System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving keywords associated with a business framework to be utilized; means for analyzing the received keywords to map to an appropriate framework; means for collecting and organizing relevant information based on the framework; means for automatically generating framework analysis results based on the collected and organized information; and means for displaying the generated framework analysis results.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] Many businesspeople and companies struggle with the time and effort required to think in line with frameworks that are effectively utilized when formulating management strategies and business plans. Furthermore, when it comes to business improvement, it is difficult to implement appropriate PDCA cycles and OODA loops. To solve these challenges, there is a need for consistent support for thinking based on business frameworks. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means: It provides a means for a user to input keywords related to a business framework; It includes a means for analyzing the input keywords and mapping them to an appropriate framework; It has a means for collecting and organizing related information based on the framework; It further includes a means for automatically generating framework analysis results based on the collected and organized information; and It provides a system including a means for displaying the generated framework analysis results to the user. It also includes a means for a user to modify the displayed framework analysis results and a means for re-analyzing the modified framework analysis results and displaying the updated results, thereby enabling high-quality thinking based on business frameworks to be carried out quickly.

[0006] A "business framework" is a systematic framework for analysis and thinking used when formulating business strategies and management plans.

[0007] "Keywords" are user-entered words and phrases that identify information needed for framework analysis.

[0008] "Means" refers to a device, system, or method used to accomplish a particular purpose.

[0009] A "server" is a central control device that processes data and provides services to other computers over a network.

[0010] A "terminal" is a device such as a computer or smartphone that is directly operated by a user.

[0011] "Receiving" is the act of taking in data or information from outside.

[0012] "Analysis" is the act of evaluating, classifying, and organizing input data and information in detail.

[0013] "Mapping" is the act of associating one piece of data with another piece of data.

[0014] "Collection" is the act of gathering relevant data or information.

[0015] "Organization" is the act of arranging collected data and information in a form that is easy to use.

[0016] "Auto-generation" is the act of a system creating a result without human intervention.

[0017] "Display" is the act of visually presenting data or information to a user.

[0018] "Framework analysis results" are analysis results derived in accordance with a framework based on collected and organized information.

[0019] "Modification" refers to the act of a user changing displayed data or information.

[0020] "Reanalysis" refers to the act of re-evaluating, classifying, and organizing the revised data and information.

[0021] "Updated results" are new analytical results obtained by reanalysis. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] System Overview

[0044] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on keywords entered by the user and automates advanced analysis based on that framework. Major frameworks include PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop.

[0045] System configuration

[0046] The system mainly consists of the following components:

[0047] 1. User Interface (Terminal)

[0048] 2. Data analysis engine (server)

[0049] 3. Data collection module (server)

[0050] 4. Framework generation module (server)

[0051] 5. Display module (terminal)

[0052] 6. Data storage module (server)

[0053] Operation of the invention

[0054] 1. User Input

[0055] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the user interface of the terminal. The input is made through a text box, and by clicking the "Submit" button, the information is sent to the server.

[0056] 2. Keyword analysis

[0057] The server analyzes the received keywords and maps them to the appropriate framework. This analysis uses a text analysis engine to perform topic modeling and keyword extraction. For example, if the inputs are "SWOT analysis" and "new product development," the analysis engine will identify the data items required for a SWOT analysis.

[0058] 3. Data Collection

[0059] The server collects relevant information using internal databases and external APIs (market databases, competitive information, etc.), including data on the company's strengths and weaknesses, competitive information, and market trends. The collected data is stored in a designated data storage device.

[0060] 4. Framework Generation

[0061] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of a SWOT analysis, the following elements are automatically generated:

[0062] Strengths: Our own technological capabilities and brand power

[0063] Weaknesses: Development costs, lack of engineers

[0064] Opportunities: Discovery of new market needs, technology trends

[0065] Threats: New products from competitors, changes in laws and regulations

[0066] 5. Display results

[0067] The display module of the terminal displays the framework analysis results sent from the server to the user. The user interface is integrated with a text editor for review and correction. The user can check the results and make any necessary corrections or additions.

[0068] 6. User Modifications and Re-analysis

[0069] When the user modifies the framework analysis results and clicks the "Reanalyze" button, the modifications are sent to the server again, which then reanalyzes the modified data, regenerates the updated framework analysis results, and displays them to the user again.

[0070] 7. Data Storage

[0071] The server stores the final framework analysis results in a database using the data storage module, allowing for future reuse or reference in other projects.

[0072] The overall processing flow is explained with concrete examples. For example, if a user requests a SWOT analysis for new product development, the system receives input such as "SWOT analysis, new product development." Based on this information, it collects appropriate market data and competitive information, automatically generates strengths, weaknesses, opportunities, and threats, and presents them to the user. If the user makes corrections, the system reanalyzes the results and saves them for future reference.

[0073] This system helps business people to conduct high-quality framework analysis quickly and efficiently.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] Enter keywords related to the business framework you want to use. For example, enter "SWOT analysis, new product development."

[0077] Step 2:

[0078] The terminal receives the user's input and sends it to the server. The input is sent to the server as text data using an HTTP request.

[0079] Step 3:

[0080] The server analyzes the data received from the user. It uses a text analysis engine to break down keywords and map them to an appropriate framework. In this example, it selects the SWOT analysis template.

[0081] Step 4:

[0082] The server uses internal databases and external APIs (market databases, competitive information, etc.) to collect information related to the entered keywords. For example, it collects market trends related to new product development, competitive information, and data on the company's strengths and weaknesses.

[0083] Step 5:

[0084] Based on the information collected by the server, each element of the SWOT analysis is generated, i.e., strengths, weaknesses, opportunities, and threats are automatically classified and organized.

[0085] Step 6:

[0086] The server sends the generated SWOT analysis results to the device, which returns the information as an HTTP response.

[0087] Step 7:

[0088] The terminal displays the SWOT analysis results provided by the server to the user. The visualized results are displayed in text boxes and graph format, allowing the user to check and modify them.

[0089] Step 8:

[0090] The user can check the displayed SWOT analysis results and make corrections or additions as necessary. For example, a new "market uncertainty" can be added to the "weaknesses" section.

[0091] Step 9:

[0092] The user sends the corrected content back to the server from the terminal, along with a request for reanalysis.

[0093] Step 10:

[0094] The server re-analyzes the modifications and regenerates an updated SWOT analysis, integrating the previous analysis data with the new input data.

[0095] Step 11:

[0096] The server sends the updated SWOT analysis results back to the terminal, and the new analysis results are returned to the terminal as an HTTP response for redisplay.

[0097] Step 12:

[0098] The terminal will then redisplay the updated SWOT analysis results to the user, who can review them and make any necessary adjustments.

[0099] Step 13:

[0100] The server saves the final SWOT analysis results in a database, which is then stored for future reference or reuse in other projects.

[0101] Example 1

[0102] 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."

[0103] Conventional analysis systems using business frameworks have limited functionality for automatically generating analysis results based on user input, and processes such as collecting related information and reanalyzing analysis results are often performed manually. Furthermore, the functionality for correcting analysis results is also extremely limited, limiting the flexibility of users. This makes it difficult to perform high-quality business framework analysis quickly and efficiently.

[0104] 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.

[0105] In this invention, the server includes means for receiving terms related to an analysis framework to be used, means for analyzing the received terms and mapping them to an appropriate framework, means for collecting and storing related information based on the framework, means for automatically generating framework analysis results based on the collected and stored information, means for displaying the generated framework analysis results, means for allowing a user to edit the displayed analysis results, means for reanalyzing the edited analysis results and displaying the updated results, and means for saving the final analysis results, thereby enabling a user to quickly and efficiently perform high-quality business framework analysis.

[0106] An "analytical framework" is a structured method or model for evaluating and planning a business or strategy. Examples include SWOT analysis, PEST analysis, and Five Forces analysis.

[0107] "Related terms" refer to keywords or topics related to the subject or theme that a user wants to evaluate using the analytical framework, such as "new product development" or "market research."

[0108] "Means for receiving" refers to the mechanism by which the system receives related terms and analysis frameworks entered by the user. Specifically, this includes the text box and submit button in the user interface.

[0109] "Means for parsing and mapping to an appropriate framework" refers to techniques and methods for analyzing received related terms and selecting the most appropriate analytical framework, including text analysis engines and topic modeling techniques.

[0110] "Means for collecting and storing relevant information" refers to the process for collecting the necessary information from internal databases and external APIs and storing it in data storage based on the analytical framework identified through the analysis.

[0111] "Means for automatically generating framework analysis results" refers to technologies or algorithms that automatically generate results for a specified analysis framework based on collected and stored information, such as generative AI models.

[0112] The "display means" refers to a technique or interface for visually presenting the generated framework analysis results to the user. Specifically, this includes a display or a display module of a user interface.

[0113] "Editing means" refers to an interface or function that allows the user to modify or add to the displayed framework analysis results. This includes a text editor and a modification button.

[0114] "Means for reanalyzing and displaying updated results" refers to the process or technology for sending the framework analysis results edited by the user back to the server, performing analysis based on the modifications, and displaying the updated results to the user again.

[0115] "Means for storing the final analysis results" refers to the technology or method for storing the finalized framework analysis results in a storage medium such as a database so that they can be reused or referenced later.

[0116] This invention is a generative AI system that provides thinking support based on business frameworks. The system selects an appropriate framework based on keywords entered by the user and automates advanced analysis based on that framework. Major frameworks include PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop.

[0117] System configuration

[0118] The system mainly consists of the following components:

[0119] 1. User Interface (Terminal)

[0120] 2. Data analysis engine (server)

[0121] 3. Data collection module (server)

[0122] 4. Framework generation module (server)

[0123] 5. Display module (terminal)

[0124] 6. Data storage module (server)

[0125] The specific operation of the system is as follows.

[0126] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the terminal's user interface. This input is done through a text box and completed by clicking the "Submit" button. Once the input is submitted, the information is sent to the server.

[0127] The server uses a text analysis engine (e.g., NLTK, SpaCy) to analyze the received keywords. During this analysis, topic modeling and keyword extraction techniques are used to map the input keywords to an appropriate framework. For example, if the keywords "SWOT analysis" and "new product development" are input, the server will identify the data items required for a SWOT analysis.

[0128] Next, the server collects relevant information. This is done using an internal database or external API (e.g., market database, competitive information API). The collected data is stored in data storage (e.g., AWS (registered trademark) S3, Google (registered trademark) Cloud Storage). For example, data on the company's technological capabilities and brand power, information on competitors, market trends, etc. are collected.

[0129] Next, the server's framework generation module automatically generates the specified framework analysis results based on the collected information. During this process, a generative AI model (e.g., OpenAI® GPT-3®) is used to perform advanced analysis. For example, in a SWOT analysis, the following elements are automatically generated:

[0130] Strengths: Our own technological capabilities and brand power

[0131] Weaknesses: Development costs, lack of engineers

[0132] Opportunities: Discovery of new market needs, technology trends

[0133] Threats: New products from competitors, changes in laws and regulations

[0134] The generated framework analysis results are sent to the terminal's display module and displayed visually to the user. This display has an integrated text editor function for review and correction. The user can check the results and make any necessary corrections or additions.

[0135] If the user makes any modifications, the modifications are sent to the server again. The server then re-analyzes the modified data and regenerates the updated framework analysis results. This allows the latest analysis results to be displayed to the user again.

[0136] The final framework analysis results are stored in a database (e.g., MongoDB, MySQL (registered trademark)) using the server's data storage module, allowing for future reuse or reference in other projects.

[0137] Specific examples

[0138] When conducting a SWOT analysis for a new product development, the system works as follows:

[0139] 1. The user enters "SWOT analysis, new product development" and clicks the "Submit" button.

[0140] 2. The server analyzes the received keywords and maps them to a SWOT analysis.

[0141] 3. The server collects relevant information from internal databases and external APIs and stores it in data storage.

[0142] 4. The server automatically generates the analysis results using an AI model.

[0143] 5. The device displays the analysis results so that the user can check and correct them.

[0144] 6. After the user makes any corrections, the server re-analyzes the data and displays the updated information.

[0145] 7. The server stores the final analysis results in a database.

[0146] An example prompt is:

[0147] SWOT analysis, new product development

[0148] This system enables users to perform high-quality business framework analysis quickly and efficiently.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] The user inputs keywords related to the business framework into the user interface of the terminal. Specifically, the user inputs "SWOT analysis" and "new product development" into the text boxes and clicks the "Submit" button.

[0152] input:

[0153] "Business framework" and "related keywords" entered by the user.

[0154] output:

[0155] Sending information from the device to the server.

[0156] Step 2:

[0157] The server analyzes the received keywords using a text analysis engine (e.g., NLTK, SpaCy). It then uses topic modeling and keyword extraction techniques to map the input content into an appropriate framework. Specifically, it analyzes the keywords "SWOT analysis" and "new product development" to identify the data items required for a SWOT analysis.

[0158] input:

[0159] "Business framework" and "related keywords" sent from the device.

[0160] output:

[0161] Analysis results from the text analysis engine.

[0162] Step 3:

[0163] The server collects relevant information using internal databases and external APIs (e.g., market databases, competitive intelligence APIs). The collected data is stored in data storage (e.g., AWS S3, Google Cloud Storage). Specifically, it collects information such as the company's strengths and weaknesses, competitive intelligence, and market trends.

[0164] input:

[0165] Data items identified by the analysis results.

[0166] output:

[0167] Relevant data collected from databases and APIs.

[0168] Step 4:

[0169] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. This process uses a generative AI model (e.g., OpenAI GPT-3). Specifically, it generates each element of the SWOT analysis (strengths, weaknesses, opportunities, and threats).

[0170] input:

[0171] Relevant data collected.

[0172] output:

[0173] Framework analysis results using a generative AI model.

[0174] Step 5:

[0175] The display module of the terminal displays the framework analysis results sent from the server to the user. The user interface is integrated with a text editor function for reviewing and modifying the results.

[0176] input:

[0177] Framework analysis results sent from the server.

[0178] output:

[0179] The analysis results that are displayed to the user.

[0180] Step 6:

[0181] The user modifies the framework analysis results and clicks the "Reanalyze" button. The modified content is sent to the server again. Specifically, the user edits the results in the text editor of the user interface and clicks the "Reanalyze" button.

[0182] input:

[0183] Analysis results modified by the user.

[0184] output:

[0185] The corrected data is sent to the server.

[0186] Step 7:

[0187] The server then re-analyzes the modified data and regenerates the updated framework analysis results, which are again displayed to the user.

[0188] input:

[0189] Corrected data.

[0190] output:

[0191] Updated framework analysis results after reanalysis.

[0192] Step 8:

[0193] The server stores the final framework analysis results in a database using the data storage module, allowing for future reuse or reference in other projects.

[0194] input:

[0195] Final framework analysis results.

[0196] output:

[0197] Analysis results stored in a database.

[0198] (Application example 1)

[0199] 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."

[0200] Real-time data collection and analysis is required for production processes within factories. However, in conventional systems, data collection and analysis is often done manually, which requires time and effort. Another problem is the lack of an accurate analytical framework for making quick decisions. This situation leads to reduced production efficiency and inefficient use of resources. Therefore, a system is needed to analyze factory data in real time and optimize production processes.

[0201] 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.

[0202] In this invention, the server includes means for receiving keywords related to a business framework to be used, means for analyzing the received keywords and mapping them to an appropriate framework, means for collecting and organizing related information based on the framework, means for automatically generating framework analysis results based on the collected and organized information, means for displaying the generated framework analysis results, and means for performing analysis in real time using data within the factory to optimize the production process. This enables rapid and accurate framework analysis in the factory production process, improving production efficiency and optimizing resource utilization.

[0203] A "business framework" is a framework for systematically planning and analyzing a business.

[0204] "Keywords" are specific words or phrases that users enter when conducting framework analysis.

[0205] "Mapping" is the process of associating received keywords with the appropriate business framework.

[0206] "Relevant information" refers to data and knowledge required to perform framework analysis.

[0207] "Automatic generation" is the process by which the system automatically creates framework analysis results based on collected and organized information.

[0208] "Display" refers to visually showing the generated framework analysis results to the user.

[0209] "Factory data" refers to information about production processes collected within the factory from sensors, cameras, etc.

[0210] "Real-time" means that processing and analysis occurs immediately, without delay.

[0211] A "production process" is the entire manufacturing process of a product in a factory.

[0212] This invention is a system designed to optimize production processes within a factory. The system consists of the following main components:

[0213] 1. Overall structure

[0214] This system consists of a user interface (terminal), a data analysis engine (server), a data collection module (server), a framework generation module (server), a display module (terminal), and a data storage module (server).The overall purpose is to analyze data in the factory in real time and optimize the production process.

[0215] 2. User Interface

[0216] The user interface (UI) operates via a smartphone, tablet, or factory robot display, and allows users to enter keywords related to the business framework they wish to use, using a text box and a submit button.

[0217] 3. Data analysis engine

[0218] The data analysis engine installed on the server analyzes keywords using a text analysis engine or a generative AI model (e.g., TENSORFLOW (registered trademark), PyTorch). Based on the analysis results, mapping is performed to an appropriate business framework.

[0219] 4. Data Collection Module

[0220] The data collection module on the server collects real-time data from an internal database and external APIs (e.g., sensor APIs and camera APIs). This data includes information from temperature sensors, pressure sensors, and cameras. The collected data is stored in a database.

[0221] 5. Framework Generation Module

[0222] The framework generation module automatically generates analysis results for a specified framework based on the collected information. A specific example of a framework is the PDCA cycle. For example, the following PDCA cycle is generated:

[0223] Plan: Introducing new materials

[0224] Do: Test production

[0225] Check: Quality test

[0226] Improvement (Act): Correction of production process

[0227] 6. Display module

[0228] The display module displays the framework analysis results sent from the server to the user, including real-time updates and a text editor for the user to review and modify the results, which are then re-analyzed and the updated analysis results are displayed.

[0229] 7. Data storage module

[0230] The final framework analysis results are stored in a database in the server's data storage module, allowing for future reference and reuse in other projects.

[0231] Examples of concrete examples and prompts

[0232] As a concrete example, let's say there is a factory that is planning to introduce a new material. This factory aims to improve the quality of its products. The user enters the keywords "PDCA cycle" and "introduction of new material" into the interface. Based on this input, the system collects appropriate data and generates the following PDCA cycle.

[0233] Example prompt sentence:

[0234] Based on the "PDCA cycle," please collect in-factory data related to "introduction of new materials" and propose the next planning stage.

[0235] This system allows users to quickly and reliably obtain accurate analysis results to optimize production processes.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Step 1:

[0238] The user enters keywords related to the business framework they want to use into the user interface. Input is done using a text box and a submit button, and by clicking the submit button, the information is sent to the server.

[0239] Input: Keywords such as "PDCA cycle" and "introduction of new materials"

[0240] Output: The keyword is sent to the server

[0241] Step 2:

[0242] The server uses a text analysis engine to analyze the received keywords, primarily leveraging generative AI models (e.g., TensorFlow, PyTorch) for topic modeling and keyword extraction.

[0243] Input: The keyword submitted in step 1

[0244] Output: Selection of an appropriate business framework

[0245] Step 3:

[0246] The server's data collection module collects real-time data from an internal database and external APIs (sensor API, camera API, etc.), and the collected data is stored in a designated data storage.

[0247] Input: Selection result of appropriate business framework

[0248] Output: Real-time data collected

[0249] Step 4:

[0250] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of the PDCA cycle, the plan, do, verify, and improve phases are automatically generated.

[0251] Input: Real-time data collected

[0252] Output: Auto-generated framework analysis results

[0253] Step 5:

[0254] The display module of the terminal displays the framework analysis results sent from the server to the user, who can check the results, review and modify them.

[0255] Input: Auto-generated framework analysis results

[0256] Output: Show framework analysis results

[0257] Step 6:

[0258] When the user modifies the framework analysis results and clicks the "Reanalyze" button, the modifications are sent to the server again. The server then reanalyzes the modified data and regenerates the updated framework analysis results.

[0259] Input: User-modified framework analysis results

[0260] Output: Re-analyzed and re-generated framework analysis results

[0261] Step 7:

[0262] The final framework analysis results are stored in a database using the server's data storage module, allowing them to be reused later or used as reference for other projects.

[0263] Input: Re-analyzed and re-generated framework analysis results

[0264] Output: Saved framework analysis results

[0265] 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.

[0266] System Overview

[0267] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on keywords entered by the user and automates advanced analysis in line with that framework. Furthermore, by combining it with an emotion engine, it has the ability to recognize the user's emotional state and adjust the display of analysis results and advice based on that.

