

[1803] Data processing: Convert text data into JSON format

[1804] Output: API request in JSON format

[1805] Step 3: The server analyzes the business idea

[1806] The server analyzes the received business ideas and extracts important keywords using natural language processing technology.

[1807] Input: Business idea in JSON format

[1808] Data processing: Extract keywords using natural language processing techniques (e.g., spaCy)

[1809] Output: Extracted keywords (e.g. "layout", "intuitive", "categorical")

[1810] Step 4: The server searches for similar services

[1811] The server generates a query to the database based on the extracted keywords and searches for similar past services.

[1812] Input: Extracted keywords

[1813] Data retrieval: Search using a database management system (e.g. MySQL)

[1814] Output: Data for similar services

[1815] Step 5: Server standardizes data for similar services

[1816] The server standardizes the data it obtains from similar services and formats it into a format that is easy for the generating AI to process.

[1817] Input: Data from similar services

[1818] Data processing: Data cleaning and standardization process

[1819] Output: Standardized dataset

[1820] Step 6: The server recognizes the emotion

[1821] The server uses an emotion engine to recognize emotions from the user's input text and generate emotion data.

[1822] Input: Text data of business ideas

[1823] Data computation: Recognizing emotions using sentiment analysis tools (e.g., IBM Watson)

[1824] Output: Sentiment data (e.g., positive, negative)

[1825] Step 7: Generative AI generates recommendations

[1826] The server inputs standardized data and emotional data into the generative AI, which generates recommendations based on factors that lead to success and failure.

[1827] Input: Standardized data, emotion data

[1828] Data computation: analysis using generative AI models

[1829] Output: Recommendation (e.g., "Use a categorized layout and add high-resolution images")

[1830] Step 8: The server generates feedback in the form of a report

[1831] The server compiles the generated recommendations into a report format and adjusts the way the feedback is presented based on the emotional data.

[1832] Input: Recommendation, sentiment data

[1833] Data processing: Report generation and presentation adjustment

[1834] Output: Feedback in report format

[1835] Step 9: Your device will display feedback

[1836] The terminal receives the report sent from the server and displays it in a format that is easily understandable to the user.

[1837] Input: Feedback in report format

[1838] Data display: how it appears in the user interface

[1839] Output: On-screen feedback

[1840] Through the above processing steps, specific and practical recommendations for the user's business idea are provided. For example, information such as the following prompt sentence is presented:

[1841] User Ideas:

[1842] Intuitive virtual store layout.

[1843] Color-code your products by category.

[1844] Visually easy to understand.

[1845] Sentiment: Positive (engaged and motivated)

[1846] Recommendation:

[1847] In a similar success story, color-coded categories increased user satisfaction by 20%.

[1848] To further highlight the visual impact of the category layout, high-resolution images and interactive feedback features should be added.

[1849] It is also important to implement concise design guidelines to avoid visual clutter.

[1850] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1851] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1852] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1853] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1854] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1855] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1856] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1857] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1858] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1859] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1860] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1861] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1862] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1863] 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.

[1864] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1865] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1866] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1867] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1868] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1869] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1870] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1871] The following is further disclosed regarding the above embodiment.

[1872] (Claim 1)

[1873] A means for receiving business ideas from users;

[1874] A means of analyzing received business ideas and extracting keywords;

[1875] A method for searching a database for similar past services based on the extracted keywords, and

[1876] A means for analyzing the data of the searched similar services using the generated artificial intelligence to identify success factors and failure factors;

[1877] A means for generating recommendations applicable to the user's business idea based on the analysis results;

[1878] a means for feeding back the generated recommendations to the user;

[1879] A system including:

[1880] (Claim 2)

[1881] 10. The system of claim 1, further comprising means for generating feedback in the form of a report.

[1882] (Claim 3)

[1883] 2. The system of claim 1, further comprising means for arranging data of similar services through a standardization process.

