System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting an idea of a new project by a user; means for searching and retrieving similar failures and successes from a historical database using generated AI; means for analyzing the retrieved information to identify causes of failures and factors of successes; means for generating and presenting strategic recommendations to the user based on the identified information; and means for monitoring market changes and competition status and updating advice in real time.SELECTED DRAWING: Figure 1

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] Learning from past failures and successes is important for leading new businesses to success, but this information is often confidential and difficult to research. In addition, markets often change and new competitors emerge during new business planning, making it necessary to respond quickly. Current methods do not provide a means to adequately resolve these issues. Therefore, the present invention aims to provide an effective consultation service that utilizes past data to adapt to market changes and increase the success rate of new businesses. [Means for solving the problem]

[0005] The system of the present invention includes a means for users to input new business ideas, a means for using generative AI to search and retrieve similar failures and successes from a past database, a means for analyzing the retrieved data to identify the causes of failure and the factors behind success, a means for generating strategic recommendations based on the identified information and presenting them to the user, and a means for monitoring market changes and competitive conditions and updating advice in real time. This system allows users to learn from past failures and provides useful strategic recommendations based on successes. It also enables users to respond to changes in market trends in real time, significantly improving the success rate of new businesses.

[0006] "User" refers to an individual or entity that uses the system of the present invention to input new business ideas and receive generated recommendations.

[0007] "New Business Idea" refers to a business plan, concept, or proposal that a user inputs into the system of the present invention.

[0008] "Device" refers to a computer, smartphone, tablet, or other electronic device that a user uses to input ideas for a new business.

[0009] "Generative AI" refers to programs or systems that use artificial intelligence technology to analyze data, search for similar cases, and generate recommendations.

[0010] The "past database" refers to an electronic database that stores and records past cases of new business failures and successes.

[0011] "Similar failure cases and success cases" refers to past business cases that have commonalities or are related to the new business idea entered by the user.

[0012] "Means of searching and retrieving" refers to the process and method of using generative AI to search for similar failure cases and success cases from a past database and retrieve them.

[0013] "Means for analyzing data" refers to the techniques and methods for analyzing the data obtained from similar cases and identifying the causes of failure and the factors for success.

[0014] "Causes of failure" refers to the specific reasons and problems that led to the business not going well, based on the failure cases obtained.

[0015] "Factors for success" refers to the specific reasons and strengths behind the success of a business, based on the success stories it has acquired.

[0016] "Strategic recommendations" refer to specific proposals and advice provided by the system of the present invention to improve a user's new business idea and lead it to success.

[0017] "Market changes" refers to changes in the situation that occur during the planning and implementation of a new business, such as fluctuations in market trends or the emergence of new competitors.

[0018] "Competitive situation" refers to the trends and activities of other competitors and companies in the same industry in the market where the new business will be developed.

[0019] "Monitoring measures" refers to methods and systems for continuously monitoring market changes and competitive conditions, and collecting and analyzing information.

[0020] The term "means for updating advice in real time" refers to a method or technology for updating the generated strategic recommendations as needed based on the latest information on market changes and competitive situations, and providing the recommendations to users. [Brief explanation of the drawings]

[0021] [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 showing 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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention provides a consultation service to help users increase the success rate of new businesses through the illustrated system. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0043] When a user wants to input an idea for a new business, they access the system using a terminal. First, the user inputs information such as the outline of the new business, its objectives, and the target market. For example, a user can input the idea of ​​launching a "new online learning platform."

[0044] The server receives user input and uses generative AI to search a database for relevant failures and successes. For example, it identifies cases where "online learning platform A" failed and "online learning platform B" was successful. In this case, the server uses natural language processing technology to extract keywords from the input business idea and search the database for the most similar cases.

[0045] The server then analyzes the data of similar cases. During this analysis, the data is examined in detail to identify the causes of failure and the factors behind success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind the success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0046] Based on the analysis results, the server generates specific strategic recommendations for the user's business idea. For example, it suggests "improving the user interface and adding interactive content" or recommends "planning and implementing a strong marketing strategy." These recommendations are presented to the user via their device, allowing them to use them to adjust and improve their business plan.

[0047] Furthermore, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has "introduced a subscription service to its online learning business model," it can use that information to generate a new strategic recommendation such as "consider introducing a subscription service."

[0048] In this way, the system of the present invention allows users to increase the probability of success of new businesses by utilizing lessons learned from past failures and factors behind successes, and by providing them with feasible strategies that adapt to changes in the market.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] A user accesses the system using a terminal and inputs a new business idea, for example, "develop an online learning platform."

[0052] Step 2:

[0053] The terminal transmits the input business idea to the server.

[0054] Step 3:

[0055] The server receives the business idea and uses natural language processing technology to extract keywords from the idea, such as "online learning" and "platform."

[0056] Step 4:

[0057] The server queries the past database based on the extracted keywords to search for similar failures and successes. For example, it retrieves "failed online learning platform A" and "successful online learning platform B" from the past database.

[0058] Step 5:

[0059] The server analyzes the data of similar cases it has acquired. For failure cases, it identifies the causes. For example, it identifies causes of failure such as "difficulty in using the user interface" or "lack of marketing." For success cases, it identifies the factors that led to success. For example, it identifies factors that led to success such as "interactive content" or "strong marketing strategy."

[0060] Step 6:

[0061] Based on the analysis results, the server generates specific strategic recommendations that can be applied to the user's business idea, such as "improve the user interface and add interactive content."

[0062] Step 7:

[0063] The server generates recommendations and sends them to the device, where they are presented to the user, who can use them to adjust and improve their business ideas.

[0064] Step 8:

[0065] The server keeps a constant eye on market changes and the competitive landscape by regularly scanning for relevant news articles, competitor activity data, and market trends.

[0066] Step 9:

[0067] When market changes or new competitors' actions are detected, the server analyzes the information in real time and generates new strategic recommendations. For example, if it detects that a new competitor has introduced a subscription service, it generates a new recommendation that says, "Consider introducing a subscription service."

[0068] Step 10:

[0069] The server then sends the latest recommendations back to the device and presents them to the user, who can then quickly modify and adapt their business strategy.

[0070] Example 1

[0071] 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."

[0072] In order to increase the success rate of new businesses, it is necessary to properly evaluate and improve business ideas. However, with conventional methods, it was difficult to effectively utilize past cases and propose specific business strategies. In addition, there was a lack of means to monitor market changes and the competitive situation in real time and respond immediately. This posed a risk of reducing the success rate of new businesses.

[0073] 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.

[0074] In this invention, the server includes a means for a user to input a new business idea, a means for searching and retrieving similar failure cases and success cases from a past database using a generative AI, a means for analyzing the retrieved data to identify causes of failure and factors of success, a means for generating strategic recommendations based on the identified information and presenting them to the user, and a means for monitoring market changes and competitive conditions and updating advice in real time. This enables users to utilize lessons learned from past cases to quickly and accurately improve their business plans and increase the probability of success for their new businesses.

[0075] "User" refers to an individual or corporation that uses this system to input ideas for new businesses.

[0076] "Terminal" refers to the hardware device, such as a PC or smartphone, that a user uses to access the system.

[0077] "Server" refers to a computing device that receives user-submitted data and performs analysis and recommendation generation.

[0078] "Generative AI" refers to programs or models that use artificial intelligence technology to generate optimal output from input data, such as natural language generation models.

[0079] A "database" refers to a collection of data that stores past failures and successes.

[0080] "Keywords" are key words or phrases related to a business idea entered by a user, and refer to information used when searching and analyzing data.

[0081] "Recommendations" refer to specific strategies and suggestions for improvement presented to users based on the analysis results.

[0082] "Natural language processing technology" refers to technology for processing natural language using a computer, including text analysis and generation.

[0083] "Market monitoring" refers to the process of continuously monitoring market trends and competitive conditions, and generating and presenting new recommendations as needed.

[0084] A "prompt sentence" is a sentence provided as input to a generative AI, containing instructions for obtaining a specific output.

[0085] This invention is a system that provides consultation services to help users increase the success rate of new businesses. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0086] First, a user accesses the system using a terminal. The user logs in to the system using a web browser or a dedicated application and enters their new business idea. For example, the user enters their idea, such as "I want to launch a new online learning platform," along with related information such as their goals and target market, into a form.

[0087] Next, the server receives the data provided by the user. The data is sent to the server as an HTTP POST request and parsed in JSON format. The server then extracts key keywords from the input data and converts them into a prompt for the generation AI. For example, this prompt might look something like this:

[0088] "I want to launch a new online learning platform. Its main function is to provide video lessons and quiz-style assessments."

[0089] The server uses generative AI (e.g., natural language generation models) to search a historical database for relevant failure and success stories, using a high-speed search engine such as Elasticsearch to identify relevant cases.

[0090] The server then performs a detailed analysis of the similar cases it has retrieved. It uses natural language processing technology (e.g., SpaCy or NLTK) to identify the causes of failure and the factors behind success using text mining and clustering techniques. For example, the causes of failure of "Online Learning Platform A" are identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind its success are identified as "interactive content" and "strong marketing strategy."

[0091] Based on these analysis results, the server uses a generative AI model to generate specific strategic recommendations, such as "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are then presented to the user via their device.

[0092] As a specific example, the server generates recommendations such as "You should improve the user interface and add interactive content. It is recommended that you plan and implement a strong marketing strategy" and displays them to the user.

[0093] Furthermore, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has introduced a subscription service into its online learning business model, it can use that information to generate and present a new strategic recommendation, such as "consider introducing a subscription service."

[0094] In this way, the system of the present invention increases the probability of success for new businesses by providing users with viable strategies that utilize lessons learned from past failures and factors behind successes, and that adapt to changes in the market.

[0095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0096] Step 1:

[0097] Users access the system using a terminal and input their new business idea. The input information includes the business outline, objectives, target market, etc. For example, a user can input an idea such as "I want to launch a new online learning platform." This input data is then sent to the server.

[0098] Step 2:

[0099] The server receives the data sent by the user. The received data is parsed in JSON format to extract key keywords for the business idea. These keywords are converted into prompts for the generation AI. For example, keywords such as "online learning," "platform," and "video lessons" are extracted from the received data. Based on this, a prompt is generated: "We want to launch a new online learning platform. Its main functions are to provide video lessons and quiz-style assessment functions."

[0100] Step 3:

[0101] The server uses the generated prompt to query the generative AI model. Based on the input prompt, the generative AI model searches a past database for relevant failure and success cases. Elasticsearch is used to efficiently identify relevant cases. A list of similar cases is output as the search results.

[0102] Step 4:

[0103] The server performs a detailed analysis of similar cases retrieved from the search results. Natural language processing technology (e.g., SpaCy or NLTK) is used here, and text mining and clustering techniques are used to identify the causes of failure and the factors behind success. The input for the analysis is the text data of past cases, and the output is a list of the causes of failure and the factors behind success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind the success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0104] Step 5:

[0105] Based on the analysis results, the server uses a generative AI model to generate specific strategic recommendations. The recommendations are tailored to the user's business idea and include specific action plans. For example, the recommendations may include "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are sent from the server to the device and displayed on the user's screen.

[0106] Step 6:

[0107] The device receives recommendations from the server and presents them to the user, who can use them to adjust or improve their business plans. The recommendations are displayed on the device screen in list or dashboard format.

[0108] Step 7:

[0109] The server continuously monitors market trends and competitive conditions in real time. It analyzes market data and news feeds to detect new trends and competitor movements. For example, if it detects that a new competitor has introduced a subscription model, it uses that information to generate a new strategic recommendation, such as "Consider introducing a subscription service," and provides it to the user. The latest advice responding to market changes is instantly displayed on the user's device.

[0110] (Application example 1)

[0111] 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."

[0112] In today's brick-and-mortar store operations, learning from past examples is crucial to increasing the success rate of new product introductions and service improvements. However, it is difficult for executives and managers to efficiently utilize vast amounts of past data. Furthermore, there is a lack of concrete support for monitoring market changes and competitive conditions in real time and quickly formulating and implementing effective strategies based on that information. For these reasons, innovative methods are needed to reduce the risk of failure and increase the success rate of new businesses.

[0113] 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.

[0114] In this invention, the server includes: means for a user to input a new business idea; means for searching and retrieving similar failure cases and success cases from a past database using a generation AI; means for analyzing the retrieved data to identify causes of failure and factors of success; means for generating strategic recommendations based on the identified information and presenting them to the user; means for monitoring market changes and competitive conditions and updating advice in real time; means for collecting and analyzing information on new product introductions and service improvements in physical stores; and means for generating strategic recommendations for physical stores using a generation AI and presenting them to the user via a smartphone application. This enables physical store operators to learn from past cases and quickly plan and implement effective strategies based on the latest market trends.

[0115] "Means for users to input new business ideas" refers to interfaces or devices that allow users to register information such as their business ideas, project outlines, objectives, target markets, etc. into the system.

[0116] "Generative AI" refers to an artificial intelligence model that uses natural language processing technology to analyze input data and provide appropriate answers or recommendations.

[0117] A "past database" is a collection of various cases and records that have been accumulated in the past, including both successful and unsuccessful cases.

[0118] "Means for searching and retrieving similar failure cases and success cases" refers to the technology and methods for finding and retrieving cases that are closest to the input business idea from a past database.

[0119] "Means for analyzing the acquired data to identify causes of failure and factors for success" refers to a method for analyzing the collected case data and identifying the reasons for failure or success in each case.

[0120] "Strategic recommendations" refers to proposing specific strategies or courses of action to users based on acquired and analyzed information.

[0121] "Means of monitoring market changes and competitive conditions and updating advice in real time" refers to methods of continuously monitoring market trends and competitor activity and using that information to keep user advice and strategies up to date.

[0122] "Means for collecting and analyzing information on new product introductions and service improvements in physical stores" refers to technologies and methods for collecting data related to the introduction of new products and services in physical stores and analyzing it in detail to gain important insights.

[0123] "Smartphone application" refers to software that allows users to input information and receive strategic recommendations via mobile communication devices.

[0124] By integrating the above, users can make effective data-driven decisions and develop strategies.

[0125] This invention is a system for providing consultation services to increase the success rate of new business ideas by users entering them. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0126] First, users access the system through a smartphone application. The process begins when the user inputs information about the new business, such as its outline, objectives, and target market. For example, a user can input their idea to launch a "new online learning platform" or a "new product sales strategy."

[0127] Next, the server receives the user's input and uses generative AI to search and retrieve related failure and success cases from a past database. For example, it identifies cases where "online learning platform A" failed and "online learning platform B" was successful. In this case, the server uses natural language processing technology (e.g., spaCy or NLTK) to extract keywords from the input business idea and search a database (e.g., PostgreSQL or MongoDB) for the most similar cases.

[0128] The data obtained in the above steps is then analyzed by the server. During this analysis process, the data is examined in detail to identify the causes of failure and the factors of success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in user interface" and "lack of marketing strategy," while the factors of success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0129] Furthermore, based on the analysis results, the server generates specific strategic recommendations for the user's business idea. For example, it suggests "improving the user interface and adding interactive content" or recommends "planning and implementing a strong marketing strategy." These recommendations are presented to the user via a smartphone application, allowing the user to adjust and improve their business plan.

[0130] In addition, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has introduced a subscription service into its online learning business model, it can use that information to generate a new strategic recommendation, such as "consider introducing a subscription service."

[0131] Below are some examples of specific prompts for a physical store:

[0132] "What approach is necessary to successfully introduce new products in physical stores? Please provide specific strategies based on past examples."

[0133] The above is a specific form for implementing the system of the present invention, which enables users to learn from past cases and quickly develop and implement effective strategies based on the latest market trends.

[0134] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0135] Step 1:

[0136] Users access the system using a terminal and enter information such as the outline of the new business, its objectives, target market, etc. The entered data is sent to the server.

[0137] Input: User-entered information such as business idea, objectives, target market, etc.

[0138] Data processing: converting user-entered information into text and formatting it

[0139] Output: User-entered data sent to the server

[0140] Step 2:

[0141] The server receives user input and uses natural language processing techniques to extract keywords for the business idea, using libraries such as spaCy and NLTK.

[0142] Input: User-entered data

[0143] Data Computing: Keyword Extraction by Natural Language Processing

[0144] Output: Extracted keywords

[0145] Step 3:

[0146] The server uses generative AI to search and retrieve similar failure and success cases from a past database, using database management systems such as PostgreSQL and MongoDB for this process.

[0147] Input: Extracted keywords

[0148] Data calculation: Retrieving similar cases through database search

[0149] Output: Retrieved related case data

[0150] Step 4:

[0151] The data acquired by the server is analyzed to identify the causes of failure and the factors behind success. Specifically, the case is examined in detail and an analysis is performed to identify each factor.

[0152] Input: Retrieved relevant case data

[0153] Data Computing: Identifying Causes of Failure and Success Factors

[0154] Output: Identified causes of failure and success factors

[0155] Step 5:

[0156] The server generates strategic recommendations based on the identified information, using a generative AI model (e.g., GPT-4) to generate specific strategies or courses of action for the user.

[0157] Input: Identified causes of failure and success factors

[0158] Data Processing and Data Arithmetic: Generating Strategic Recommendations with Generative AI

[0159] Output: Generated strategy recommendations

[0160] Step 6:

[0161] The server presents the generated strategic recommendations to the user via their terminal, allowing the user to adjust and improve their own business plans based on these recommendations.

[0162] Input: Generated strategy recommendations

[0163] Data output: Display on user terminal

[0164] Step 7:

[0165] The server monitors market changes and competitive conditions in real time, regularly scanning for relevant news articles and competitor activity data.

[0166] Input: Market trend information, competitor trend information

[0167] Data Computing: Scanning and Analyzing News Articles and Competitive Trends

[0168] Output: Real-time updated market and competitive landscape data

[0169] Step 8:

[0170] Based on newly acquired market and competitive data, strategic recommendations are updated to provide users with the most up-to-date advice.

[0171] Input: Updated market and competitive landscape data

[0172] Data processing and data calculations: Updating strategic recommendations

[0173] Output: Providing updated strategy recommendations to the user

[0174] By following these steps, users can learn from past cases and quickly develop and implement effective strategies based on the latest market trends.