[0268] System configuration

[0269] The system mainly consists of the following components:

[0270] 1. User Interface (Terminal)

[0271] 2. Data analysis engine (server)

[0272] 3. Data collection module (server)

[0273] 4. Framework generation module (server)

[0274] 5. Display module (terminal)

[0275] 6. Data storage module (server)

[0276] 7. Emotion engine (server)

[0277] Operation of the invention

[0278] 1. User Input

[0279] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the user interface of the terminal. The input is made through a text box, and by clicking the "Submit" button, the information is sent to the server.

[0280] 2. Keyword analysis

[0281] The server analyzes the received keywords and maps them to the appropriate framework. This analysis uses a text analysis engine to perform topic modeling and keyword extraction. For example, if the inputs are "SWOT analysis" and "new product development," the analysis engine will identify the data items required for a SWOT analysis.

[0282] 3. Data Collection

[0283] The server collects relevant information using internal databases and external APIs (market databases, competitive information, etc.), including data on the company's strengths and weaknesses, competitive information, and market trends. The collected data is stored in a designated data storage device.

[0284] 4. Framework Generation

[0285] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of a SWOT analysis, each element (strengths, weaknesses, opportunities, and threats) is automatically generated:

[0286] Strengths: Our own technological capabilities and brand power

[0287] Weaknesses: Development costs, lack of engineers

[0288] Opportunities: Discovery of new market needs, technology trends

[0289] Threats: New products from competitors, changes in laws and regulations

[0290] 5. Emotion recognition

[0291] The server's emotion engine recognizes the user's emotions based on the user's input, operation history, and even biometric and voice analysis acquired through the user interface. For example, it analyzes the speed and pattern of text input, the tone of voice, and other factors to determine whether the user is feeling stressed or positive.

[0292] 6. Results display

[0293] The server sends the generated SWOT analysis results to the device, and the emotion engine adjusts the content and tone of the display based on the user's emotional state. For example, if the user is feeling stressed, an encouraging message is added to the analysis results.

[0294] 7. User Modifications and Re-analysis

[0295] The user checks the displayed SWOT analysis results and makes corrections or additions as necessary. The corrections are then sent back to the server from the device.

[0296] 8. Reanalysis and result updates

[0297] The server re-analyzes the changes and regenerates the updated SWOT analysis results, at which point the emotion engine also performs emotion analysis again based on the updated data.

[0298] 9. Displaying new results

[0299] The server sends the updated SWOT analysis results to the device and redisplays them, adjusting them according to the user's emotional state.

[0300] 10. Data Retention

[0301] The server stores the final SWOT analysis results in a database, and also stores the sentiment analysis history for future reference.

[0302] Specific examples

[0303] For example, suppose a user requests a SWOT analysis of a new product development and expresses anxiety while entering data. In this case, the emotion engine will detect this anxiety and add an encouraging message to the results display, such as, "New product development involves risks, but based on the data you entered, there is a good chance of success." In this way, the engine helps users feel at ease when using the analysis results.

[0304] This system not only helps business people to efficiently perform high-quality framework analysis, but also provides customization according to the user's emotional state, thereby achieving a better user experience.

[0305] The processing flow will be explained below.

[0306] Step 1:

[0307] The user enters the business framework (e.g., SWOT analysis) they want to use and related keywords (e.g., new product development) into the user interface of the device. For example, they enter "SWOT analysis" and "new product development" in the text boxes and click the "Submit" button.

[0308] Step 2:

[0309] The device receives the user's input and sends the data to the server, which is sent as an HTTP request.

[0310] Step 3:

[0311] The server analyzes the received data, using a text analysis engine to break down the keywords entered and map them to an appropriate framework (in this case, a SWOT analysis).

[0312] Step 4:

[0313] The server uses internal databases and external APIs (market databases, competitive information, etc.) to collect relevant information, such as market trends related to new product development, competitive information, and data on the company's strengths and weaknesses.

[0314] Step 5:

[0315] Based on the information collected by the server, each element of a SWOT analysis (strengths, weaknesses, opportunities, and threats) is automatically generated. For example, a company's technological capabilities are classified as a "strength" and development costs as a "weakness."

[0316] Step 6:

[0317] The server's emotion engine analyzes the user's operation history and input content to recognize the user's emotions. For example, it determines whether the user is feeling stressed based on the input speed and text content.

[0318] Step 7:

[0319] The SWOT analysis results generated by the server are adjusted based on the analysis results of the emotion engine. For example, if the user is feeling anxious, an encouraging message is added.

[0320] Step 8:

[0321] The server sends the generated SWOT analysis results to the device, which are returned as an HTTP response, including any adjusted information.

[0322] Step 9:

[0323] The terminal displays the SWOT analysis results provided by the server to the user, who can then review the results and make corrections or additions as necessary.

[0324] Step 10:

[0325] The user makes corrections to the SWOT analysis results and sends the corrections back to the server via the terminal.

[0326] Step 11:

[0327] The server receives the modifications and performs the analysis again, updating the SWOT analysis results based on the modified data and generating new analysis results.

[0328] Step 12:

[0329] The server sends the updated SWOT analysis results back to the device, where the emotion engine is reanalyzed and adjusted as necessary.

[0330] Step 13:

[0331] The terminal will then redisplay the updated SWOT analysis results to the user, who can review them and make any necessary adjustments.

[0332] Step 14:

[0333] The server saves the final SWOT analysis results in a database. This data is stored in storage for future reference and reuse in other projects. The sentiment analysis history is also saved.

[0334] Example 2

[0335] 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."

[0336] In conventional business framework analysis systems, the selection of an appropriate framework for keywords entered by the user, followed by the subsequent data collection, organization, and analysis are often done manually, requiring time and effort.In addition, there is a problem in that the user experience is not optimized because the display adjustment according to the user's emotional state is not taken into consideration.

[0337] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving terms related to a business framework to be used, a means for analyzing the received terms and associating them with an appropriate framework, a means for collecting and organizing related information based on the framework, a means for automatically generating framework analysis results based on the collected and organized information, a means for recognizing the user's emotional state, and a means for adjusting display content based on the recognized emotional state. This allows the user to quickly and efficiently receive an appropriate business framework analysis, and further improves the user experience through optimal display according to the user's emotional state.

[0338] A "business framework" refers to a framework or method for systematically analyzing and organizing strategies and plans in business or corporate activities.

[0339] "Terms" refer to keywords and phrases that a user inputs to be used in analyzing the business framework.

[0340] "Means for receiving" refers to the interface or software for importing terms and information entered by the user into the server via the network.

[0341] "Means of analyzing and associating with an appropriate framework" refers to algorithms and modules that use methods such as text analysis based on received terms to select the optimal business framework.

[0342] "Means of collecting and organizing relevant information" refers to systems and software that utilize internal databases and external information sources to collect the data required for the business framework, organize it, and make it usable.

[0343] "Means for automatically generating framework analysis results" refers to programs or algorithms for automatically generating each element of a business framework based on collected and organized information.

[0344] "Means for recognizing the user's emotional state" refers to a system or engine that analyzes the user's input, operation history, biometric information, voice, etc. to evaluate and determine the user's emotions.

[0345] "Means for adjusting displayed content based on the user's perceived emotional state" refers to algorithms or modules that modify or adjust the displayed content or tone in response to the user's emotional state.

[0346] System Overview and Configuration

[0347] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on terms entered by the user and automates advanced analysis in line with that framework. This system is primarily composed of a server and terminals working together. Furthermore, by combining it with an emotion engine, it has the ability to recognize the user's emotional state and adjust the display of analysis results and advice based on that.

[0348] Hardware and software used

[0349] The terminal includes a user interface and a display module.

[0350] The server includes a data analysis engine, a data collection module, a framework generation module, a data storage module, and an emotion engine.

[0351] Use a text analysis engine to analyze the received terms.

[0352] We collect relevant information using internal databases and external APIs.

[0353] The collected data storage stores the collected data.

[0354] Explanation of program processing

[0355] The user inputs terms related to the business framework (e.g., SWOT analysis) through the user interface of the terminal. The input is done using a text box, and by clicking the "Submit" button, this information is sent to the server.

[0356] The server then uses a text analysis engine to analyze the received terms and map them to the appropriate framework, specifically using topic modeling and keyword extraction.

[0357] Next, the server uses the internal database and external APIs (market database, competitive information, etc.) to collect relevant information through the collection module, including the company's strengths and weaknesses, competitive information, market trend data, etc. The collected data is stored in the collected data storage.

[0358] Based on the collected information, the server's framework generation module automatically generates analysis results for the specified framework (e.g., SWOT analysis). For example, in a SWOT analysis, each element (strengths, weaknesses, opportunities, and threats) is generated.

[0359] Furthermore, the server's emotion engine recognizes the user's emotional state through analysis of the user's input, operation history, biometric information, and voice, for example, by analyzing the speed and pattern of text input and the tone of voice.

[0360] Based on the recognized emotional state, the server adjusts the generated framework analysis results and reflects them in the displayed content, which may include adding an encouraging message if the user is feeling stressed.

[0361] Finally, the server sends the generated framework analysis results to the terminal and displays them. The user can check the displayed results and make corrections as necessary. Any corrections are sent back to the server for re-analysis and updated results. The final framework analysis results are saved in a database via the data storage module.

[0362] Examples of concrete examples and prompts

[0363] For example, if a user requests a SWOT analysis on "New Product Development," the system works as follows:

[0364] 1. The user enters "New Product Development" and clicks the submit button.

[0365] 2. The server analyzes the term and determines that a SWOT analysis is appropriate.

[0366] 3. The server collects relevant information (such as the company's technical capabilities and market trends) from internal databases and external APIs.

[0367] 4. The server generates the SWOT analysis and the emotion engine recognizes the user's emotional state.

[0368] 5. The results are sent to the device, and the display content is adjusted depending on the user's emotions. For example, if the user is feeling stressed, an encouraging message will be added: "Developing a new product involves risks, but based on the data you entered, there is a good chance of success."

[0369] Prompt Sentence Examples

[0370] "I'd like to conduct a SWOT analysis of new product development. Gather data on your company's technological capabilities and market trends to identify strengths and opportunities, and add encouraging messages if you feel unsure."

[0371] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0372] Step 1: User Input

[0373] The user uses the terminal's user interface to input a business framework (e.g., SWOT analysis) and related terms (e.g., new product development). Input is completed by using the text box and clicking the "Submit" button, which sends the terms and information to the server.

[0374] Step 2: Keyword analysis

[0375] The server receives the received terms as input and analyzes them using a text analysis engine. Specifically, it selects an appropriate business framework through topic modeling and keyword extraction. For example, if the keyword "new product development" is entered, the server analyzes this information and determines that "SWOT analysis" is appropriate. The selected framework is obtained as the output of this analysis.

[0376] Step 3: Data collection

[0377] The server collects relevant information from internal databases and external APIs. The data items required for the selected framework are used as input. Specifically, data such as the company's strengths and weaknesses, competitive information, and market trends is acquired and stored in the designated data storage. This collected data is used as input for the next step.

[0378] Step 4: Framework Generation

[0379] The framework generation module on the server automatically generates framework analysis results based on the collected data. The collected data is used as input, and each element (strengths, weaknesses, opportunities, and threats) is generated as output. For example, in the case of a SWOT analysis, the following elements are generated:

[0380] Strengths: Our own technological capabilities and brand power

[0381] Weaknesses: Development costs, lack of engineers

[0382] Opportunities: Discovery of new market needs, technology trends

[0383] Threats: New products from competitors, changes in laws and regulations

[0384] Step 5: Emotion Recognition

[0385] The server's emotion engine recognizes the user's emotions through input content, operation history, biometric information, and voice analysis. Inputs include the speed and pattern of text input and voice tone, and the output determines the user's emotional state. For example, it determines whether the user is feeling stressed or positive.

[0386] Step 6: View the results

[0387] The server sends the generated framework analysis results to the terminal and reflects them in the display content. The generated framework analysis results and the recognized emotional state are used as input. For example, if the user is feeling stressed, an encouraging message such as "New product development involves risks, but based on the data you entered, there is a good chance of success" is added to the display content.

[0388] Step 7: User modifications and reanalysis

[0389] The user checks the displayed framework analysis results and makes corrections or additions as necessary. The corrected content is used as input and is sent back to the server from the terminal. This corrected content is used as input for the next step.

[0390] Step 8: Reprocess and update results

[0391] The server receives the modifications and performs a re-analysis. The modifications are used as input and the analysis is performed based on the new data. The updated framework analysis results are generated as output, and these results are used as input for the next step. The emotion engine also performs a re-evaluation of the emotions based on the updated data.

[0392] Step 9: View the new results

[0393] The server sends the updated framework analysis results to the terminal and redisplays them. The updated framework analysis results and the new emotional state are used as input. The redisplayed content is adjusted according to the user's emotional state.

[0394] Step 10: Save Data

[0395] The server saves the final framework analysis results in a database. The final analysis results and sentiment analysis history are used as input, and these data are stored in the database as output for future reference.

[0396] (Application example 2)

[0397] 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."

[0398] Modern business environments demand rapid and accurate decision-making, especially in virtual store operations, where rapid response to market trends and changes in the competitive landscape is essential. However, conventional business framework analysis systems lack feedback that takes into account the user's emotional state, which can lead to stress. Furthermore, human error can occur in the selection of business frameworks and the display of analysis results. Therefore, there is a need for a system that recognizes the user's emotional state and provides appropriate feedback to provide a better experience and streamline decision-making.

[0399] 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.

[0400] In this invention, the server includes means for receiving keywords related to a business framework to be used, means for analyzing the received keywords and mapping them to an appropriate framework, means for collecting and organizing related information based on the framework, means for automatically generating framework analysis results based on the collected and organized information, means for displaying the generated framework analysis results, and means for recognizing the user's emotional state and adjusting the result display and feedback content. This enables the user to quickly obtain highly accurate business framework analysis results without feeling stressed.

[0401] A "business framework" is a systematic method or model for analyzing the business environment and market.

[0402] "Keywords" are important words or short phrases related to the selection and analysis of a business framework.

[0403] "Mapping" is the process of matching received keywords to an appropriate business framework.

[0404] "Information collection" is the process of obtaining the necessary data using internal databases or external APIs.

[0405] "Information organization" is the process of structuring collected data based on a business framework and converting it into a format suitable for analysis.

[0406] "Automatic generation" is the process in which the system mechanically generates business framework analysis results based on collected and organized information.

[0407] "Display" is the process of providing the generated business framework analysis results to the user through a user interface.

[0408] "Emotional state recognition" is the process of determining a user's emotions by analyzing the user's input, operation history, biometric information, etc.

[0409] "Adjusting feedback content" is the process of appropriately changing the way analysis results are displayed and advice given based on the user's perceived emotional state.

[0410] System configuration

[0411] This invention is a generative AI system that provides thinking support based on business frameworks, automatically selecting an appropriate business model based on keywords entered by the user and performing advanced data analysis. Furthermore, it incorporates an emotion engine and can adjust the analysis results and feedback content according to the user's emotional state.

[0412] The system consists of the following components:

[0413] 1. User Interface (Terminal)

[0414] Users enter keywords via their mobile device (smartphone or tablet) and the analysis results are displayed.

[0415] 2. Data analysis engine (server)

[0416] The engine performs text analysis and topic modeling to map keywords into appropriate business frameworks.

[0417] 3. Data collection module (server)

[0418] Automatically gather relevant data using internal databases and external APIs (market databases, competitive intelligence, etc.).

[0419] 4. Framework generation module (server)

[0420] Based on the collected information, the specified business framework analysis results are automatically generated.

[0421] 5. Emotion engine (server)

[0422] The system recognizes the user's emotional state based on input content and biometric information obtained from the user interface.

[0423] 6. Display module (terminal)

[0424] The generated framework analysis results are displayed in an easy-to-understand manner to the user, and the content and tone are adjusted as needed.

[0425] 7. Data storage module (server)

[0426] The analysis results and sentiment analysis history are stored in a database for future reference.

[0427] Program processing

[0428] The server's data analysis engine uses Python libraries (e.g., TextBlob) to perform text analysis for keywords. For example, if a user enters keywords such as "new product launch" or "improving customer satisfaction," the engine analyzes them and maps them to an appropriate framework (e.g., SWOT analysis).

[0429] Next, the data collection module obtains relevant information using internal databases or external APIs (e.g., market databases). The obtained information is saved in data storage. The framework generation module analyzes this data and automatically generates the results of a business framework (e.g., SWOT analysis). For example, "strengths" could include "brand power" or "technical ability," while "weaknesses" could include "development costs" or "lack of engineers."

[0430] The emotion engine determines the user's emotional state from the speed and pattern of input, tone of voice, etc. For example, if the user is feeling anxious, it will add an encouraging feedback message along with the analysis results.

[0431] Examples of concrete examples and prompts

[0432] Examples:

[0433] If a user requests a SWOT analysis of a new product launch in a virtual store and feels anxious while entering the information, the emotion engine can analyze the user's anxiety and provide a feedback message such as, "Although launching a new product involves risks, there is a high chance of success if you leverage your company's brand power."

[0434] Example prompt sentence:

[0435] Virtual Product New Launch Market Trends Competitor SWOT Analysis

[0436] In this way, the user can more relaxedly check the business framework analysis results and make decisions efficiently.

[0437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0438] Step 1:

[0439] The user enters a keyword

[0440] The user inputs keywords related to the business framework (e.g., "launching a new product" or "improving customer satisfaction") into the user interface of the terminal. Once input is complete, the information is sent to the server by clicking the "Submit" button.

[0441] Input: keyword

[0442] Output: Keywords sent to the server

[0443] Step 2:

[0444] The server analyzes the keywords

[0445] The server uses a text analysis engine (e.g., TextBlob) to analyze the received keywords. The text analysis engine performs natural language processing on the received keywords and maps them into an appropriate business framework (e.g., SWOT analysis).

[0446] Input: keyword

[0447] Output: Mapped business framework

[0448] Step 3:

[0449] The server collects relevant information

[0450] The server's data collection module collects relevant information using internal databases and external APIs (market databases, competitive information, etc.). For example, regarding "new product launches," market trends and competitive information are obtained through API calls.

[0451] Input: Business framework and related keywords

[0452] Output: Collected relevant information data

[0453] Step 4:

[0454] Organizing the information collected by the server

[0455] The server organizes the collected data into a certain format, for example, categorizing market trend information and competitor information stored in the database into required categories (e.g., strengths, weaknesses, opportunities, and threats).

[0456] Input: Collected relevant information data

[0457] Output: Organized data

[0458] Step 5:

[0459] The server automatically generates framework analysis results

[0460] The server's framework generation module automatically generates analysis results for a business framework (e.g., SWOT analysis) based on the organized data. For example, "strengths" could include "brand power" and "technical ability," while "weaknesses" could include "development costs" and "lack of engineers."

[0461] Input: Organized data

[0462] Output: Auto-generated framework analysis results

[0463] Step 6:

[0464] The server recognizes the user's emotional state

[0465] The server's emotion engine collects information such as the user's input speed, patterns, and tone of voice to recognize their emotional state. For example, if the user is typing slowly, it will interpret this as anxiety.

[0466] Input: User input, operation history, biometric information, etc.

[0467] Output: Perceived emotional state

[0468] Step 7:

[0469] The server coordinates the results display and feedback

[0470] The server's display module displays the analysis results based on the user's emotional state and adjusts the feedback as needed. For example, if a user feels anxious, the server might add an encouraging message such as, "Launching a new product involves risks, but if you leverage your company's brand power, you have a high chance of success."

[0471] Input: Automatically generated framework analysis results, recognized emotional states

[0472] Output: Analysis results and feedback displayed to the user

[0473] Step 8:

[0474] The user checks and corrects the results

[0475] The user checks the business framework analysis results displayed on the terminal and makes corrections or additions as necessary. Once corrections are complete, the user clicks the "Send" button again to send the information to the server.

[0476] Input: User modifications

[0477] Output: Modifications sent to the server

[0478] Step 9:

[0479] The server re-analyzes the corrected analysis results and updates them.

[0480] The server re-analyzes the received modifications and regenerates the updated business framework analysis results, at which time the emotion engine also runs again to generate analysis results based on the latest emotional state.

[0481] Input: Modified Business Framework analysis results

[0482] Output: Re-analysis and updated analysis results

[0483] Step 10:

[0484] Server displays updated results

[0485] The server's display module displays the updated business framework analysis results to the user again, and updates the emotion-based feedback as needed.

