system

Abstract

The system according to this embodiment aims to efficiently organize and visually present the structure of a business model. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of companies, contracts, commercial flows, financial flows, handling data, and information to be realized. The analysis unit analyzes the information received by the reception unit and organizes the structure of the business model. The generation unit generates the structure organized by the analysis unit as a visual diagram. The provision unit provides the diagram generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0007] The system according to this embodiment can efficiently organize and visually present the structure of a business model. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied. [[ID=​​​​​​​​​​​​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The framework organization system according to an embodiment of the present invention is a system that uses an AI tool to streamline framework organization, thereby supporting improved order rates and project success. This framework organization system takes input such as companies, contracts, trade flows, financial flows, data handled, and desired outcomes, and the AI ​​analyzes this information to organize the business model framework. The organized framework is generated as a visually appealing and easy-to-understand diagram and provided to the user. This diagram improves communication and sharing processes and contributes to project success. For example, the user inputs companies, contracts, trade flows, financial flows, data handled, and desired outcomes. This information is input to the AI. Next, the AI ​​analyzes the input information and organizes the business model framework. The AI ​​calculates the optimal framework based on successful business models and organizes the framework based on the results. The organized framework is generated as a visually appealing and easy-to-understand diagram. For example, a diagram showing the relationships between companies and the flow of trade is generated. This diagram is provided to the user, improving communication and sharing processes. As a result, even employees unfamiliar with framework organization can easily use the system, achieving increased efficiency in framework organization and improved order rates. Furthermore, to prevent incorrect team compositions, the system includes a function that uses AI to detect and correct erroneous compositions based on the analysis results. This improves the project success rate and reduces wasted effort. In this way, the team composition organization system can streamline team composition organization, boost the order acceptance rate, and contribute to project success.

[0029] The framework organization system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of companies, contracts, trade flows, financial flows, data to be handled, and information to be realized. Companies include, for example, trading partners, partner companies, and competitors. Contracts include, for example, transaction agreements, partnership agreements, and license agreements. Trade flows include, for example, product distribution channels and transaction processes. Financial flows include, for example, payment flows and fund flows. Data to be handled includes, for example, customer data, transaction data, and product data. Information to be realized includes, for example, project goals and business goals. The analysis unit analyzes the information received by the reception unit and organizes the framework of the business model. The analysis unit analyzes the information using, for example, data analysis techniques. For example, the analysis unit analyzes the information using data mining techniques. The analysis unit can also analyze the information using machine learning algorithms. Furthermore, the analysis unit can also analyze the information using natural language processing techniques. The generation unit generates a visual diagram of the organizational structure organized by the analysis unit. The generation unit can generate, for example, a flowchart. It can also generate a mind map. Furthermore, it can generate a relationship diagram. The provision unit provides the diagram generated by the generation unit to the user. The provision unit provides the diagram, for example, through a web application. It can also provide the diagram through a mobile application. Furthermore, it can provide the diagram via email. As a result, the organizational structure organization system according to this embodiment can streamline organizational structure organization and support improved order acceptance rates and project success.

[0030] The reception desk inputs information such as companies, contracts, trade flows, financial flows, data to be handled, and the information to be realized. Companies include, for example, trading partners, partner companies, and competitors. This company information includes detailed data such as company name, address, contact information, industry, and transaction history. Contracts include, for example, trade agreements, partnership agreements, and license agreements. Contract information includes detailed data such as contract type, contract period, contract amount, contract terms, and contract performance status. Trade flows include, for example, product distribution channels and transaction processes. Trade flow information includes detailed data such as product origin, destination, delivery method, delivery schedule, and transaction status. Financial flows include, for example, payment flows and fund flows. Financial flow information includes detailed data such as payer, payee, payment method, payment schedule, and fund movement history. Data to be handled includes, for example, customer data, transaction data, and product data. Data to be handled includes detailed data such as customer name, contact information, purchase history, product type, quantity, price, and transaction date and time. The information to be processed includes, for example, project goals and business objectives. This information also includes detailed data such as specific project objectives, deadlines, required resources, and expected outcomes. This allows the reception unit to centrally input diverse information and collect foundational data for the entire system. Furthermore, the reception unit has the functionality to automatically classify the input information and save it in the appropriate format. This ensures smoother subsequent analysis and generation processes, improving the overall efficiency of the system.