[0175] 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.

[0176] This invention is a system that provides consultation services to help users increase the success rate of new businesses. This system is implemented using a terminal, a server, a generative AI, a historical database, and an emotion engine.

[0177] When a user wants to enter an idea for a new business, they access the system using a terminal. They enter information such as the outline of the new business, its objectives, and the target market. For example, they can enter the idea of ​​launching a "new online learning platform."

[0178] After the terminal transmits the input business idea to the server, the server uses natural language processing technology to extract keywords from the business idea, such as "online learning" and "platform."

[0179] Next, the server searches the past database based on the extracted keywords to obtain similar failure and success cases, for example, "failed online learning platform A" and "successful online learning platform B" from the past database.

[0180] The server analyzes data on similar cases and identifies the causes of failure and the factors behind success. For failure cases, causes of failure such as "difficulty in using the user interface" and "lack of marketing" are identified. For success cases, factors of success such as "interactive content" and "strong marketing strategy" are identified.

[0181] Based on the analysis results, the server generates specific strategic recommendations that can be applied to the user's business idea. For example, it may suggest "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are presented to the user via their device.

[0182] As another feature of the present invention, the server is equipped with an emotion engine. This emotion engine can recognize the user's emotions when inputting a business idea or when presenting recommendations. For example, when a user inputs an "idea for an online learning platform," the emotion engine can recognize emotions such as "expectation" or "anxiety" from the user's tone and choice of words.

[0183] The user's emotions recognized through the emotion engine are reflected in the recommendations generated by the server. For example, if the user is feeling anxious, the recommendation may include a specific action plan to alleviate the anxiety. Also, if the user has strong expectations, proactive strategies to fulfill those expectations will be presented.

[0184] Furthermore, the server continuously monitors market changes and the competitive situation in real time. When new market trends or competitors' moves are detected, the server generates and updates new strategic recommendations based on these. At this time, the emotion engine re-evaluates the user's current emotional state and adjusts the content of the recommendations.

[0185] For example, if the server detects that a new competitor has introduced a subscription service, the emotion engine will recognize that the user is feeling anxious about that information, and will then recommend that the user should consider introducing a subscription service, along with specific implementation steps and risk mitigation measures.

[0186] This allows the system of the present invention to not only learn from past failures and provide strategic advice based on successes, but also take into account the user's emotional state to generate more personalized recommendations, thereby further increasing the success rate of new businesses.

[0187] The processing flow will be explained below.

[0188] Step 1:

[0189] A user accesses the system using a terminal and inputs a new business idea, for example, "Develop a new online learning platform."

[0190] Step 2:

[0191] The emotion engine analyzes the user's input and recognizes their emotions, for example, analyzing "expectation" or "anxiety" from the tone and vocabulary of the input.

[0192] Step 3:

[0193] The terminal transmits the user's input and analyzed emotion information to the server.

[0194] Step 4:

[0195] The server receives the business idea and uses natural language processing technology to extract keywords from the idea, such as "online learning" and "platform."

[0196] Step 5:

[0197] The server searches a past database based on the extracted keywords to retrieve similar failure cases and success cases, for example, "failed online learning platform A" and "successful online learning platform B" from the past database.

[0198] Step 6:

[0199] The server analyzes data on similar cases and identifies the causes of failure and factors for success. For example, the causes of failure of "Online Learning Platform A" can be identified as "difficulty in using the user interface" and "lack of marketing," while the factors for success of "Online Learning Platform B" can be identified as "interactive content" and "strong marketing strategy."

[0200] Step 7:

[0201] Based on the analysis results, the server generates specific strategic recommendations taking into account the user's emotional state. For example, if the user is feeling anxious, the server will suggest improving the user interface and adding interactive content, as well as providing detailed instructions on specific steps for the marketing strategy.

[0202] Step 8:

[0203] The server generates recommendations and sends them to the device, where they are presented to the user, who can use them to adjust and improve their business ideas.

[0204] Step 9:

[0205] The server periodically scans for relevant news articles, competitor activity data, and market trends to monitor market changes and the competitive landscape.

[0206] Step 10:

[0207] When market changes or new competitor moves are detected, the server analyzes the information in real time and generates new strategic recommendations, while the emotion engine again evaluates the user's current emotional state and adjusts the recommendations accordingly.

[0208] Step 11:

[0209] The server sends the latest recommendations generated by the server to the device and presents them to the user, who can then quickly modify and adapt their business strategy.

[0210] In this way, users' new business ideas not only learn from past cases but also receive personalized advice that takes into account the user's emotional state, improving the chances of success.

[0211] Example 2

[0212] 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."

[0213] Conventional new business support systems have the problem that they are not only unable to learn from past cases, but also have difficulty in providing personalized recommendations that take into account the emotional state of the user.It is also difficult to monitor market changes and the competitive situation in real time and provide prompt and appropriate advice.

[0214] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input an idea for a new business; a means for searching and retrieving similar failure cases and success cases from a past database using a generation AI; a means for analyzing the retrieved data to identify causes of failure and factors of success; a means for generating strategic recommendations based on the identified information and presenting them to the user; a means for monitoring market changes and the competitive situation and updating the advice in real time; a means for recognizing the user's emotions at the time of user input or when recommendations are presented using an emotion engine; and a means for personalizing recommendations based on the user's emotional information. This makes it possible to provide personalized advice that takes the user's emotional state into consideration, further increasing the probability of success for new businesses.

[0215] "Users" are entities that use the system to provide new business ideas and receive recommendations.

[0216] "New business ideas" are new business concepts and plans provided by users.

[0217] "Generative AI" is a system that uses artificial intelligence technology to automatically analyze data and generate recommendations.

[0218] A "past database" is a data storage that accumulates records of past failures and successes.

[0219] A "failed case" is a business case that has been attempted in the past but was unsuccessful.

[0220] A "success story" is a business case that has been attempted in the past and succeeded.

[0221] "Strategic recommendations" are proposals for specific countermeasures and strategies for potential problems and success factors based on the analysis results.

[0222] "Market changes" are fluctuations in factors that affect the entire market, such as the business environment and consumer demand.

[0223] "Competitive situation" refers to the trends and market share of competitors in the market to which the user belongs.

[0224] "Monitoring" is the process of continuously observing market changes and competitive conditions, and collecting and analyzing data.

[0225] An "emotion engine" is a technology that analyzes and recognizes emotions from user input and reactions.

[0226] "Emotion information" is emotional data obtained from the user by the emotion engine.

[0227] "Personalization" means customizing content and services based on a user's individual characteristics and feelings.

[0228] The present invention is a system that provides consultation services to help users increase the success rate of new businesses. This system is realized by a configuration including a terminal, a server, a generative AI, a historical database, and an emotion engine.

[0229] First, a user accesses the system using a terminal and inputs their new business idea. Specifically, the user logs into the system using a browser or a dedicated application and enters information such as the business outline, objectives, and target market into an input form. For example, a user can enter a specific idea such as "I want to launch a new online learning platform."

[0230] Next, the device sends the business idea to a server, which uses natural language processing technology to extract keywords from the business idea. For example, it uses Python's NLTK library to extract keywords such as "online learning" and "platform."

[0231] Based on the extracted keywords, the server searches a historical database (e.g., a MySQL database) to obtain similar failure and success cases, such as "failed online learning platform A" and "successful online learning platform B."

[0232] The server analyzes the acquired similar cases using data mining technology (e.g., machine learning algorithms) to identify the causes of failure and the factors behind success. For example, factors such as "difficulty in using the user interface" and "lack of marketing" can be extracted from failure cases, while factors such as "interactive content" and "strong marketing strategies" can be extracted from success cases.

[0233] Based on the analysis results, the server generates strategic recommendations, such as "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are presented to the user via their device.

[0234] In addition, the server is equipped with an emotion engine (e.g., Microsoft Azure's emotion analysis API) that recognizes the user's emotions when they input data and when presenting recommendations. For example, when a user inputs "ideas for an online learning platform," the emotion engine recognizes "expectation" or "anxiety" from the user's tone and vocabulary.

[0235] The recognized emotional information is reflected in the recommendations. For example, if the user is feeling anxious, a recommendation including a "specific action plan to alleviate anxiety" will be provided. In addition, the server monitors market changes and competitive situations in real time to generate and update new strategic recommendations according to the user's emotional state.

[0236] For example, if the server detects that a new competitor has introduced a subscription service, the emotion engine will recognize that the user is feeling anxious about that information. The server will then recommend that users consider introducing a subscription service, along with specific implementation steps and risk mitigation measures. This allows users to quickly adopt an appropriate strategy in line with market trends, further increasing the chances of success for their new business.

[0237] As a concrete example, below is an example of a prompt sentence that is sent to a generative AI model when an idea for a new online learning platform is input.

[0238] "I have an idea for a new online learning platform. What strategy should I use to differentiate it from the competition? Please provide specific guidelines for a strong marketing strategy and interactive content."

[0239] In this way, the system of the present invention aims to provide personalized recommendations that take into account the user's emotional state, thereby increasing the chances of success for the user's new business.

[0240] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0241] Step 1:

[0242] A user accesses the system using a terminal and inputs a new business idea. Specifically, the user logs into the system using a browser or a dedicated application and enters information such as the business outline, objectives, and target market into an input form. For example, a specific idea such as "I want to launch a new online learning platform" is input. The input is text data of the business plan that the user has come up with, which serves as the starting point for processing.

[0243] Step 2:

[0244] The device sends the entered business idea to the server. The input is business proposal data in text format. The device sends this data to the server in the form of an API request. The server receives the request and begins preparations to analyze the business idea data contained therein. The output is the request data sent to the server.

[0245] Step 3:

[0246] The server uses natural language processing technology to extract keywords for business ideas. Specifically, it uses Python's NLTK library to extract keywords such as "online learning" and "platform" from the received text data. The input is the text data of the business idea received by the server, and the output is a list of extracted keywords.

[0247] Step 4:

[0248] The server searches a past database based on the extracted keywords. Specifically, it issues an SQL query to search a database (e.g., a MySQL database) that stores past failure and success cases. The input is a list of keywords, and the output is a data list of similar failure and success cases.

[0249] Step 5:

[0250] The server analyzes the acquired data on similar cases and identifies the causes of failure and the factors behind success. Specifically, it uses machine learning algorithms to cluster the acquired data and extracts factors such as "difficulty in using the user interface" and "lack of marketing" from failure cases, and "interactive content" and "strong marketing strategy" from success cases. The input is a list of data, and the output is a list of identified causes of failure and factors behind success.

[0251] Step 6:

[0252] Based on the analysis results, the server generates strategic recommendations for the user's business idea. Specifically, based on the identified factors, it suggests "improving the user interface and adding interactive content" and "planning and implementing a powerful marketing strategy." The input is a list of causes of failure and factors of success, and the output is a list of strategic recommendations.

[0253] Step 7:

[0254] The server uses an emotion engine to recognize the user's emotions when they input data or when recommendations are presented. Specifically, it uses Microsoft Azure's emotion analysis API to recognize "expectation" or "anxiety" from the user's tone and vocabulary. The input is the text data of the business idea and the text data of the recommendation, and the output is recognized emotion data.

[0255] Step 8:

[0256] The server personalizes recommendations based on emotional data. Specifically, if a user feels anxious, it generates recommendations that include a specific action plan to alleviate the anxiety. The input is emotional data and a list of strategic recommendations, and the output is a list of personalized recommendations that take emotions into account.

[0257] Step 9:

[0258] The server monitors market changes and competitive conditions in real time and updates its recommendations. Specifically, it periodically scans relevant news articles and competitor activity data to generate new recommendations that reflect market trends and competitor actions. The input is market data and the user's current situation, and the output is updated strategic recommendations based on the latest market information.

[0259] (Application example 2)

[0260] 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."

[0261] Conventional consultation systems for increasing the success rate of new businesses generate recommendations by searching and retrieving similar cases from a past database for the business idea entered by the user, but because they cannot reflect the user's emotional state or market trends in real time, it is difficult to provide personalized advice.In addition, because they cannot take into account the user's emotions such as anxiety and expectations, the proposed recommendations may not match the user's actual needs.

[0262] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0263] In this invention, the server includes: a means for a user to input a new business idea; a means for searching and retrieving similar failure cases and success cases from a past database using a generative AI; a means for analyzing the retrieved data to identify causes of failure and factors of success; a means for generating strategic recommendations based on the identified information and presenting them to the user; a means for monitoring market changes and competitive conditions and updating the advice in real time; and a means for recognizing the user's emotions and adjusting the recommendations based on those emotions. This makes it possible to provide more personalized advice that not only learns from past cases but also takes into account the user's emotional state and market trends.

[0264] The "means for users to input ideas for new businesses" is an interface that allows users to input information about their own new businesses into the system.

[0265] "Generative AI" is an engine that uses artificial intelligence technology to perform natural language processing and data analysis, and has the ability to search and analyze past cases based on user input.

[0266] The "past database" is a collection of information that records the success and failure of new businesses, and is the source of information that stores the target data that the generation AI searches and retrieves.

[0267] "Means for searching and retrieving similar failure cases and success cases" refers to the process by which the generative AI finds business cases similar to the user input from a past database and retrieves the necessary information.

[0268] "Methods for analyzing acquired data to identify causes of failure and factors for success" refers to methods for analyzing the searched and acquired case data in detail to clarify the reasons for the success or failure of those businesses.

[0269] "Strategic recommendations" are advice that propose specific and effective strategies to users based on identified success factors and causes of failure.

[0270] The "means for presenting to the user" is an interface for displaying the generated recommendations to the user in an easy-to-understand manner.

[0271] "Means of monitoring market changes and competitive conditions and updating advice in real time" refers to methods for constantly monitoring changes in the external environment and competitor trends and keeping recommendations up to date accordingly.

[0272] "Means for recognizing user emotions and adjusting recommendations based on those emotions" refers to the process of using an emotion engine to analyze the user's psychological state and change the content of recommendations to match that state.

[0273] As an embodiment of the present invention, the following system configuration is provided.

[0274] The system is equipped with a terminal that allows users to input ideas for new businesses. Users use this terminal to input information such as their business idea, objectives, and target market. For example, they could input an idea such as, "I want to devise new security measures to prevent the leaking of personal information."

[0275] The business idea sent from the device is sent to a server. The server uses natural language processing technology to extract keywords from the business idea. This process uses a generative AI model. For example, keywords such as "personal information leakage" and "security measures" are extracted.

[0276] Next, the server searches and retrieves similar failure and success cases from a past database based on the extracted keywords, for example, "Failed personal information security measure A" and "Successful personal information security measure B."

[0277] The server analyzes the data of similar cases and identifies the causes of failure and the factors behind success. For failure cases, it identifies causes of failure such as "lack of proper data encryption" and "insufficient user training." For success cases, it identifies factors of success such as "strong data encryption" and "regular security training."

[0278] Furthermore, the server generates specific strategic recommendations based on the analysis results that can be applied to the user's business idea and presents them to the user via their device, such as "strengthen data encryption and conduct regular security training."

[0279] A feature of the present invention is that the server is equipped with an emotion engine that has the ability to recognize the user's emotions. When entering a business idea or presenting recommendations, the emotion engine can recognize emotions such as "expectation" or "anxiety" from the user's tone and vocabulary and reflect them in the recommendations. For example, if the user is feeling anxious, the recommendation will include a "specific action plan to alleviate the anxiety." Furthermore, if the user has strong expectations, proactive strategies to fulfill those expectations will be presented.

[0280] Furthermore, the server continuously monitors market trends and the competitive situation in real time. When new market trends or competitors' actions are detected, the server generates and updates new recommendations based on these, and presents them to the user along with new sentiment analysis results from the emotion engine. For example, if a competitor is detected to have "introduced a subscription service," and the emotion engine recognizes that the user is feeling "impatient," the server will recommend that the user "consider introducing a subscription service," along with specific implementation steps and risk mitigation measures.

[0281] This allows users to not only learn from past cases, but also receive more specific and personalized advice based on their own emotional state and market trends, further increasing the chances of success for new businesses.

[0282] Example prompt sentence:

[0283] "Business idea: I want to develop new security measures to prevent personal information leaks."

[0284] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0285] Step 1:

[0286] The user inputs a new business idea into the terminal. The input includes the business outline, objectives, target market, etc. An example of an input is a business idea such as "I want to devise new security measures to prevent the leakage of personal information." This information is sent to the server via the terminal's input interface.

[0287] input:

[0288] "I want to devise new security measures to prevent personal information leaks."

[0289] output:

[0290] Business idea data on the server

[0291] Step 2:

[0292] The server analyzes the received business ideas using natural language processing technology and extracts keywords. This process uses a generative AI model. For example, the keywords "personal information leakage" and "security measures" are extracted.

[0293] input:

[0294] Business idea data

[0295] output:

[0296] Keywords (e.g., "personal information leakage," "security measures")

[0297] Step 3:

[0298] The server searches and retrieves similar failure and success cases from a past database based on the extracted keywords. For example, cases such as "failed personal information security measure A" and "successful personal information security measure B" are retrieved.

[0299] input:

[0300] keyword

[0301] output:

[0302] Similar failure and success case data

[0303] Step 4:

[0304] The server analyzes the acquired case data and identifies the causes of failure and the factors of success. For example, causes of failure such as "lack of proper data encryption" and "lack of regular security training" are identified, while success factors such as "strong data encryption" and "regular security training" are identified.

[0305] input:

[0306] Similar failure and success case data

[0307] output:

[0308] Data on causes of failure and factors behind success

[0309] Step 5:

[0310] The server generates specific strategic recommendations applicable to the user's business idea based on the identified success factors and causes of failure, and presents them to the user via the terminal. For example, a recommendation such as "strengthen data encryption and conduct regular security training" may be presented.

[0311] input:

[0312] Data on causes of failure and factors behind success

[0313] output:

[0314] Specific strategic recommendations

[0315] Step 6:

[0316] The server uses an emotion engine to recognize the user's emotions when entering a business idea or presenting recommendations, and reflects these in the recommendations. For example, if the user is feeling anxious, the recommendation will include a "specific action plan to alleviate anxiety."