[0486] Input: Updated business framework analysis results, latest emotional state

[0487] Output: Analysis results and feedback that are displayed back to the user

[0488] Step 11:

[0489] The server stores the data

[0490] The server's data storage module stores the final business framework analysis results and the history of sentiment analysis in a database, which can be used for future analysis.

[0491] Input: Final business framework analysis results, sentiment analysis history

[0492] Output: Data stored in the database

[0493] 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.

[0494] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0495] 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.

[0496] [Second embodiment]

[0497] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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).

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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."

[0509] System Overview

[0510] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on keywords entered by the user and automates advanced analysis based on that framework. Major frameworks include PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop.

[0511] System configuration

[0512] The system mainly consists of the following components:

[0513] 1. User Interface (Terminal)

[0514] 2. Data analysis engine (server)

[0515] 3. Data collection module (server)

[0516] 4. Framework generation module (server)

[0517] 5. Display module (terminal)

[0518] 6. Data storage module (server)

[0519] Operation of the invention

[0520] 1. User Input

[0521] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the user interface of the terminal. The input is made through a text box, and by clicking the "Submit" button, the information is sent to the server.

[0522] 2. Keyword analysis

[0523] The server analyzes the received keywords and maps them to the appropriate framework. This analysis uses a text analysis engine to perform topic modeling and keyword extraction. For example, if the inputs are "SWOT analysis" and "new product development," the analysis engine will identify the data items required for a SWOT analysis.

[0524] 3. Data Collection

[0525] The server collects relevant information using internal databases and external APIs (market databases, competitive information, etc.), including data on the company's strengths and weaknesses, competitive information, and market trends. The collected data is stored in a designated data storage device.

[0526] 4. Framework Generation

[0527] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of a SWOT analysis, the following elements are automatically generated:

[0528] Strengths: Our own technological capabilities and brand power

[0529] Weaknesses: Development costs, lack of engineers

[0530] Opportunities: Discovery of new market needs, technology trends

[0531] Threats: New products from competitors, changes in laws and regulations

[0532] 5. Display results

[0533] The display module of the terminal displays the framework analysis results sent from the server to the user. The user interface is integrated with a text editor for review and correction. The user can check the results and make any necessary corrections or additions.

[0534] 6. User Modifications and Re-analysis

[0535] When the user modifies the framework analysis results and clicks the "Reanalyze" button, the modifications are sent to the server again, which then reanalyzes the modified data, regenerates the updated framework analysis results, and displays them to the user again.

[0536] 7. Data Storage

[0537] The server stores the final framework analysis results in a database using the data storage module, allowing for future reuse or reference in other projects.

[0538] The overall processing flow is explained with concrete examples. For example, if a user requests a SWOT analysis for new product development, the system receives input such as "SWOT analysis, new product development." Based on this information, it collects appropriate market data and competitive information, automatically generates strengths, weaknesses, opportunities, and threats, and presents them to the user. If the user makes corrections, the system reanalyzes the results and saves them for future reference.

[0539] This system helps business people to conduct high-quality framework analysis quickly and efficiently.

[0540] The processing flow will be explained below.

[0541] Step 1:

[0542] Enter keywords related to the business framework you want to use. For example, enter "SWOT analysis, new product development."

[0543] Step 2:

[0544] The terminal receives the user's input and sends it to the server. The input is sent to the server as text data using an HTTP request.

[0545] Step 3:

[0546] The server analyzes the data received from the user. It uses a text analysis engine to break down keywords and map them to an appropriate framework. In this example, it selects the SWOT analysis template.

[0547] Step 4:

[0548] The server uses internal databases and external APIs (market databases, competitive information, etc.) to collect information related to the entered keywords. For example, it collects market trends related to new product development, competitive information, and data on the company's strengths and weaknesses.

[0549] Step 5:

[0550] Based on the information collected by the server, each element of the SWOT analysis is generated, i.e., strengths, weaknesses, opportunities, and threats are automatically classified and organized.

[0551] Step 6:

[0552] The server sends the generated SWOT analysis results to the device, which returns the information as an HTTP response.

[0553] Step 7:

[0554] The terminal displays the SWOT analysis results provided by the server to the user. The visualized results are displayed in text boxes and graph format, allowing the user to check and modify them.

[0555] Step 8:

[0556] The user can check the displayed SWOT analysis results and make corrections or additions as necessary. For example, a new "market uncertainty" can be added to the "weaknesses" section.

[0557] Step 9:

[0558] The user sends the corrected content back to the server from the terminal, along with a request for reanalysis.

[0559] Step 10:

[0560] The server re-analyzes the modifications and regenerates an updated SWOT analysis, integrating the previous analysis data with the new input data.

[0561] Step 11:

[0562] The server sends the updated SWOT analysis results back to the terminal, and the new analysis results are returned to the terminal as an HTTP response for redisplay.

[0563] Step 12:

[0564] The terminal will then redisplay the updated SWOT analysis results to the user, who can review them and make any necessary adjustments.

[0565] Step 13:

[0566] The server saves the final SWOT analysis results in a database, which is then stored for future reference or reuse in other projects.

[0567] Example 1

[0568] 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."

[0569] Conventional analysis systems using business frameworks have limited functionality for automatically generating analysis results based on user input, and processes such as collecting related information and reanalyzing analysis results are often performed manually. Furthermore, the functionality for correcting analysis results is also extremely limited, limiting the flexibility of users. This makes it difficult to perform high-quality business framework analysis quickly and efficiently.

[0570] 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.

[0571] In this invention, the server includes means for receiving terms related to an analysis framework to be used, means for analyzing the received terms and mapping them to an appropriate framework, means for collecting and storing related information based on the framework, means for automatically generating framework analysis results based on the collected and stored information, means for displaying the generated framework analysis results, means for allowing a user to edit the displayed analysis results, means for reanalyzing the edited analysis results and displaying the updated results, and means for saving the final analysis results, thereby enabling a user to quickly and efficiently perform high-quality business framework analysis.

[0572] An "analytical framework" is a structured method or model for evaluating and planning a business or strategy. Examples include SWOT analysis, PEST analysis, and Five Forces analysis.

[0573] "Related terms" refer to keywords or topics related to the subject or theme that a user wants to evaluate using the analytical framework, such as "new product development" or "market research."

[0574] "Means for receiving" refers to the mechanism by which the system receives related terms and analysis frameworks entered by the user. Specifically, this includes the text box and submit button in the user interface.

[0575] "Means for parsing and mapping to an appropriate framework" refers to techniques and methods for analyzing received related terms and selecting the most appropriate analytical framework, including text analysis engines and topic modeling techniques.

[0576] "Means for collecting and storing relevant information" refers to the process for collecting the necessary information from internal databases and external APIs and storing it in data storage based on the analytical framework identified through the analysis.

[0577] "Means for automatically generating framework analysis results" refers to technologies or algorithms that automatically generate results for a specified analysis framework based on collected and stored information, such as generative AI models.

[0578] The "display means" refers to a technique or interface for visually presenting the generated framework analysis results to the user. Specifically, this includes a display or a display module of a user interface.

[0579] "Editing means" refers to an interface or function that allows the user to modify or add to the displayed framework analysis results. This includes a text editor and a modification button.

[0580] "Means for reanalyzing and displaying updated results" refers to the process or technology for sending the framework analysis results edited by the user back to the server, performing analysis based on the modifications, and displaying the updated results to the user again.

[0581] "Means for storing the final analysis results" refers to the technology or method for storing the finalized framework analysis results in a storage medium such as a database so that they can be reused or referenced later.

[0582] This invention is a generative AI system that provides thinking support based on business frameworks. The system selects an appropriate framework based on keywords entered by the user and automates advanced analysis based on that framework. Major frameworks include PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop.

[0583] System configuration

[0584] The system mainly consists of the following components:

[0585] 1. User Interface (Terminal)

[0586] 2. Data analysis engine (server)

[0587] 3. Data collection module (server)

[0588] 4. Framework generation module (server)

[0589] 5. Display module (terminal)

[0590] 6. Data storage module (server)

[0591] The specific operation of the system is as follows.

[0592] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the terminal's user interface. This input is done through a text box and completed by clicking the "Submit" button. Once the input is submitted, the information is sent to the server.

[0593] The server uses a text analysis engine (e.g., NLTK, SpaCy) to analyze the received keywords. During this analysis, topic modeling and keyword extraction techniques are used to map the input keywords to an appropriate framework. For example, if the keywords "SWOT analysis" and "new product development" are input, the server will identify the data items required for a SWOT analysis.

[0594] Next, the server collects relevant information. This is done using an internal database or external API (e.g., market database, competitive information API). The collected data is stored in data storage (e.g., AWS S3, Google Cloud Storage). For example, data on the company's technological capabilities and brand power, information on competitors, and market trends are collected.

[0595] Next, the server's framework generation module automatically generates the specified framework analysis results based on the collected information. During this process, a generative AI model (e.g., OpenAI GPT-3) is used to perform advanced analysis. For example, in a SWOT analysis, the following elements are automatically generated:

[0596] Strengths: Our own technological capabilities and brand power

[0597] Weaknesses: Development costs, lack of engineers

[0598] Opportunities: Discovery of new market needs, technology trends

[0599] Threats: New products from competitors, changes in laws and regulations

[0600] The generated framework analysis results are sent to the terminal's display module and displayed visually to the user. This display has an integrated text editor function for review and correction. The user can check the results and make any necessary corrections or additions.

[0601] If the user makes any modifications, the modifications are sent to the server again. The server then re-analyzes the modified data and regenerates the updated framework analysis results. This allows the latest analysis results to be displayed to the user again.

[0602] The final framework analysis results are stored in a database (e.g., MongoDB, MySQL, etc.) using the server's data storage module, allowing for future reuse or reference in other projects.

[0603] Specific examples

[0604] When conducting a SWOT analysis for a new product development, the system works as follows:

[0605] 1. The user enters "SWOT analysis, new product development" and clicks the "Submit" button.

[0606] 2. The server analyzes the received keywords and maps them to a SWOT analysis.

[0607] 3. The server collects relevant information from internal databases and external APIs and stores it in data storage.

[0608] 4. The server automatically generates the analysis results using an AI model.

[0609] 5. The device displays the analysis results so that the user can check and correct them.

[0610] 6. After the user makes any corrections, the server re-analyzes the data and displays the updated information.

[0611] 7. The server stores the final analysis results in a database.

[0612] An example prompt is:

[0613] SWOT analysis, new product development

[0614] This system enables users to perform high-quality business framework analysis quickly and efficiently.

[0615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0616] Step 1:

[0617] The user inputs keywords related to the business framework into the user interface of the terminal. Specifically, the user inputs "SWOT analysis" and "new product development" into the text boxes and clicks the "Submit" button.

[0618] input:

[0619] "Business framework" and "related keywords" entered by the user.

[0620] output:

[0621] Sending information from the device to the server.

[0622] Step 2:

[0623] The server analyzes the received keywords using a text analysis engine (e.g., NLTK, SpaCy). It then uses topic modeling and keyword extraction techniques to map the input content into an appropriate framework. Specifically, it analyzes the keywords "SWOT analysis" and "new product development" to identify the data items required for a SWOT analysis.

[0624] input:

[0625] "Business framework" and "related keywords" sent from the device.

[0626] output:

[0627] Analysis results from the text analysis engine.

[0628] Step 3:

[0629] The server collects relevant information using internal databases and external APIs (e.g., market databases, competitive intelligence APIs). The collected data is stored in data storage (e.g., AWS S3, Google Cloud Storage). Specifically, it collects information such as the company's strengths and weaknesses, competitive intelligence, and market trends.

[0630] input:

[0631] Data items identified by the analysis results.

[0632] output:

[0633] Relevant data collected from databases and APIs.

[0634] Step 4:

[0635] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. This process uses a generative AI model (e.g., OpenAI GPT-3). Specifically, it generates each element of the SWOT analysis (strengths, weaknesses, opportunities, and threats).

[0636] input:

[0637] Relevant data collected.

[0638] output:

[0639] Framework analysis results using a generative AI model.

[0640] Step 5:

[0641] The display module of the terminal displays the framework analysis results sent from the server to the user. The user interface is integrated with a text editor function for reviewing and modifying the results.

[0642] input:

[0643] Framework analysis results sent from the server.

[0644] output:

[0645] The analysis results that are displayed to the user.

[0646] Step 6:

[0647] The user modifies the framework analysis results and clicks the "Reanalyze" button. The modified content is sent to the server again. Specifically, the user edits the results in the text editor of the user interface and clicks the "Reanalyze" button.

[0648] input:

[0649] Analysis results modified by the user.

[0650] output:

[0651] The corrected data is sent to the server.

[0652] Step 7:

[0653] The server then re-analyzes the modified data and regenerates the updated framework analysis results, which are again displayed to the user.

[0654] input:

[0655] Corrected data.

[0656] output:

[0657] Updated framework analysis results after reanalysis.

[0658] Step 8:

[0659] The server stores the final framework analysis results in a database using the data storage module, allowing for future reuse or reference in other projects.

[0660] input:

[0661] Final framework analysis results.

[0662] output:

[0663] Analysis results stored in a database.

[0664] (Application example 1)

[0665] 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."

[0666] Real-time data collection and analysis is required for production processes within factories. However, in conventional systems, data collection and analysis is often done manually, which requires time and effort. Another problem is the lack of an accurate analytical framework for making quick decisions. This situation leads to reduced production efficiency and inefficient use of resources. Therefore, a system is needed to analyze factory data in real time and optimize production processes.

[0667] 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.

[0668] In this invention, the server includes means for receiving keywords related to a business framework to be used, means for analyzing the received keywords and mapping them to an appropriate framework, means for collecting and organizing related information based on the framework, means for automatically generating framework analysis results based on the collected and organized information, means for displaying the generated framework analysis results, and means for performing analysis in real time using data within the factory to optimize the production process. This enables rapid and accurate framework analysis in the factory production process, improving production efficiency and optimizing resource utilization.

[0669] A "business framework" is a framework for systematically planning and analyzing a business.

[0670] "Keywords" are specific words or phrases that users enter when conducting framework analysis.

[0671] "Mapping" is the process of associating received keywords with the appropriate business framework.

[0672] "Relevant information" refers to data and knowledge required to perform framework analysis.

[0673] "Automatic generation" is the process by which the system automatically creates framework analysis results based on collected and organized information.

[0674] "Display" refers to visually showing the generated framework analysis results to the user.

[0675] "Factory data" refers to information about production processes collected within the factory from sensors, cameras, etc.

[0676] "Real-time" means that processing and analysis occurs immediately, without delay.

[0677] A "production process" is the entire manufacturing process of a product in a factory.

[0678] This invention is a system designed to optimize production processes within a factory. The system consists of the following main components:

[0679] 1. Overall structure

[0680] This system consists of a user interface (terminal), a data analysis engine (server), a data collection module (server), a framework generation module (server), a display module (terminal), and a data storage module (server).The overall purpose is to analyze data in the factory in real time and optimize the production process.

[0681] 2. User Interface

[0682] The user interface (UI) operates via a smartphone, tablet, or factory robot display, and allows users to enter keywords related to the business framework they wish to use, using a text box and a submit button.

[0683] 3. Data analysis engine

[0684] The server-based data analysis engine analyzes keywords using text analysis engines and generative AI models (e.g., TensorFlow, PyTorch). Based on the analysis results, the keywords are mapped to the appropriate business framework.

[0685] 4. Data Collection Module

[0686] The data collection module on the server collects real-time data from an internal database and external APIs (e.g., sensor APIs and camera APIs). This data includes information from temperature sensors, pressure sensors, and cameras. The collected data is stored in a database.

[0687] 5. Framework Generation Module

[0688] The framework generation module automatically generates analysis results for a specified framework based on the collected information. A specific example of a framework is the PDCA cycle. For example, the following PDCA cycle is generated:

[0689] Plan: Introducing new materials

[0690] Do: Test production

[0691] Check: Quality test

[0692] Improvement (Act): Correction of production process

[0693] 6. Display module

[0694] The display module displays the framework analysis results sent from the server to the user, including real-time updates and a text editor for the user to review and modify the results, which are then re-analyzed and the updated analysis results are displayed.

[0695] 7. Data storage module

[0696] The final framework analysis results are stored in a database in the server's data storage module, allowing for future reference and reuse in other projects.

[0697] Examples of concrete examples and prompts

[0698] As a concrete example, let's say there is a factory that is planning to introduce a new material. This factory aims to improve the quality of its products. The user enters the keywords "PDCA cycle" and "introduction of new material" into the interface. Based on this input, the system collects appropriate data and generates the following PDCA cycle.

[0699] Example prompt sentence:

[0700] Based on the "PDCA cycle," please collect in-factory data related to "introduction of new materials" and propose the next planning stage.

[0701] This system allows users to quickly and reliably obtain accurate analysis results to optimize production processes.

[0702] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0703] Step 1:

[0704] The user enters keywords related to the business framework they want to use into the user interface. Input is done using a text box and a submit button, and by clicking the submit button, the information is sent to the server.

[0705] Input: Keywords such as "PDCA cycle" and "introduction of new materials"

[0706] Output: The keyword is sent to the server

[0707] Step 2:

[0708] The server uses a text analysis engine to analyze the received keywords, primarily leveraging generative AI models (e.g., TensorFlow, PyTorch) for topic modeling and keyword extraction.

[0709] Input: The keyword submitted in step 1

[0710] Output: Selection of an appropriate business framework

[0711] Step 3:

[0712] The server's data collection module collects real-time data from an internal database and external APIs (sensor API, camera API, etc.), and the collected data is stored in a designated data storage.

[0713] Input: Selection result of appropriate business framework

[0714] Output: Real-time data collected

[0715] Step 4:

[0716] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of the PDCA cycle, the plan, do, verify, and improve phases are automatically generated.

[0717] Input: Real-time data collected

[0718] Output: Auto-generated framework analysis results

[0719] Step 5:

[0720] The display module of the terminal displays the framework analysis results sent from the server to the user, who can check the results, review and modify them.

[0721] Input: Auto-generated framework analysis results

[0722] Output: Show framework analysis results

[0723] Step 6:

[0724] When the user modifies the framework analysis results and clicks the "Reanalyze" button, the modifications are sent to the server again. The server then reanalyzes the modified data and regenerates the updated framework analysis results.

[0725] Input: User-modified framework analysis results

[0726] Output: Re-analyzed and re-generated framework analysis results

[0727] Step 7:

[0728] The final framework analysis results are stored in a database using the server's data storage module, allowing them to be reused later or used as reference for other projects.

[0729] Input: Re-analyzed and re-generated framework analysis results

[0730] Output: Saved framework analysis results

[0731] 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.

[0732] System Overview

[0733] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on keywords entered by the user and automates advanced analysis in line with that framework. Furthermore, by combining it with an emotion engine, it has the ability to recognize the user's emotional state and adjust the display of analysis results and advice based on that.

[0734] System configuration

[0735] The system mainly consists of the following components:

[0736] 1. User Interface (Terminal)

[0737] 2. Data analysis engine (server)

[0738] 3. Data collection module (server)

[0739] 4. Framework generation module (server)

[0740] 5. Display module (terminal)

[0741] 6. Data storage module (server)

[0742] 7. Emotion engine (server)

[0743] Operation of the invention

[0744] 1. User Input

[0745] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the user interface of the terminal. The input is made through a text box, and by clicking the "Submit" button, the information is sent to the server.

[0746] 2. Keyword analysis

[0747] The server analyzes the received keywords and maps them to the appropriate framework. This analysis uses a text analysis engine to perform topic modeling and keyword extraction. For example, if the inputs are "SWOT analysis" and "new product development," the analysis engine will identify the data items required for a SWOT analysis.

[0748] 3. Data Collection

[0749] The server collects relevant information using internal databases and external APIs (market databases, competitive information, etc.), including data on the company's strengths and weaknesses, competitive information, and market trends. The collected data is stored in a designated data storage device.

[0750] 4. Framework Generation

[0751] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of a SWOT analysis, each element (strengths, weaknesses, opportunities, and threats) is automatically generated:

[0752] Strengths: Our own technological capabilities and brand power

[0753] Weaknesses: Development costs, lack of engineers

[0754] Opportunities: Discovery of new market needs, technology trends

[0755] Threats: New products from competitors, changes in laws and regulations

[0756] 5. Emotion recognition

[0757] The server's emotion engine recognizes the user's emotions based on the user's input, operation history, and even biometric and voice analysis acquired through the user interface. For example, it analyzes the speed and pattern of text input, the tone of voice, and other factors to determine whether the user is feeling stressed or positive.