[0031] The analysis department analyzes information received by the reception department and organizes the business model structure. The analysis department analyzes information using data analysis techniques, specifically using data mining technology to discover hidden patterns and trends. For example, it analyzes transaction history data to identify factors influencing increases or decreases in sales during specific periods. The analysis department can also analyze information using machine learning algorithms. For example, it builds predictive models based on past purchase data to predict customer purchasing behavior. Furthermore, the analysis department can analyze information using natural language processing technology. For example, it analyzes the content of contracts and emails to extract important keywords and phrases and evaluate the risks and opportunities of contracts. This allows the analysis department to analyze diverse information received from the reception department from multiple perspectives and efficiently organize the business model structure. Additionally, the analysis department can update data in real time and provide analysis results based on the latest information. For example, when new transaction data is entered, it immediately performs analysis and updates the business model structure. The analysis department can also compare historical and current data to grasp long-term trends and fluctuations. This allows the analysis unit to contribute not only to short-term decision-making but also to long-term strategic planning.

[0032] The generation unit generates a visual diagram of the structure organized by the analysis unit. For example, the generation unit can generate a flowchart. A flowchart visually represents the flow of a business process, clarifying the relationships and order of each step. The generation unit can also generate a mind map. A mind map visually represents the overall picture of a business model, clarifying the relationships and hierarchical structure of each element. Furthermore, the generation unit can generate a relationship diagram. A relationship diagram visually represents the relationships between companies and the flow of transactions, clarifying the positioning of each company and transaction. This allows the generation unit to visually represent the analysis results in an easy-to-understand way, enabling users to understand them intuitively. In addition, the generation unit has a function to customize the generated diagrams. For example, users can highlight specific elements or change the colors and shapes. The generation unit also has a function to save the generated diagrams in multiple formats. For example, it can save them in formats such as PDF, PNG, and SVG, allowing users to use them as needed. This enables the generation unit to flexibly generate and provide diagrams that meet user needs, promoting the understanding and sharing of business models.

[0033] The service provider provides users with diagrams generated by the generation unit. For example, the service provider can provide diagrams through a web application. A web application is a platform accessible to users via the internet, allowing them to view, download, and share generated diagrams. The service provider can also provide diagrams through a mobile application. A mobile application is available on mobile devices such as smartphones and tablets, allowing users to view generated diagrams while on the go. Furthermore, the service provider can provide diagrams via email. Email sends the generated diagrams as attachments, allowing users to download them directly from the received email. This enables the service provider to provide users with access to generated diagrams anytime, anywhere, promoting the sharing and utilization of business models. Additionally, the service provider has the functionality to collect user feedback and continuously improve the delivery method and content of the diagrams. For example, it can implement a feedback form where users can provide opinions on the usability and display of the diagrams, and use the collected feedback to improve the system. The service provider also has enhanced security measures to prevent unauthorized access to generated diagrams and information leaks. This enables the service provider to provide users with safe and reliable diagrams, supporting the effective utilization of business models.

[0034] The analysis unit can calculate the optimal configuration based on successful business models and organize the configuration based on the results. For example, the analysis unit can calculate the optimal configuration based on past success stories. The analysis unit can also calculate the optimal configuration based on success factors in a specific industry. Furthermore, the analysis unit can learn patterns of successful business models and calculate the optimal configuration based on those patterns. This improves the accuracy of the configuration by calculating the optimal configuration based on successful business models. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on successful business models into a generating AI and have the generating AI perform the calculation of the optimal configuration.

[0035] The generation unit can generate diagrams that show the relationships between companies and the flow of trade. For example, the generation unit can generate flowcharts that show the transaction relationships between companies. It can also generate mind maps that show the flow of trade. Furthermore, the generation unit can generate relationship diagrams that show the relationships between companies. This makes it possible to provide easy-to-understand diagrams by visually representing the relationships between companies and the flow of trade. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the relationships between companies and the flow of trade into a generation AI and have the generation AI perform the diagram generation.

[0036] The service provider can provide the generated diagrams to users, improving the communication and sharing process. For example, the service provider can provide the generated diagrams to users through a web application. It can also provide the generated diagrams to users through a mobile application. Furthermore, it can provide the generated diagrams to users via email. This improves the communication and sharing process by providing the generated diagrams. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated diagrams into a generation AI and have the generation AI provide the diagrams.

[0037] The analysis unit can detect and correct incorrect configurations based on the analysis results. For example, the analysis unit can detect and correct inefficient configurations based on the analysis results. It can also detect and correct high-risk configurations based on the analysis results. Furthermore, the analysis unit can recalculate and correct the optimal configuration based on the analysis results. This improves the project success rate by detecting and correcting incorrect configurations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the analysis results into a generating AI and have the generating AI perform the detection and correction of incorrect configurations.