[0317] input:

[0318] User emotional state data

[0319] output:

[0320] Strategic recommendations tailored based on emotions

[0321] Step 7:

[0322] The server monitors market changes and the competitive situation in real time and updates recommendations accordingly. For example, if it detects that a competitor has introduced a subscription service, it will recommend that the company should consider introducing a subscription service, along with specific implementation steps and risk mitigation measures.

[0323] input:

[0324] Market trends and competitive landscape data

[0325] output:

[0326] Updated strategic recommendations based on the latest market trends and competitive landscape

[0327] 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.

[0328] 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.

[0329] 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.

[0330] [Second embodiment]

[0331] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0332] 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.

[0333] 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).

[0334] 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.

[0335] 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.

[0336] 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).

[0337] 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.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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."

[0343] The present invention provides a consultation service to help users increase the success rate of new businesses through the illustrated system. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0344] When a user wants to input an idea for a new business, they access the system using a terminal. First, the user inputs information such as the outline of the new business, its objectives, and the target market. For example, a user can input the idea of ​​launching a "new online learning platform."

[0345] The server receives user input and uses generative AI to search a database for relevant failures and successes. For example, it identifies cases where "online learning platform A" failed and "online learning platform B" was successful. In this case, the server uses natural language processing technology to extract keywords from the input business idea and search the database for the most similar cases.

[0346] The server then analyzes the data of similar cases. During this analysis, the data is examined in detail to identify the causes of failure and the factors behind success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind the success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0347] Based on the analysis results, the server generates specific strategic recommendations for the user's business idea. For example, it suggests "improving the user interface and adding interactive content" or recommends "planning and implementing a strong marketing strategy." These recommendations are presented to the user via their device, allowing them to use them to adjust and improve their business plan.

[0348] Furthermore, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has "introduced a subscription service to its online learning business model," it can use that information to generate a new strategic recommendation such as "consider introducing a subscription service."

[0349] In this way, the system of the present invention allows users to increase the probability of success of new businesses by utilizing lessons learned from past failures and factors behind successes, and by providing them with feasible strategies that adapt to changes in the market.

[0350] The processing flow will be explained below.

[0351] Step 1:

[0352] A user accesses the system using a terminal and inputs a new business idea, for example, "develop an online learning platform."

[0353] Step 2:

[0354] The terminal transmits the input business idea to the server.

[0355] Step 3:

[0356] The server receives the business idea and uses natural language processing technology to extract keywords from the idea, such as "online learning" and "platform."

[0357] Step 4:

[0358] The server queries the past database based on the extracted keywords to search for similar failures and successes. For example, it retrieves "failed online learning platform A" and "successful online learning platform B" from the past database.

[0359] Step 5:

[0360] The server analyzes the data of similar cases it has acquired. For failure cases, it identifies the causes. For example, it identifies causes of failure such as "difficulty in using the user interface" or "lack of marketing." For success cases, it identifies the factors that led to success. For example, it identifies factors that led to success such as "interactive content" or "strong marketing strategy."

[0361] Step 6:

[0362] Based on the analysis results, the server generates specific strategic recommendations that can be applied to the user's business idea, such as "improve the user interface and add interactive content."

[0363] Step 7:

[0364] The server generates recommendations and sends them to the device, where they are presented to the user, who can use them to adjust and improve their business ideas.

[0365] Step 8:

[0366] The server keeps a constant eye on market changes and the competitive landscape by regularly scanning for relevant news articles, competitor activity data, and market trends.

[0367] Step 9:

[0368] When market changes or new competitors' actions are detected, the server analyzes the information in real time and generates new strategic recommendations. For example, if it detects that a new competitor has introduced a subscription service, it generates a new recommendation that says, "Consider introducing a subscription service."

[0369] Step 10:

[0370] The server then sends the latest recommendations back to the device and presents them to the user, who can then quickly modify and adapt their business strategy.

[0371] Example 1

[0372] 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."

[0373] In order to increase the success rate of new businesses, it is necessary to properly evaluate and improve business ideas. However, with conventional methods, it was difficult to effectively utilize past cases and propose specific business strategies. In addition, there was a lack of means to monitor market changes and the competitive situation in real time and respond immediately. This posed a risk of reducing the success rate of new businesses.

[0374] 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.

[0375] In this invention, the server includes a means for a user to input a new business idea, a means for searching and retrieving similar failure cases and success cases from a past database using a generative AI, a means for analyzing the retrieved data to identify causes of failure and factors of success, a means for generating strategic recommendations based on the identified information and presenting them to the user, and a means for monitoring market changes and competitive conditions and updating advice in real time. This enables users to utilize lessons learned from past cases to quickly and accurately improve their business plans and increase the probability of success for their new businesses.

[0376] "User" refers to an individual or corporation that uses this system to input ideas for new businesses.

[0377] "Terminal" refers to the hardware device, such as a PC or smartphone, that a user uses to access the system.

[0378] "Server" refers to a computing device that receives user-submitted data and performs analysis and recommendation generation.

[0379] "Generative AI" refers to programs or models that use artificial intelligence technology to generate optimal output from input data, such as natural language generation models.

[0380] A "database" refers to a collection of data that stores past failures and successes.

[0381] "Keywords" are key words or phrases related to a business idea entered by a user, and refer to information used when searching and analyzing data.

[0382] "Recommendations" refer to specific strategies and suggestions for improvement presented to users based on the analysis results.

[0383] "Natural language processing technology" refers to technology for processing natural language using a computer, including text analysis and generation.

[0384] "Market monitoring" refers to the process of continuously monitoring market trends and competitive conditions, and generating and presenting new recommendations as needed.

[0385] A "prompt sentence" is a sentence provided as input to a generative AI, containing instructions for obtaining a specific output.

[0386] This invention is a system that provides consultation services to help users increase the success rate of new businesses. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0387] First, a user accesses the system using a terminal. The user logs in to the system using a web browser or a dedicated application and enters their new business idea. For example, the user enters their idea, such as "I want to launch a new online learning platform," along with related information such as their goals and target market, into a form.

[0388] Next, the server receives the data provided by the user. The data is sent to the server as an HTTP POST request and parsed in JSON format. The server then extracts key keywords from the input data and converts them into a prompt for the generation AI. For example, this prompt might look something like this:

[0389] "I want to launch a new online learning platform. Its main function is to provide video lessons and quiz-style assessments."

[0390] The server uses generative AI (e.g., natural language generation models) to search a historical database for relevant failure and success stories, using a high-speed search engine such as Elasticsearch to identify relevant cases.

[0391] The server then performs a detailed analysis of the similar cases it has retrieved. It uses natural language processing technology (e.g., SpaCy or NLTK) to identify the causes of failure and the factors behind success using text mining and clustering techniques. For example, the causes of failure of "Online Learning Platform A" are identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind its success are identified as "interactive content" and "strong marketing strategy."

[0392] Based on these analysis results, the server uses a generative AI model to generate specific strategic recommendations, such as "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are then presented to the user via their device.

[0393] As a specific example, the server generates recommendations such as "You should improve the user interface and add interactive content. It is recommended that you plan and implement a strong marketing strategy" and displays them to the user.

[0394] Furthermore, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has introduced a subscription service into its online learning business model, it can use that information to generate and present a new strategic recommendation, such as "consider introducing a subscription service."

[0395] In this way, the system of the present invention increases the probability of success for new businesses by providing users with viable strategies that utilize lessons learned from past failures and factors behind successes, and that adapt to changes in the market.

[0396] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0397] Step 1:

[0398] Users access the system using a terminal and input their new business idea. The input information includes the business outline, objectives, target market, etc. For example, a user can input an idea such as "I want to launch a new online learning platform." This input data is then sent to the server.

[0399] Step 2:

[0400] The server receives the data sent by the user. The received data is parsed in JSON format to extract key keywords for the business idea. These keywords are converted into prompts for the generation AI. For example, keywords such as "online learning," "platform," and "video lessons" are extracted from the received data. Based on this, a prompt is generated: "We want to launch a new online learning platform. Its main functions are to provide video lessons and quiz-style assessment functions."

[0401] Step 3:

[0402] The server uses the generated prompt to query the generative AI model. Based on the input prompt, the generative AI model searches a past database for relevant failure and success cases. Elasticsearch is used to efficiently identify relevant cases. A list of similar cases is output as the search results.

[0403] Step 4:

[0404] The server performs a detailed analysis of similar cases retrieved from the search results. Natural language processing technology (e.g., SpaCy or NLTK) is used here, and text mining and clustering techniques are used to identify the causes of failure and the factors behind success. The input for the analysis is the text data of past cases, and the output is a list of the causes of failure and the factors behind success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind the success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0405] Step 5:

[0406] Based on the analysis results, the server uses a generative AI model to generate specific strategic recommendations. The recommendations are tailored to the user's business idea and include specific action plans. For example, the recommendations may include "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are sent from the server to the device and displayed on the user's screen.

[0407] Step 6:

[0408] The device receives recommendations from the server and presents them to the user, who can use them to adjust or improve their business plans. The recommendations are displayed on the device screen in list or dashboard format.

[0409] Step 7:

[0410] The server continuously monitors market trends and competitive conditions in real time. It analyzes market data and news feeds to detect new trends and competitor movements. For example, if it detects that a new competitor has introduced a subscription model, it uses that information to generate a new strategic recommendation, such as "Consider introducing a subscription service," and provides it to the user. The latest advice responding to market changes is instantly displayed on the user's device.

[0411] (Application example 1)

[0412] 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."

[0413] In today's brick-and-mortar store operations, learning from past examples is crucial to increasing the success rate of new product introductions and service improvements. However, it is difficult for executives and managers to efficiently utilize vast amounts of past data. Furthermore, there is a lack of concrete support for monitoring market changes and competitive conditions in real time and quickly formulating and implementing effective strategies based on that information. For these reasons, innovative methods are needed to reduce the risk of failure and increase the success rate of new businesses.

[0414] 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.

[0415] In this invention, the server includes: means for a user to input a new business idea; means for searching and retrieving similar failure cases and success cases from a past database using a generation AI; means for analyzing the retrieved data to identify causes of failure and factors of success; means for generating strategic recommendations based on the identified information and presenting them to the user; means for monitoring market changes and competitive conditions and updating advice in real time; means for collecting and analyzing information on new product introductions and service improvements in physical stores; and means for generating strategic recommendations for physical stores using a generation AI and presenting them to the user via a smartphone application. This enables physical store operators to learn from past cases and quickly plan and implement effective strategies based on the latest market trends.

[0416] "Means for users to input new business ideas" refers to interfaces or devices that allow users to register information such as their business ideas, project outlines, objectives, target markets, etc. into the system.

[0417] "Generative AI" refers to an artificial intelligence model that uses natural language processing technology to analyze input data and provide appropriate answers or recommendations.

[0418] A "past database" is a collection of various cases and records that have been accumulated in the past, including both successful and unsuccessful cases.

[0419] "Means for searching and retrieving similar failure cases and success cases" refers to the technology and methods for finding and retrieving cases that are closest to the input business idea from a past database.

[0420] "Means for analyzing the acquired data to identify causes of failure and factors for success" refers to a method for analyzing the collected case data and identifying the reasons for failure or success in each case.

[0421] "Strategic recommendations" refers to proposing specific strategies or courses of action to users based on acquired and analyzed information.

[0422] "Means of monitoring market changes and competitive conditions and updating advice in real time" refers to methods of continuously monitoring market trends and competitor activity and using that information to keep user advice and strategies up to date.

[0423] "Means for collecting and analyzing information on new product introductions and service improvements in physical stores" refers to technologies and methods for collecting data related to the introduction of new products and services in physical stores and analyzing it in detail to gain important insights.

[0424] "Smartphone application" refers to software that allows users to input information and receive strategic recommendations via mobile communication devices.

[0425] By integrating the above, users can make effective data-driven decisions and develop strategies.

[0426] This invention is a system for providing consultation services to increase the success rate of new business ideas by users entering them. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0427] First, users access the system through a smartphone application. The process begins when the user inputs information about the new business, such as its outline, objectives, and target market. For example, a user can input their idea to launch a "new online learning platform" or a "new product sales strategy."

[0428] Next, the server receives the user's input and uses generative AI to search and retrieve related failure and success cases from a past database. For example, it identifies cases where "online learning platform A" failed and "online learning platform B" was successful. In this case, the server uses natural language processing technology (e.g., spaCy or NLTK) to extract keywords from the input business idea and search a database (e.g., PostgreSQL or MongoDB) for the most similar cases.

[0429] The data obtained in the above steps is then analyzed by the server. During this analysis process, the data is examined in detail to identify the causes of failure and the factors of success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in user interface" and "lack of marketing strategy," while the factors of success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0430] Furthermore, based on the analysis results, the server generates specific strategic recommendations for the user's business idea. For example, it suggests "improving the user interface and adding interactive content" or recommends "planning and implementing a strong marketing strategy." These recommendations are presented to the user via a smartphone application, allowing the user to adjust and improve their business plan.

[0431] In addition, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has introduced a subscription service into its online learning business model, it can use that information to generate a new strategic recommendation, such as "consider introducing a subscription service."

[0432] Below are some examples of specific prompts for a physical store:

[0433] "What approach is necessary to successfully introduce new products in physical stores? Please provide specific strategies based on past examples."

[0434] The above is a specific form for implementing the system of the present invention, which enables users to learn from past cases and quickly plan and execute effective strategies based on the latest market trends.

[0435] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0436] Step 1:

[0437] Users access the system using a terminal and enter information such as the outline of the new business, its objectives, target market, etc. The entered data is sent to the server.

[0438] Input: User-entered information such as business idea, objectives, target market, etc.

[0439] Data processing: converting user-entered information into text and formatting it

[0440] Output: User-entered data sent to the server

[0441] Step 2:

[0442] The server receives user input and uses natural language processing techniques to extract keywords for the business idea, using libraries such as spaCy and NLTK.

[0443] Input: User-entered data

[0444] Data Computing: Keyword Extraction by Natural Language Processing

[0445] Output: Extracted keywords

[0446] Step 3:

[0447] The server uses generative AI to search and retrieve similar failure and success cases from a past database, using database management systems such as PostgreSQL and MongoDB for this process.

[0448] Input: Extracted keywords

[0449] Data calculation: Retrieving similar cases through database search

[0450] Output: Retrieved related case data

[0451] Step 4:

[0452] The data acquired by the server is analyzed to identify the causes of failure and the factors behind success. Specifically, the case is examined in detail and an analysis is performed to identify each factor.

[0453] Input: Retrieved relevant case data

[0454] Data Computing: Identifying Causes of Failure and Success Factors

[0455] Output: Identified causes of failure and success factors

[0456] Step 5:

[0457] The server generates strategic recommendations based on the identified information, using a generative AI model (e.g., GPT-4) to generate specific strategies or courses of action for the user.

[0458] Input: Identified causes of failure and success factors

[0459] Data Processing and Data Arithmetic: Generating Strategic Recommendations with Generative AI

[0460] Output: Generated strategy recommendations

[0461] Step 6:

[0462] The server presents the generated strategic recommendations to the user via their terminal, allowing the user to adjust and improve their own business plans based on these recommendations.

[0463] Input: Generated strategy recommendations

[0464] Data output: Display on user terminal

[0465] Step 7:

[0466] The server monitors market changes and competitive conditions in real time, regularly scanning for relevant news articles and competitor activity data.

[0467] Input: Market trend information, competitor trend information

[0468] Data Computing: Scanning and Analyzing News Articles and Competitive Trends

[0469] Output: Real-time updated market and competitive landscape data

[0470] Step 8:

[0471] Based on newly acquired market and competitive data, strategic recommendations are updated to provide users with the most up-to-date advice.

[0472] Input: Updated market and competitive landscape data

[0473] Data processing and data calculations: Updating strategic recommendations

[0474] Output: Providing updated strategy recommendations to the user

[0475] By following these steps, users can learn from past cases and quickly develop and implement effective strategies based on the latest market trends.

[0476] 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.

[0477] This invention is a system that provides consultation services to help users increase the success rate of new businesses. This system is implemented using a terminal, a server, a generative AI, a historical database, and an emotion engine.

[0478] When a user wants to enter an idea for a new business, they access the system using a terminal. They enter information such as the outline of the new business, its objectives, and the target market. For example, they can enter the idea of ​​launching a "new online learning platform."

[0479] After the terminal transmits the input business idea to the server, the server uses natural language processing technology to extract keywords from the business idea, such as "online learning" and "platform."

[0480] Next, the server searches the past database based on the extracted keywords to obtain similar failure and success cases, for example, "failed online learning platform A" and "successful online learning platform B" from the past database.

[0481] The server analyzes data on similar cases and identifies the causes of failure and the factors behind success. For failure cases, causes of failure such as "difficulty in using the user interface" and "lack of marketing" are identified. For success cases, factors of success such as "interactive content" and "strong marketing strategy" are identified.

[0482] Based on the analysis results, the server generates specific strategic recommendations that can be applied to the user's business idea. For example, it may suggest "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are presented to the user via their device.

[0483] As another feature of the present invention, the server is equipped with an emotion engine. This emotion engine can recognize the user's emotions when inputting a business idea or when presenting recommendations. For example, when a user inputs an "idea for an online learning platform," the emotion engine can recognize emotions such as "expectation" or "anxiety" from the user's tone and choice of words.

[0484] The user's emotions recognized through the emotion engine are reflected in the recommendations generated by the server. For example, if the user is feeling anxious, the recommendation may include a specific action plan to alleviate the anxiety. Also, if the user has strong expectations, proactive strategies to fulfill those expectations will be presented.

[0485] Furthermore, the server continuously monitors market changes and the competitive situation in real time. When new market trends or competitors' moves are detected, the server generates and updates new strategic recommendations based on these. At this time, the emotion engine re-evaluates the user's current emotional state and adjusts the content of the recommendations.