[0758] 6. Results display

[0759] The server sends the generated SWOT analysis results to the device, and the emotion engine adjusts the content and tone of the display based on the user's emotional state. For example, if the user is feeling stressed, an encouraging message is added to the analysis results.

[0760] 7. User Modifications and Re-analysis

[0761] The user checks the displayed SWOT analysis results and makes corrections or additions as necessary. The corrections are then sent back to the server from the device.

[0762] 8. Reanalysis and result updates

[0763] The server re-analyzes the changes and regenerates the updated SWOT analysis results, at which point the emotion engine also performs emotion analysis again based on the updated data.

[0764] 9. Displaying new results

[0765] The server sends the updated SWOT analysis results to the device and redisplays them, adjusting them according to the user's emotional state.

[0766] 10. Data Retention

[0767] The server stores the final SWOT analysis results in a database, and also stores the sentiment analysis history for future reference.

[0768] Specific examples

[0769] For example, suppose a user requests a SWOT analysis of a new product development and expresses anxiety while entering data. In this case, the emotion engine will detect this anxiety and add an encouraging message to the results display, such as, "New product development involves risks, but based on the data you entered, there is a good chance of success." In this way, the engine helps users feel at ease when using the analysis results.

[0770] This system not only helps business people to efficiently perform high-quality framework analysis, but also provides customization according to the user's emotional state, thereby achieving a better user experience.

[0771] The processing flow will be explained below.

[0772] Step 1:

[0773] The user enters the business framework (e.g., SWOT analysis) they want to use and related keywords (e.g., new product development) into the user interface of the device. For example, they enter "SWOT analysis" and "new product development" in the text boxes and click the "Submit" button.

[0774] Step 2:

[0775] The device receives the user's input and sends the data to the server, which is sent as an HTTP request.

[0776] Step 3:

[0777] The server analyzes the received data, using a text analysis engine to break down the keywords entered and map them to an appropriate framework (in this case, a SWOT analysis).

[0778] Step 4:

[0779] The server uses internal databases and external APIs (market databases, competitive information, etc.) to collect relevant information, such as market trends related to new product development, competitive information, and data on the company's strengths and weaknesses.

[0780] Step 5:

[0781] Based on the information collected by the server, each element of a SWOT analysis (strengths, weaknesses, opportunities, and threats) is automatically generated. For example, a company's technological capabilities are classified as a "strength" and development costs as a "weakness."

[0782] Step 6:

[0783] The server's emotion engine analyzes the user's operation history and input content to recognize the user's emotions. For example, it determines whether the user is feeling stressed based on the input speed and text content.

[0784] Step 7:

[0785] The SWOT analysis results generated by the server are adjusted based on the analysis results of the emotion engine. For example, if the user is feeling anxious, an encouraging message is added.

[0786] Step 8:

[0787] The server sends the generated SWOT analysis results to the device, which are returned as an HTTP response, including any adjusted information.

[0788] Step 9:

[0789] The terminal displays the SWOT analysis results provided by the server to the user, who can then review the results and make corrections or additions as necessary.

[0790] Step 10:

[0791] The user makes corrections to the SWOT analysis results and sends the corrections back to the server via the terminal.

[0792] Step 11:

[0793] The server receives the modifications and performs the analysis again, updating the SWOT analysis results based on the modified data and generating new analysis results.

[0794] Step 12:

[0795] The server sends the updated SWOT analysis results back to the device, where the emotion engine is reanalyzed and adjusted as necessary.

[0796] Step 13:

[0797] The terminal will then redisplay the updated SWOT analysis results to the user, who can review them and make any necessary adjustments.

[0798] Step 14:

[0799] The server saves the final SWOT analysis results in a database. This data is stored in storage for future reference and reuse in other projects. The sentiment analysis history is also saved.

[0800] Example 2

[0801] 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."

[0802] In conventional business framework analysis systems, the selection of an appropriate framework for keywords entered by the user, followed by the subsequent data collection, organization, and analysis are often done manually, requiring time and effort.In addition, there is a problem in that the user experience is not optimized because the display adjustment according to the user's emotional state is not taken into consideration.

[0803] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving terms related to a business framework to be used, a means for analyzing the received terms and associating them with an appropriate framework, a means for collecting and organizing related information based on the framework, a means for automatically generating framework analysis results based on the collected and organized information, a means for recognizing the user's emotional state, and a means for adjusting display content based on the recognized emotional state. This allows the user to quickly and efficiently receive an appropriate business framework analysis, and further improves the user experience through optimal display according to the user's emotional state.

[0804] A "business framework" refers to a framework or method for systematically analyzing and organizing strategies and plans in business or corporate activities.

[0805] "Terms" refer to keywords and phrases that a user inputs to be used in analyzing the business framework.

[0806] "Means for receiving" refers to the interface or software for importing terms and information entered by the user into the server via the network.

[0807] "Means of analyzing and associating with an appropriate framework" refers to algorithms and modules that use methods such as text analysis based on received terms to select the optimal business framework.

[0808] "Means of collecting and organizing relevant information" refers to systems and software that utilize internal databases and external information sources to collect the data required for the business framework, organize it, and make it usable.

[0809] "Means for automatically generating framework analysis results" refers to programs or algorithms for automatically generating each element of a business framework based on collected and organized information.

[0810] "Means for recognizing the user's emotional state" refers to a system or engine that analyzes the user's input, operation history, biometric information, voice, etc. to evaluate and determine the user's emotions.

[0811] "Means for adjusting displayed content based on the user's perceived emotional state" refers to algorithms or modules that modify or adjust the displayed content or tone in response to the user's emotional state.

[0812] System Overview and Configuration

[0813] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on terms entered by the user and automates advanced analysis in line with that framework. This system is primarily composed of a server and terminals working together. Furthermore, by combining it with an emotion engine, it has the ability to recognize the user's emotional state and adjust the display of analysis results and advice based on that.

[0814] Hardware and software used

[0815] The terminal includes a user interface and a display module.

[0816] The server includes a data analysis engine, a data collection module, a framework generation module, a data storage module, and an emotion engine.

[0817] Use a text analysis engine to analyze the received terms.

[0818] We collect relevant information using internal databases and external APIs.

[0819] The collected data storage stores the collected data.

[0820] Explanation of program processing

[0821] The user inputs terms related to the business framework (e.g., SWOT analysis) through the user interface of the terminal. The input is done using a text box, and by clicking the "Submit" button, this information is sent to the server.

[0822] The server then uses a text analysis engine to analyze the received terms and map them to the appropriate framework, specifically using topic modeling and keyword extraction.

[0823] Next, the server uses the internal database and external APIs (market database, competitive information, etc.) to collect relevant information through the collection module, including the company's strengths and weaknesses, competitive information, market trend data, etc. The collected data is stored in the collected data storage.

[0824] Based on the collected information, the server's framework generation module automatically generates analysis results for the specified framework (e.g., SWOT analysis). For example, in a SWOT analysis, each element (strengths, weaknesses, opportunities, and threats) is generated.

[0825] Furthermore, the server's emotion engine recognizes the user's emotional state through analysis of the user's input, operation history, biometric information, and voice, for example, by analyzing the speed and pattern of text input and the tone of voice.

[0826] Based on the recognized emotional state, the server adjusts the generated framework analysis results and reflects them in the displayed content, which may include adding an encouraging message if the user is feeling stressed.

[0827] Finally, the server sends the generated framework analysis results to the terminal and displays them. The user can check the displayed results and make corrections as necessary. Any corrections are sent back to the server for re-analysis and updated results. The final framework analysis results are saved in a database via the data storage module.

[0828] Examples of concrete examples and prompts

[0829] For example, if a user requests a SWOT analysis on "New Product Development," the system works as follows:

[0830] 1. The user enters "New Product Development" and clicks the submit button.

[0831] 2. The server analyzes the term and determines that a SWOT analysis is appropriate.

[0832] 3. The server collects relevant information (such as the company's technical capabilities and market trends) from internal databases and external APIs.

[0833] 4. The server generates the SWOT analysis and the emotion engine recognizes the user's emotional state.

[0834] 5. The results are sent to the device, and the display content is adjusted depending on the user's emotions. For example, if the user is feeling stressed, an encouraging message will be added: "Developing a new product involves risks, but based on the data you entered, there is a good chance of success."

[0835] Prompt Sentence Examples

[0836] "I'd like to conduct a SWOT analysis of new product development. Gather data on your company's technological capabilities and market trends to identify strengths and opportunities, and add encouraging messages if you feel unsure."

[0837] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0838] Step 1: User Input

[0839] The user uses the terminal's user interface to input a business framework (e.g., SWOT analysis) and related terms (e.g., new product development). Input is completed by using the text box and clicking the "Submit" button, which sends the terms and information to the server.

[0840] Step 2: Keyword analysis

[0841] The server receives the received terms as input and analyzes them using a text analysis engine. Specifically, it selects an appropriate business framework through topic modeling and keyword extraction. For example, if the keyword "new product development" is entered, the server analyzes this information and determines that "SWOT analysis" is appropriate. The selected framework is obtained as the output of this analysis.

[0842] Step 3: Data collection

[0843] The server collects relevant information from internal databases and external APIs. The data items required for the selected framework are used as input. Specifically, data such as the company's strengths and weaknesses, competitive information, and market trends is acquired and stored in the designated data storage. This collected data is used as input for the next step.

[0844] Step 4: Framework Generation

[0845] The framework generation module on the server automatically generates framework analysis results based on the collected data. The collected data is used as input, and each element (strengths, weaknesses, opportunities, and threats) is generated as output. For example, in the case of a SWOT analysis, the following elements are generated:

[0846] Strengths: Our own technological capabilities and brand power

[0847] Weaknesses: Development costs, lack of engineers

[0848] Opportunities: Discovery of new market needs, technology trends

[0849] Threats: New products from competitors, changes in laws and regulations

[0850] Step 5: Emotion Recognition

[0851] The server's emotion engine recognizes the user's emotions through input content, operation history, biometric information, and voice analysis. Inputs include the speed and pattern of text input and voice tone, and the output determines the user's emotional state. For example, it determines whether the user is feeling stressed or positive.

[0852] Step 6: View the results

[0853] The server sends the generated framework analysis results to the terminal and reflects them in the display content. The generated framework analysis results and the recognized emotional state are used as input. For example, if the user is feeling stressed, an encouraging message such as "New product development involves risks, but based on the data you entered, there is a good chance of success" is added to the display content.

[0854] Step 7: User modifications and reanalysis

[0855] The user checks the displayed framework analysis results and makes corrections or additions as necessary. The corrected content is used as input and is sent back to the server from the terminal. This corrected content is used as input for the next step.

[0856] Step 8: Reprocess and update results

[0857] The server receives the modifications and performs a re-analysis. The modifications are used as input and the analysis is performed based on the new data. The updated framework analysis results are generated as output, and these results are used as input for the next step. The emotion engine also performs a re-evaluation of the emotions based on the updated data.

[0858] Step 9: View the new results

[0859] The server sends the updated framework analysis results to the terminal and redisplays them. The updated framework analysis results and the new emotional state are used as input. The redisplayed content is adjusted according to the user's emotional state.

[0860] Step 10: Save Data

[0861] The server saves the final framework analysis results in a database. The final analysis results and sentiment analysis history are used as input, and these data are stored in the database as output for future reference.

[0862] (Application example 2)

[0863] 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."

[0864] Modern business environments demand rapid and accurate decision-making, especially in virtual store operations, where rapid response to market trends and changes in the competitive landscape is essential. However, conventional business framework analysis systems lack feedback that takes into account the user's emotional state, which can lead to stress. Furthermore, human error can occur in the selection of business frameworks and the display of analysis results. Therefore, there is a need for a system that recognizes the user's emotional state and provides appropriate feedback to provide a better experience and streamline decision-making.

[0865] 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.

[0866] In this invention, the server includes means for receiving keywords related to a business framework to be used, means for analyzing the received keywords and mapping them to an appropriate framework, means for collecting and organizing related information based on the framework, means for automatically generating framework analysis results based on the collected and organized information, means for displaying the generated framework analysis results, and means for recognizing the user's emotional state and adjusting the result display and feedback content. This enables the user to quickly obtain highly accurate business framework analysis results without feeling stressed.

[0867] A "business framework" is a systematic method or model for analyzing the business environment and market.

[0868] "Keywords" are important words or short phrases related to the selection and analysis of a business framework.

[0869] "Mapping" is the process of matching received keywords to an appropriate business framework.

[0870] "Information collection" is the process of obtaining the necessary data using internal databases or external APIs.

[0871] "Information organization" is the process of structuring collected data based on a business framework and converting it into a format suitable for analysis.

[0872] "Automatic generation" is the process in which the system mechanically generates business framework analysis results based on collected and organized information.

[0873] "Display" is the process of providing the generated business framework analysis results to the user through a user interface.

[0874] "Emotional state recognition" is the process of determining a user's emotions by analyzing the user's input, operation history, biometric information, etc.

[0875] "Adjusting feedback content" is the process of appropriately changing the way analysis results are displayed and advice given based on the user's perceived emotional state.

[0876] System configuration

[0877] This invention is a generative AI system that provides thinking support based on business frameworks, automatically selecting an appropriate business model based on keywords entered by the user and performing advanced data analysis. Furthermore, it incorporates an emotion engine and can adjust the analysis results and feedback content according to the user's emotional state.

[0878] The system consists of the following components:

[0879] 1. User Interface (Terminal)

[0880] Users enter keywords via their mobile device (smartphone or tablet) and the analysis results are displayed.

[0881] 2. Data analysis engine (server)

[0882] The engine performs text analysis and topic modeling to map keywords into appropriate business frameworks.

[0883] 3. Data collection module (server)

[0884] Automatically gather relevant data using internal databases and external APIs (market databases, competitive intelligence, etc.).

[0885] 4. Framework generation module (server)

[0886] Based on the collected information, the specified business framework analysis results are automatically generated.

[0887] 5. Emotion engine (server)

[0888] The system recognizes the user's emotional state based on input content and biometric information obtained from the user interface.

[0889] 6. Display module (terminal)

[0890] The generated framework analysis results are displayed in an easy-to-understand manner to the user, and the content and tone are adjusted as needed.

[0891] 7. Data storage module (server)

[0892] The analysis results and sentiment analysis history are stored in a database for future reference.

[0893] Program processing

[0894] The server's data analysis engine uses Python libraries (e.g., TextBlob) to perform text analysis for keywords. For example, if a user enters keywords such as "new product launch" or "improving customer satisfaction," the engine analyzes them and maps them to an appropriate framework (e.g., SWOT analysis).

[0895] Next, the data collection module obtains relevant information using internal databases or external APIs (e.g., market databases). The obtained information is saved in data storage. The framework generation module analyzes this data and automatically generates the results of a business framework (e.g., SWOT analysis). For example, "strengths" could include "brand power" or "technical ability," while "weaknesses" could include "development costs" or "lack of engineers."

[0896] The emotion engine determines the user's emotional state from the speed and pattern of input, tone of voice, etc. For example, if the user is feeling anxious, it will add an encouraging feedback message along with the analysis results.

[0897] Examples of concrete examples and prompts

[0898] Examples:

[0899] If a user requests a SWOT analysis of a new product launch in a virtual store and feels anxious while entering the information, the emotion engine can analyze the user's anxiety and provide a feedback message such as, "Although launching a new product involves risks, there is a high chance of success if you leverage your company's brand power."

[0900] Example prompt sentence:

[0901] Virtual Product New Launch Market Trends Competitor SWOT Analysis

[0902] In this way, the user can more relaxedly check the business framework analysis results and make decisions efficiently.

[0903] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0904] Step 1:

[0905] The user enters a keyword

[0906] The user inputs keywords related to the business framework (e.g., "launching a new product" or "improving customer satisfaction") into the user interface of the terminal. Once input is complete, the information is sent to the server by clicking the "Submit" button.

[0907] Input: keyword

[0908] Output: Keywords sent to the server

[0909] Step 2:

[0910] The server analyzes the keywords

[0911] The server uses a text analysis engine (e.g., TextBlob) to analyze the received keywords. The text analysis engine performs natural language processing on the received keywords and maps them into an appropriate business framework (e.g., SWOT analysis).

[0912] Input: keyword

[0913] Output: Mapped business framework

[0914] Step 3:

[0915] The server collects relevant information

[0916] The server's data collection module collects relevant information using internal databases and external APIs (market databases, competitive information, etc.). For example, regarding "new product launches," market trends and competitive information are obtained through API calls.

[0917] Input: Business framework and related keywords

[0918] Output: Collected relevant information data

[0919] Step 4:

[0920] Organizing the information collected by the server

[0921] The server organizes the collected data into a certain format, for example, categorizing market trend information and competitor information stored in the database into required categories (e.g., strengths, weaknesses, opportunities, and threats).

[0922] Input: Collected relevant information data

[0923] Output: Organized data

[0924] Step 5:

[0925] The server automatically generates framework analysis results

[0926] The server's framework generation module automatically generates analysis results for a business framework (e.g., SWOT analysis) based on the organized data. For example, "strengths" could include "brand power" and "technical ability," while "weaknesses" could include "development costs" and "lack of engineers."

[0927] Input: Organized data

[0928] Output: Auto-generated framework analysis results

[0929] Step 6:

[0930] The server recognizes the user's emotional state

[0931] The server's emotion engine collects information such as the user's input speed, patterns, and tone of voice to recognize their emotional state. For example, if the user is typing slowly, it will interpret this as anxiety.

[0932] Input: User input, operation history, biometric information, etc.

[0933] Output: Perceived emotional state

[0934] Step 7:

[0935] The server coordinates the results display and feedback

[0936] The server's display module displays the analysis results based on the user's emotional state and adjusts the feedback as needed. For example, if a user feels anxious, the server might add an encouraging message such as, "Launching a new product involves risks, but if you leverage your company's brand power, you have a high chance of success."

[0937] Input: Automatically generated framework analysis results, recognized emotional states

[0938] Output: Analysis results and feedback displayed to the user

[0939] Step 8:

[0940] The user checks and corrects the results

[0941] The user checks the business framework analysis results displayed on the terminal and makes corrections or additions as necessary. Once corrections are complete, the user clicks the "Send" button again to send the information to the server.

[0942] Input: User modifications

[0943] Output: Modifications sent to the server

[0944] Step 9:

[0945] The server re-analyzes the corrected analysis results and updates them.

[0946] The server re-analyzes the received modifications and regenerates the updated business framework analysis results, at which time the emotion engine also runs again to generate analysis results based on the latest emotional state.

[0947] Input: Modified Business Framework analysis results

[0948] Output: Re-analysis and updated analysis results

[0949] Step 10:

[0950] Server displays updated results

[0951] The server's display module displays the updated business framework analysis results to the user again, and updates the emotion-based feedback as needed.

[0952] Input: Updated business framework analysis results, latest emotional state

[0953] Output: Analysis results and feedback that are displayed back to the user

[0954] Step 11:

[0955] The server stores the data

[0956] The server's data storage module stores the final business framework analysis results and the history of sentiment analysis in a database, which can be used for future analysis.

[0957] Input: Final business framework analysis results, sentiment analysis history

[0958] Output: Data stored in the database

[0959] 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.

[0960] 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.

[0961] 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.

[0962] [Third embodiment]

[0963] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0964] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0965] 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).

[0966] 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.

[0967] 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.

[0968] 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).

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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."

[0975] System Overview

[0976] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on keywords entered by the user and automates advanced analysis based on that framework. Major frameworks include PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop.

[0977] System configuration

[0978] The system mainly consists of the following components:

[0979] 1. User Interface (Terminal)

[0980] 2. Data analysis engine (server)

[0981] 3. Data collection module (server)

[0982] 4. Framework generation module (server)

[0983] 5. Display module (terminal)

[0984] 6. Data storage module (server)

[0985] Operation of the invention

[0986] 1. User Input

[0987] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the user interface of the terminal. The input is made through a text box, and by clicking the "Submit" button, the information is sent to the server.

[0988] 2. Keyword analysis

[0989] The server analyzes the received keywords and maps them to the appropriate framework. This analysis uses a text analysis engine to perform topic modeling and keyword extraction. For example, if the inputs are "SWOT analysis" and "new product development," the analysis engine will identify the data items required for a SWOT analysis.

[0990] 3. Data Collection

[0991] The server collects relevant information using internal databases and external APIs (market databases, competitive information, etc.), including data on the company's strengths and weaknesses, competitive information, and market trends. The collected data is stored in a designated data storage device.