[0038] The reception desk can analyze past input data and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input data, the reception desk can suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input data into a generating AI and have the generating AI suggest the optimal input method.

[0039] The reception unit can filter input content based on the user's current project status and areas of interest during input. For example, the reception unit prioritizes input of information related to the user's current ongoing project. The reception unit can also automatically filter highly relevant information based on the user's areas of interest. Furthermore, the reception unit can enable the user to input only the necessary information depending on their project status. This allows the user to input only the necessary information by filtering input content based on project status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's project status and areas of interest into a generating AI and have the generating AI perform the filtering of the input content.

[0040] The reception unit can prioritize inputting highly relevant information based on the user's geographical location during data entry. For example, if the user is in a specific region, the reception unit will prioritize inputting information related to that region. Furthermore, if the user is on the move, the reception unit can input necessary information based on their current location. In addition, if the user is in a specific location, the reception unit can automatically input information related to that location. This enables efficient information input by prioritizing the input of highly relevant information based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI input highly relevant information.

[0041] The reception unit can analyze the user's social media activity and input relevant information during data entry. For example, the reception unit can input relevant information based on information the user has shared on social media. The reception unit can also analyze the content of the user's social media posts and automatically input the necessary information. Furthermore, the reception unit can input highly relevant information based on the user's social media activity history. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis for important information and a simplified analysis for less important information. The analysis unit can also adjust the depth of the analysis according to the importance of the information. Furthermore, the analysis unit can provide detailed graphs and charts for important information and perform simple text analysis for less important information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. It can also apply a specific marketing analysis algorithm to marketing data. Furthermore, it can apply a specific human resources analysis algorithm to human resources data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the information submission date. For example, the analysis unit may prioritize the analysis of the latest information and postpone the analysis of older information. The analysis unit may also prioritize the analysis of information with an approaching submission deadline. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission date data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information and postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. Furthermore, the analysis unit can group highly relevant information and analyze it all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0046] The generation unit can adjust the level of detail of the figures based on the importance of the information. For example, the generation unit can generate detailed figures for important information and simplified figures for less important information. The generation unit can also adjust the level of detail of the figures according to the importance of the information. Furthermore, the generation unit can provide detailed graphs and charts for important information and generate simple text figures for less important information. By adjusting the level of detail of the figures according to the importance of the information, visibility is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the figures.

[0047] The generation unit can apply different generation algorithms depending on the category of information. For example, the generation unit can apply a specific financial chart generation algorithm to financial data. It can also apply a specific marketing chart generation algorithm to marketing data. Furthermore, it can apply a specific personnel chart generation algorithm to personnel data. By applying the appropriate generation algorithm according to the category of information, visibility is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0048] The generation unit can determine the priority of the diagrams based on the information submission timing. For example, the generation unit can prioritize the inclusion of the latest information in the diagrams and postpone older information. It can also prioritize the inclusion of information with an approaching submission deadline. Furthermore, the generation unit can adjust the generation order of the diagrams based on the submission timing. This enables efficient diagram generation by determining the priority of the diagrams based on the information submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information submission timing data into a generation AI and have the generation AI perform the determination of the diagram priorities.

[0049] The generation unit can adjust the order of the figures based on the relevance of the information. For example, the generation unit can prioritize reflecting highly relevant information in the figures and postpone reflecting less relevant information. The generation unit can also adjust the order of the figures based on the relevance of the information. Furthermore, the generation unit can group highly relevant information and reflect it in the figures all at once. This improves visibility by adjusting the order of the figures based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the figure order.

[0050] The service provider can select the optimal display method when providing a figure by referring to the user's past operation history. For example, the service provider can prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's operation history and provide the most efficient display method. In this way, the optimal display method can be provided by referring to past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's operation history data into a generating AI and have the generating AI select the optimal display method.

[0051] The data provider can select the optimal display method when providing a figure, taking into account the user's device information. For example, if the user is using a smartphone, the data provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the data provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the data provider can provide a concise and highly visible display method. This allows the data provider to provide the optimal display method by considering device information. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0052] The service provider can provide multilingual displays according to the user's language settings when providing figures. For example, the service provider can automatically set the display language based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide displays in a specific language if the user selects one. This improves user convenience by providing multilingual displays. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI execute the multilingual display.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze past input data and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input data, the reception desk can suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input data into a generating AI and have the generating AI suggest the optimal input method.