[1884] "Example 1"

[1885] (Claim 1)

[1886] A means for receiving business ideas from users;

[1887] A means of analyzing received business ideas and extracting keywords;

[1888] A means to search the information library for similar past services based on the extracted keywords,

[1889] A means for analyzing the data of the searched similar services using the generated artificial intelligence to identify success factors and failure factors;

[1890] A means for generating recommendations applicable to the user's business idea based on the analysis results using numerical analysis technology and the generated artificial intelligence;

[1891] a means for feeding back the generated recommendations to the user in the form of a report;

[1892] A system including:

[1893] (Claim 2)

[1894] 10. The system of claim 1, further comprising means for generating and displaying feedback in the form of a report on the user's terminal.

[1895] (Claim 3)

[1896] 2. The system of claim 1, further comprising means for arranging data of similar services through a standardization process.

[1897] "Application Example 1"

[1898] (Claim 1)

[1899] A means for receiving business ideas from users;

[1900] A means of analyzing received business ideas and extracting keywords;

[1901] A method for searching a database for similar past services based on the extracted keywords, and

[1902] A means for analyzing the data of the searched similar services using the generated artificial intelligence to identify success factors and failure factors;

[1903] A means for generating recommendations applicable to the user's business idea based on the analysis results to support service design for autonomous vehicles;

[1904] a means for feeding back the generated recommendations to the user;

[1905] A system including:

[1906] (Claim 2)

[1907] 10. The system of claim 1, further comprising means for generating feedback in the form of a report.

[1908] (Claim 3)

[1909] 2. The system of claim 1, further comprising means for arranging data of similar services through a standardization process.

[1910] "Example 2: Combining Emotion Engines"

[1911] (Claim 1)

[1912] A means for receiving business ideas from users;

[1913] A means of analyzing received business ideas and extracting keywords;

[1914] A method for searching a database for similar past services based on the extracted keywords, and

[1915] A means for arranging the data of the searched similar services through a standardization process;

[1916] A means for identifying success factors and failure factors using generated artificial intelligence based on the received business idea and past similar service data;

[1917] A means for recognizing a user's feelings about the user's business idea and adjusting the content of the proposal based on the emotion data;

[1918] A means for generating recommendations applicable to the user's business idea based on the analysis results and the sentiment data;

[1919] a means for feeding back the generated recommendations to the user;

[1920] A system including:

[1921] (Claim 2)

[1922] 10. The system of claim 1, further comprising means for generating feedback in the form of a report.

[1923] (Claim 3)

[1924] 10. The system of claim 1, further comprising means for adjusting the manner in which feedback is expressed based on emotion data.

[1925] "Application example 2 when combining emotion engines"

[1926] (Claim 1)

[1927] A means for receiving business ideas from users;

[1928] A means of analyzing received business ideas and extracting keywords;

[1929] A method for searching a database for similar past services based on the extracted keywords, and

[1930] A means for analyzing the data of the searched similar services using the generated artificial intelligence to identify success factors and failure factors;

[1931] A means for recognizing the user's emotions, analyzing the emotion data, and reflecting the data in the feedback;

[1932] means for generating recommendations applicable to the user's business idea based on the analysis results and the sentiment data;

[1933] a means for feeding back the generated recommendations to the user;

[1934] A system including:

[1935] (Claim 2)

[1936] 10. The system of claim 1, further comprising means for generating feedback in the form of a report and adjusting presentation based on the emotion data.

[1937] (Claim 3)

[1938] 2. The system of claim 1, further comprising means for arran...

Claims

1. A means for receiving business ideas from users; A means of analyzing received business ideas and extracting keywords; A method for searching a database for similar past services based on the extracted keywords, and A means for analyzing the data of the searched similar services using the generated artificial intelligence to identify success factors and failure factors; A means for generating recommendations applicable to the user's business idea based on the analysis results; a means for feeding back the generated recommendations to the user; A system including:

2. 10. The system of claim 1, further comprising means for generating feedback in the form of a report.

3. 2. The system of claim 1, further comprising means for arranging data of similar services through a standardization process.

Images