[0992] 4. Framework Generation

[0993] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of a SWOT analysis, the following elements are automatically generated:

[0994] Strengths: Our own technological capabilities and brand power

[0995] Weaknesses: Development costs, lack of engineers

[0996] Opportunities: Discovery of new market needs, technology trends

[0997] Threats: New products from competitors, changes in laws and regulations

[0998] 5. Display results

[0999] The display module of the terminal displays the framework analysis results sent from the server to the user. The user interface is integrated with a text editor for review and correction. The user can check the results and make any necessary corrections or additions.

[1000] 6. User Modifications and Re-analysis

[1001] When the user modifies the framework analysis results and clicks the "Reanalyze" button, the modifications are sent to the server again, which then reanalyzes the modified data, regenerates the updated framework analysis results, and displays them to the user again.

[1002] 7. Data Storage

[1003] The server stores the final framework analysis results in a database using the data storage module, allowing for future reuse or reference in other projects.

[1004] The overall processing flow is explained with concrete examples. For example, if a user requests a SWOT analysis for new product development, the system receives input such as "SWOT analysis, new product development." Based on this information, it collects appropriate market data and competitive information, automatically generates strengths, weaknesses, opportunities, and threats, and presents them to the user. If the user makes corrections, the system reanalyzes the results and saves them for future reference.

[1005] This system helps business people to conduct high-quality framework analysis quickly and efficiently.

[1006] The processing flow will be explained below.

[1007] Step 1:

[1008] Enter keywords related to the business framework you want to use. For example, enter "SWOT analysis, new product development."

[1009] Step 2:

[1010] The terminal receives the user's input and sends it to the server. The input is sent to the server as text data using an HTTP request.

[1011] Step 3:

[1012] The server analyzes the data received from the user. It uses a text analysis engine to break down keywords and map them to an appropriate framework. In this example, it selects the SWOT analysis template.

[1013] Step 4:

[1014] The server uses internal databases and external APIs (market databases, competitive information, etc.) to collect information related to the entered keywords. For example, it collects market trends related to new product development, competitive information, and data on the company's strengths and weaknesses.

[1015] Step 5:

[1016] Based on the information collected by the server, each element of the SWOT analysis is generated, i.e., strengths, weaknesses, opportunities, and threats are automatically classified and organized.

[1017] Step 6:

[1018] The server sends the generated SWOT analysis results to the device, which returns the information as an HTTP response.

[1019] Step 7:

[1020] The terminal displays the SWOT analysis results provided by the server to the user. The visualized results are displayed in text boxes and graph format, allowing the user to check and modify them.

[1021] Step 8:

[1022] The user can check the displayed SWOT analysis results and make corrections or additions as necessary. For example, a new "market uncertainty" can be added to the "weaknesses" section.

[1023] Step 9:

[1024] The user sends the corrected content back to the server from the terminal, along with a request for reanalysis.

[1025] Step 10:

[1026] The server re-analyzes the modifications and regenerates an updated SWOT analysis, integrating the previous analysis data with the new input data.

[1027] Step 11:

[1028] The server sends the updated SWOT analysis results back to the terminal, and the new analysis results are returned to the terminal as an HTTP response for redisplay.

[1029] Step 12:

[1030] The terminal will then redisplay the updated SWOT analysis results to the user, who can review them and make any necessary adjustments.

[1031] Step 13:

[1032] The server saves the final SWOT analysis results in a database, which is then stored for future reference or reuse in other projects.

[1033] Example 1

[1034] 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."

[1035] Conventional analysis systems using business frameworks have limited functionality for automatically generating analysis results based on user input, and processes such as collecting related information and reanalyzing analysis results are often performed manually. Furthermore, the functionality for correcting analysis results is also extremely limited, limiting the flexibility of users. This makes it difficult to perform high-quality business framework analysis quickly and efficiently.

[1036] 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.

[1037] In this invention, the server includes means for receiving terms related to an analysis framework to be used, means for analyzing the received terms and mapping them to an appropriate framework, means for collecting and storing related information based on the framework, means for automatically generating framework analysis results based on the collected and stored information, means for displaying the generated framework analysis results, means for allowing a user to edit the displayed analysis results, means for reanalyzing the edited analysis results and displaying the updated results, and means for saving the final analysis results, thereby enabling a user to quickly and efficiently perform high-quality business framework analysis.

[1038] An "analytical framework" is a structured method or model for evaluating and planning a business or strategy. Examples include SWOT analysis, PEST analysis, and Five Forces analysis.

[1039] "Related terms" refer to keywords or topics related to the subject or theme that a user wants to evaluate using the analytical framework, such as "new product development" or "market research."

[1040] "Means for receiving" refers to the mechanism by which the system receives related terms and analysis frameworks entered by the user. Specifically, this includes the text box and submit button in the user interface.

[1041] "Means for parsing and mapping to an appropriate framework" refers to techniques and methods for analyzing received related terms and selecting the most appropriate analytical framework, including text analysis engines and topic modeling techniques.

[1042] "Means for collecting and storing relevant information" refers to the process for collecting the necessary information from internal databases and external APIs and storing it in data storage based on the analytical framework identified through the analysis.

[1043] "Means for automatically generating framework analysis results" refers to technologies or algorithms that automatically generate results for a specified analysis framework based on collected and stored information, such as generative AI models.

[1044] The "display means" refers to a technique or interface for visually presenting the generated framework analysis results to the user. Specifically, this includes a display or a display module of a user interface.

[1045] "Editing means" refers to an interface or function that allows the user to modify or add to the displayed framework analysis results. This includes a text editor and a modification button.

[1046] "Means for reanalyzing and displaying updated results" refers to the process or technology for sending the framework analysis results edited by the user back to the server, performing analysis based on the modifications, and displaying the updated results to the user again.

[1047] "Means for storing the final analysis results" refers to the technology or method for storing the finalized framework analysis results in a storage medium such as a database so that they can be reused or referenced later.

[1048] This invention is a generative AI system that provides thinking support based on business frameworks. The system selects an appropriate framework based on keywords entered by the user and automates advanced analysis based on that framework. Major frameworks include PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop.

[1049] System configuration

[1050] The system mainly consists of the following components:

[1051] 1. User Interface (Terminal)

[1052] 2. Data analysis engine (server)

[1053] 3. Data collection module (server)

[1054] 4. Framework generation module (server)

[1055] 5. Display module (terminal)

[1056] 6. Data storage module (server)

[1057] The specific operation of the system is as follows.

[1058] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the terminal's user interface. This input is done through a text box and completed by clicking the "Submit" button. Once the input is submitted, the information is sent to the server.

[1059] The server uses a text analysis engine (e.g., NLTK, SpaCy) to analyze the received keywords. During this analysis, topic modeling and keyword extraction techniques are used to map the input keywords to an appropriate framework. For example, if the keywords "SWOT analysis" and "new product development" are input, the server will identify the data items required for a SWOT analysis.

[1060] Next, the server collects relevant information. This is done using an internal database or external API (e.g., market database, competitive information API). The collected data is stored in data storage (e.g., AWS S3, Google Cloud Storage). For example, data on the company's technological capabilities and brand power, information on competitors, and market trends are collected.

[1061] Next, the server's framework generation module automatically generates the specified framework analysis results based on the collected information. During this process, a generative AI model (e.g., OpenAI GPT-3) is used to perform advanced analysis. For example, in a SWOT analysis, the following elements are automatically generated:

[1062] Strengths: Our own technological capabilities and brand power

[1063] Weaknesses: Development costs, lack of engineers

[1064] Opportunities: Discovery of new market needs, technology trends

[1065] Threats: New products from competitors, changes in laws and regulations

[1066] The generated framework analysis results are sent to the terminal's display module and displayed visually to the user. This display has an integrated text editor function for review and correction. The user can check the results and make any necessary corrections or additions.

[1067] If the user makes any modifications, the modifications are sent to the server again. The server then re-analyzes the modified data and regenerates the updated framework analysis results. This allows the latest analysis results to be displayed to the user again.

[1068] The final framework analysis results are stored in a database (e.g., MongoDB, MySQL, etc.) using the server's data storage module, allowing for future reuse or reference in other projects.

[1069] Specific examples

[1070] When conducting a SWOT analysis for a new product development, the system works as follows:

[1071] 1. The user enters "SWOT analysis, new product development" and clicks the "Submit" button.

[1072] 2. The server analyzes the received keywords and maps them to a SWOT analysis.

[1073] 3. The server collects relevant information from internal databases and external APIs and stores it in data storage.

[1074] 4. The server automatically generates the analysis results using an AI model.

[1075] 5. The device displays the analysis results so that the user can check and correct them.

[1076] 6. After the user makes any corrections, the server re-analyzes the data and displays the updated information.

[1077] 7. The server stores the final analysis results in a database.

[1078] An example prompt is:

[1079] SWOT analysis, new product development

[1080] This system enables users to perform high-quality business framework analysis quickly and efficiently.

[1081] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1082] Step 1:

[1083] The user inputs keywords related to the business framework into the user interface of the terminal. Specifically, the user inputs "SWOT analysis" and "new product development" into the text boxes and clicks the "Submit" button.

[1084] input:

[1085] "Business framework" and "related keywords" entered by the user.

[1086] output:

[1087] Sending information from the device to the server.

[1088] Step 2:

[1089] The server analyzes the received keywords using a text analysis engine (e.g., NLTK, SpaCy). It then uses topic modeling and keyword extraction techniques to map the input content into an appropriate framework. Specifically, it analyzes the keywords "SWOT analysis" and "new product development" to identify the data items required for a SWOT analysis.

[1090] input:

[1091] "Business framework" and "related keywords" sent from the device.

[1092] output:

[1093] Analysis results from the text analysis engine.

[1094] Step 3:

[1095] The server collects relevant information using internal databases and external APIs (e.g., market databases, competitive intelligence APIs). The collected data is stored in data storage (e.g., AWS S3, Google Cloud Storage). Specifically, it collects information such as the company's strengths and weaknesses, competitive intelligence, and market trends.

[1096] input:

[1097] Data items identified by the analysis results.

[1098] output:

[1099] Relevant data collected from databases and APIs.

[1100] Step 4:

[1101] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. This process uses a generative AI model (e.g., OpenAI GPT-3). Specifically, it generates each element of the SWOT analysis (strengths, weaknesses, opportunities, and threats).

[1102] input:

[1103] Relevant data collected.

[1104] output:

[1105] Framework analysis results using a generative AI model.

[1106] Step 5:

[1107] The display module of the terminal displays the framework analysis results sent from the server to the user. The user interface is integrated with a text editor function for reviewing and modifying the results.

[1108] input:

[1109] Framework analysis results sent from the server.

[1110] output:

[1111] The analysis results that are displayed to the user.

[1112] Step 6:

[1113] The user modifies the framework analysis results and clicks the "Reanalyze" button. The modified content is sent to the server again. Specifically, the user edits the results in the text editor of the user interface and clicks the "Reanalyze" button.

[1114] input:

[1115] Analysis results modified by the user.

[1116] output:

[1117] The corrected data is sent to the server.

[1118] Step 7:

[1119] The server then re-analyzes the modified data and regenerates the updated framework analysis results, which are again displayed to the user.

[1120] input:

[1121] Corrected data.

[1122] output:

[1123] Updated framework analysis results after reanalysis.

[1124] Step 8:

[1125] The server stores the final framework analysis results in a database using the data storage module, allowing for future reuse or reference in other projects.

[1126] input:

[1127] Final framework analysis results.

[1128] output:

[1129] Analysis results stored in a database.

[1130] (Application example 1)

[1131] 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."

[1132] Real-time data collection and analysis is required for production processes within factories. However, in conventional systems, data collection and analysis is often done manually, which requires time and effort. Another problem is the lack of an accurate analytical framework for making quick decisions. This situation leads to reduced production efficiency and inefficient use of resources. Therefore, a system is needed to analyze factory data in real time and optimize production processes.

[1133] 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.

[1134] In this invention, the server includes means for receiving keywords related to a business framework to be used, means for analyzing the received keywords and mapping them to an appropriate framework, means for collecting and organizing related information based on the framework, means for automatically generating framework analysis results based on the collected and organized information, means for displaying the generated framework analysis results, and means for performing analysis in real time using data within the factory to optimize the production process. This enables rapid and accurate framework analysis in the factory production process, improving production efficiency and optimizing resource utilization.

[1135] A "business framework" is a framework for systematically planning and analyzing a business.

[1136] "Keywords" are specific words or phrases that users enter when conducting framework analysis.

[1137] "Mapping" is the process of associating received keywords with the appropriate business framework.

[1138] "Relevant information" refers to data and knowledge required to perform framework analysis.

[1139] "Automatic generation" is the process by which the system automatically creates framework analysis results based on collected and organized information.

[1140] "Display" refers to visually showing the generated framework analysis results to the user.

[1141] "Factory data" refers to information about production processes collected within the factory from sensors, cameras, etc.

[1142] "Real-time" means that processing and analysis occurs immediately, without delay.

[1143] A "production process" is the entire manufacturing process of a product in a factory.

[1144] This invention is a system designed to optimize production processes within a factory. The system consists of the following main components:

[1145] 1. Overall structure

[1146] This system consists of a user interface (terminal), a data analysis engine (server), a data collection module (server), a framework generation module (server), a display module (terminal), and a data storage module (server).The overall purpose is to analyze data in the factory in real time and optimize the production process.

[1147] 2. User Interface

[1148] The user interface (UI) operates via a smartphone, tablet, or factory robot display, and allows users to enter keywords related to the business framework they wish to use, using a text box and a submit button.

[1149] 3. Data analysis engine

[1150] The server-based data analysis engine analyzes keywords using text analysis engines and generative AI models (e.g., TensorFlow, PyTorch). Based on the analysis results, the keywords are mapped to the appropriate business framework.

[1151] 4. Data Collection Module

[1152] The data collection module on the server collects real-time data from an internal database and external APIs (e.g., sensor APIs and camera APIs). This data includes information from temperature sensors, pressure sensors, and cameras. The collected data is stored in a database.

[1153] 5. Framework Generation Module

[1154] The framework generation module automatically generates analysis results for a specified framework based on the collected information. A specific example of a framework is the PDCA cycle. For example, the following PDCA cycle is generated:

[1155] Plan: Introducing new materials

[1156] Do: Test production

[1157] Check: Quality test

[1158] Improvement (Act): Correction of production process

[1159] 6. Display module

[1160] The display module displays the framework analysis results sent from the server to the user, including real-time updates and a text editor for the user to review and modify the results, which are then re-analyzed and the updated analysis results are displayed.

[1161] 7. Data storage module

[1162] The final framework analysis results are stored in a database in the server's data storage module, allowing for future reference and reuse in other projects.

[1163] Examples of concrete examples and prompts

[1164] As a concrete example, let's say there is a factory that is planning to introduce a new material. This factory aims to improve the quality of its products. The user enters the keywords "PDCA cycle" and "introduction of new material" into the interface. Based on this input, the system collects appropriate data and generates the following PDCA cycle.

[1165] Example prompt sentence:

[1166] Based on the "PDCA cycle," please collect in-factory data related to "introduction of new materials" and propose the next planning stage.

[1167] This system allows users to quickly and reliably obtain accurate analysis results to optimize production processes.

[1168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1169] Step 1:

[1170] The user enters keywords related to the business framework they want to use into the user interface. Input is done using a text box and a submit button, and by clicking the submit button, the information is sent to the server.

[1171] Input: Keywords such as "PDCA cycle" and "introduction of new materials"

[1172] Output: The keyword is sent to the server

[1173] Step 2:

[1174] The server uses a text analysis engine to analyze the received keywords, primarily leveraging generative AI models (e.g., TensorFlow, PyTorch) for topic modeling and keyword extraction.

[1175] Input: The keyword submitted in step 1

[1176] Output: Selection of an appropriate business framework

[1177] Step 3:

[1178] The server's data collection module collects real-time data from an internal database and external APIs (sensor API, camera API, etc.), and the collected data is stored in a designated data storage.

[1179] Input: Selection result of appropriate business framework

[1180] Output: Real-time data collected

[1181] Step 4:

[1182] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of the PDCA cycle, the plan, do, verify, and improve phases are automatically generated.

[1183] Input: Real-time data collected

[1184] Output: Auto-generated framework analysis results

[1185] Step 5:

[1186] The display module of the terminal displays the framework analysis results sent from the server to the user, who can check the results, review and modify them.

[1187] Input: Auto-generated framework analysis results

[1188] Output: Show framework analysis results

[1189] Step 6:

[1190] When the user modifies the framework analysis results and clicks the "Reanalyze" button, the modifications are sent to the server again. The server then reanalyzes the modified data and regenerates the updated framework analysis results.

[1191] Input: User-modified framework analysis results

[1192] Output: Re-analyzed and re-generated framework analysis results

[1193] Step 7:

[1194] The final framework analysis results are stored in a database using the server's data storage module, allowing them to be reused later or used as reference for other projects.

[1195] Input: Re-analyzed and re-generated framework analysis results

[1196] Output: Saved framework analysis results

[1197] 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.

[1198] System Overview

[1199] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on keywords entered by the user and automates advanced analysis in line with that framework. Furthermore, by combining it with an emotion engine, it has the ability to recognize the user's emotional state and adjust the display of analysis results and advice based on that.

[1200] System configuration

[1201] The system mainly consists of the following components:

[1202] 1. User Interface (Terminal)

[1203] 2. Data analysis engine (server)

[1204] 3. Data collection module (server)

[1205] 4. Framework generation module (server)

[1206] 5. Display module (terminal)

[1207] 6. Data storage module (server)

[1208] 7. Emotion engine (server)

[1209] Operation of the invention

[1210] 1. User Input

[1211] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the user interface of the terminal. The input is made through a text box, and by clicking the "Submit" button, the information is sent to the server.

[1212] 2. Keyword analysis

[1213] The server analyzes the received keywords and maps them to the appropriate framework. This analysis uses a text analysis engine to perform topic modeling and keyword extraction. For example, if the inputs are "SWOT analysis" and "new product development," the analysis engine will identify the data items required for a SWOT analysis.

[1214] 3. Data Collection

[1215] The server collects relevant information using internal databases and external APIs (market databases, competitive information, etc.), including data on the company's strengths and weaknesses, competitive information, and market trends. The collected data is stored in a designated data storage device.

[1216] 4. Framework Generation

[1217] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of a SWOT analysis, each element (strengths, weaknesses, opportunities, and threats) is automatically generated:

[1218] Strengths: Our own technological capabilities and brand power

[1219] Weaknesses: Development costs, lack of engineers

[1220] Opportunities: Discovery of new market needs, technology trends

[1221] Threats: New products from competitors, changes in laws and regulations

[1222] 5. Emotion recognition

[1223] The server's emotion engine recognizes the user's emotions based on the user's input, operation history, and even biometric and voice analysis acquired through the user interface. For example, it analyzes the speed and pattern of text input, the tone of voice, and other factors to determine whether the user is feeling stressed or positive.

[1224] 6. Results display

[1225] The server sends the generated SWOT analysis results to the device, and the emotion engine adjusts the content and tone of the display based on the user's emotional state. For example, if the user is feeling stressed, an encouraging message is added to the analysis results.

[1226] 7. User Modifications and Re-analysis

[1227] The user checks the displayed SWOT analysis results and makes corrections or additions as necessary. The corrections are then sent back to the server from the device.

[1228] 8. Reanalysis and result updates

[1229] The server re-analyzes the changes and regenerates the updated SWOT analysis results, at which point the emotion engine also performs emotion analysis again based on the updated data.

[1230] 9. Displaying new results

[1231] The server sends the updated SWOT analysis results to the device and redisplays them, adjusting them according to the user's emotional state.

[1232] 10. Data Retention

[1233] The server stores the final SWOT analysis results in a database, and also stores the sentiment analysis history for future reference.

[1234] Specific examples

[1235] For example, suppose a user requests a SWOT analysis of a new product development and expresses anxiety while entering data. In this case, the emotion engine will detect this anxiety and add an encouraging message to the results display, such as, "New product development involves risks, but based on the data you entered, there is a good chance of success." In this way, the engine helps users feel at ease when using the analysis results.

[1236] This system not only helps business people to efficiently perform high-quality framework analysis, but also provides customization according to the user's emotional state, thereby achieving a better user experience.

[1237] The processing flow will be explained below.

[1238] Step 1:

[1239] The user enters the business framework (e.g., SWOT analysis) they want to use and related keywords (e.g., new product development) into the user interface of the device. For example, they enter "SWOT analysis" and "new product development" in the text boxes and click the "Submit" button.

[1240] Step 2:

[1241] The device receives the user's input and sends the data to the server, which is sent as an HTTP request.

[1242] Step 3:

[1243] The server analyzes the received data, using a text analysis engine to break down the keywords entered and map them to an appropriate framework (in this case, a SWOT analysis).