[0486] For example, if the server detects that a new competitor has introduced a subscription service, the emotion engine will recognize that the user is feeling anxious about that information, and will then recommend that the user should consider introducing a subscription service, along with specific implementation steps and risk mitigation measures.

[0487] This allows the system of the present invention to not only learn from past failures and provide strategic advice based on successes, but also take into account the user's emotional state to generate more personalized recommendations, thereby further increasing the success rate of new businesses.

[0488] The processing flow will be explained below.

[0489] Step 1:

[0490] A user accesses the system using a terminal and inputs a new business idea, for example, "Develop a new online learning platform."

[0491] Step 2:

[0492] The emotion engine analyzes the user's input and recognizes their emotions, for example, analyzing "expectation" or "anxiety" from the tone and vocabulary of the input.

[0493] Step 3:

[0494] The terminal transmits the user's input and analyzed emotion information to the server.

[0495] Step 4:

[0496] The server receives the business idea and uses natural language processing technology to extract keywords from the idea, such as "online learning" and "platform."

[0497] Step 5:

[0498] The server searches a past database based on the extracted keywords to retrieve similar failure cases and success cases, for example, "failed online learning platform A" and "successful online learning platform B" from the past database.

[0499] Step 6:

[0500] The server analyzes data on similar cases and identifies the causes of failure and factors for success. For example, the causes of failure of "Online Learning Platform A" can be identified as "difficulty in using the user interface" and "lack of marketing," while the factors for success of "Online Learning Platform B" can be identified as "interactive content" and "strong marketing strategy."

[0501] Step 7:

[0502] Based on the analysis results, the server generates specific strategic recommendations taking into account the user's emotional state. For example, if the user is feeling anxious, the server will suggest improving the user interface and adding interactive content, as well as providing detailed instructions on specific steps for the marketing strategy.

[0503] Step 8:

[0504] The server generates recommendations and sends them to the device, where they are presented to the user, who can use them to adjust and improve their business ideas.

[0505] Step 9:

[0506] The server periodically scans for relevant news articles, competitor activity data, and market trends to monitor market changes and the competitive landscape.

[0507] Step 10:

[0508] When market changes or new competitor moves are detected, the server analyzes the information in real time and generates new strategic recommendations, while the emotion engine again evaluates the user's current emotional state and adjusts the recommendations accordingly.

[0509] Step 11:

[0510] The server sends the latest recommendations generated by the server to the device and presents them to the user, who can then quickly modify and adapt their business strategy.

[0511] In this way, users' new business ideas not only learn from past cases but also receive personalized advice that takes into account the user's emotional state, improving the chances of success.

[0512] Example 2

[0513] 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."

[0514] Conventional new business support systems have the problem that they are not only unable to learn from past cases, but also have difficulty in providing personalized recommendations that take into account the emotional state of the user.It is also difficult to monitor market changes and the competitive situation in real time and provide prompt and appropriate advice.

[0515] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input an idea for a new business; a means for searching and retrieving similar failure cases and success cases from a past database using a generation AI; a means for analyzing the retrieved data to identify causes of failure and factors of success; a means for generating strategic recommendations based on the identified information and presenting them to the user; a means for monitoring market changes and the competitive situation and updating the advice in real time; a means for recognizing the user's emotions at the time of user input or when recommendations are presented using an emotion engine; and a means for personalizing recommendations based on the user's emotional information. This makes it possible to provide personalized advice that takes the user's emotional state into consideration, further increasing the probability of success for new businesses.

[0516] "Users" are entities that use the system to provide new business ideas and receive recommendations.

[0517] "New business ideas" are new business concepts and plans provided by users.

[0518] "Generative AI" is a system that uses artificial intelligence technology to automatically analyze data and generate recommendations.

[0519] A "past database" is a data storage that accumulates records of past failures and successes.

[0520] A "failed case" is a business case that has been attempted in the past but was unsuccessful.

[0521] A "success story" is a business case that has been attempted in the past and succeeded.

[0522] "Strategic recommendations" are proposals for specific countermeasures and strategies for potential problems and success factors based on the analysis results.

[0523] "Market changes" are fluctuations in factors that affect the entire market, such as the business environment and consumer demand.

[0524] "Competitive situation" refers to the trends and market share of competitors in the market to which the user belongs.

[0525] "Monitoring" is the process of continuously observing market changes and competitive conditions, and collecting and analyzing data.

[0526] An "emotion engine" is a technology that analyzes and recognizes emotions from user input and reactions.

[0527] "Emotion information" is emotional data obtained from the user by the emotion engine.

[0528] "Personalization" refers to customizing content and services based on a user's individual characteristics and feelings.

[0529] The present invention is a system that provides consultation services to help users increase the success rate of new businesses. This system is realized by a configuration including a terminal, a server, a generative AI, a historical database, and an emotion engine.

[0530] First, a user accesses the system using a terminal and inputs their new business idea. Specifically, the user logs into the system using a browser or a dedicated application and enters information such as the business outline, objectives, and target market into an input form. For example, a user can enter a specific idea such as "I want to launch a new online learning platform."

[0531] Next, the device sends the business idea to a server, which uses natural language processing technology to extract keywords from the business idea. For example, it uses Python's NLTK library to extract keywords such as "online learning" and "platform."

[0532] Based on the extracted keywords, the server searches a historical database (e.g., a MySQL database) to obtain similar failure and success cases, such as "failed online learning platform A" and "successful online learning platform B."

[0533] The server analyzes the acquired similar cases using data mining technology (e.g., machine learning algorithms) to identify the causes of failure and the factors behind success. For example, factors such as "difficulty in using the user interface" and "lack of marketing" can be extracted from failure cases, while factors such as "interactive content" and "strong marketing strategies" can be extracted from success cases.

[0534] Based on the analysis results, the server generates strategic recommendations, such as "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are presented to the user via their device.

[0535] In addition, the server is equipped with an emotion engine (e.g., Microsoft Azure's emotion analysis API) that recognizes the user's emotions when they input data and when presenting recommendations. For example, when a user inputs "ideas for an online learning platform," the emotion engine recognizes "expectation" or "anxiety" from the user's tone and vocabulary.

[0536] The recognized emotional information is reflected in the recommendations. For example, if the user is feeling anxious, a recommendation including a "specific action plan to alleviate anxiety" will be provided. In addition, the server monitors market changes and competitive situations in real time to generate and update new strategic recommendations according to the user's emotional state.

[0537] For example, if the server detects that a new competitor has introduced a subscription service, the emotion engine will recognize that the user is feeling anxious about that information. The server will then recommend that users consider introducing a subscription service, along with specific implementation steps and risk mitigation measures. This allows users to quickly adopt an appropriate strategy in line with market trends, further increasing the chances of success for their new business.

[0538] As a concrete example, below is an example of a prompt sentence that is sent to a generative AI model when an idea for a new online learning platform is input.

[0539] "I have an idea for a new online learning platform. What strategy should I use to differentiate it from the competition? Please provide specific guidelines for a strong marketing strategy and interactive content."

[0540] In this way, the system of the present invention aims to provide personalized recommendations that take into account the user's emotional state, thereby increasing the chances of success for the user's new business.

[0541] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0542] Step 1:

[0543] A user accesses the system using a terminal and inputs a new business idea. Specifically, the user logs into the system using a browser or a dedicated application and enters information such as the business outline, objectives, and target market into an input form. For example, a specific idea such as "I want to launch a new online learning platform" is input. The input is text data of the business plan that the user has come up with, which serves as the starting point for processing.

[0544] Step 2:

[0545] The device sends the entered business idea to the server. The input is business proposal data in text format. The device sends this data to the server in the form of an API request. The server receives the request and begins preparations to analyze the business idea data contained therein. The output is the request data sent to the server.

[0546] Step 3:

[0547] The server uses natural language processing technology to extract keywords for business ideas. Specifically, it uses Python's NLTK library to extract keywords such as "online learning" and "platform" from the received text data. The input is the text data of the business idea received by the server, and the output is a list of extracted keywords.

[0548] Step 4:

[0549] The server searches a past database based on the extracted keywords. Specifically, it issues an SQL query to search a database (e.g., a MySQL database) that stores past failure and success cases. The input is a list of keywords, and the output is a data list of similar failure and success cases.

[0550] Step 5:

[0551] The server analyzes the acquired data on similar cases and identifies the causes of failure and the factors behind success. Specifically, it uses machine learning algorithms to cluster the acquired data and extracts factors such as "difficulty in using the user interface" and "lack of marketing" from failure cases, and "interactive content" and "strong marketing strategy" from success cases. The input is a list of data, and the output is a list of identified causes of failure and factors behind success.

[0552] Step 6:

[0553] Based on the analysis results, the server generates strategic recommendations for the user's business idea. Specifically, based on the identified factors, it suggests "improving the user interface and adding interactive content" and "planning and implementing a powerful marketing strategy." The input is a list of causes of failure and factors of success, and the output is a list of strategic recommendations.

[0554] Step 7:

[0555] The server uses an emotion engine to recognize the user's emotions when they input data or when recommendations are presented. Specifically, it uses Microsoft Azure's emotion analysis API to recognize "expectation" or "anxiety" from the user's tone and vocabulary. The input is the text data of the business idea and the text data of the recommendation, and the output is recognized emotion data.

[0556] Step 8:

[0557] The server personalizes recommendations based on emotional data. Specifically, if a user feels anxious, it generates recommendations that include a specific action plan to alleviate the anxiety. The input is emotional data and a list of strategic recommendations, and the output is a list of personalized recommendations that take emotions into account.

[0558] Step 9:

[0559] The server monitors market changes and competitive conditions in real time and updates its recommendations. Specifically, it periodically scans relevant news articles and competitor activity data to generate new recommendations that reflect market trends and competitor actions. The input is market data and the user's current situation, and the output is updated strategic recommendations based on the latest market information.

[0560] (Application example 2)

[0561] 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."

[0562] Conventional consultation systems for increasing the success rate of new businesses generate recommendations by searching and retrieving similar cases from a past database for the business idea entered by the user, but because they cannot reflect the user's emotional state or market trends in real time, it is difficult to provide personalized advice.In addition, because they cannot take into account the user's emotions such as anxiety and expectations, the proposed recommendations may not match the user's actual needs.

[0563] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0564] In this invention, the server includes: a means for a user to input a new business idea; a means for searching and retrieving similar failure cases and success cases from a past database using a generative AI; a means for analyzing the retrieved data to identify causes of failure and factors of success; a means for generating strategic recommendations based on the identified information and presenting them to the user; a means for monitoring market changes and competitive conditions and updating the advice in real time; and a means for recognizing the user's emotions and adjusting the recommendations based on those emotions. This makes it possible to provide more personalized advice that not only learns from past cases but also takes into account the user's emotional state and market trends.

[0565] The "means for users to input ideas for new businesses" is an interface that allows users to input information about their own new businesses into the system.

[0566] "Generative AI" is an engine that uses artificial intelligence technology to perform natural language processing and data analysis, and has the ability to search and analyze past cases based on user input.

[0567] The "past database" is a collection of information that records the success and failure of new businesses, and is the source of information that stores the target data that the generation AI searches and retrieves.

[0568] "Means for searching and retrieving similar failure cases and success cases" refers to the process by which the generative AI finds business cases similar to the user input from a past database and retrieves the necessary information.

[0569] "Methods for analyzing acquired data to identify causes of failure and factors for success" refers to methods for analyzing the searched and acquired case data in detail to clarify the reasons for the success or failure of those businesses.

[0570] "Strategic recommendations" are advice that propose specific and effective strategies to users based on identified success factors and causes of failure.

[0571] The "means for presenting to the user" is an interface for displaying the generated recommendations to the user in an easy-to-understand manner.

[0572] "Means of monitoring market changes and competitive conditions and updating advice in real time" refers to methods for constantly monitoring changes in the external environment and competitor trends and keeping recommendations up to date accordingly.

[0573] "Means for recognizing user emotions and adjusting recommendations based on those emotions" refers to the process of using an emotion engine to analyze the user's psychological state and change the content of recommendations to match that state.

[0574] As an embodiment of the present invention, the following system configuration is provided.

[0575] The system is equipped with a terminal that allows users to input ideas for new businesses. Users use this terminal to input information such as their business idea, objectives, and target market. For example, they could input an idea such as, "I want to devise new security measures to prevent the leaking of personal information."

[0576] The business idea sent from the device is sent to a server. The server uses natural language processing technology to extract keywords from the business idea. This process uses a generative AI model. For example, keywords such as "personal information leakage" and "security measures" are extracted.

[0577] Next, the server searches and retrieves similar failure and success cases from a past database based on the extracted keywords, for example, "Failed personal information security measure A" and "Successful personal information security measure B."

[0578] The server analyzes the data of similar cases and identifies the causes of failure and the factors behind success. For failure cases, it identifies causes of failure such as "lack of proper data encryption" and "insufficient user training." For success cases, it identifies factors of success such as "strong data encryption" and "regular security training."

[0579] Furthermore, the server generates specific strategic recommendations based on the analysis results that can be applied to the user's business idea and presents them to the user via their device, such as "strengthen data encryption and conduct regular security training."

[0580] A feature of the present invention is that the server is equipped with an emotion engine that has the ability to recognize the user's emotions. When entering a business idea or presenting recommendations, the emotion engine can recognize emotions such as "expectation" or "anxiety" from the user's tone and vocabulary and reflect them in the recommendations. For example, if the user is feeling anxious, the recommendation will include a "specific action plan to alleviate the anxiety." Furthermore, if the user has strong expectations, proactive strategies to fulfill those expectations will be presented.

[0581] Furthermore, the server continuously monitors market trends and the competitive situation in real time. When new market trends or competitors' actions are detected, the server generates and updates new recommendations based on these, and presents them to the user along with new sentiment analysis results from the emotion engine. For example, if a competitor is detected to have "introduced a subscription service," and the emotion engine recognizes that the user is feeling "impatient," the server will recommend that the user "consider introducing a subscription service," along with specific implementation steps and risk mitigation measures.

[0582] This allows users to not only learn from past cases, but also receive more specific and personalized advice based on their own emotional state and market trends, further increasing the chances of success for new businesses.

[0583] Example prompt sentence:

[0584] "Business idea: I want to develop new security measures to prevent personal information leaks."

[0585] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0586] Step 1:

[0587] The user inputs a new business idea into the terminal. The input includes the business outline, objectives, target market, etc. An example of an input is a business idea such as "I want to devise new security measures to prevent the leakage of personal information." This information is sent to the server via the terminal's input interface.

[0588] input:

[0589] "I want to devise new security measures to prevent personal information leaks."

[0590] output:

[0591] Business idea data on the server

[0592] Step 2:

[0593] The server analyzes the received business ideas using natural language processing technology and extracts keywords. This process uses a generative AI model. For example, the keywords "personal information leakage" and "security measures" are extracted.

[0594] input:

[0595] Business idea data

[0596] output:

[0597] Keywords (e.g., "personal information leakage," "security measures")

[0598] Step 3:

[0599] The server searches and retrieves similar failure and success cases from a past database based on the extracted keywords. For example, cases such as "failed personal information security measure A" and "successful personal information security measure B" are retrieved.

[0600] input:

[0601] keyword

[0602] output:

[0603] Similar failure and success case data

[0604] Step 4:

[0605] The server analyzes the acquired case data and identifies the causes of failure and the factors of success. For example, causes of failure such as "lack of proper data encryption" and "lack of regular security training" are identified, while success factors such as "strong data encryption" and "regular security training" are identified.

[0606] input:

[0607] Similar failure and success case data

[0608] output:

[0609] Data on causes of failure and factors behind success

[0610] Step 5:

[0611] The server generates specific strategic recommendations applicable to the user's business idea based on the identified success factors and causes of failure, and presents them to the user via the terminal. For example, a recommendation such as "strengthen data encryption and conduct regular security training" may be presented.

[0612] input:

[0613] Data on causes of failure and factors behind success

[0614] output:

[0615] Specific strategic recommendations

[0616] Step 6:

[0617] The server uses an emotion engine to recognize the user's emotions when entering a business idea or presenting recommendations, and reflects these in the recommendations. For example, if the user is feeling anxious, the recommendation will include a "specific action plan to alleviate anxiety."

[0618] input:

[0619] User emotional state data

[0620] output:

[0621] Strategic recommendations tailored based on emotions

[0622] Step 7:

[0623] The server monitors market changes and the competitive situation in real time and updates recommendations accordingly. For example, if it detects that a competitor has introduced a subscription service, it will recommend that the company should consider introducing a subscription service, along with specific implementation steps and risk mitigation measures.

[0624] input:

[0625] Market trends and competitive landscape data

[0626] output:

[0627] Updated strategic recommendations based on the latest market trends and competitive landscape

[0628] 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.

[0629] 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.

[0630] 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.

[0631] [Third embodiment]

[0632] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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."

[0644] The present invention provides a consultation service to help users increase the success rate of new businesses through the illustrated system. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0645] When a user wants to input an idea for a new business, they access the system using a terminal. First, the user inputs information such as the outline of the new business, its objectives, and the target market. For example, a user can input the idea of ​​launching a "new online learning platform."

[0646] The server receives user input and uses generative AI to search a database for relevant failures and successes. For example, it identifies cases where "online learning platform A" failed and "online learning platform B" was successful. In this case, the server uses natural language processing technology to extract keywords from the input business idea and search the database for the most similar cases.

[0647] The server then analyzes the data of similar cases. During this analysis, the data is examined in detail to identify the causes of failure and the factors behind success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind the success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0648] Based on the analysis results, the server generates specific strategic recommendations for the user's business idea. For example, it suggests "improving the user interface and adding interactive content" or recommends "planning and implementing a strong marketing strategy." These recommendations are presented to the user via their device, allowing them to use them to adjust and improve their business plan.

[0649] Furthermore, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has "introduced a subscription service to its online learning business model," it can use that information to generate a new strategic recommendation such as "consider introducing a subscription service."

[0650] In this way, the system of the present invention allows users to increase the probability of success of new businesses by utilizing lessons learned from past failures and factors behind successes, and by providing them with feasible strategies that adapt to changes in the market.

[0651] The processing flow will be explained below.