[0055] The reception unit can filter input content based on the user's current project status and areas of interest during input. For example, the reception unit prioritizes input of information related to the user's current ongoing project. The reception unit can also automatically filter highly relevant information based on the user's areas of interest. Furthermore, the reception unit can enable the user to input only the necessary information depending on their project status. This allows the user to input only the necessary information by filtering input content based on project status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's project status and areas of interest into a generating AI and have the generating AI perform the filtering of the input content.

[0056] The reception unit can prioritize inputting highly relevant information based on the user's geographical location during data entry. For example, if the user is in a specific region, the reception unit will prioritize inputting information related to that region. Furthermore, if the user is on the move, the reception unit can input necessary information based on their current location. In addition, if the user is in a specific location, the reception unit can automatically input information related to that location. This enables efficient information input by prioritizing the input of highly relevant information based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI input highly relevant information.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis for important information and a simplified analysis for less important information. The analysis unit can also adjust the depth of the analysis according to the importance of the information. Furthermore, the analysis unit can provide detailed graphs and charts for important information and perform simple text analysis for less important information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0058] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. It can also apply a specific marketing analysis algorithm to marketing data. Furthermore, it can apply a specific human resources analysis algorithm to human resources data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0059] The generation unit can adjust the level of detail of the figures based on the importance of the information. For example, the generation unit can generate detailed figures for important information and simplified figures for less important information. The generation unit can also adjust the level of detail of the figures according to the importance of the information. Furthermore, the generation unit can provide detailed graphs and charts for important information and generate simple text figures for less important information. By adjusting the level of detail of the figures according to the importance of the information, visibility is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the figures.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk inputs information about the company, contracts, trade flow, financial flow, data to be handled, and desired information. Companies include trading partners, partner companies, and competitors, while contracts include trade agreements, partnership agreements, and license agreements. Trade flow includes product distribution channels and transaction processes, while financial flow includes payment flows and fund flows. Data to be handled includes customer data, transaction data, and product data, while desired information includes project goals and business goals. Step 2: The analysis unit analyzes the information received by the reception unit and organizes the business model structure. The analysis unit analyzes the information using data analysis methods, such as data mining techniques, machine learning algorithms, and natural language processing techniques. Step 3: The generation unit generates a visual diagram of the structure organized by the analysis unit. The generation unit can generate flowcharts, mind maps, relationship diagrams, etc. Step 4: The provider unit provides the user with the diagram generated by the generator unit. The provider unit can provide the diagram via web application, mobile application, or email.

[0062] (Example of form 2) The framework organization system according to an embodiment of the present invention is a system that uses an AI tool to streamline framework organization, thereby supporting improved order rates and project success. This framework organization system takes input such as companies, contracts, trade flows, financial flows, data handled, and desired outcomes, and the AI ​​analyzes this information to organize the business model framework. The organized framework is generated as a visually appealing and easy-to-understand diagram and provided to the user. This diagram improves communication and sharing processes and contributes to project success. For example, the user inputs companies, contracts, trade flows, financial flows, data handled, and desired outcomes. This information is input to the AI. Next, the AI ​​analyzes the input information and organizes the business model framework. The AI ​​calculates the optimal framework based on successful business models and organizes the framework based on the results. The organized framework is generated as a visually appealing and easy-to-understand diagram. For example, a diagram showing the relationships between companies and the flow of trade is generated. This diagram is provided to the user, improving communication and sharing processes. As a result, even employees unfamiliar with framework organization can easily use the system, achieving increased efficiency in framework organization and improved order rates. Furthermore, to prevent incorrect team compositions, the system includes a function that uses AI to detect and correct erroneous compositions based on the analysis results. This improves the project success rate and reduces wasted effort. In this way, the team composition organization system can streamline team composition organization, boost the order acceptance rate, and contribute to project success.