[1244] Step 4:

[1245] The server uses internal databases and external APIs (market databases, competitive information, etc.) to collect relevant information, such as market trends related to new product development, competitive information, and data on the company's strengths and weaknesses.

[1246] Step 5:

[1247] Based on the information collected by the server, each element of a SWOT analysis (strengths, weaknesses, opportunities, and threats) is automatically generated. For example, a company's technological capabilities are classified as a "strength" and development costs as a "weakness."

[1248] Step 6:

[1249] The server's emotion engine analyzes the user's operation history and input content to recognize the user's emotions. For example, it determines whether the user is feeling stressed based on the input speed and text content.

[1250] Step 7:

[1251] The SWOT analysis results generated by the server are adjusted based on the analysis results of the emotion engine. For example, if the user is feeling anxious, an encouraging message is added.

[1252] Step 8:

[1253] The server sends the generated SWOT analysis results to the device, which are returned as an HTTP response, including any adjusted information.

[1254] Step 9:

[1255] The terminal displays the SWOT analysis results provided by the server to the user, who can then review the results and make corrections or additions as necessary.

[1256] Step 10:

[1257] The user makes corrections to the SWOT analysis results and sends the corrections back to the server via the terminal.

[1258] Step 11:

[1259] The server receives the modifications and performs the analysis again, updating the SWOT analysis results based on the modified data and generating new analysis results.

[1260] Step 12:

[1261] The server sends the updated SWOT analysis results back to the device, where the emotion engine is reanalyzed and adjusted as necessary.

[1262] Step 13:

[1263] The terminal will then redisplay the updated SWOT analysis results to the user, who can review them and make any necessary adjustments.

[1264] Step 14:

[1265] The server saves the final SWOT analysis results in a database. This data is stored in storage for future reference and reuse in other projects. The sentiment analysis history is also saved.

[1266] Example 2

[1267] 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."

[1268] In conventional business framework analysis systems, the selection of an appropriate framework for keywords entered by the user, followed by the subsequent data collection, organization, and analysis are often done manually, requiring time and effort.In addition, there is a problem in that the user experience is not optimized because the display adjustment according to the user's emotional state is not taken into consideration.

[1269] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving terms related to a business framework to be used, a means for analyzing the received terms and associating them with an appropriate framework, a means for collecting and organizing related information based on the framework, a means for automatically generating framework analysis results based on the collected and organized information, a means for recognizing the user's emotional state, and a means for adjusting display content based on the recognized emotional state. This allows the user to quickly and efficiently receive an appropriate business framework analysis, and further improves the user experience through optimal display according to the user's emotional state.

[1270] A "business framework" refers to a framework or method for systematically analyzing and organizing strategies and plans in business or corporate activities.

[1271] "Terms" refer to keywords and phrases that a user inputs to be used in analyzing the business framework.

[1272] "Means for receiving" refers to the interface or software for importing terms and information entered by the user into the server via the network.

[1273] "Means of analyzing and associating with an appropriate framework" refers to algorithms and modules that use methods such as text analysis based on received terms to select the optimal business framework.

[1274] "Means of collecting and organizing relevant information" refers to systems and software that utilize internal databases and external information sources to collect the data required for the business framework, organize it, and make it usable.

[1275] "Means for automatically generating framework analysis results" refers to programs or algorithms for automatically generating each element of a business framework based on collected and organized information.

[1276] "Means for recognizing the user's emotional state" refers to a system or engine that analyzes the user's input, operation history, biometric information, voice, etc. to evaluate and determine the user's emotions.

[1277] "Means for adjusting displayed content based on the user's perceived emotional state" refers to algorithms or modules that modify or adjust the displayed content or tone in response to the user's emotional state.

[1278] System Overview and Configuration

[1279] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on terms entered by the user and automates advanced analysis in line with that framework. This system is primarily composed of a server and terminals working together. Furthermore, by combining it with an emotion engine, it has the ability to recognize the user's emotional state and adjust the display of analysis results and advice based on that.

[1280] Hardware and software used

[1281] The terminal includes a user interface and a display module.

[1282] The server includes a data analysis engine, a data collection module, a framework generation module, a data storage module, and an emotion engine.

[1283] Use a text analysis engine to analyze the received terms.

[1284] We collect relevant information using internal databases and external APIs.

[1285] The collected data storage stores the collected data.

[1286] Explanation of program processing

[1287] The user inputs terms related to the business framework (e.g., SWOT analysis) through the user interface of the terminal. The input is done using a text box, and by clicking the "Submit" button, this information is sent to the server.

[1288] The server then uses a text analysis engine to analyze the received terms and map them to the appropriate framework, specifically using topic modeling and keyword extraction.

[1289] Next, the server uses the internal database and external APIs (market database, competitive information, etc.) to collect relevant information through the collection module, including the company's strengths and weaknesses, competitive information, market trend data, etc. The collected data is stored in the collected data storage.

[1290] Based on the collected information, the server's framework generation module automatically generates analysis results for the specified framework (e.g., SWOT analysis). For example, in a SWOT analysis, each element (strengths, weaknesses, opportunities, and threats) is generated.

[1291] Furthermore, the server's emotion engine recognizes the user's emotional state through analysis of the user's input, operation history, biometric information, and voice, for example, by analyzing the speed and pattern of text input and the tone of voice.

[1292] Based on the recognized emotional state, the server adjusts the generated framework analysis results and reflects them in the displayed content, which may include adding an encouraging message if the user is feeling stressed.

[1293] Finally, the server sends the generated framework analysis results to the terminal and displays them. The user can check the displayed results and make corrections as necessary. Any corrections are sent back to the server for re-analysis and updated results. The final framework analysis results are saved in a database via the data storage module.

[1294] Examples of concrete examples and prompts

[1295] For example, if a user requests a SWOT analysis on "New Product Development," the system works as follows:

[1296] 1. The user enters "New Product Development" and clicks the submit button.

[1297] 2. The server analyzes the term and determines that a SWOT analysis is appropriate.

[1298] 3. The server collects relevant information (such as the company's technical capabilities and market trends) from internal databases and external APIs.

[1299] 4. The server generates the SWOT analysis and the emotion engine recognizes the user's emotional state.

[1300] 5. The results are sent to the device, and the display content is adjusted depending on the user's emotions. For example, if the user is feeling stressed, an encouraging message will be added: "Developing a new product involves risks, but based on the data you entered, there is a good chance of success."

[1301] Prompt Sentence Examples

[1302] "I'd like to conduct a SWOT analysis of new product development. Gather data on your company's technological capabilities and market trends to identify strengths and opportunities, and add encouraging messages if you feel unsure."

[1303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1304] Step 1: User Input

[1305] The user uses the terminal's user interface to input a business framework (e.g., SWOT analysis) and related terms (e.g., new product development). Input is completed by using the text box and clicking the "Submit" button, which sends the terms and information to the server.

[1306] Step 2: Keyword analysis

[1307] The server receives the received terms as input and analyzes them using a text analysis engine. Specifically, it selects an appropriate business framework through topic modeling and keyword extraction. For example, if the keyword "new product development" is entered, the server analyzes this information and determines that "SWOT analysis" is appropriate. The selected framework is obtained as the output of this analysis.

[1308] Step 3: Data collection

[1309] The server collects relevant information from internal databases and external APIs. The data items required for the selected framework are used as input. Specifically, data such as the company's strengths and weaknesses, competitive information, and market trends is acquired and stored in the designated data storage. This collected data is used as input for the next step.

[1310] Step 4: Framework Generation

[1311] The framework generation module on the server automatically generates framework analysis results based on the collected data. The collected data is used as input, and each element (strengths, weaknesses, opportunities, and threats) is generated as output. For example, in the case of a SWOT analysis, the following elements are generated:

[1312] Strengths: Our own technological capabilities and brand power

[1313] Weaknesses: Development costs, lack of engineers

[1314] Opportunities: Discovery of new market needs, technology trends

[1315] Threats: New products from competitors, changes in laws and regulations

[1316] Step 5: Emotion Recognition

[1317] The server's emotion engine recognizes the user's emotions through input content, operation history, biometric information, and voice analysis. Inputs include the speed and pattern of text input and voice tone, and the output determines the user's emotional state. For example, it determines whether the user is feeling stressed or positive.

[1318] Step 6: View the results

[1319] The server sends the generated framework analysis results to the terminal and reflects them in the display content. The generated framework analysis results and the recognized emotional state are used as input. For example, if the user is feeling stressed, an encouraging message such as "New product development involves risks, but based on the data you entered, there is a good chance of success" is added to the display content.

[1320] Step 7: User modifications and reanalysis

[1321] The user checks the displayed framework analysis results and makes corrections or additions as necessary. The corrected content is used as input and is sent back to the server from the terminal. This corrected content is used as input for the next step.

[1322] Step 8: Reprocess and update results

[1323] The server receives the modifications and performs a re-analysis. The modifications are used as input and the analysis is performed based on the new data. The updated framework analysis results are generated as output, and these results are used as input for the next step. The emotion engine also performs a re-evaluation of the emotions based on the updated data.

[1324] Step 9: View the new results

[1325] The server sends the updated framework analysis results to the terminal and redisplays them. The updated framework analysis results and the new emotional state are used as input. The redisplayed content is adjusted according to the user's emotional state.

[1326] Step 10: Save Data

[1327] The server saves the final framework analysis results in a database. The final analysis results and sentiment analysis history are used as input, and these data are stored in the database as output for future reference.

[1328] (Application example 2)

[1329] 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."

[1330] Modern business environments demand rapid and accurate decision-making, especially in virtual store operations, where rapid response to market trends and changes in the competitive landscape is essential. However, conventional business framework analysis systems lack feedback that takes into account the user's emotional state, which can lead to stress. Furthermore, human error can occur in the selection of business frameworks and the display of analysis results. Therefore, there is a need for a system that recognizes the user's emotional state and provides appropriate feedback to provide a better experience and streamline decision-making.

[1331] 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.

[1332] In this invention, the server includes means for receiving keywords related to a business framework to be used, means for analyzing the received keywords and mapping them to an appropriate framework, means for collecting and organizing related information based on the framework, means for automatically generating framework analysis results based on the collected and organized information, means for displaying the generated framework analysis results, and means for recognizing the user's emotional state and adjusting the result display and feedback content. This enables the user to quickly obtain highly accurate business framework analysis results without feeling stressed.

[1333] A "business framework" is a systematic method or model for analyzing the business environment and market.

[1334] "Keywords" are important words or short phrases related to the selection and analysis of a business framework.

[1335] "Mapping" is the process of matching received keywords to an appropriate business framework.

[1336] "Information collection" is the process of obtaining the necessary data using internal databases or external APIs.

[1337] "Information organization" is the process of structuring collected data based on a business framework and converting it into a format suitable for analysis.

[1338] "Automatic generation" is the process in which the system mechanically generates business framework analysis results based on collected and organized information.

[1339] "Display" is the process of providing the generated business framework analysis results to the user through a user interface.

[1340] "Emotional state recognition" is the process of determining a user's emotions by analyzing the user's input, operation history, biometric information, etc.

[1341] "Adjusting feedback content" is the process of appropriately changing the way analysis results are displayed and advice given based on the user's perceived emotional state.

[1342] System configuration

[1343] This invention is a generative AI system that provides thinking support based on business frameworks, automatically selecting an appropriate business model based on keywords entered by the user and performing advanced data analysis. Furthermore, it incorporates an emotion engine and can adjust the analysis results and feedback content according to the user's emotional state.

[1344] The system consists of the following components:

[1345] 1. User Interface (Terminal)

[1346] Users enter keywords via their mobile device (smartphone or tablet) and the analysis results are displayed.

[1347] 2. Data analysis engine (server)

[1348] The engine performs text analysis and topic modeling to map keywords into appropriate business frameworks.

[1349] 3. Data collection module (server)

[1350] Automatically gather relevant data using internal databases and external APIs (market databases, competitive intelligence, etc.).

[1351] 4. Framework generation module (server)

[1352] Based on the collected information, the specified business framework analysis results are automatically generated.

[1353] 5. Emotion engine (server)

[1354] The system recognizes the user's emotional state based on input content and biometric information obtained from the user interface.

[1355] 6. Display module (terminal)

[1356] The generated framework analysis results are displayed in an easy-to-understand manner to the user, and the content and tone are adjusted as needed.

[1357] 7. Data storage module (server)

[1358] The analysis results and sentiment analysis history are stored in a database for future reference.

[1359] Program processing

[1360] The server's data analysis engine uses Python libraries (e.g., TextBlob) to perform text analysis for keywords. For example, if a user enters keywords such as "new product launch" or "improving customer satisfaction," the engine analyzes them and maps them to an appropriate framework (e.g., SWOT analysis).

[1361] Next, the data collection module obtains relevant information using internal databases or external APIs (e.g., market databases). The obtained information is saved in data storage. The framework generation module analyzes this data and automatically generates the results of a business framework (e.g., SWOT analysis). For example, "strengths" could include "brand power" or "technical ability," while "weaknesses" could include "development costs" or "lack of engineers."

[1362] The emotion engine determines the user's emotional state from the speed and pattern of input, tone of voice, etc. For example, if the user is feeling anxious, it will add an encouraging feedback message along with the analysis results.

[1363] Examples of concrete examples and prompts

[1364] Examples:

[1365] If a user requests a SWOT analysis of a new product launch in a virtual store and feels anxious while entering the information, the emotion engine can analyze the user's anxiety and provide a feedback message such as, "Although launching a new product involves risks, there is a high chance of success if you leverage your company's brand power."

[1366] Example prompt sentence:

[1367] Virtual Product New Launch Market Trends Competitor SWOT Analysis

[1368] In this way, the user can more relaxedly check the business framework analysis results and make decisions efficiently.

[1369] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1370] Step 1:

[1371] The user enters a keyword

[1372] The user inputs keywords related to the business framework (e.g., "launching a new product" or "improving customer satisfaction") into the user interface of the terminal. Once input is complete, the information is sent to the server by clicking the "Submit" button.

[1373] Input: keyword

[1374] Output: Keywords sent to the server

[1375] Step 2:

[1376] The server analyzes the keywords

[1377] The server uses a text analysis engine (e.g., TextBlob) to analyze the received keywords. The text analysis engine performs natural language processing on the received keywords and maps them into an appropriate business framework (e.g., SWOT analysis).

[1378] Input: keyword

[1379] Output: Mapped business framework

[1380] Step 3:

[1381] The server collects relevant information

[1382] The server's data collection module collects relevant information using internal databases and external APIs (market databases, competitive information, etc.). For example, regarding "new product launches," market trends and competitive information are obtained through API calls.

[1383] Input: Business framework and related keywords

[1384] Output: Collected relevant information data

[1385] Step 4:

[1386] Organizing the information collected by the server

[1387] The server organizes the collected data into a certain format, for example, categorizing market trend information and competitor information stored in the database into required categories (e.g., strengths, weaknesses, opportunities, and threats).

[1388] Input: Collected relevant information data

[1389] Output: Organized data

[1390] Step 5:

[1391] The server automatically generates framework analysis results

[1392] The server's framework generation module automatically generates analysis results for a business framework (e.g., SWOT analysis) based on the organized data. For example, "strengths" could include "brand power" and "technical ability," while "weaknesses" could include "development costs" and "lack of engineers."

[1393] Input: Organized data

[1394] Output: Auto-generated framework analysis results

[1395] Step 6:

[1396] The server recognizes the user's emotional state

[1397] The server's emotion engine collects information such as the user's input speed, patterns, and tone of voice to recognize their emotional state. For example, if the user is typing slowly, it will interpret this as anxiety.

[1398] Input: User input, operation history, biometric information, etc.

[1399] Output: Perceived emotional state

[1400] Step 7:

[1401] The server coordinates the results display and feedback

[1402] The server's display module displays the analysis results based on the user's emotional state and adjusts the feedback as needed. For example, if a user feels anxious, the server might add an encouraging message such as, "Launching a new product involves risks, but if you leverage your company's brand power, you have a high chance of success."

[1403] Input: Automatically generated framework analysis results, recognized emotional states

[1404] Output: Analysis results and feedback displayed to the user

[1405] Step 8:

[1406] The user checks and corrects the results

[1407] The user checks the business framework analysis results displayed on the terminal and makes corrections or additions as necessary. Once corrections are complete, the user clicks the "Send" button again to send the information to the server.

[1408] Input: User modifications

[1409] Output: Modifications sent to the server

[1410] Step 9:

[1411] The server re-analyzes the corrected analysis results and updates them.

[1412] The server re-analyzes the received modifications and regenerates the updated business framework analysis results, at which time the emotion engine also runs again to generate analysis results based on the latest emotional state.

[1413] Input: Modified Business Framework analysis results

[1414] Output: Re-analysis and updated analysis results

[1415] Step 10:

[1416] Server displays updated results

[1417] The server's display module displays the updated business framework analysis results to the user again, and updates the emotion-based feedback as needed.

[1418] Input: Updated business framework analysis results, latest emotional state

[1419] Output: Analysis results and feedback that are displayed back to the user

[1420] Step 11:

[1421] The server stores the data

[1422] The server's data storage module stores the final business framework analysis results and the history of sentiment analysis in a database, which can be used for future analysis.

[1423] Input: Final business framework analysis results, sentiment analysis history

[1424] Output: Data stored in the database

[1425] 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.

[1426] 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.

[1427] 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.

[1428] [Fourth embodiment]

[1429] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1430] 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.

[1431] 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).

[1432] 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.

[1433] 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.

[1434] 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).

[1435] 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.

[1436] 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.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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."

[1442] System Overview

[1443] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on keywords entered by the user and automates advanced analysis based on that framework. Major frameworks include PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop.

[1444] System configuration

[1445] The system mainly consists of the following components:

[1446] 1. User Interface (Terminal)

[1447] 2. Data analysis engine (server)

[1448] 3. Data collection module (server)

[1449] 4. Framework generation module (server)

[1450] 5. Display module (terminal)

[1451] 6. Data storage module (server)

[1452] Operation of the invention

[1453] 1. User Input

[1454] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the user interface of the terminal. The input is made through a text box, and by clicking the "Submit" button, the information is sent to the server.

[1455] 2. Keyword analysis

[1456] The server analyzes the received keywords and maps them to the appropriate framework. This analysis uses a text analysis engine to perform topic modeling and keyword extraction. For example, if the inputs are "SWOT analysis" and "new product development," the analysis engine will identify the data items required for a SWOT analysis.

[1457] 3. Data Collection

[1458] The server collects relevant information using internal databases and external APIs (market databases, competitive information, etc.), including data on the company's strengths and weaknesses, competitive information, and market trends. The collected data is stored in a designated data storage device.

[1459] 4. Framework Generation

[1460] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of a SWOT analysis, the following elements are automatically generated:

[1461] Strengths: Our own technological capabilities and brand power

[1462] Weaknesses: Development costs, lack of engineers

[1463] Opportunities: Discovery of new market needs, technology trends

[1464] Threats: New products from competitors, changes in laws and regulations

[1465] 5. Display results

[1466] The display module of the terminal displays the framework analysis results sent from the server to the user. The user interface is integrated with a text editor for review and correction. The user can check the results and make any necessary corrections or additions.

[1467] 6. User Modifications and Re-analysis

[1468] When the user modifies the framework analysis results and clicks the "Reanalyze" button, the modifications are sent to the server again, which then reanalyzes the modified data, regenerates the updated framework analysis results, and displays them to the user again.

[1469] 7. Data Storage

[1470] The server stores the final framework analysis results in a database using the data storage module, allowing for future reuse or reference in other projects.

[1471] The overall processing flow is explained with concrete examples. For example, if a user requests a SWOT analysis for new product development, the system receives input such as "SWOT analysis, new product development." Based on this information, it collects appropriate market data and competitive information, automatically generates strengths, weaknesses, opportunities, and threats, and presents them to the user. If the user makes corrections, the system reanalyzes the results and saves them for future reference.

[1472] This system helps business people to conduct high-quality framework analysis quickly and efficiently.

[1473] The processing flow will be explained below.

[1474] Step 1:

[1475] Enter keywords related to the business framework you want to use. For example, enter "SWOT analysis, new product development."

[1476] Step 2:

[1477] The terminal receives the user's input and sends it to the server. The input is sent to the server as text data using an HTTP request.

[1478] Step 3:

[1479] The server analyzes the data received from the user. It uses a text analysis engine to break down keywords and map them to an appropriate framework. In this example, it selects the SWOT analysis template.