[0652] Step 1:

[0653] A user accesses the system using a terminal and inputs a new business idea, for example, "develop an online learning platform."

[0654] Step 2:

[0655] The terminal transmits the input business idea to the server.

[0656] Step 3:

[0657] The server receives the business idea and uses natural language processing technology to extract keywords from the idea, such as "online learning" and "platform."

[0658] Step 4:

[0659] The server queries the past database based on the extracted keywords to search for similar failures and successes. For example, it retrieves "failed online learning platform A" and "successful online learning platform B" from the past database.

[0660] Step 5:

[0661] The server analyzes the data of similar cases it has acquired. For failure cases, it identifies the causes. For example, it identifies causes of failure such as "difficulty in using the user interface" or "lack of marketing." For success cases, it identifies the factors that led to success. For example, it identifies factors that led to success such as "interactive content" or "strong marketing strategy."

[0662] Step 6:

[0663] Based on the analysis results, the server generates specific strategic recommendations that can be applied to the user's business idea, such as "improve the user interface and add interactive content."

[0664] Step 7:

[0665] The server generates recommendations and sends them to the device, where they are presented to the user, who can use them to adjust and improve their business ideas.

[0666] Step 8:

[0667] The server keeps a constant eye on market changes and the competitive landscape by periodically scanning for relevant news articles, competitor activity data, and market trends.

[0668] Step 9:

[0669] When market changes or new competitors' actions are detected, the server analyzes the information in real time and generates new strategic recommendations. For example, if it detects that a new competitor has introduced a subscription service, it generates a new recommendation that says, "Consider introducing a subscription service."

[0670] Step 10:

[0671] The server then sends the latest recommendations back to the device and presents them to the user, who can then quickly modify and adapt their business strategy.

[0672] Example 1

[0673] 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."

[0674] In order to increase the success rate of new businesses, it is necessary to properly evaluate and improve business ideas. However, with conventional methods, it was difficult to effectively utilize past cases and propose specific business strategies. In addition, there was a lack of means to monitor market changes and the competitive situation in real time and respond immediately. This posed a risk of reducing the success rate of new businesses.

[0675] 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.

[0676] In this invention, the server includes a means for a user to input a new business idea, a means for searching and retrieving similar failure cases and success cases from a past database using a generative AI, a means for analyzing the retrieved data to identify causes of failure and factors of success, a means for generating strategic recommendations based on the identified information and presenting them to the user, and a means for monitoring market changes and competitive conditions and updating advice in real time. This enables users to utilize lessons learned from past cases to quickly and accurately improve their business plans and increase the probability of success for their new businesses.

[0677] "User" refers to an individual or corporation that uses this system to input ideas for new businesses.

[0678] "Terminal" refers to the hardware device, such as a PC or smartphone, that a user uses to access the system.

[0679] "Server" refers to a computing device that receives user-submitted data and performs analysis and recommendation generation.

[0680] "Generative AI" refers to programs or models that use artificial intelligence technology to generate optimal output from input data, such as natural language generation models.

[0681] A "database" refers to a collection of data that stores past failures and successes.

[0682] "Keywords" are key words or phrases related to a business idea entered by a user, and refer to information used when searching and analyzing data.

[0683] "Recommendations" refer to specific strategies and suggestions for improvement presented to users based on the analysis results.

[0684] "Natural language processing technology" refers to technology for processing natural language using a computer, including text analysis and generation.

[0685] "Market monitoring" refers to the process of continuously monitoring market trends and competitive conditions, and generating and presenting new recommendations as needed.

[0686] A "prompt sentence" is a sentence provided as input to a generative AI, containing instructions for obtaining a specific output.

[0687] This invention is a system that provides consultation services to help users increase the success rate of new businesses. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0688] First, a user accesses the system using a terminal. The user logs in to the system using a web browser or a dedicated application and enters their new business idea. For example, the user enters their idea, such as "I want to launch a new online learning platform," along with related information such as their goals and target market into a form.

[0689] Next, the server receives the data provided by the user. The data is sent to the server as an HTTP POST request and parsed in JSON format. The server then extracts key keywords from the input data and converts them into a prompt for the generation AI. For example, this prompt might look something like this:

[0690] "I want to launch a new online learning platform. Its main function is to provide video lessons and quiz-style assessments."

[0691] The server uses generative AI (e.g., natural language generation models) to search a historical database for relevant failure and success stories, using a high-speed search engine such as Elasticsearch to identify relevant cases.

[0692] The server then performs a detailed analysis of the similar cases it has retrieved. It uses natural language processing technology (e.g., SpaCy or NLTK) to identify the causes of failure and the factors behind success using text mining and clustering techniques. For example, the causes of failure of "Online Learning Platform A" are identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind its success are identified as "interactive content" and "strong marketing strategy."

[0693] Based on these analysis results, the server uses a generative AI model to generate specific strategic recommendations, such as "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are then presented to the user via their device.

[0694] As a specific example, the server generates recommendations such as "You should improve the user interface and add interactive content. It is recommended that you plan and implement a strong marketing strategy" and displays them to the user.

[0695] Furthermore, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has introduced a subscription service into its online learning business model, it can use that information to generate and present a new strategic recommendation, such as "consider introducing a subscription service."

[0696] In this way, the system of the present invention increases the probability of success for new businesses by providing users with viable strategies that utilize lessons learned from past failures and factors behind successes, and that adapt to changes in the market.

[0697] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0698] Step 1:

[0699] Users access the system using a terminal and input their new business idea. The input information includes the business outline, objectives, target market, etc. For example, a user can input an idea such as "I want to launch a new online learning platform." This input data is then sent to the server.

[0700] Step 2:

[0701] The server receives the data sent by the user. The received data is parsed in JSON format to extract key keywords for the business idea. These keywords are converted into prompts for the generation AI. For example, keywords such as "online learning," "platform," and "video lessons" are extracted from the received data. Based on this, a prompt is generated: "We want to launch a new online learning platform. Its main functions are to provide video lessons and quiz-style assessment functions."

[0702] Step 3:

[0703] The server uses the generated prompt to query the generative AI model. Based on the input prompt, the generative AI model searches a past database for relevant failure and success cases. Elasticsearch is used to efficiently identify relevant cases. A list of similar cases is output as the search results.

[0704] Step 4:

[0705] The server performs a detailed analysis of similar cases retrieved from the search results. Natural language processing technology (e.g., SpaCy or NLTK) is used here, and text mining and clustering techniques are used to identify the causes of failure and the factors behind success. The input for the analysis is the text data of past cases, and the output is a list of the causes of failure and the factors behind success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind the success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0706] Step 5:

[0707] Based on the analysis results, the server uses a generative AI model to generate specific strategic recommendations. The recommendations are tailored to the user's business idea and include specific action plans. For example, the recommendations may include "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are sent from the server to the device and displayed on the user's screen.

[0708] Step 6:

[0709] The device receives recommendations from the server and presents them to the user, who can use them to adjust or improve their business plans. The recommendations are displayed on the device screen in list or dashboard format.

[0710] Step 7:

[0711] The server continuously monitors market trends and competitive conditions in real time. It analyzes market data and news feeds to detect new trends and competitor movements. For example, if it detects that a new competitor has introduced a subscription model, it uses that information to generate a new strategic recommendation, such as "Consider introducing a subscription service," and provides it to the user. The latest advice responding to market changes is instantly displayed on the user's device.

[0712] (Application example 1)

[0713] 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."

[0714] In today's brick-and-mortar store operations, learning from past examples is crucial to increasing the success rate of new product introductions and service improvements. However, it is difficult for executives and managers to efficiently utilize vast amounts of past data. Furthermore, there is a lack of concrete support for monitoring market changes and competitive conditions in real time and quickly formulating and implementing effective strategies based on that information. For these reasons, innovative methods are needed to reduce the risk of failure and increase the success rate of new businesses.

[0715] 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.

[0716] In this invention, the server includes: means for a user to input a new business idea; means for searching and retrieving similar failure cases and success cases from a past database using a generation AI; means for analyzing the retrieved data to identify causes of failure and factors of success; means for generating strategic recommendations based on the identified information and presenting them to the user; means for monitoring market changes and competitive conditions and updating advice in real time; means for collecting and analyzing information on new product introductions and service improvements in physical stores; and means for generating strategic recommendations for physical stores using a generation AI and presenting them to the user via a smartphone application. This enables physical store operators to learn from past cases and quickly plan and implement effective strategies based on the latest market trends.

[0717] "Means for users to input new business ideas" refers to interfaces or devices that allow users to register information such as their business ideas, project outlines, objectives, target markets, etc. into the system.

[0718] "Generative AI" refers to an artificial intelligence model that uses natural language processing technology to analyze input data and provide appropriate answers or recommendations.

[0719] A "past database" is a collection of various cases and records that have been accumulated in the past, including both successful and unsuccessful cases.

[0720] "Means for searching and retrieving similar failure cases and success cases" refers to the technology and methods for finding and retrieving cases that are closest to the input business idea from a past database.

[0721] "Means for analyzing the acquired data to identify causes of failure and factors for success" refers to a method for analyzing the collected case data and identifying the reasons for failure or success in each case.

[0722] "Strategic recommendations" refers to proposing specific strategies or courses of action to users based on acquired and analyzed information.

[0723] "Means of monitoring market changes and competitive conditions and updating advice in real time" refers to methods of continuously monitoring market trends and competitor activity and using that information to keep user advice and strategies up to date.

[0724] "Means for collecting and analyzing information on new product introductions and service improvements in physical stores" refers to technologies and methods for collecting data related to the introduction of new products and services in physical stores and analyzing it in detail to gain important insights.

[0725] "Smartphone application" refers to software that allows users to input information and receive strategic recommendations via mobile communication devices.

[0726] By integrating the above, users can make effective data-driven decisions and develop strategies.

[0727] This invention is a system for providing consultation services to increase the success rate of new business ideas by users entering them. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0728] First, users access the system through a smartphone application. The process begins when the user inputs information about the new business, such as its outline, objectives, and target market. For example, a user can input their idea to launch a "new online learning platform" or a "new product sales strategy."

[0729] Next, the server receives the user's input and uses generative AI to search and retrieve related failure and success cases from a past database. For example, it identifies cases where "online learning platform A" failed and "online learning platform B" was successful. In this case, the server uses natural language processing technology (e.g., spaCy or NLTK) to extract keywords from the input business idea and search a database (e.g., PostgreSQL or MongoDB) for the most similar cases.

[0730] The data obtained in the above steps is then analyzed by the server. During this analysis process, the data is examined in detail to identify the causes of failure and the factors of success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in user interface" and "lack of marketing strategy," while the factors of success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0731] Furthermore, based on the analysis results, the server generates specific strategic recommendations for the user's business idea. For example, it suggests "improving the user interface and adding interactive content" or recommends "planning and implementing a strong marketing strategy." These recommendations are presented to the user via a smartphone application, allowing the user to adjust and improve their business plan.

[0732] In addition, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has introduced a subscription service into its online learning business model, it can use that information to generate a new strategic recommendation, such as "consider introducing a subscription service."

[0733] Below are some examples of specific prompts for a physical store:

[0734] "What approach is necessary to successfully introduce new products in physical stores? Please provide specific strategies based on past examples."

[0735] The above is a specific form for implementing the system of the present invention, which enables users to learn from past cases and quickly plan and execute effective strategies based on the latest market trends.

[0736] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0737] Step 1:

[0738] Users access the system using a terminal and enter information such as the outline of the new business, its objectives, target market, etc. The entered data is sent to the server.

[0739] Input: User-entered information such as business idea, objectives, target market, etc.

[0740] Data processing: converting user-entered information into text and formatting it

[0741] Output: User-entered data sent to the server

[0742] Step 2:

[0743] The server receives user input and uses natural language processing techniques to extract keywords for the business idea, using libraries such as spaCy and NLTK.

[0744] Input: User-entered data

[0745] Data Computing: Keyword Extraction by Natural Language Processing

[0746] Output: Extracted keywords

[0747] Step 3:

[0748] The server uses generative AI to search and retrieve similar failure and success cases from a past database, using database management systems such as PostgreSQL and MongoDB for this process.

[0749] Input: Extracted keywords

[0750] Data calculation: Retrieving similar cases through database search

[0751] Output: Retrieved related case data

[0752] Step 4:

[0753] The data acquired by the server is analyzed to identify the causes of failure and the factors behind success. Specifically, the case is examined in detail and an analysis is performed to identify each factor.

[0754] Input: Retrieved relevant case data

[0755] Data Computing: Identifying Causes of Failure and Success Factors

[0756] Output: Identified causes of failure and success factors

[0757] Step 5:

[0758] The server generates strategic recommendations based on the identified information, using a generative AI model (e.g., GPT-4) to generate specific strategies or courses of action for the user.

[0759] Input: Identified causes of failure and success factors

[0760] Data Processing and Data Arithmetic: Generating Strategic Recommendations with Generative AI

[0761] Output: Generated strategy recommendations

[0762] Step 6:

[0763] The server presents the generated strategic recommendations to the user via their terminal, allowing the user to adjust and improve their own business plans based on these recommendations.

[0764] Input: Generated strategy recommendations

[0765] Data output: Display on user terminal

[0766] Step 7:

[0767] The server monitors market changes and competitive conditions in real time, regularly scanning for relevant news articles and competitor activity data.

[0768] Input: Market trend information, competitor trend information

[0769] Data Computing: Scanning and Analyzing News Articles and Competitive Trends

[0770] Output: Real-time updated market and competitive landscape data

[0771] Step 8:

[0772] Based on newly acquired market and competitive data, strategic recommendations are updated to provide users with the most up-to-date advice.

[0773] Input: Updated market and competitive landscape data

[0774] Data processing and data calculations: Updating strategic recommendations

[0775] Output: Providing updated strategy recommendations to the user

[0776] By following these steps, users can learn from past cases and quickly develop and implement effective strategies based on the latest market trends.

[0777] 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.

[0778] This invention is a system that provides consultation services to help users increase the success rate of new businesses. This system is implemented using a terminal, a server, a generative AI, a historical database, and an emotion engine.

[0779] When a user wants to enter an idea for a new business, they access the system using a terminal. They enter information such as the outline of the new business, its objectives, and the target market. For example, they can enter the idea of ​​launching a "new online learning platform."

[0780] After the terminal transmits the input business idea to the server, the server uses natural language processing technology to extract keywords from the business idea, such as "online learning" and "platform."

[0781] Next, the server searches the past database based on the extracted keywords to obtain similar failure and success cases, for example, "failed online learning platform A" and "successful online learning platform B" from the past database.

[0782] The server analyzes data on similar cases and identifies the causes of failure and the factors behind success. For failure cases, causes of failure such as "difficulty in using the user interface" and "lack of marketing" are identified. For success cases, factors of success such as "interactive content" and "strong marketing strategy" are identified.

[0783] Based on the analysis results, the server generates specific strategic recommendations that can be applied to the user's business idea. For example, it may suggest "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are presented to the user via their device.

[0784] As another feature of the present invention, the server is equipped with an emotion engine. This emotion engine can recognize the user's emotions when inputting a business idea or when presenting recommendations. For example, when a user inputs an "idea for an online learning platform," the emotion engine can recognize emotions such as "expectation" or "anxiety" from the user's tone and choice of words.

[0785] The user's emotions recognized through the emotion engine are reflected in the recommendations generated by the server. For example, if the user is feeling anxious, the recommendation may include a specific action plan to alleviate the anxiety. Also, if the user has strong expectations, proactive strategies to fulfill those expectations will be presented.

[0786] Furthermore, the server continuously monitors market changes and the competitive situation in real time. When new market trends or competitors' moves are detected, the server generates and updates new strategic recommendations based on these. At this time, the emotion engine re-evaluates the user's current emotional state and adjusts the content of the recommendations.

[0787] For example, if the server detects that a new competitor has introduced a subscription service, the emotion engine will recognize that the user is feeling anxious about that information, and will then recommend that the user should consider introducing a subscription service, along with specific implementation steps and risk mitigation measures.

[0788] This allows the system of the present invention to not only learn from past failures and provide strategic advice based on successes, but also take into account the user's emotional state to generate more personalized recommendations, thereby further increasing the success rate of new businesses.

[0789] The processing flow will be explained below.

[0790] Step 1:

[0791] A user accesses the system using a terminal and inputs a new business idea, for example, "Develop a new online learning platform."

[0792] Step 2:

[0793] The emotion engine analyzes the user's input and recognizes their emotions, for example, analyzing "expectation" or "anxiety" from the tone and vocabulary of the input.

[0794] Step 3:

[0795] The terminal transmits the user's input and analyzed emotion information to the server.

[0796] Step 4:

[0797] The server receives the business idea and uses natural language processing technology to extract keywords from the idea, such as "online learning" and "platform."

[0798] Step 5:

[0799] The server searches a past database based on the extracted keywords to retrieve similar failure cases and success cases, for example, "failed online learning platform A" and "successful online learning platform B" from the past database.

[0800] Step 6:

[0801] The server analyzes data on similar cases and identifies the causes of failure and factors for success. For example, the causes of failure of "Online Learning Platform A" can be identified as "difficulty in using the user interface" and "lack of marketing," while the factors for success of "Online Learning Platform B" can be identified as "interactive content" and "strong marketing strategy."

[0802] Step 7:

[0803] Based on the analysis results, the server generates specific strategic recommendations taking into account the user's emotional state. For example, if the user is feeling anxious, the server will suggest improving the user interface and adding interactive content, as well as providing detailed instructions on specific steps for the marketing strategy.

[0804] Step 8:

[0805] The server generates recommendations and sends them to the device, where they are presented to the user, who can use them to adjust and improve their business ideas.

[0806] Step 9:

[0807] The server periodically scans for relevant news articles, competitor activity data, and market trends to monitor market changes and the competitive landscape.

[0808] Step 10:

[0809] When market changes or new competitor moves are detected, the server analyzes the information in real time and generates new strategic recommendations, while the emotion engine again evaluates the user's current emotional state and adjusts the recommendations accordingly.