[0063] The framework organization system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of companies, contracts, trade flows, financial flows, data to be handled, and information to be realized. Companies include, for example, trading partners, partner companies, and competitors. Contracts include, for example, transaction agreements, partnership agreements, and license agreements. Trade flows include, for example, product distribution channels and transaction processes. Financial flows include, for example, payment flows and fund flows. Data to be handled includes, for example, customer data, transaction data, and product data. Information to be realized includes, for example, project goals and business goals. The analysis unit analyzes the information received by the reception unit and organizes the framework of the business model. The analysis unit analyzes the information using, for example, data analysis techniques. For example, the analysis unit analyzes the information using data mining techniques. The analysis unit can also analyze the information using machine learning algorithms. Furthermore, the analysis unit can also analyze the information using natural language processing techniques. The generation unit generates a visual diagram of the organizational structure organized by the analysis unit. The generation unit can generate, for example, a flowchart. It can also generate a mind map. Furthermore, it can generate a relationship diagram. The provision unit provides the diagram generated by the generation unit to the user. The provision unit provides the diagram, for example, through a web application. It can also provide the diagram through a mobile application. Furthermore, it can provide the diagram via email. As a result, the organizational structure organization system according to this embodiment can streamline organizational structure organization and support improved order acceptance rates and project success.

[0064] The reception desk inputs information such as companies, contracts, trade flows, financial flows, data to be handled, and the information to be realized. Companies include, for example, trading partners, partner companies, and competitors. This company information includes detailed data such as company name, address, contact information, industry, and transaction history. Contracts include, for example, trade agreements, partnership agreements, and license agreements. Contract information includes detailed data such as contract type, contract period, contract amount, contract terms, and contract performance status. Trade flows include, for example, product distribution channels and transaction processes. Trade flow information includes detailed data such as product origin, destination, delivery method, delivery schedule, and transaction status. Financial flows include, for example, payment flows and fund flows. Financial flow information includes detailed data such as payer, payee, payment method, payment schedule, and fund movement history. Data to be handled includes, for example, customer data, transaction data, and product data. Data to be handled includes detailed data such as customer name, contact information, purchase history, product type, quantity, price, and transaction date and time. The information to be processed includes, for example, project goals and business objectives. This information also includes detailed data such as specific project objectives, deadlines, required resources, and expected outcomes. This allows the reception unit to centrally input diverse information and collect foundational data for the entire system. Furthermore, the reception unit has the functionality to automatically classify the input information and save it in the appropriate format. This ensures smoother subsequent analysis and generation processes, improving the overall efficiency of the system.

[0065] The analysis department analyzes information received by the reception department and organizes the business model structure. The analysis department analyzes information using data analysis techniques, specifically using data mining technology to discover hidden patterns and trends. For example, it analyzes transaction history data to identify factors influencing increases or decreases in sales during specific periods. The analysis department can also analyze information using machine learning algorithms. For example, it builds predictive models based on past purchase data to predict customer purchasing behavior. Furthermore, the analysis department can analyze information using natural language processing technology. For example, it analyzes the content of contracts and emails to extract important keywords and phrases and evaluate the risks and opportunities of contracts. This allows the analysis department to analyze diverse information received from the reception department from multiple perspectives and efficiently organize the business model structure. Additionally, the analysis department can update data in real time and provide analysis results based on the latest information. For example, when new transaction data is entered, it immediately performs analysis and updates the business model structure. The analysis department can also compare historical and current data to grasp long-term trends and fluctuations. This allows the analysis unit to contribute not only to short-term decision-making but also to long-term strategic planning.

[0066] The generation unit generates a visual diagram of the structure organized by the analysis unit. For example, the generation unit can generate a flowchart. A flowchart visually represents the flow of a business process, clarifying the relationships and order of each step. The generation unit can also generate a mind map. A mind map visually represents the overall picture of a business model, clarifying the relationships and hierarchical structure of each element. Furthermore, the generation unit can generate a relationship diagram. A relationship diagram visually represents the relationships between companies and the flow of transactions, clarifying the positioning of each company and transaction. This allows the generation unit to visually represent the analysis results in an easy-to-understand way, enabling users to understand them intuitively. In addition, the generation unit has a function to customize the generated diagrams. For example, users can highlight specific elements or change the colors and shapes. The generation unit also has a function to save the generated diagrams in multiple formats. For example, it can save them in formats such as PDF, PNG, and SVG, allowing users to use them as needed. This enables the generation unit to flexibly generate and provide diagrams that meet user needs, promoting the understanding and sharing of business models.