[1480] Step 4:

[1481] The server uses internal databases and external APIs (market databases, competitive information, etc.) to collect information related to the entered keywords. For example, it collects market trends related to new product development, competitive information, and data on the company's strengths and weaknesses.

[1482] Step 5:

[1483] Based on the information collected by the server, each element of the SWOT analysis is generated, i.e., strengths, weaknesses, opportunities, and threats are automatically classified and organized.

[1484] Step 6:

[1485] The server sends the generated SWOT analysis results to the device, which returns the information as an HTTP response.

[1486] Step 7:

[1487] The terminal displays the SWOT analysis results provided by the server to the user. The visualized results are displayed in text boxes and graph format, allowing the user to check and modify them.

[1488] Step 8:

[1489] The user can check the displayed SWOT analysis results and make corrections or additions as necessary. For example, a new "market uncertainty" can be added to the "weaknesses" section.

[1490] Step 9:

[1491] The user sends the corrected content back to the server from the terminal, along with a request for reanalysis.

[1492] Step 10:

[1493] The server re-analyzes the modifications and regenerates an updated SWOT analysis, integrating the previous analysis data with the new input data.

[1494] Step 11:

[1495] The server sends the updated SWOT analysis results back to the terminal, and the new analysis results are returned to the terminal as an HTTP response for redisplay.

[1496] Step 12:

[1497] The terminal will then redisplay the updated SWOT analysis results to the user, who can review them and make any necessary adjustments.

[1498] Step 13:

[1499] The server saves the final SWOT analysis results in a database, which is then stored for future reference or reuse in other projects.

[1500] Example 1

[1501] 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."

[1502] Conventional analysis systems using business frameworks have limited functionality for automatically generating analysis results based on user input, and processes such as collecting related information and reanalyzing analysis results are often performed manually. Furthermore, the functionality for correcting analysis results is also extremely limited, limiting the flexibility of users. This makes it difficult to perform high-quality business framework analysis quickly and efficiently.

[1503] 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.

[1504] In this invention, the server includes means for receiving terms related to an analysis framework to be used, means for analyzing the received terms and mapping them to an appropriate framework, means for collecting and storing related information based on the framework, means for automatically generating framework analysis results based on the collected and stored information, means for displaying the generated framework analysis results, means for allowing a user to edit the displayed analysis results, means for reanalyzing the edited analysis results and displaying the updated results, and means for saving the final analysis results, thereby enabling a user to quickly and efficiently perform high-quality business framework analysis.

[1505] An "analytical framework" is a structured method or model for evaluating and planning a business or strategy. Examples include SWOT analysis, PEST analysis, and Five Forces analysis.

[1506] "Related terms" refer to keywords or topics related to the subject or theme that a user wants to evaluate using the analytical framework, such as "new product development" or "market research."

[1507] "Means for receiving" refers to the mechanism by which the system receives related terms and analysis frameworks entered by the user. Specifically, this includes the text box and submit button in the user interface.

[1508] "Means for parsing and mapping to an appropriate framework" refers to techniques and methods for analyzing received related terms and selecting the most appropriate analytical framework, including text analysis engines and topic modeling techniques.

[1509] "Means for collecting and storing relevant information" refers to the process for collecting the necessary information from internal databases and external APIs and storing it in data storage based on the analytical framework identified through the analysis.

[1510] "Means for automatically generating framework analysis results" refers to technologies or algorithms that automatically generate results for a specified analysis framework based on collected and stored information, such as generative AI models.

[1511] The "display means" refers to a technique or interface for visually presenting the generated framework analysis results to the user. Specifically, this includes a display or a display module of a user interface.

[1512] "Editing means" refers to an interface or function that allows the user to modify or add to the displayed framework analysis results. This includes a text editor and a modification button.

[1513] "Means for reanalyzing and displaying updated results" refers to the process or technology for sending the framework analysis results edited by the user back to the server, performing analysis based on the modifications, and displaying the updated results to the user again.

[1514] "Means for storing the final analysis results" refers to the technology or method for storing the finalized framework analysis results in a storage medium such as a database so that they can be reused or referenced later.

[1515] This invention is a generative AI system that provides thinking support based on business frameworks. The system selects an appropriate framework based on keywords entered by the user and automates advanced analysis based on that framework. Major frameworks include PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop.

[1516] System configuration

[1517] The system mainly consists of the following components:

[1518] 1. User Interface (Terminal)

[1519] 2. Data analysis engine (server)

[1520] 3. Data collection module (server)

[1521] 4. Framework generation module (server)

[1522] 5. Display module (terminal)

[1523] 6. Data storage module (server)

[1524] The specific operation of the system is as follows.

[1525] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the terminal's user interface. This input is done through a text box and completed by clicking the "Submit" button. Once the input is submitted, the information is sent to the server.

[1526] The server uses a text analysis engine (e.g., NLTK, SpaCy) to analyze the received keywords. During this analysis, topic modeling and keyword extraction techniques are used to map the input keywords to an appropriate framework. For example, if the keywords "SWOT analysis" and "new product development" are input, the server will identify the data items required for a SWOT analysis.

[1527] Next, the server collects relevant information. This is done using an internal database or external API (e.g., market database, competitive information API). The collected data is stored in data storage (e.g., AWS S3, Google Cloud Storage). For example, data on the company's technological capabilities and brand power, information on competitors, and market trends are collected.

[1528] Next, the server's framework generation module automatically generates the specified framework analysis results based on the collected information. During this process, a generative AI model (e.g., OpenAI GPT-3) is used to perform advanced analysis. For example, in a SWOT analysis, the following elements are automatically generated:

[1529] Strengths: Our own technological capabilities and brand power

[1530] Weaknesses: Development costs, lack of engineers

[1531] Opportunities: Discovery of new market needs, technology trends

[1532] Threats: New products from competitors, changes in laws and regulations

[1533] The generated framework analysis results are sent to the terminal's display module and displayed visually to the user. This display has an integrated text editor function for review and correction. The user can check the results and make any necessary corrections or additions.

[1534] If the user makes any modifications, the modifications are sent to the server again. The server then re-analyzes the modified data and regenerates the updated framework analysis results. This allows the latest analysis results to be displayed to the user again.

[1535] The final framework analysis results are stored in a database (e.g., MongoDB, MySQL, etc.) using the server's data storage module, allowing for future reuse or reference in other projects.

[1536] Specific examples

[1537] When conducting a SWOT analysis for a new product development, the system works as follows:

[1538] 1. The user enters "SWOT analysis, new product development" and clicks the "Submit" button.

[1539] 2. The server analyzes the received keywords and maps them to a SWOT analysis.

[1540] 3. The server collects relevant information from internal databases and external APIs and stores it in data storage.

[1541] 4. The server automatically generates the analysis results using an AI model.

[1542] 5. The device displays the analysis results so that the user can check and correct them.

[1543] 6. After the user makes any corrections, the server re-analyzes the data and displays the updated information.

[1544] 7. The server stores the final analysis results in a database.

[1545] An example prompt is:

[1546] SWOT analysis, new product development

[1547] This system enables users to perform high-quality business framework analysis quickly and efficiently.

[1548] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1549] Step 1:

[1550] The user inputs keywords related to the business framework into the user interface of the terminal. Specifically, the user inputs "SWOT analysis" and "new product development" into the text boxes and clicks the "Submit" button.

[1551] input:

[1552] "Business framework" and "related keywords" entered by the user.

[1553] output:

[1554] Sending information from the device to the server.

[1555] Step 2:

[1556] The server analyzes the received keywords using a text analysis engine (e.g., NLTK, SpaCy). It then uses topic modeling and keyword extraction techniques to map the input content into an appropriate framework. Specifically, it analyzes the keywords "SWOT analysis" and "new product development" to identify the data items required for a SWOT analysis.

[1557] input:

[1558] "Business framework" and "related keywords" sent from the device.

[1559] output:

[1560] Analysis results from the text analysis engine.

[1561] Step 3:

[1562] The server collects relevant information using internal databases and external APIs (e.g., market databases, competitive intelligence APIs). The collected data is stored in data storage (e.g., AWS S3, Google Cloud Storage). Specifically, it collects information such as the company's strengths and weaknesses, competitive intelligence, and market trends.

[1563] input:

[1564] Data items identified by the analysis results.

[1565] output:

[1566] Relevant data collected from databases and APIs.

[1567] Step 4:

[1568] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. This process uses a generative AI model (e.g., OpenAI GPT-3). Specifically, it generates each element of the SWOT analysis (strengths, weaknesses, opportunities, and threats).

[1569] input:

[1570] Relevant data collected.

[1571] output:

[1572] Framework analysis results using a generative AI model.

[1573] Step 5:

[1574] The display module of the terminal displays the framework analysis results sent from the server to the user. The user interface is integrated with a text editor function for reviewing and modifying the results.

[1575] input:

[1576] Framework analysis results sent from the server.

[1577] output:

[1578] The analysis results that are displayed to the user.

[1579] Step 6:

[1580] The user modifies the framework analysis results and clicks the "Reanalyze" button. The modified content is sent to the server again. Specifically, the user edits the results in the text editor of the user interface and clicks the "Reanalyze" button.

[1581] input:

[1582] Analysis results modified by the user.

[1583] output:

[1584] The corrected data is sent to the server.

[1585] Step 7:

[1586] The server then re-analyzes the modified data and regenerates the updated framework analysis results, which are again displayed to the user.

[1587] input:

[1588] Corrected data.

[1589] output:

[1590] Updated framework analysis results after reanalysis.

[1591] Step 8:

[1592] The server stores the final framework analysis results in a database using the data storage module, allowing for future reuse or reference in other projects.

[1593] input:

[1594] Final framework analysis results.

[1595] output:

[1596] Analysis results stored in a database.

[1597] (Application example 1)

[1598] 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."

[1599] Real-time data collection and analysis is required for production processes within factories. However, in conventional systems, data collection and analysis is often done manually, which requires time and effort. Another problem is the lack of an accurate analytical framework for making quick decisions. This situation leads to reduced production efficiency and inefficient use of resources. Therefore, a system is needed to analyze factory data in real time and optimize production processes.

[1600] 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.

[1601] In this invention, the server includes means for receiving keywords related to a business framework to be used, means for analyzing the received keywords and mapping them to an appropriate framework, means for collecting and organizing related information based on the framework, means for automatically generating framework analysis results based on the collected and organized information, means for displaying the generated framework analysis results, and means for performing analysis in real time using data within the factory to optimize the production process. This enables rapid and accurate framework analysis in the factory production process, improving production efficiency and optimizing resource utilization.

[1602] A "business framework" is a framework for systematically planning and analyzing a business.

[1603] "Keywords" are specific words or phrases that users enter when conducting framework analysis.

[1604] "Mapping" is the process of associating received keywords with the appropriate business framework.

[1605] "Relevant information" refers to data and knowledge required to perform framework analysis.

[1606] "Automatic generation" is the process by which the system automatically creates framework analysis results based on collected and organized information.

[1607] "Display" refers to visually showing the generated framework analysis results to the user.

[1608] "Factory data" refers to information about production processes collected within the factory from sensors, cameras, etc.

[1609] "Real-time" means that processing and analysis occurs immediately, without delay.

[1610] A "production process" is the entire manufacturing process of a product in a factory.

[1611] This invention is a system designed to optimize production processes within a factory. The system consists of the following main components:

[1612] 1. Overall structure

[1613] This system consists of a user interface (terminal), a data analysis engine (server), a data collection module (server), a framework generation module (server), a display module (terminal), and a data storage module (server).The overall purpose is to analyze data in the factory in real time and optimize the production process.

[1614] 2. User Interface

[1615] The user interface (UI) operates via a smartphone, tablet, or factory robot display, and allows users to enter keywords related to the business framework they wish to use, using a text box and a submit button.

[1616] 3. Data analysis engine

[1617] The server-based data analysis engine analyzes keywords using text analysis engines and generative AI models (e.g., TensorFlow, PyTorch). Based on the analysis results, the keywords are mapped to the appropriate business framework.

[1618] 4. Data Collection Module

[1619] The data collection module on the server collects real-time data from an internal database and external APIs (e.g., sensor APIs and camera APIs). This data includes information from temperature sensors, pressure sensors, and cameras. The collected data is stored in a database.

[1620] 5. Framework Generation Module

[1621] The framework generation module automatically generates analysis results for a specified framework based on the collected information. A specific example of a framework is the PDCA cycle. For example, the following PDCA cycle is generated:

[1622] Plan: Introducing new materials

[1623] Do: Test production

[1624] Check: Quality test

[1625] Improvement (Act): Correction of production process

[1626] 6. Display module

[1627] The display module displays the framework analysis results sent from the server to the user, including real-time updates and a text editor for the user to review and modify the results, which are then re-analyzed and the updated analysis results are displayed.

[1628] 7. Data storage module

[1629] The final framework analysis results are stored in a database in the server's data storage module, allowing for future reference and reuse in other projects.

[1630] Examples of concrete examples and prompts

[1631] As a concrete example, let's say there is a factory that is planning to introduce a new material. This factory aims to improve the quality of its products. The user enters the keywords "PDCA cycle" and "introduction of new material" into the interface. Based on this input, the system collects appropriate data and generates the following PDCA cycle.

[1632] Example prompt sentence:

[1633] Based on the "PDCA cycle," please collect in-factory data related to "introduction of new materials" and propose the next planning stage.

[1634] This system allows users to quickly and reliably obtain accurate analysis results to optimize production processes.

[1635] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1636] Step 1:

[1637] The user enters keywords related to the business framework they want to use into the user interface. Input is done using a text box and a submit button, and by clicking the submit button, the information is sent to the server.

[1638] Input: Keywords such as "PDCA cycle" and "introduction of new materials"

[1639] Output: The keyword is sent to the server

[1640] Step 2:

[1641] The server uses a text analysis engine to analyze the received keywords, primarily leveraging generative AI models (e.g., TensorFlow, PyTorch) for topic modeling and keyword extraction.

[1642] Input: The keyword submitted in step 1

[1643] Output: Selection of an appropriate business framework

[1644] Step 3:

[1645] The server's data collection module collects real-time data from an internal database and external APIs (sensor API, camera API, etc.), and the collected data is stored in a designated data storage.

[1646] Input: Selection result of appropriate business framework

[1647] Output: Real-time data collected

[1648] Step 4:

[1649] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of the PDCA cycle, the plan, do, verify, and improve phases are automatically generated.

[1650] Input: Real-time data collected

[1651] Output: Auto-generated framework analysis results

[1652] Step 5:

[1653] The display module of the terminal displays the framework analysis results sent from the server to the user, who can check the results, review and modify them.

[1654] Input: Auto-generated framework analysis results

[1655] Output: Show framework analysis results

[1656] Step 6:

[1657] When the user modifies the framework analysis results and clicks the "Reanalyze" button, the modifications are sent to the server again. The server then reanalyzes the modified data and regenerates the updated framework analysis results.

[1658] Input: User-modified framework analysis results

[1659] Output: Re-analyzed and re-generated framework analysis results

[1660] Step 7:

[1661] The final framework analysis results are stored in a database using the server's data storage module, allowing them to be reused later or used as reference for other projects.

[1662] Input: Re-analyzed and re-generated framework analysis results

[1663] Output: Saved framework analysis results

[1664] 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.

[1665] System Overview

[1666] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on keywords entered by the user and automates advanced analysis in line with that framework. Furthermore, by combining it with an emotion engine, it has the ability to recognize the user's emotional state and adjust the display of analysis results and advice based on that.

[1667] System configuration

[1668] The system mainly consists of the following components:

[1669] 1. User Interface (Terminal)

[1670] 2. Data analysis engine (server)

[1671] 3. Data collection module (server)

[1672] 4. Framework generation module (server)

[1673] 5. Display module (terminal)

[1674] 6. Data storage module (server)

[1675] 7. Emotion engine (server)

[1676] Operation of the invention

[1677] 1. User Input

[1678] The user inputs a business framework (e.g., SWOT analysis) and related keywords (e.g., new product development) into the user interface of the terminal. The input is made through a text box, and by clicking the "Submit" button, the information is sent to the server.

[1679] 2. Keyword analysis

[1680] The server analyzes the received keywords and maps them to the appropriate framework. This analysis uses a text analysis engine to perform topic modeling and keyword extraction. For example, if the inputs are "SWOT analysis" and "new product development," the analysis engine will identify the data items required for a SWOT analysis.

[1681] 3. Data Collection

[1682] The server collects relevant information using internal databases and external APIs (market databases, competitive information, etc.), including data on the company's strengths and weaknesses, competitive information, and market trends. The collected data is stored in a designated data storage device.

[1683] 4. Framework Generation

[1684] The server's framework generation module automatically generates the specified framework analysis results based on the collected information. For example, in the case of a SWOT analysis, each element (strengths, weaknesses, opportunities, and threats) is automatically generated:

[1685] Strengths: Our own technological capabilities and brand power

[1686] Weaknesses: Development costs, lack of engineers

[1687] Opportunities: Discovery of new market needs, technology trends

[1688] Threats: New products from competitors, changes in laws and regulations

[1689] 5. Emotion recognition

[1690] The server's emotion engine recognizes the user's emotions based on the user's input, operation history, and even biometric and voice analysis acquired through the user interface. For example, it analyzes the speed and pattern of text input, the tone of voice, and other factors to determine whether the user is feeling stressed or positive.

[1691] 6. Results display

[1692] The server sends the generated SWOT analysis results to the device, and the emotion engine adjusts the content and tone of the display based on the user's emotional state. For example, if the user is feeling stressed, an encouraging message is added to the analysis results.

[1693] 7. User Modifications and Re-analysis

[1694] The user checks the displayed SWOT analysis results and makes corrections or additions as necessary. The corrections are then sent back to the server from the device.

[1695] 8. Reanalysis and result updates

[1696] The server re-analyzes the changes and regenerates the updated SWOT analysis results, at which point the emotion engine also performs emotion analysis again based on the updated data.

[1697] 9. Displaying new results

[1698] The server sends the updated SWOT analysis results to the device and redisplays them, adjusting them according to the user's emotional state.

[1699] 10. Data Retention

[1700] The server stores the final SWOT analysis results in a database, and also stores the sentiment analysis history for future reference.

[1701] Specific examples

[1702] For example, suppose a user requests a SWOT analysis of a new product development and expresses anxiety while entering data. In this case, the emotion engine will detect this anxiety and add an encouraging message to the results display, such as, "New product development involves risks, but based on the data you entered, there is a good chance of success." In this way, the engine helps users feel at ease when using the analysis results.

[1703] This system not only helps business people to efficiently perform high-quality framework analysis, but also provides customization according to the user's emotional state, thereby achieving a better user experience.

[1704] The processing flow will be explained below.

[1705] Step 1:

[1706] The user enters the business framework (e.g., SWOT analysis) they want to use and related keywords (e.g., new product development) into the user interface of the device. For example, they enter "SWOT analysis" and "new product development" in the text boxes and click the "Submit" button.

[1707] Step 2:

[1708] The device receives the user's input and sends the data to the server, which is sent as an HTTP request.

[1709] Step 3:

[1710] The server analyzes the received data, using a text analysis engine to break down the keywords entered and map them to an appropriate framework (in this case, a SWOT analysis).

[1711] Step 4:

[1712] The server uses internal databases and external APIs (market databases, competitive information, etc.) to collect relevant information, such as market trends related to new product development, competitive information, and data on the company's strengths and weaknesses.

[1713] Step 5:

[1714] Based on the information collected by the server, each element of a SWOT analysis (strengths, weaknesses, opportunities, and threats) is automatically generated. For example, a company's technological capabilities are classified as a "strength" and development costs as a "weakness."

[1715] Step 6:

[1716] The server's emotion engine analyzes the user's operation history and input content to recognize the user's emotions. For example, it determines whether the user is feeling stressed based on the input speed and text content.

[1717] Step 7:

[1718] The SWOT analysis results generated by the server are adjusted based on the analysis results of the emotion engine. For example, if the user is feeling anxious, an encouraging message is added.

[1719] Step 8:

[1720] The server sends the generated SWOT analysis results to the device, which are returned as an HTTP response, including any adjusted information.

[1721] Step 9:

[1722] The terminal displays the SWOT analysis results provided by the server to the user, who can then review the results and make corrections or additions as necessary.

[1723] Step 10:

[1724] The user makes corrections to the SWOT analysis results and sends the corrections back to the server via the terminal.

[1725] Step 11:

[1726] The server receives the modifications and performs the analysis again, updating the SWOT analysis results based on the modified data and generating new analysis results.

[1727] Step 12:

[1728] The server sends the updated SWOT analysis results back to the device, where the emotion engine is reanalyzed and adjusted as necessary.