[0810] Step 11:

[0811] The server sends the latest recommendations generated by the server to the device and presents them to the user, who can then quickly modify and adapt their business strategy.

[0812] In this way, users' new business ideas not only learn from past cases but also receive personalized advice that takes into account the user's emotional state, improving the chances of success.

[0813] Example 2

[0814] 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."

[0815] Conventional new business support systems have the problem that they are not only unable to learn from past cases, but also have difficulty in providing personalized recommendations that take into account the emotional state of the user.It is also difficult to monitor market changes and the competitive situation in real time and provide prompt and appropriate advice.

[0816] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input an idea for a new business; a means for searching and retrieving similar failure cases and success cases from a past database using a generation AI; a means for analyzing the retrieved data to identify causes of failure and factors of success; a means for generating strategic recommendations based on the identified information and presenting them to the user; a means for monitoring market changes and the competitive situation and updating the advice in real time; a means for recognizing the user's emotions at the time of user input or when recommendations are presented using an emotion engine; and a means for personalizing recommendations based on the user's emotional information. This makes it possible to provide personalized advice that takes the user's emotional state into consideration, further increasing the probability of success for new businesses.

[0817] "Users" are entities that use the system to provide new business ideas and receive recommendations.

[0818] "New business ideas" are new business concepts and plans provided by users.

[0819] "Generative AI" is a system that uses artificial intelligence technology to automatically analyze data and generate recommendations.

[0820] A "past database" is a data storage that accumulates records of past failures and successes.

[0821] A "failed case" is a business case that has been attempted in the past but was unsuccessful.

[0822] A "success story" is a business case that has been attempted in the past and succeeded.

[0823] "Strategic recommendations" are proposals for specific countermeasures and strategies for potential problems and success factors based on the analysis results.

[0824] "Market changes" are fluctuations in factors that affect the entire market, such as the business environment and consumer demand.

[0825] "Competitive situation" refers to the trends and market share of competitors in the market to which the user belongs.

[0826] "Monitoring" is the process of continuously observing market changes and competitive conditions, and collecting and analyzing data.

[0827] An "emotion engine" is a technology that analyzes and recognizes emotions from user input and reactions.

[0828] "Emotion information" is emotional data obtained from the user by the emotion engine.

[0829] "Personalization" refers to customizing content and services based on a user's individual characteristics and feelings.

[0830] The present invention is a system that provides consultation services to help users increase the success rate of new businesses. This system is realized by a configuration including a terminal, a server, a generative AI, a historical database, and an emotion engine.

[0831] First, a user accesses the system using a terminal and inputs their new business idea. Specifically, the user logs into the system using a browser or a dedicated application and enters information such as the business outline, objectives, and target market into an input form. For example, a user can enter a specific idea such as "I want to launch a new online learning platform."

[0832] Next, the device sends the business idea to a server, which uses natural language processing technology to extract keywords from the business idea. For example, it uses Python's NLTK library to extract keywords such as "online learning" and "platform."

[0833] Based on the extracted keywords, the server searches a historical database (e.g., a MySQL database) to obtain similar failure and success cases, such as "failed online learning platform A" and "successful online learning platform B."

[0834] The server analyzes the acquired similar cases using data mining technology (e.g., machine learning algorithms) to identify the causes of failure and the factors behind success. For example, factors such as "difficulty in using the user interface" and "lack of marketing" can be extracted from failure cases, while factors such as "interactive content" and "strong marketing strategies" can be extracted from success cases.

[0835] Based on the analysis results, the server generates strategic recommendations, such as "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are presented to the user via their device.

[0836] In addition, the server is equipped with an emotion engine (e.g., Microsoft Azure's emotion analysis API) that recognizes the user's emotions when they input data and when presenting recommendations. For example, when a user inputs "ideas for an online learning platform," the emotion engine recognizes "expectation" or "anxiety" from the user's tone and vocabulary.

[0837] The recognized emotional information is reflected in the recommendations. For example, if the user is feeling anxious, a recommendation including a "specific action plan to alleviate anxiety" will be provided. In addition, the server monitors market changes and competitive situations in real time to generate and update new strategic recommendations according to the user's emotional state.

[0838] For example, if the server detects that a new competitor has introduced a subscription service, the emotion engine will recognize that the user is feeling anxious about that information. The server will then recommend that users consider introducing a subscription service, along with specific implementation steps and risk mitigation measures. This allows users to quickly adopt an appropriate strategy in line with market trends, further increasing the chances of success for their new business.

[0839] As a concrete example, below is an example of a prompt sentence that is sent to a generative AI model when an idea for a new online learning platform is input.

[0840] "I have an idea for a new online learning platform. What strategy should I use to differentiate it from the competition? Please provide specific guidelines for a strong marketing strategy and interactive content."

[0841] In this way, the system of the present invention aims to provide personalized recommendations that take into account the user's emotional state, thereby increasing the chances of success for the user's new business.

[0842] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0843] Step 1:

[0844] A user accesses the system using a terminal and inputs a new business idea. Specifically, the user logs into the system using a browser or a dedicated application and enters information such as the business outline, objectives, and target market into an input form. For example, a specific idea such as "I want to launch a new online learning platform" is input. The input is text data of the business plan that the user has come up with, which serves as the starting point for processing.

[0845] Step 2:

[0846] The device sends the entered business idea to the server. The input is business proposal data in text format. The device sends this data to the server in the form of an API request. The server receives the request and begins preparations to analyze the business idea data contained therein. The output is the request data sent to the server.

[0847] Step 3:

[0848] The server uses natural language processing technology to extract keywords for business ideas. Specifically, it uses Python's NLTK library to extract keywords such as "online learning" and "platform" from the received text data. The input is the text data of the business idea received by the server, and the output is a list of extracted keywords.

[0849] Step 4:

[0850] The server searches a past database based on the extracted keywords. Specifically, it issues an SQL query to search a database (e.g., a MySQL database) that stores past failure and success cases. The input is a list of keywords, and the output is a data list of similar failure and success cases.

[0851] Step 5:

[0852] The server analyzes the acquired data on similar cases and identifies the causes of failure and the factors behind success. Specifically, it uses machine learning algorithms to cluster the acquired data and extracts factors such as "difficulty in using the user interface" and "lack of marketing" from failure cases, and "interactive content" and "strong marketing strategy" from success cases. The input is a list of data, and the output is a list of identified causes of failure and factors behind success.

[0853] Step 6:

[0854] Based on the analysis results, the server generates strategic recommendations for the user's business idea. Specifically, based on the identified factors, it suggests "improving the user interface and adding interactive content" and "planning and implementing a powerful marketing strategy." The input is a list of causes of failure and factors of success, and the output is a list of strategic recommendations.

[0855] Step 7:

[0856] The server uses an emotion engine to recognize the user's emotions when they input data or when recommendations are presented. Specifically, it uses Microsoft Azure's emotion analysis API to recognize "expectation" or "anxiety" from the user's tone and vocabulary. The input is the text data of the business idea and the text data of the recommendation, and the output is recognized emotion data.

[0857] Step 8:

[0858] The server personalizes recommendations based on emotional data. Specifically, if a user feels anxious, it generates recommendations that include a specific action plan to alleviate the anxiety. The input is emotional data and a list of strategic recommendations, and the output is a list of personalized recommendations that take emotions into account.

[0859] Step 9:

[0860] The server monitors market changes and competitive conditions in real time and updates its recommendations. Specifically, it periodically scans relevant news articles and competitor activity data to generate new recommendations that reflect market trends and competitor actions. The input is market data and the user's current situation, and the output is updated strategic recommendations based on the latest market information.

[0861] (Application example 2)

[0862] 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."

[0863] Conventional consultation systems for increasing the success rate of new businesses generate recommendations by searching and retrieving similar cases from a past database for the business idea entered by the user, but because they cannot reflect the user's emotional state or market trends in real time, it is difficult to provide personalized advice.In addition, because they cannot take into account the user's emotions such as anxiety and expectations, the proposed recommendations may not match the user's actual needs.

[0864] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0865] In this invention, the server includes: a means for a user to input a new business idea; a means for searching and retrieving similar failure cases and success cases from a past database using a generative AI; a means for analyzing the retrieved data to identify causes of failure and factors of success; a means for generating strategic recommendations based on the identified information and presenting them to the user; a means for monitoring market changes and competitive conditions and updating the advice in real time; and a means for recognizing the user's emotions and adjusting the recommendations based on those emotions. This makes it possible to provide more personalized advice that not only learns from past cases but also takes into account the user's emotional state and market trends.

[0866] The "means for users to input ideas for new businesses" is an interface that allows users to input information about their own new businesses into the system.

[0867] "Generative AI" is an engine that uses artificial intelligence technology to perform natural language processing and data analysis, and has the ability to search and analyze past cases based on user input.

[0868] The "past database" is a collection of information that records the success and failure of new businesses, and is the source of information that stores the target data that the generation AI searches and retrieves.

[0869] "Means for searching and retrieving similar failure cases and success cases" refers to the process by which the generative AI finds business cases similar to the user input from a past database and retrieves the necessary information.

[0870] "Methods for analyzing acquired data to identify causes of failure and factors for success" refers to methods for analyzing the searched and acquired case data in detail to clarify the reasons for the success or failure of those businesses.

[0871] "Strategic recommendations" are advice that propose specific and effective strategies to users based on identified success factors and causes of failure.

[0872] The "means for presenting to the user" is an interface for displaying the generated recommendations to the user in an easy-to-understand manner.

[0873] "Means of monitoring market changes and competitive conditions and updating advice in real time" refers to methods for constantly monitoring changes in the external environment and competitor trends and keeping recommendations up to date accordingly.

[0874] "Means for recognizing user emotions and adjusting recommendations based on those emotions" refers to the process of using an emotion engine to analyze the user's psychological state and change the content of recommendations to match that state.

[0875] As an embodiment of the present invention, the following system configuration is provided.

[0876] The system is equipped with a terminal that allows users to input ideas for new businesses. Users use this terminal to input information such as the business idea, objectives, and target market. For example, they could input an idea such as, "I want to devise new security measures to prevent the leaking of personal information."

[0877] The business idea sent from the device is sent to a server. The server uses natural language processing technology to extract keywords from the business idea. This process uses a generative AI model. For example, keywords such as "personal information leakage" and "security measures" are extracted.

[0878] Next, the server searches and retrieves similar failure and success cases from a past database based on the extracted keywords, for example, "failed personal information security measure A" and "successful personal information security measure B."

[0879] The server analyzes the data of similar cases and identifies the causes of failure and the factors behind success. For failure cases, it identifies causes of failure such as "lack of proper data encryption" and "insufficient user training." For success cases, it identifies factors of success such as "strong data encryption" and "regular security training."

[0880] Furthermore, the server generates specific strategic recommendations based on the analysis results that can be applied to the user's business idea and presents them to the user via their device, such as "strengthen data encryption and conduct regular security training."

[0881] A feature of the present invention is that the server is equipped with an emotion engine that has the ability to recognize the user's emotions. When entering a business idea or presenting recommendations, the emotion engine can recognize emotions such as "expectation" or "anxiety" from the user's tone and vocabulary and reflect them in the recommendations. For example, if the user is feeling anxious, the recommendation will include a "specific action plan to alleviate the anxiety." Furthermore, if the user has strong expectations, proactive strategies to fulfill those expectations will be presented.

[0882] Furthermore, the server continuously monitors market trends and the competitive situation in real time. When new market trends or competitors' actions are detected, the server generates and updates new recommendations based on these, and presents them to the user along with new sentiment analysis results from the emotion engine. For example, if a competitor is detected to have "introduced a subscription service," and the emotion engine recognizes that the user is feeling "impatient," the server will recommend that the user "consider introducing a subscription service," along with specific implementation steps and risk mitigation measures.

[0883] This allows users to not only learn from past cases, but also receive more specific and personalized advice based on their own emotional state and market trends, further increasing the chances of success for new businesses.

[0884] Example prompt sentence:

[0885] "Business idea: I want to develop new security measures to prevent personal information leaks."

[0886] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0887] Step 1:

[0888] The user inputs a new business idea into the terminal. The input includes the business outline, objectives, target market, etc. An example of an input is a business idea such as "I want to devise new security measures to prevent the leakage of personal information." This information is sent to the server via the terminal's input interface.

[0889] input:

[0890] "I want to devise new security measures to prevent personal information leaks."

[0891] output:

[0892] Business idea data on the server

[0893] Step 2:

[0894] The server analyzes the received business ideas using natural language processing technology and extracts keywords. This process uses a generative AI model. For example, the keywords "personal information leakage" and "security measures" are extracted.

[0895] input:

[0896] Business idea data

[0897] output:

[0898] Keywords (e.g., "personal information leakage," "security measures")

[0899] Step 3:

[0900] The server searches and retrieves similar failure and success cases from a past database based on the extracted keywords. For example, cases such as "failed personal information security measure A" and "successful personal information security measure B" are retrieved.

[0901] input:

[0902] keyword

[0903] output:

[0904] Similar failure and success case data

[0905] Step 4:

[0906] The server analyzes the acquired case data and identifies the causes of failure and the factors of success. For example, causes of failure such as "lack of proper data encryption" and "lack of regular security training" are identified, while success factors such as "strong data encryption" and "regular security training" are identified.

[0907] input:

[0908] Similar failure and success case data

[0909] output:

[0910] Data on causes of failure and factors behind success

[0911] Step 5:

[0912] The server generates specific strategic recommendations applicable to the user's business idea based on the identified success factors and causes of failure, and presents them to the user via the terminal. For example, a recommendation such as "strengthen data encryption and conduct regular security training" may be presented.

[0913] input:

[0914] Data on causes of failure and factors behind success

[0915] output:

[0916] Specific strategic recommendations

[0917] Step 6:

[0918] The server uses an emotion engine to recognize the user's emotions when entering a business idea or presenting recommendations, and reflects these in the recommendations. For example, if the user is feeling anxious, the recommendation will include a "specific action plan to alleviate anxiety."

[0919] input:

[0920] User emotional state data

[0921] output:

[0922] Strategic recommendations tailored based on emotions

[0923] Step 7:

[0924] The server monitors market changes and the competitive situation in real time and updates recommendations accordingly. For example, if it detects that a competitor has introduced a subscription service, it will recommend that the company should consider introducing a subscription service, along with specific implementation steps and risk mitigation measures.

[0925] input:

[0926] Market trends and competitive landscape data

[0927] output:

[0928] Updated strategic recommendations based on the latest market trends and competitive landscape

[0929] 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.

[0930] 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.

[0931] 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.

[0932] [Fourth embodiment]

[0933] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0934] 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.

[0935] 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).

[0936] 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.

[0937] 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.

[0938] 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).

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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."

[0946] The present invention provides a consultation service to help users increase the success rate of new businesses through the illustrated system. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0947] When a user wants to input an idea for a new business, they access the system using a terminal. First, the user inputs information such as the outline of the new business, its objectives, and the target market. For example, a user can input the idea of ​​launching a "new online learning platform."

[0948] The server receives user input and uses generative AI to search a database for relevant failures and successes. For example, it identifies cases where "online learning platform A" failed and "online learning platform B" was successful. In this case, the server uses natural language processing technology to extract keywords from the input business idea and search the database for the most similar cases.

[0949] The server then analyzes the data of similar cases. During this analysis, the data is examined in detail to identify the causes of failure and the factors behind success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind the success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[0950] Based on the analysis results, the server generates specific strategic recommendations for the user's business idea. For example, it suggests "improving the user interface and adding interactive content" or recommends "planning and implementing a strong marketing strategy." These recommendations are presented to the user via their device, allowing them to use them to adjust and improve their business plan.

[0951] Furthermore, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has "introduced a subscription service to its online learning business model," it can use that information to generate a new strategic recommendation such as "consider introducing a subscription service."

[0952] In this way, the system of the present invention allows users to increase the probability of success of new businesses by utilizing lessons learned from past failures and factors behind successes, and by providing them with feasible strategies that adapt to changes in the market.

[0953] The processing flow will be explained below.

[0954] Step 1:

[0955] A user accesses the system using a terminal and inputs a new business idea, for example, "develop an online learning platform."

[0956] Step 2:

[0957] The terminal transmits the input business idea to the server.

[0958] Step 3:

[0959] The server receives the business idea and uses natural language processing technology to extract keywords from the idea, such as "online learning" and "platform."

[0960] Step 4:

[0961] The server queries the past database based on the extracted keywords to search for similar failures and successes. For example, it retrieves "failed online learning platform A" and "successful online learning platform B" from the past database.

[0962] Step 5:

[0963] The server analyzes the data of similar cases it has acquired. For failure cases, it identifies the causes. For example, it identifies causes of failure such as "difficulty in using the user interface" or "lack of marketing." For success cases, it identifies the factors that led to success. For example, it identifies factors that led to success such as "interactive content" or "strong marketing strategy."

[0964] Step 6:

[0965] Based on the analysis results, the server generates specific strategic recommendations that can be applied to the user's business idea, such as "improve the user interface and add interactive content."

[0966] Step 7:

[0967] The server generates recommendations and sends them to the device, where they are presented to the user, who can use them to adjust and improve their business ideas.

[0968] Step 8:

[0969] The server keeps a constant eye on market changes and the competitive landscape by periodically scanning for relevant news articles, competitor activity data, and market trends.

[0970] Step 9:

[0971] When market changes or new competitors' actions are detected, the server analyzes the information in real time and generates new strategic recommendations. For example, if it detects that a new competitor has introduced a subscription service, it generates a new recommendation that says, "Consider introducing a subscription service."

[0972] Step 10:

[0973] The server then sends the latest recommendations back to the device and presents them to the user, who can then quickly modify and adapt their business strategy.

[0974] Example 1

[0975] 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."

[0976] In order to increase the success rate of new businesses, it is necessary to properly evaluate and improve business ideas. However, with conventional methods, it was difficult to effectively utilize past cases and propose specific business strategies. In addition, there was a lack of means to monitor market changes and the competitive situation in real time and respond immediately. This posed a risk of reducing the success rate of new businesses.

[0977] 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.