[0067] The service provider provides users with diagrams generated by the generation unit. For example, the service provider can provide diagrams through a web application. A web application is a platform accessible to users via the internet, allowing them to view, download, and share generated diagrams. The service provider can also provide diagrams through a mobile application. A mobile application is available on mobile devices such as smartphones and tablets, allowing users to view generated diagrams while on the go. Furthermore, the service provider can provide diagrams via email. Email sends the generated diagrams as attachments, allowing users to download them directly from the received email. This enables the service provider to provide users with access to generated diagrams anytime, anywhere, promoting the sharing and utilization of business models. Additionally, the service provider has the functionality to collect user feedback and continuously improve the delivery method and content of the diagrams. For example, it can implement a feedback form where users can provide opinions on the usability and display of the diagrams, and use the collected feedback to improve the system. The service provider also has enhanced security measures to prevent unauthorized access to generated diagrams and information leaks. This enables the service provider to provide users with safe and reliable diagrams, supporting the effective utilization of business models.

[0068] The analysis unit can calculate the optimal configuration based on successful business models and organize the configuration based on the results. For example, the analysis unit can calculate the optimal configuration based on past success stories. The analysis unit can also calculate the optimal configuration based on success factors in a specific industry. Furthermore, the analysis unit can learn patterns of successful business models and calculate the optimal configuration based on those patterns. This improves the accuracy of the configuration by calculating the optimal configuration based on successful business models. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on successful business models into a generating AI and have the generating AI perform the calculation of the optimal configuration.

[0069] The generation unit can generate diagrams that show the relationships between companies and the flow of trade. For example, the generation unit can generate flowcharts that show the transaction relationships between companies. It can also generate mind maps that show the flow of trade. Furthermore, the generation unit can generate relationship diagrams that show the relationships between companies. This makes it possible to provide easy-to-understand diagrams by visually representing the relationships between companies and the flow of trade. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the relationships between companies and the flow of trade into a generation AI and have the generation AI perform the diagram generation.

[0070] The service provider can provide the generated diagrams to users, improving the communication and sharing process. For example, the service provider can provide the generated diagrams to users through a web application. It can also provide the generated diagrams to users through a mobile application. Furthermore, it can provide the generated diagrams to users via email. This improves the communication and sharing process by providing the generated diagrams. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated diagrams into a generation AI and have the generation AI provide the diagrams.

[0071] The analysis unit can detect and correct incorrect configurations based on the analysis results. For example, the analysis unit can detect and correct inefficient configurations based on the analysis results. It can also detect and correct high-risk configurations based on the analysis results. Furthermore, the analysis unit can recalculate and correct the optimal configuration based on the analysis results. This improves the project success rate by detecting and correcting incorrect configurations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the analysis results into a generating AI and have the generating AI perform the detection and correction of incorrect configurations.

[0072] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception unit may divide the input into smaller, step-by-step steps. If the user is relaxed, the reception unit may allow all information to be entered at once. Furthermore, if the user is in a hurry, the reception unit may prompt the user to prioritize the input of the most important information. This improves input efficiency by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information input.

[0073] The reception desk can analyze past input data and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input data, the reception desk can suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input data into a generating AI and have the generating AI suggest the optimal input method.

[0074] The reception unit can filter input content based on the user's current project status and areas of interest during input. For example, the reception unit prioritizes input of information related to the user's current ongoing project. The reception unit can also automatically filter highly relevant information based on the user's areas of interest. Furthermore, the reception unit can enable the user to input only the necessary information depending on their project status. This allows the user to input only the necessary information by filtering input content based on project status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's project status and areas of interest into a generating AI and have the generating AI perform the filtering of the input content.

[0075] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prompt the user to enter the most important information first. If the user is relaxed, the reception desk may also prompt the user to enter more detailed information. Furthermore, if the user is in a hurry, the reception desk may ask the user to enter only the minimum necessary information. This ensures that important information is entered first by prioritizing the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0076] The reception unit can prioritize inputting highly relevant information based on the user's geographical location during data entry. For example, if the user is in a specific region, the reception unit will prioritize inputting information related to that region. Furthermore, if the user is on the move, the reception unit can input necessary information based on their current location. In addition, if the user is in a specific location, the reception unit can automatically input information related to that location. This enables efficient information input by prioritizing the input of highly relevant information based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI input highly relevant information.