[1729] Step 13:

[1730] The terminal will then redisplay the updated SWOT analysis results to the user, who can review them and make any necessary adjustments.

[1731] Step 14:

[1732] The server saves the final SWOT analysis results in a database. This data is stored in storage for future reference and reuse in other projects. The sentiment analysis history is also saved.

[1733] Example 2

[1734] 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."

[1735] In conventional business framework analysis systems, the selection of an appropriate framework for keywords entered by the user, followed by the subsequent data collection, organization, and analysis are often done manually, requiring time and effort.In addition, there is a problem in that the user experience is not optimized because the display adjustment according to the user's emotional state is not taken into consideration.

[1736] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving terms related to a business framework to be used, a means for analyzing the received terms and associating them with an appropriate framework, a means for collecting and organizing related information based on the framework, a means for automatically generating framework analysis results based on the collected and organized information, a means for recognizing the user's emotional state, and a means for adjusting display content based on the recognized emotional state. This allows the user to quickly and efficiently receive an appropriate business framework analysis, and further improves the user experience through optimal display according to the user's emotional state.

[1737] A "business framework" refers to a framework or method for systematically analyzing and organizing strategies and plans in business or corporate activities.

[1738] "Terms" refer to keywords and phrases that a user inputs to be used in analyzing the business framework.

[1739] "Means for receiving" refers to the interface or software for importing terms and information entered by the user into the server via the network.

[1740] "Means of analyzing and associating with an appropriate framework" refers to algorithms and modules that use methods such as text analysis based on received terms to select the optimal business framework.

[1741] "Means of collecting and organizing relevant information" refers to systems and software that utilize internal databases and external information sources to collect the data required for the business framework, organize it, and make it usable.

[1742] "Means for automatically generating framework analysis results" refers to programs or algorithms for automatically generating each element of a business framework based on collected and organized information.

[1743] "Means for recognizing the user's emotional state" refers to a system or engine that analyzes the user's input, operation history, biometric information, voice, etc. to evaluate and determine the user's emotions.

[1744] "Means for adjusting displayed content based on the user's perceived emotional state" refers to algorithms or modules that modify or adjust the displayed content or tone in response to the user's emotional state.

[1745] System Overview and Configuration

[1746] This invention is a generative AI system that provides thinking support based on business frameworks. It selects an appropriate framework based on terms entered by the user and automates advanced analysis in line with that framework. This system is primarily composed of a server and terminals working together. Furthermore, by combining it with an emotion engine, it has the ability to recognize the user's emotional state and adjust the display of analysis results and advice based on that.

[1747] Hardware and software used

[1748] The terminal includes a user interface and a display module.

[1749] The server includes a data analysis engine, a data collection module, a framework generation module, a data storage module, and an emotion engine.

[1750] Use a text analysis engine to analyze the received terms.

[1751] We collect relevant information using internal databases and external APIs.

[1752] The collected data storage stores the collected data.

[1753] Explanation of program processing

[1754] The user inputs terms related to the business framework (e.g., SWOT analysis) through the user interface of the terminal. The input is done using a text box, and by clicking the "Submit" button, this information is sent to the server.

[1755] The server then uses a text analysis engine to analyze the received terms and map them to the appropriate framework, specifically using topic modeling and keyword extraction.

[1756] Next, the server uses the internal database and external APIs (market database, competitive information, etc.) to collect relevant information through the collection module, including the company's strengths and weaknesses, competitive information, market trend data, etc. The collected data is stored in the collected data storage.

[1757] Based on the collected information, the server's framework generation module automatically generates analysis results for the specified framework (e.g., SWOT analysis). For example, in a SWOT analysis, each element (strengths, weaknesses, opportunities, and threats) is generated.

[1758] Furthermore, the server's emotion engine recognizes the user's emotional state through analysis of the user's input, operation history, biometric information, and voice, for example, by analyzing the speed and pattern of text input and the tone of voice.

[1759] Based on the recognized emotional state, the server adjusts the generated framework analysis results and reflects them in the displayed content, which may include adding an encouraging message if the user is feeling stressed.

[1760] Finally, the server sends the generated framework analysis results to the terminal and displays them. The user can check the displayed results and make corrections as necessary. Any corrections are sent back to the server for re-analysis and updated results. The final framework analysis results are saved in a database via the data storage module.

[1761] Examples of concrete examples and prompts

[1762] For example, if a user requests a SWOT analysis on "New Product Development," the system works as follows:

[1763] 1. The user enters "New Product Development" and clicks the submit button.

[1764] 2. The server analyzes the term and determines that a SWOT analysis is appropriate.

[1765] 3. The server collects relevant information (such as the company's technical capabilities and market trends) from internal databases and external APIs.

[1766] 4. The server generates the SWOT analysis and the emotion engine recognizes the user's emotional state.

[1767] 5. The results are sent to the device, and the display content is adjusted depending on the user's emotions. For example, if the user is feeling stressed, an encouraging message will be added: "Developing a new product involves risks, but based on the data you entered, there is a good chance of success."

[1768] Prompt Sentence Examples

[1769] "I'd like to conduct a SWOT analysis of new product development. Gather data on your company's technological capabilities and market trends to identify strengths and opportunities, and add encouraging messages if you feel unsure."

[1770] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1771] Step 1: User Input

[1772] The user uses the terminal's user interface to input a business framework (e.g., SWOT analysis) and related terms (e.g., new product development). Input is completed by using the text box and clicking the "Submit" button, which sends the terms and information to the server.

[1773] Step 2: Keyword analysis

[1774] The server receives the received terms as input and analyzes them using a text analysis engine. Specifically, it selects an appropriate business framework through topic modeling and keyword extraction. For example, if the keyword "new product development" is entered, the server analyzes this information and determines that "SWOT analysis" is appropriate. The selected framework is obtained as the output of this analysis.

[1775] Step 3: Data collection

[1776] The server collects relevant information from internal databases and external APIs. The data items required for the selected framework are used as input. Specifically, data such as the company's strengths and weaknesses, competitive information, and market trends is acquired and stored in the designated data storage. This collected data is used as input for the next step.

[1777] Step 4: Framework Generation

[1778] The framework generation module on the server automatically generates framework analysis results based on the collected data. The collected data is used as input, and each element (strengths, weaknesses, opportunities, and threats) is generated as output. For example, in the case of a SWOT analysis, the following elements are generated:

[1779] Strengths: Our own technological capabilities and brand power

[1780] Weaknesses: Development costs, lack of engineers

[1781] Opportunities: Discovery of new market needs, technology trends

[1782] Threats: New products from competitors, changes in laws and regulations

[1783] Step 5: Emotion Recognition

[1784] The server's emotion engine recognizes the user's emotions through input content, operation history, biometric information, and voice analysis. Inputs include the speed and pattern of text input and voice tone, and the output determines the user's emotional state. For example, it determines whether the user is feeling stressed or positive.

[1785] Step 6: View the results

[1786] The server sends the generated framework analysis results to the terminal and reflects them in the display content. The generated framework analysis results and the recognized emotional state are used as input. For example, if the user is feeling stressed, an encouraging message such as "New product development involves risks, but based on the data you entered, there is a good chance of success" is added to the display content.

[1787] Step 7: User modifications and reanalysis

[1788] The user checks the displayed framework analysis results and makes corrections or additions as necessary. The corrected content is used as input and is sent back to the server from the terminal. This corrected content is used as input for the next step.

[1789] Step 8: Reprocess and update results

[1790] The server receives the modifications and performs a re-analysis. The modifications are used as input and the analysis is performed based on the new data. The updated framework analysis results are generated as output, and these results are used as input for the next step. The emotion engine also performs a re-evaluation of the emotions based on the updated data.

[1791] Step 9: View the new results

[1792] The server sends the updated framework analysis results to the terminal and redisplays them. The updated framework analysis results and the new emotional state are used as input. The redisplayed content is adjusted according to the user's emotional state.

[1793] Step 10: Save Data

[1794] The server saves the final framework analysis results in a database. The final analysis results and sentiment analysis history are used as input, and these data are stored in the database as output for future reference.

[1795] (Application example 2)

[1796] 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."

[1797] Modern business environments demand rapid and accurate decision-making, especially in virtual store operations, where rapid response to market trends and changes in the competitive landscape is essential. However, conventional business framework analysis systems lack feedback that takes into account the user's emotional state, which can lead to stress. Furthermore, human error can occur in the selection of business frameworks and the display of analysis results. Therefore, there is a need for a system that recognizes the user's emotional state and provides appropriate feedback to provide a better experience and streamline decision-making.

[1798] 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.

[1799] In this invention, the server includes means for receiving keywords related to a business framework to be used, means for analyzing the received keywords and mapping them to an appropriate framework, means for collecting and organizing related information based on the framework, means for automatically generating framework analysis results based on the collected and organized information, means for displaying the generated framework analysis results, and means for recognizing the user's emotional state and adjusting the result display and feedback content. This enables the user to quickly obtain highly accurate business framework analysis results without feeling stressed.

[1800] A "business framework" is a systematic method or model for analyzing the business environment and market.

[1801] "Keywords" are important words or short phrases related to the selection and analysis of a business framework.

[1802] "Mapping" is the process of matching received keywords to an appropriate business framework.

[1803] "Information collection" is the process of obtaining the necessary data using internal databases or external APIs.

[1804] "Information organization" is the process of structuring collected data based on a business framework and converting it into a format suitable for analysis.

[1805] "Automatic generation" is the process in which the system mechanically generates business framework analysis results based on collected and organized information.

[1806] "Display" is the process of providing the generated business framework analysis results to the user through a user interface.

[1807] "Emotional state recognition" is the process of determining a user's emotions by analyzing the user's input, operation history, biometric information, etc.

[1808] "Adjusting feedback content" is the process of appropriately changing the way analysis results are displayed and advice given based on the user's perceived emotional state.

[1809] System configuration

[1810] This invention is a generative AI system that provides thinking support based on business frameworks, automatically selecting an appropriate business model based on keywords entered by the user and performing advanced data analysis. Furthermore, it incorporates an emotion engine and can adjust the analysis results and feedback content according to the user's emotional state.

[1811] The system consists of the following components:

[1812] 1. User Interface (Terminal)

[1813] Users enter keywords via their mobile device (smartphone or tablet) and the analysis results are displayed.

[1814] 2. Data analysis engine (server)

[1815] The engine performs text analysis and topic modeling to map keywords into appropriate business frameworks.

[1816] 3. Data collection module (server)

[1817] Automatically gather relevant data using internal databases and external APIs (market databases, competitive intelligence, etc.).

[1818] 4. Framework generation module (server)

[1819] Based on the collected information, the specified business framework analysis results are automatically generated.

[1820] 5. Emotion engine (server)

[1821] The system recognizes the user's emotional state based on input content and biometric information obtained from the user interface.

[1822] 6. Display module (terminal)

[1823] The generated framework analysis results are displayed in an easy-to-understand manner to the user, and the content and tone are adjusted as needed.

[1824] 7. Data storage module (server)

[1825] The analysis results and sentiment analysis history are stored in a database for future reference.

[1826] Program processing

[1827] The server's data analysis engine uses Python libraries (e.g., TextBlob) to perform text analysis for keywords. For example, if a user enters keywords such as "new product launch" or "improving customer satisfaction," the engine analyzes them and maps them to an appropriate framework (e.g., SWOT analysis).

[1828] Next, the data collection module obtains relevant information using internal databases or external APIs (e.g., market databases). The obtained information is saved in data storage. The framework generation module analyzes this data and automatically generates the results of a business framework (e.g., SWOT analysis). For example, "strengths" could include "brand power" or "technical ability," while "weaknesses" could include "development costs" or "lack of engineers."

[1829] The emotion engine determines the user's emotional state from the speed and pattern of input, tone of voice, etc. For example, if the user is feeling anxious, it will add an encouraging feedback message along with the analysis results.

[1830] Examples of concrete examples and prompts

[1831] Examples:

[1832] If a user requests a SWOT analysis of a new product launch in a virtual store and feels anxious while entering the information, the emotion engine can analyze the user's anxiety and provide a feedback message such as, "Although launching a new product involves risks, there is a high chance of success if you leverage your company's brand power."

[1833] Example prompt sentence:

[1834] Virtual Product New Launch Market Trends Competitor SWOT Analysis

[1835] In this way, the user can more relaxedly check the business framework analysis results and make decisions efficiently.

[1836] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1837] Step 1:

[1838] The user enters a keyword

[1839] The user inputs keywords related to the business framework (e.g., "launching a new product" or "improving customer satisfaction") into the user interface of the terminal. Once input is complete, the information is sent to the server by clicking the "Submit" button.

[1840] Input: keyword

[1841] Output: Keywords sent to the server

[1842] Step 2:

[1843] The server analyzes the keywords

[1844] The server uses a text analysis engine (e.g., TextBlob) to analyze the received keywords. The text analysis engine performs natural language processing on the received keywords and maps them into an appropriate business framework (e.g., SWOT analysis).

[1845] Input: keyword

[1846] Output: Mapped business framework

[1847] Step 3:

[1848] The server collects relevant information

[1849] The server's data collection module collects relevant information using internal databases and external APIs (market databases, competitive information, etc.). For example, regarding "new product launches," market trends and competitive information are obtained through API calls.

[1850] Input: Business framework and related keywords

[1851] Output: Collected relevant information data

[1852] Step 4:

[1853] Organizing the information collected by the server

[1854] The server organizes the collected data into a certain format, for example, categorizing market trend information and competitor information stored in the database into required categories (e.g., strengths, weaknesses, opportunities, and threats).

[1855] Input: Collected relevant information data

[1856] Output: Organized data

[1857] Step 5:

[1858] The server automatically generates framework analysis results

[1859] The server's framework generation module automatically generates analysis results for a business framework (e.g., SWOT analysis) based on the organized data. For example, "strengths" could include "brand power" and "technical ability," while "weaknesses" could include "development costs" and "lack of engineers."

[1860] Input: Organized data

[1861] Output: Auto-generated framework analysis results

[1862] Step 6:

[1863] The server recognizes the user's emotional state

[1864] The server's emotion engine collects information such as the user's input speed, patterns, and tone of voice to recognize their emotional state. For example, if the user is typing slowly, it will interpret this as anxiety.

[1865] Input: User input, operation history, biometric information, etc.

[1866] Output: Perceived emotional state

[1867] Step 7:

[1868] The server coordinates the results display and feedback

[1869] The server's display module displays the analysis results based on the user's emotional state and adjusts the feedback as needed. For example, if a user feels anxious, the server might add an encouraging message such as, "Launching a new product involves risks, but if you leverage your company's brand power, you have a high chance of success."

[1870] Input: Automatically generated framework analysis results, recognized emotional states

[1871] Output: Analysis results and feedback displayed to the user

[1872] Step 8:

[1873] The user checks and corrects the results

[1874] The user checks the business framework analysis results displayed on the terminal and makes corrections or additions as necessary. Once corrections are complete, the user clicks the "Send" button again to send the information to the server.

[1875] Input: User modifications

[1876] Output: Modifications sent to the server

[1877] Step 9:

[1878] The server re-analyzes the corrected analysis results and updates them.

[1879] The server re-analyzes the received modifications and regenerates the updated business framework analysis results, at which time the emotion engine also runs again to generate analysis results based on the latest emotional state.

[1880] Input: Modified Business Framework analysis results

[1881] Output: Re-analysis and updated analysis results

[1882] Step 10:

[1883] Server displays updated results

[1884] The server's display module displays the updated business framework analysis results to the user again, and updates the emotion-based feedback as needed.

[1885] Input: Updated business framework analysis results, latest emotional state

[1886] Output: Analysis results and feedback that are displayed back to the user

[1887] Step 11:

[1888] The server stores the data

[1889] The server's data storage module stores the final business framework analysis results and the history of sentiment analysis in a database, which can be used for future analysis.

[1890] Input: Final business framework analysis results, sentiment analysis history

[1891] Output: Data stored in the database

[1892] 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.

[1893] 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.

[1894] 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.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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).

[1899] 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.

[1900] 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."

[1901] 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.

[1902] 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).

[1903] 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.

[1904] 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.

[1905] 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.

[1906] 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.

[1907] 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.

[1908] 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.

[1909] 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.

[1910] 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.

[1911] 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.

[1912] 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.

[1913] The following is further disclosed regarding the above embodiment.

[1914] (Claim 1)

[1915] A means of receiving keywords related to the business framework you want to use;

[1916] A means of parsing and mapping incoming keywords into an appropriate framework;

[1917] A means of collecting and organizing relevant information based on the framework;

[1918] A means for automatically generating framework analysis results based on the collected and organized information;

[1919] a means for displaying the generated framework analysis results;

[1920] A system including:

[1921] (Claim 2)

[1922] and means for a user to modify the displayed framework analysis results.

[1923] 10. The system of claim 1.

[1924] (Claim 3)

[1925] The modified framework analysis result is analyzed again, and an updated result is displayed.

[1926] 10. The system of claim 1.

[1927] "Example 1"

[1928] (Claim 1)

[1929] a means for receiving terms associated with the analytical framework desired to be utilized;

[1930] a means for parsing and mapping received words into an appropriate framework;

[1931] means for collecting and storing relevant information based on the framework;

[1932] means for automatically generating framework analysis results based on the collected and stored information;

[1933] a means for displaying the generated framework analysis results;

[1934] A means for the user to edit the displayed analysis results;

[1935] means for reanalyzing the edited analysis results and displaying the updated results;

[1936] A means of saving the final analysis results;

[1937] A system including:

[1938] (Claim 2)

[1939] The present invention further includes a means for a user to make corrections to the displayed framework analysis results.

[1940] 10. The system of claim 1.

[1941] (Claim 3)

[1942] The modified framework analysis result is analyzed again, and an updated result is displayed.

[1943] 10. The system of claim 1.

[1944] "Application Example 1"

[1945] (Claim 1)

[1946] A means of receiving keywords related to the business framework you want to use;

[1947] A means of parsing and mapping incoming keywords into an appropriate framework;

[1948] A means of collecting and organizing relevant information based on the framework;

[1949] A means for automatically generating framework analysis results based on the collected and organized information;

[1950] a means for displaying the generated framework analysis results;

[1951] A means to use data from within the factory to perform real-time analysis and optimize the production process.

[1952] A system including:

[1953] (Claim 2)

[1954] 10. The system of claim 1, further comprising means for a user to modify the displayed framework analysis results.

[1955] (Claim 3)

[1956] 10. The system of claim 1, further comprising means for re-analyzing the framework analysis results after the modification and displaying the updated results.

[1957] "Example 2: Combining Emotion Engines"

[1958] (Claim 1)

[1959] A means for receiving terms related to the business framework you want to use;

[1960] a means for parsing the received terms and mapping them to an appropriate framework;

[1961] A means of collecting and organizing relevant information based on the framework;

[1962] A means for automatically generating framework analysis results based on the collected and organized information;

[1963] A means for displaying the generated framework analysis results;

[1964] means for recognizing the emotional state of a user;

[1965] means for adjusting the displayed content based on the recognized emotional state;

[1966] A system including:

[1967] (Claim 2)

[1968] 10. The system of claim 1, further comprising means for a user to modify the displayed framework analysis results.

[1969] (Claim 3)

[1970] 10. The system of claim 1, further comprising means for re-analyzing the revised framework analysis results and displaying the updated results.

[1971] "Application example 2 when combining emotion engines"

[1972] (Claim 1)

[1973] A means of receiving keywords related to the business framework you want to use;

[1974] A means of parsing and mapping incoming keywords into an appropriate framework;

[1975] A means of collecting and organizing relevant information based on the framework;

[1976] A means for automatically generating framework analysis results based on the collected and organized information;

[1977] a means for displaying the generated framework analysis results;

[1978] A means for recognizing the user's emotional state and adjusting the results display and feedback content;

[1979] A system including:

[1980] (Claim 2)

[1981] 10. The system of claim 1, further comprising means for a user to modify the displayed framework analysis results.

[1982] (Claim 3)

[1983] 10. The system of claim 1, further comprising means for re-analyzing the framework analysis results after the modification and displaying the updated results. [Explanation of symbols]

[1984] 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 of receiving keywords related to the business framework you want to use; A means of parsing and mapping incoming keywords into an appropriate framework; A means of collecting and organizing relevant information based on the framework; A means for automatically generating framework analysis results based on the collected and organized information; a means for displaying the generated framework analysis results; A system including:

2. and means for a user to modify the displayed framework analysis results. The system of claim 1 .

3. The modified framework analysis result is analyzed again, and an updated result is displayed. The system of claim 1 .

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