[0978] In this invention, the server includes a means for a user to input a new business idea, a means for searching and retrieving similar failure cases and success cases from a past database using a generative AI, a means for analyzing the retrieved data to identify causes of failure and factors of success, a means for generating strategic recommendations based on the identified information and presenting them to the user, and a means for monitoring market changes and competitive conditions and updating advice in real time. This enables users to utilize lessons learned from past cases to quickly and accurately improve their business plans and increase the probability of success for their new businesses.

[0979] "User" refers to an individual or corporation that uses this system to input ideas for new businesses.

[0980] "Terminal" refers to the hardware device, such as a PC or smartphone, that a user uses to access the system.

[0981] "Server" refers to a computing device that receives user-submitted data and performs analysis and recommendation generation.

[0982] "Generative AI" refers to programs or models that use artificial intelligence technology to generate optimal output from input data, such as natural language generation models.

[0983] A "database" refers to a collection of data that stores past failures and successes.

[0984] "Keywords" are key words or phrases related to a business idea entered by a user, and refer to information used when searching and analyzing data.

[0985] "Recommendations" refer to specific strategies and suggestions for improvement presented to users based on the analysis results.

[0986] "Natural language processing technology" refers to technology for processing natural language using a computer, including text analysis and generation.

[0987] "Market monitoring" refers to the process of continuously monitoring market trends and competitive conditions, and generating and presenting new recommendations as needed.

[0988] A "prompt sentence" is a sentence provided as input to a generative AI, containing instructions for obtaining a specific output.

[0989] This invention is a system that provides consultation services to help users increase the success rate of new businesses. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[0990] First, a user accesses the system using a terminal. The user logs in to the system using a web browser or a dedicated application and enters their new business idea. For example, the user enters their idea, such as "I want to launch a new online learning platform," along with related information such as their goals and target market into a form.

[0991] Next, the server receives the data provided by the user. The data is sent to the server as an HTTP POST request and parsed in JSON format. The server then extracts key keywords from the input data and converts them into a prompt for the generation AI. For example, this prompt might look something like this:

[0992] "I want to launch a new online learning platform. Its main function is to provide video lessons and quiz-style assessments."

[0993] The server uses generative AI (e.g., natural language generation models) to search a historical database for relevant failure and success stories, using a high-speed search engine such as Elasticsearch to identify relevant cases.

[0994] The server then performs a detailed analysis of the similar cases it has retrieved. It uses natural language processing technology (e.g., SpaCy or NLTK) to identify the causes of failure and the factors behind success using text mining and clustering techniques. For example, the causes of failure of "Online Learning Platform A" are identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind its success are identified as "interactive content" and "strong marketing strategy."

[0995] Based on these analysis results, the server uses a generative AI model to generate specific strategic recommendations, such as "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are then presented to the user via their device.

[0996] As a specific example, the server generates recommendations such as "You should improve the user interface and add interactive content. It is recommended that you plan and implement a strong marketing strategy" and displays them to the user.

[0997] Furthermore, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has introduced a subscription service into its online learning business model, it can use that information to generate and present a new strategic recommendation, such as "consider introducing a subscription service."

[0998] In this way, the system of the present invention increases the probability of success for new businesses by providing users with viable strategies that utilize lessons learned from past failures and factors behind successes, and that adapt to changes in the market.

[0999] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1000] Step 1:

[1001] Users access the system using a terminal and input their new business idea. The input information includes the business outline, objectives, target market, etc. For example, a user can input an idea such as "I want to launch a new online learning platform." This input data is then sent to the server.

[1002] Step 2:

[1003] The server receives the data sent by the user. The received data is parsed in JSON format to extract key keywords for the business idea. These keywords are converted into prompts for the generation AI. For example, keywords such as "online learning," "platform," and "video lessons" are extracted from the received data. Based on this, a prompt is generated: "We want to launch a new online learning platform. Its main functions are to provide video lessons and quiz-style assessment functions."

[1004] Step 3:

[1005] The server uses the generated prompt to query the generative AI model. Based on the input prompt, the generative AI model searches a past database for relevant failure and success cases. Elasticsearch is used to efficiently identify relevant cases. A list of similar cases is output as the search results.

[1006] Step 4:

[1007] The server performs a detailed analysis of similar cases retrieved from the search results. Natural language processing technology (e.g., SpaCy or NLTK) is used here, and text mining and clustering techniques are used to identify the causes of failure and the factors behind success. The input for the analysis is the text data of past cases, and the output is a list of the causes of failure and the factors behind success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in using the user interface" and "lack of a marketing strategy," while the factors behind the success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[1008] Step 5:

[1009] Based on the analysis results, the server uses a generative AI model to generate specific strategic recommendations. The recommendations are tailored to the user's business idea and include specific action plans. For example, the recommendations may include "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are sent from the server to the device and displayed on the user's screen.

[1010] Step 6:

[1011] The device receives recommendations from the server and presents them to the user, who can use them to adjust or improve their business plans. The recommendations are displayed on the device screen in list or dashboard format.

[1012] Step 7:

[1013] The server continuously monitors market trends and competitive conditions in real time. It analyzes market data and news feeds to detect new trends and competitor movements. For example, if it detects that a new competitor has introduced a subscription model, it uses that information to generate a new strategic recommendation, such as "Consider introducing a subscription service," and provides it to the user. The latest advice responding to market changes is instantly displayed on the user's device.

[1014] (Application example 1)

[1015] 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."

[1016] In today's brick-and-mortar store operations, learning from past examples is crucial to increasing the success rate of new product introductions and service improvements. However, it is difficult for executives and managers to efficiently utilize vast amounts of past data. Furthermore, there is a lack of concrete support for monitoring market changes and competitive conditions in real time and quickly formulating and implementing effective strategies based on that information. For these reasons, innovative methods are needed to reduce the risk of failure and increase the success rate of new businesses.

[1017] 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.

[1018] In this invention, the server includes: means for a user to input a new business idea; means for searching and retrieving similar failure cases and success cases from a past database using a generation AI; means for analyzing the retrieved data to identify causes of failure and factors of success; means for generating strategic recommendations based on the identified information and presenting them to the user; means for monitoring market changes and competitive conditions and updating advice in real time; means for collecting and analyzing information on new product introductions and service improvements in physical stores; and means for generating strategic recommendations for physical stores using a generation AI and presenting them to the user via a smartphone application. This enables physical store operators to learn from past cases and quickly plan and implement effective strategies based on the latest market trends.

[1019] "Means for users to input new business ideas" refers to interfaces or devices that allow users to register information such as their business ideas, project outlines, objectives, target markets, etc. into the system.

[1020] "Generative AI" refers to an artificial intelligence model that uses natural language processing technology to analyze input data and provide appropriate answers or recommendations.

[1021] A "past database" is a collection of various cases and records that have been accumulated in the past, including both successful and unsuccessful cases.

[1022] "Means for searching and retrieving similar failure cases and success cases" refers to the technology and methods for finding and retrieving cases that are closest to the input business idea from a past database.

[1023] "Means for analyzing the acquired data to identify causes of failure and factors for success" refers to a method for analyzing the collected case data and identifying the reasons for failure or success in each case.

[1024] "Strategic recommendations" refers to proposing specific strategies or courses of action to users based on acquired and analyzed information.

[1025] "Means of monitoring market changes and competitive conditions and updating advice in real time" refers to methods of continuously monitoring market trends and competitor activity and using that information to keep user advice and strategies up to date.

[1026] "Means for collecting and analyzing information on new product introductions and service improvements in physical stores" refers to technologies and methods for collecting data related to the introduction of new products and services in physical stores and analyzing it in detail to gain important insights.

[1027] "Smartphone application" refers to software that allows users to input information and receive strategic recommendations via mobile communication devices.

[1028] By integrating the above, users can make effective data-driven decisions and develop strategies.

[1029] This invention is a system for providing consultation services to increase the success rate of new business ideas by users entering them. The system is implemented using a terminal, a server, a generation AI, and a historical database.

[1030] First, users access the system through a smartphone application. The process begins when the user inputs information about the new business, such as its outline, objectives, and target market. For example, a user can input their idea to launch a "new online learning platform" or a "new product sales strategy."

[1031] Next, the server receives the user's input and uses generative AI to search and retrieve related failure and success cases from a past database. For example, it identifies cases where "online learning platform A" failed and "online learning platform B" was successful. In this case, the server uses natural language processing technology (e.g., spaCy or NLTK) to extract keywords from the input business idea and search a database (e.g., PostgreSQL or MongoDB) for the most similar cases.

[1032] The data obtained in the above steps is then analyzed by the server. During this analysis process, the data is examined in detail to identify the causes of failure and the factors of success. For example, the causes of failure of "Online Learning Platform A" may be identified as "difficulty in user interface" and "lack of marketing strategy," while the factors of success of "Online Learning Platform B" may be identified as "interactive content" and "strong marketing strategy."

[1033] Furthermore, based on the analysis results, the server generates specific strategic recommendations for the user's business idea. For example, it suggests "improving the user interface and adding interactive content" or recommends "planning and implementing a strong marketing strategy." These recommendations are presented to the user via a smartphone application, allowing the user to adjust and improve their business plan.

[1034] In addition, the server has the ability to monitor market changes and the competitive situation in real time. When market trends or new moves by competitors are detected, it immediately analyzes them and provides updated advice to users. For example, if the server detects that a newly emerged competitor has introduced a subscription service into its online learning business model, it can use that information to generate a new strategic recommendation, such as "consider introducing a subscription service."

[1035] Below are some examples of specific prompts for a physical store:

[1036] "What approach is necessary to successfully introduce new products in physical stores? Please provide specific strategies based on past examples."

[1037] The above is a specific form for implementing the system of the present invention, which enables users to learn from past cases and quickly develop and implement effective strategies based on the latest market trends.

[1038] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1039] Step 1:

[1040] Users access the system using a terminal and enter information such as the outline of the new business, its objectives, target market, etc. The entered data is sent to the server.

[1041] Input: User-entered information such as business idea, objectives, target market, etc.

[1042] Data processing: converting user-entered information into text and formatting it

[1043] Output: User-entered data sent to the server

[1044] Step 2:

[1045] The server receives user input and uses natural language processing techniques to extract keywords for the business idea, using libraries such as spaCy and NLTK.

[1046] Input: User-entered data

[1047] Data Computing: Keyword Extraction by Natural Language Processing

[1048] Output: Extracted keywords

[1049] Step 3:

[1050] The server uses generative AI to search and retrieve similar failure and success cases from a past database, using database management systems such as PostgreSQL and MongoDB for this process.

[1051] Input: Extracted keywords

[1052] Data calculation: Retrieving similar cases through database search

[1053] Output: Retrieved related case data

[1054] Step 4:

[1055] The data acquired by the server is analyzed to identify the causes of failure and the factors behind success. Specifically, the case is examined in detail and an analysis is performed to identify each factor.

[1056] Input: Retrieved relevant case data

[1057] Data Computing: Identifying Causes of Failure and Success Factors

[1058] Output: Identified causes of failure and success factors

[1059] Step 5:

[1060] The server generates strategic recommendations based on the identified information, using a generative AI model (e.g., GPT-4) to generate specific strategies or courses of action for the user.

[1061] Input: Identified causes of failure and success factors

[1062] Data Processing and Data Arithmetic: Generating Strategic Recommendations with Generative AI

[1063] Output: Generated strategy recommendations

[1064] Step 6:

[1065] The server presents the generated strategic recommendations to the user via their terminal, allowing the user to adjust and improve their own business plans based on these recommendations.

[1066] Input: Generated strategy recommendations

[1067] Data output: Display on user terminal

[1068] Step 7:

[1069] The server monitors market changes and competitive conditions in real time, regularly scanning for relevant news articles and competitor activity data.

[1070] Input: Market trend information, competitor trend information

[1071] Data Computing: Scanning and Analyzing News Articles and Competitive Trends

[1072] Output: Real-time updated market and competitive landscape data

[1073] Step 8:

[1074] Based on newly acquired market and competitive data, strategic recommendations are updated to provide users with the most up-to-date advice.

[1075] Input: Updated market and competitive landscape data

[1076] Data processing and data calculations: Updating strategic recommendations

[1077] Output: Providing updated strategy recommendations to the user

[1078] By following these steps, users can learn from past cases and quickly develop and implement effective strategies based on the latest market trends.

[1079] 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.

[1080] This invention is a system that provides consultation services to help users increase the success rate of new businesses. This system is implemented using a terminal, a server, a generative AI, a historical database, and an emotion engine.

[1081] When a user wants to enter an idea for a new business, they access the system using a terminal. They enter information such as the outline of the new business, its objectives, and the target market. For example, they can enter the idea of ​​launching a "new online learning platform."

[1082] After the terminal transmits the input business idea to the server, the server uses natural language processing technology to extract keywords from the business idea, such as "online learning" and "platform."

[1083] Next, the server searches the past database based on the extracted keywords to obtain similar failure and success cases, for example, "failed online learning platform A" and "successful online learning platform B" from the past database.

[1084] The server analyzes data on similar cases and identifies the causes of failure and the factors behind success. For failure cases, causes of failure such as "difficulty in using the user interface" and "lack of marketing" are identified. For success cases, factors of success such as "interactive content" and "strong marketing strategy" are identified.

[1085] Based on the analysis results, the server generates specific strategic recommendations that can be applied to the user's business idea. For example, it may suggest "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are presented to the user via their device.

[1086] As another feature of the present invention, the server is equipped with an emotion engine. This emotion engine can recognize the user's emotions when inputting a business idea or when presenting recommendations. For example, when a user inputs an "idea for an online learning platform," the emotion engine can recognize emotions such as "expectation" or "anxiety" from the user's tone and choice of words.

[1087] The user's emotions recognized through the emotion engine are reflected in the recommendations generated by the server. For example, if the user is feeling anxious, the recommendation may include a specific action plan to alleviate the anxiety. Also, if the user has strong expectations, proactive strategies to fulfill those expectations will be presented.

[1088] Furthermore, the server continuously monitors market changes and the competitive situation in real time. When new market trends or competitors' moves are detected, the server generates and updates new strategic recommendations based on these. At this time, the emotion engine re-evaluates the user's current emotional state and adjusts the content of the recommendations.

[1089] For example, if the server detects that a new competitor has introduced a subscription service, the emotion engine will recognize that the user is feeling anxious about that information, and will then recommend that the user should consider introducing a subscription service, along with specific implementation steps and risk mitigation measures.

[1090] This allows the system of the present invention to not only learn from past failures and provide strategic advice based on successes, but also take into account the user's emotional state to generate more personalized recommendations, thereby further increasing the success rate of new businesses.

[1091] The processing flow will be explained below.

[1092] Step 1:

[1093] A user accesses the system using a terminal and inputs a new business idea, for example, "Develop a new online learning platform."

[1094] Step 2:

[1095] The emotion engine analyzes the user's input and recognizes their emotions, for example, analyzing "expectation" or "anxiety" from the tone and vocabulary of the input.

[1096] Step 3:

[1097] The terminal transmits the user's input and analyzed emotion information to the server.

[1098] Step 4:

[1099] The server receives the business idea and uses natural language processing technology to extract keywords from the idea, such as "online learning" and "platform."

[1100] Step 5:

[1101] The server searches a past database based on the extracted keywords to retrieve similar failure cases and success cases, for example, "failed online learning platform A" and "successful online learning platform B" from the past database.

[1102] Step 6:

[1103] The server analyzes data on similar cases and identifies the causes of failure and factors for success. For example, the causes of failure of "Online Learning Platform A" can be identified as "difficulty in using the user interface" and "lack of marketing," while the factors for success of "Online Learning Platform B" can be identified as "interactive content" and "strong marketing strategy."

[1104] Step 7:

[1105] Based on the analysis results, the server generates specific strategic recommendations taking into account the user's emotional state. For example, if the user is feeling anxious, the server will suggest improving the user interface and adding interactive content, as well as providing detailed instructions on specific steps for the marketing strategy.

[1106] Step 8:

[1107] The server generates recommendations and sends them to the device, where they are presented to the user, who can use them to adjust and improve their business ideas.

[1108] Step 9:

[1109] The server periodically scans for relevant news articles, competitor activity data, and market trends to monitor market changes and the competitive landscape.

[1110] Step 10:

[1111] When market changes or new competitor moves are detected, the server analyzes the information in real time and generates new strategic recommendations, while the emotion engine again evaluates the user's current emotional state and adjusts the recommendations accordingly.

[1112] Step 11:

[1113] The server sends the latest recommendations generated by the server to the device and presents them to the user, who can then quickly modify and adapt their business strategy.

[1114] In this way, users' new business ideas not only learn from past cases but also receive personalized advice that takes into account the user's emotional state, improving the chances of success.

[1115] Example 2

[1116] 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."

[1117] Conventional new business support systems have the problem that they are not only unable to learn from past cases, but also have difficulty in providing personalized recommendations that take into account the emotional state of the user.It is also difficult to monitor market changes and the competitive situation in real time and provide prompt and appropriate advice.

[1118] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input an idea for a new business; a means for searching and retrieving similar failure cases and success cases from a past database using a generation AI; a means for analyzing the retrieved data to identify causes of failure and factors of success; a means for generating strategic recommendations based on the identified information and presenting them to the user; a means for monitoring market changes and the competitive situation and updating the advice in real time; a means for recognizing the user's emotions at the time of user input or when recommendations are presented using an emotion engine; and a means for personalizing recommendations based on the user's emotional information. This makes it possible to provide personalized advice that takes the user's emotional state into consideration, further increasing the probability of success for new businesses.

[1119] "Users" are entities that use the system to provide new business ideas and receive recommendations.

[1120] "New business ideas" are new business concepts and plans provided by users.

[1121] "Generative AI" is a system that uses artificial intelligence technology to automatically analyze data and generate recommendations.

[1122] A "past database" is a data storage that accumulates records of past failures and successes.

[1123] A "failed case" is a business case that has been attempted in the past but was unsuccessful.

[1124] A "success story" is a business case that has been attempted in the past and succeeded.

[1125] "Strategic recommendations" are proposals for specific countermeasures and strategies for potential problems and success factors based on the analysis results.