[0077] The reception unit can analyze the user's social media activity and input relevant information during data entry. For example, the reception unit can input relevant information based on information the user has shared on social media. The reception unit can also analyze the content of the user's social media posts and automatically input the necessary information. Furthermore, the reception unit can input highly relevant information based on the user's social media activity history. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. This allows for a deeper understanding of the analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis for important information and a simplified analysis for less important information. The analysis unit can also adjust the depth of the analysis according to the importance of the information. Furthermore, the analysis unit can provide detailed graphs and charts for important information and perform simple text analysis for less important information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. It can also apply a specific marketing analysis algorithm to marketing data. Furthermore, it can apply a specific human resources analysis algorithm to human resources data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the understanding of the analysis result is deepened. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the information submission date. For example, the analysis unit may prioritize the analysis of the latest information and postpone the analysis of older information. The analysis unit may also prioritize the analysis of information with an approaching submission deadline. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission date data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information and postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. Furthermore, the analysis unit can group highly relevant information and analyze it all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0084] The generation unit can estimate the user's emotions and adjust the representation of the generated diagram based on the estimated emotions. For example, if the user is tense, the generation unit can generate a simple and highly visible diagram. If the user is relaxed, the generation unit can also generate a diagram containing detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a diagram that gets straight to the point. By adjusting the representation of the diagram according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the representation of the diagram.

[0085] The generation unit can adjust the level of detail of the figures based on the importance of the information. For example, the generation unit can generate detailed figures for important information and simplified figures for less important information. The generation unit can also adjust the level of detail of the figures according to the importance of the information. Furthermore, the generation unit can provide detailed graphs and charts for important information and generate simple text figures for less important information. By adjusting the level of detail of the figures according to the importance of the information, visibility is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the figures.

[0086] The generation unit can apply different generation algorithms depending on the category of information. For example, the generation unit can apply a specific financial chart generation algorithm to financial data. It can also apply a specific marketing chart generation algorithm to marketing data. Furthermore, it can apply a specific personnel chart generation algorithm to personnel data. By applying the appropriate generation algorithm according to the category of information, visibility is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0087] The generation unit can estimate the user's emotions and adjust the length of the generated diagrams based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise diagrams. If the user is relaxed, the generation unit can also generate longer diagrams with detailed explanations. Furthermore, if the user is excited, the generation unit can generate diagrams with visually stimulating effects. By adjusting the length of the diagrams according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the diagrams.

[0088] The generation unit can determine the priority of the diagrams based on the information submission timing. For example, the generation unit can prioritize the inclusion of the latest information in the diagrams and postpone older information. It can also prioritize the inclusion of information with an approaching submission deadline. Furthermore, the generation unit can adjust the generation order of the diagrams based on the submission timing. This enables efficient diagram generation by determining the priority of the diagrams based on the information submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information submission timing data into a generation AI and have the generation AI perform the determination of the diagram priorities.

[0089] The generation unit can adjust the order of the figures based on the relevance of the information. For example, the generation unit can prioritize reflecting highly relevant information in the figures and postpone reflecting less relevant information. The generation unit can also adjust the order of the figures based on the relevance of the information. Furthermore, the generation unit can group highly relevant information and reflect it in the figures all at once. This improves visibility by adjusting the order of the figures based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the figure order.

[0090] The service provider can estimate the user's emotions and adjust the display method of the provided diagrams based on the estimated user emotions. For example, if the user is tense, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the display method of the diagrams according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the display method of the diagrams.

[0091] The service provider can select the optimal display method when providing a figure by referring to the user's past operation history. For example, the service provider can prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's operation history and provide the most efficient display method. In this way, the optimal display method can be provided by referring to past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's operation history data into a generating AI and have the generating AI select the optimal display method.

[0092] The service provider can estimate the user's emotions and adjust the operation procedures of the provided diagrams based on the estimated user emotions. For example, if the user is nervous, the service provider can provide simple and intuitive operation procedures. If the user is relaxed, the service provider can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the service provider can provide the shortest possible operation procedures. This improves usability by adjusting the operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of operation procedures.

[0093] The data provider can select the optimal display method when providing a figure, taking into account the user's device information. For example, if the user is using a smartphone, the data provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the data provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the data provider can provide a concise and highly visible display method. This allows the data provider to provide the optimal display method by considering device information. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0094] The service provider can provide multilingual displays according to the user's language settings when providing figures. For example, the service provider can automatically set the display language based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide displays in a specific language if the user selects one. This improves user convenience by providing multilingual displays. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI execute the multilingual display.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception unit may divide the input into smaller, step-by-step steps. If the user is relaxed, the reception unit may allow all information to be entered at once. Furthermore, if the user is in a hurry, the reception unit may prompt the user to prioritize the input of the most important information. This improves input efficiency by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information input.

[0097] The reception desk can analyze past input data and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input data, the reception desk can suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input data into a generating AI and have the generating AI suggest the optimal input method.