[1126] "Market changes" are fluctuations in factors that affect the entire market, such as the business environment and consumer demand.

[1127] "Competitive situation" refers to the trends and market share of competitors in the market to which the user belongs.

[1128] "Monitoring" is the process of continuously observing market changes and competitive conditions, and collecting and analyzing data.

[1129] An "emotion engine" is a technology that analyzes and recognizes emotions from user input and reactions.

[1130] "Emotion information" is emotional data obtained from the user by the emotion engine.

[1131] "Personalization" refers to customizing content and services based on a user's individual characteristics and feelings.

[1132] The present invention is a system that provides consultation services to help users increase the success rate of new businesses. This system is realized by a configuration including a terminal, a server, a generative AI, a historical database, and an emotion engine.

[1133] First, a user accesses the system using a terminal and inputs their new business idea. Specifically, the user logs into the system using a browser or a dedicated application and enters information such as the business outline, objectives, and target market into an input form. For example, a user can enter a specific idea such as "I want to launch a new online learning platform."

[1134] Next, the device sends the business idea to a server, which uses natural language processing technology to extract keywords from the business idea. For example, it uses Python's NLTK library to extract keywords such as "online learning" and "platform."

[1135] Based on the extracted keywords, the server searches a historical database (e.g., a MySQL database) to obtain similar failure and success cases, such as "failed online learning platform A" and "successful online learning platform B."

[1136] The server analyzes the acquired similar cases using data mining technology (e.g., machine learning algorithms) to identify the causes of failure and the factors behind success. For example, factors such as "difficulty in using the user interface" and "lack of marketing" can be extracted from failure cases, while factors such as "interactive content" and "strong marketing strategies" can be extracted from success cases.

[1137] Based on the analysis results, the server generates strategic recommendations, such as "improving the user interface and adding interactive content" or "planning and implementing a powerful marketing strategy." These recommendations are presented to the user via their device.

[1138] In addition, the server is equipped with an emotion engine (e.g., Microsoft Azure's emotion analysis API) that recognizes the user's emotions when they input data and when presenting recommendations. For example, when a user inputs "ideas for an online learning platform," the emotion engine recognizes "expectation" or "anxiety" from the user's tone and vocabulary.

[1139] The recognized emotional information is reflected in the recommendations. For example, if the user is feeling anxious, a recommendation including a "specific action plan to alleviate anxiety" will be provided. In addition, the server monitors market changes and competitive situations in real time to generate and update new strategic recommendations according to the user's emotional state.

[1140] For example, if the server detects that a new competitor has introduced a subscription service, the emotion engine will recognize that the user is feeling anxious about that information. The server will then recommend that users consider introducing a subscription service, along with specific implementation steps and risk mitigation measures. This allows users to quickly adopt an appropriate strategy in line with market trends, further increasing the chances of success for their new business.

[1141] As a concrete example, below is an example of a prompt sentence that is sent to a generative AI model when an idea for a new online learning platform is input.

[1142] "I have an idea for a new online learning platform. What strategy should I use to differentiate it from the competition? Please provide specific guidelines for a strong marketing strategy and interactive content."

[1143] In this way, the system of the present invention aims to provide personalized recommendations that take into account the user's emotional state, thereby increasing the chances of success for the user's new business.

[1144] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1145] Step 1:

[1146] A user accesses the system using a terminal and inputs a new business idea. Specifically, the user logs into the system using a browser or a dedicated application and enters information such as the business outline, objectives, and target market into an input form. For example, a specific idea such as "I want to launch a new online learning platform" is input. The input is text data of the business plan that the user has come up with, which serves as the starting point for processing.

[1147] Step 2:

[1148] The device sends the entered business idea to the server. The input is business proposal data in text format. The device sends this data to the server in the form of an API request. The server receives the request and begins preparations to analyze the business idea data contained therein. The output is the request data sent to the server.

[1149] Step 3:

[1150] The server uses natural language processing technology to extract keywords for business ideas. Specifically, it uses Python's NLTK library to extract keywords such as "online learning" and "platform" from the received text data. The input is the text data of the business idea received by the server, and the output is a list of extracted keywords.

[1151] Step 4:

[1152] The server searches a past database based on the extracted keywords. Specifically, it issues an SQL query to search a database (e.g., a MySQL database) that stores past failure and success cases. The input is a list of keywords, and the output is a data list of similar failure and success cases.

[1153] Step 5:

[1154] The server analyzes the acquired data on similar cases and identifies the causes of failure and the factors behind success. Specifically, it uses machine learning algorithms to cluster the acquired data and extracts factors such as "difficulty in using the user interface" and "lack of marketing" from failure cases, and "interactive content" and "strong marketing strategy" from success cases. The input is a list of data, and the output is a list of identified causes of failure and factors behind success.

[1155] Step 6:

[1156] Based on the analysis results, the server generates strategic recommendations for the user's business idea. Specifically, based on the identified factors, it suggests "improving the user interface and adding interactive content" and "planning and implementing a powerful marketing strategy." The input is a list of causes of failure and factors of success, and the output is a list of strategic recommendations.

[1157] Step 7:

[1158] The server uses an emotion engine to recognize the user's emotions when they input data or when recommendations are presented. Specifically, it uses Microsoft Azure's emotion analysis API to recognize "expectation" or "anxiety" from the user's tone and vocabulary. The input is the text data of the business idea and the text data of the recommendation, and the output is recognized emotion data.

[1159] Step 8:

[1160] The server personalizes recommendations based on emotional data. Specifically, if a user feels anxious, it generates recommendations that include a specific action plan to alleviate the anxiety. The input is emotional data and a list of strategic recommendations, and the output is a list of personalized recommendations that take emotions into account.

[1161] Step 9:

[1162] The server monitors market changes and competitive conditions in real time and updates its recommendations. Specifically, it periodically scans relevant news articles and competitor activity data to generate new recommendations that reflect market trends and competitor actions. The input is market data and the user's current situation, and the output is updated strategic recommendations based on the latest market information.

[1163] (Application example 2)

[1164] 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."

[1165] Conventional consultation systems for increasing the success rate of new businesses generate recommendations by searching and retrieving similar cases from a past database for the business idea entered by the user, but because they cannot reflect the user's emotional state or market trends in real time, it is difficult to provide personalized advice.In addition, because they cannot take into account the user's emotions such as anxiety and expectations, the proposed recommendations may not match the user's actual needs.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1167] In this invention, the server includes: a means for a user to input a new business idea; a means for searching and retrieving similar failure cases and success cases from a past database using a generative AI; a means for analyzing the retrieved data to identify causes of failure and factors of success; a means for generating strategic recommendations based on the identified information and presenting them to the user; a means for monitoring market changes and competitive conditions and updating the advice in real time; and a means for recognizing the user's emotions and adjusting the recommendations based on those emotions. This makes it possible to provide more personalized advice that not only learns from past cases but also takes into account the user's emotional state and market trends.

[1168] The "means for users to input ideas for new businesses" is an interface that allows users to input information about their own new businesses into the system.

[1169] "Generative AI" is an engine that uses artificial intelligence technology to perform natural language processing and data analysis, and has the ability to search and analyze past cases based on user input.

[1170] The "past database" is a collection of information that records the success and failure of new businesses, and is the source of information that stores the target data that the generation AI searches and retrieves.

[1171] "Means for searching and retrieving similar failure cases and success cases" refers to the process by which the generative AI finds business cases similar to the user input from a past database and retrieves the necessary information.

[1172] "Methods for analyzing acquired data to identify causes of failure and factors for success" refers to methods for analyzing the searched and acquired case data in detail to clarify the reasons for the success or failure of those businesses.

[1173] "Strategic recommendations" are advice that propose specific and effective strategies to users based on identified success factors and causes of failure.

[1174] The "means for presenting to the user" is an interface for displaying the generated recommendations to the user in an easy-to-understand manner.

[1175] "Means of monitoring market changes and competitive conditions and updating advice in real time" refers to methods for constantly monitoring changes in the external environment and competitor trends and keeping recommendations up to date accordingly.

[1176] "Means for recognizing user emotions and adjusting recommendations based on those emotions" refers to the process of using an emotion engine to analyze the user's psychological state and change the content of recommendations to match that state.

[1177] As an embodiment of the present invention, the following system configuration is provided.

[1178] The system is equipped with a terminal that allows users to input ideas for new businesses. Users use this terminal to input information such as the business idea, objectives, and target market. For example, they could input an idea such as, "I want to devise new security measures to prevent the leaking of personal information."

[1179] The business idea sent from the device is sent to a server. The server uses natural language processing technology to extract keywords from the business idea. This process uses a generative AI model. For example, keywords such as "personal information leakage" and "security measures" are extracted.

[1180] Next, the server searches and retrieves similar failure and success cases from a past database based on the extracted keywords, for example, "failed personal information security measure A" and "successful personal information security measure B."

[1181] The server analyzes the data of similar cases and identifies the causes of failure and the factors behind success. For failure cases, it identifies causes of failure such as "lack of proper data encryption" and "insufficient user training." For success cases, it identifies factors of success such as "strong data encryption" and "regular security training."

[1182] Furthermore, the server generates specific strategic recommendations based on the analysis results that can be applied to the user's business idea and presents them to the user via their device, such as "strengthen data encryption and conduct regular security training."

[1183] A feature of the present invention is that the server is equipped with an emotion engine that has the ability to recognize the user's emotions. When entering a business idea or presenting recommendations, the emotion engine can recognize emotions such as "expectation" or "anxiety" from the user's tone and vocabulary and reflect them in the recommendations. For example, if the user is feeling anxious, the recommendation will include a "specific action plan to alleviate the anxiety." Furthermore, if the user has strong expectations, proactive strategies to fulfill those expectations will be presented.

[1184] Furthermore, the server continuously monitors market trends and the competitive situation in real time. When new market trends or competitors' actions are detected, the server generates and updates new recommendations based on these, and presents them to the user along with new sentiment analysis results from the emotion engine. For example, if a competitor is detected to have "introduced a subscription service," and the emotion engine recognizes that the user is feeling "impatient," the server will recommend that the user "consider introducing a subscription service," along with specific implementation steps and risk mitigation measures.

[1185] This allows users to not only learn from past cases, but also receive more specific and personalized advice based on their own emotional state and market trends, further increasing the chances of success for new businesses.

[1186] Example prompt sentence:

[1187] "Business idea: I want to develop new security measures to prevent personal information leaks."

[1188] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1189] Step 1:

[1190] The user inputs a new business idea into the terminal. The input includes the business outline, objectives, target market, etc. An example of an input is a business idea such as "I want to devise new security measures to prevent the leakage of personal information." This information is sent to the server via the terminal's input interface.

[1191] input:

[1192] "I want to devise new security measures to prevent personal information leaks."

[1193] output:

[1194] Business idea data on the server

[1195] Step 2:

[1196] The server analyzes the received business ideas using natural language processing technology and extracts keywords. This process uses a generative AI model. For example, the keywords "personal information leakage" and "security measures" are extracted.

[1197] input:

[1198] Business idea data

[1199] output:

[1200] Keywords (e.g., "personal information leakage," "security measures")

[1201] Step 3:

[1202] The server searches and retrieves similar failure and success cases from a past database based on the extracted keywords. For example, cases such as "failed personal information security measure A" and "successful personal information security measure B" are retrieved.

[1203] input:

[1204] keyword

[1205] output:

[1206] Similar failure and success case data

[1207] Step 4:

[1208] The server analyzes the acquired case data and identifies the causes of failure and the factors of success. For example, causes of failure such as "lack of proper data encryption" and "lack of regular security training" are identified, while success factors such as "strong data encryption" and "regular security training" are identified.

[1209] input:

[1210] Similar failure and success case data

[1211] output:

[1212] Data on causes of failure and factors behind success

[1213] Step 5:

[1214] The server generates specific strategic recommendations applicable to the user's business idea based on the identified success factors and causes of failure, and presents them to the user via the terminal. For example, a recommendation such as "strengthen data encryption and conduct regular security training" may be presented.

[1215] input:

[1216] Data on causes of failure and factors behind success

[1217] output:

[1218] Specific strategic recommendations

[1219] Step 6:

[1220] The server uses an emotion engine to recognize the user's emotions when entering a business idea or presenting recommendations, and reflects these in the recommendations. For example, if the user is feeling anxious, the recommendation will include a "specific action plan to alleviate anxiety."

[1221] input:

[1222] User emotional state data

[1223] output:

[1224] Strategic recommendations tailored based on emotions

[1225] Step 7:

[1226] The server monitors market changes and the competitive situation in real time and updates recommendations accordingly. For example, if it detects that a competitor has introduced a subscription service, it will recommend that the company should consider introducing a subscription service, along with specific implementation steps and risk mitigation measures.

[1227] input:

[1228] Market trends and competitive landscape data

[1229] output:

[1230] Updated strategic recommendations based on the latest market trends and competitive landscape

[1231] 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.

[1232] 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.

[1233] 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.

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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).

[1238] 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.

[1239] 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."

[1240] 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.

[1241] 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).

[1242] 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.

[1243] 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.

[1244] 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.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] The following is further disclosed regarding the above embodiment.

[1253] (Claim 1)

[1254] A means for users to input new business ideas;

[1255] A means of searching and retrieving similar failure cases and success cases from a past database using generative AI;

[1256] A means of analyzing the data obtained to identify causes of failure and factors of success;

[1257] means for generating and presenting strategic recommendations to the user based on the identified information;

[1258] A means of monitoring market changes and competitive conditions and updating advice in real time;

[1259] A system including:

[1260] (Claim 2)

[1261] 2. The system according to claim 1, further comprising means for extracting keywords of a business idea input by a user.

[1262] (Claim 3)

[1263] 10. The system of claim 1, further comprising means for periodically scanning relevant news articles and competitor activity data to monitor market changes and competitive conditions.

[1264] "Example 1"

[1265] (Claim 1)

[1266] A means for users to input new business ideas;

[1267] A means of searching and retrieving similar failure cases and success cases from a past database using generative AI;

[1268] A means of analyzing the data obtained to identify causes of failure and factors of success;

[1269] means for generating and presenting strategic recommendations to the user based on the identified information;

[1270] A means of monitoring market changes and competitive conditions and updating advice in real time;

[1271] A system including:

[1272] (Claim 2)

[1273] 10. The system of claim 1, including user system access and input of a business idea.

[1274] (Claim 3)

[1275] 2. The system according to claim 1, further comprising means for performing detailed analysis of the data received by the server using natural language processing technology to identify causes of failure and factors of success.

[1276] (Claim 4)

[1277] 2. The system according to claim 1, further comprising means for monitoring market trends and competitor information in real time, and providing the user with updated strategic recommendations immediately upon detection of new information.

[1278] (Claim 5)

[1279] The system according to claim 1, further comprising a means for generating individual prompt sentences using a generative AI model based on a user's business idea, and searching and analyzing past cases based on the generated prompt sentences.

[1280] "Application Example 1"

[1281] (Claim 1)

[1282] A means for users to input new business ideas;

[1283] A means of searching and retrieving similar failure cases and success cases from a past database using generative AI;

[1284] A means of analyzing the data obtained to identify causes of failure and factors of success;

[1285] means for generating and presenting strategic recommendations to the user based on the identified information;

[1286] A means of monitoring market changes and competitive conditions and updating advice in real time;

[1287] A means to collect and analyze information on new product introductions and service improvements in physical stores,

[1288] A means for generating strategic recommendations for physical stores using generative AI and presenting them to users via a smartphone application;

[1289] A system including:

[1290] (Claim 2)

[1291] 2. The system according to claim 1, further comprising means for extracting keywords of a business idea input by a user.

[1292] (Claim 3)

[1293] 10. The system of claim 1, further comprising means for periodically scanning relevant news articles and competitor activity data to monitor market changes and competitive conditions.

[1294] "Example 2: Combining Emotion Engines"

[1295] (Claim 1)

[1296] A means for users to input new business ideas;

[1297] A means of searching and retrieving similar failure cases and success cases from a past database using generative AI;

[1298] A means of analyzing the data obtained to identify causes of failure and factors of success;

[1299] means for generating and presenting strategic recommendations to the user based on the identified information;

[1300] A means of monitoring market changes and competitive conditions and updating advice in real time;

[1301] a means for recognizing a user's emotion during user input and during recommendation presentation using an emotion engine;

[1302] A means for personalizing recommendations based on user emotional information;

[1303] A system including:

[1304] (Claim 2)

[1305] 2. The system according to claim 1, further comprising means for extracting keywords of a business idea input by a user.

[1306] (Claim 3)

[1307] 10. The system of claim 1, further comprising means for periodically scanning relevant news articles and competitor activity data to monitor market changes and competitive conditions.

[1308] "Application example 2 when combining emotion engines"

[1309] (Claim 1)

[1310] A means for users to input new business ideas;

[1311] A means of searching and retrieving similar failure cases and success cases from a past database using generative AI;

[1312] A means of analyzing the data obtained to identify causes of failure and factors of success;

[1313] means for generating and presenting strategic recommendations to the user based on the identified information;

[1314] A means of monitoring market changes and competitive conditions and updating advice in real time;

[1315] a means for recognizing a user's emotion and adjusting recommendations based on the emotion;

[1316] A system including:

[1317] (Claim 2)

[1318] 2. The system according to claim 1, further comprising means for extracting keywords of a business idea input by a user.

[1319] (Claim 3)

[1320] 10. The system of claim 1, further comprising means for periodically scanning relevant news articles and competitor activity data to monitor market changes and competitive conditions. [Explanation of symbols]

[1321] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input new business ideas; A means of searching and retrieving similar failure and success cases from a past database using generative AI, A means of analyzing the data obtained to identify causes of failure and factors of success; means for generating and presenting strategic recommendations to the user based on the identified information; A means of monitoring market changes and competitive conditions and updating advice in real time; A system including:

2. 2. The system according to claim 1, further comprising means for extracting keywords of a business idea input by a user.

3. 10. The system of claim 1, further comprising means for periodically scanning relevant news articles and competitor activity data to monitor market changes and competitive conditions.

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