[0098] The reception unit can filter input content based on the user's current project status and areas of interest during input. For example, the reception unit prioritizes input of information related to the user's current ongoing project. The reception unit can also automatically filter highly relevant information based on the user's areas of interest. Furthermore, the reception unit can enable the user to input only the necessary information depending on their project status. This allows the user to input only the necessary information by filtering input content based on project status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's project status and areas of interest into a generating AI and have the generating AI perform the filtering of the input content.

[0099] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prompt the user to enter the most important information first. If the user is relaxed, the reception desk may also prompt the user to enter more detailed information. Furthermore, if the user is in a hurry, the reception desk may ask the user to enter only the minimum necessary information. This ensures that important information is entered first by prioritizing the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0100] The reception unit can prioritize inputting highly relevant information based on the user's geographical location during data entry. For example, if the user is in a specific region, the reception unit will prioritize inputting information related to that region. Furthermore, if the user is on the move, the reception unit can input necessary information based on their current location. In addition, if the user is in a specific location, the reception unit can automatically input information related to that location. This enables efficient information input by prioritizing the input of highly relevant information based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI input highly relevant information.

[0101] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. This allows for a deeper understanding of the analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0102] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis for important information and a simplified analysis for less important information. The analysis unit can also adjust the depth of the analysis according to the importance of the information. Furthermore, the analysis unit can provide detailed graphs and charts for important information and perform simple text analysis for less important information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0103] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. It can also apply a specific marketing analysis algorithm to marketing data. Furthermore, it can apply a specific human resources analysis algorithm to human resources data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0104] The generation unit can estimate the user's emotions and adjust the representation of the generated diagram based on the estimated emotions. For example, if the user is tense, the generation unit can generate a simple and highly visible diagram. If the user is relaxed, the generation unit can also generate a diagram containing detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a diagram that gets straight to the point. By adjusting the representation of the diagram according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the representation of the diagram.

[0105] The generation unit can adjust the level of detail of the figures based on the importance of the information. For example, the generation unit can generate detailed figures for important information and simplified figures for less important information. The generation unit can also adjust the level of detail of the figures according to the importance of the information. Furthermore, the generation unit can provide detailed graphs and charts for important information and generate simple text figures for less important information. By adjusting the level of detail of the figures according to the importance of the information, visibility is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the figures.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The reception desk inputs information about the company, contracts, trade flow, financial flow, data to be handled, and desired information. Companies include trading partners, partner companies, and competitors, while contracts include trade agreements, partnership agreements, and license agreements. Trade flow includes product distribution channels and transaction processes, while financial flow includes payment flows and fund flows. Data to be handled includes customer data, transaction data, and product data, while desired information includes project goals and business goals. Step 2: The analysis unit analyzes the information received by the reception unit and organizes the business model structure. The analysis unit analyzes the information using data analysis methods, such as data mining techniques, machine learning algorithms, and natural language processing techniques. Step 3: The generation unit generates a visual diagram of the structure organized by the analysis unit. The generation unit can generate flowcharts, mind maps, relationship diagrams, etc. Step 4: The provider unit provides the user with the diagram generated by the generator unit. The provider unit can provide the diagram via web application, mobile application, or email.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs information such as companies, contracts, commercial flows, financial flows, data to be handled, and information to be implemented. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the information received by the reception unit and organizes the structure of the business model. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates the structure organized by the analysis unit as a visual diagram. The provision unit is implemented by, for example, the output device 40 of the smart device 14, which provides the generated diagram to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, and inputs information such as company, contract, trade flow, financial flow, handling data, and information to be implemented. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the information received by the reception unit to organize the business model structure. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates the structure organized by the analysis unit as a visual diagram. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214, and provides the generated diagram to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, and inputs information such as company, contract, commercial flow, financial flow, handling data, and information to be implemented. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the information received by the reception unit to organize the business model structure. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates the structure organized by the analysis unit as a visual diagram. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, and provides the generated diagram to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives input of company, contract, commercial flow, financial flow, handling data, and information to be implemented. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to organize the business model structure. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates the structure organized by the analysis unit as a visual diagram. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated diagram to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) A reception area where you input information about companies, contracts, trade flows, financial flows, data handled, and the information you want to realize. The analysis unit analyzes the information received by the reception unit and organizes the structure of the business model, A generation unit generates a visual diagram of the structure organized by the analysis unit, The system includes a providing unit that provides the diagram generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We calculate the optimal team structure based on successful business models, and then organize the team structure based on those results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate diagrams showing inter-company relationships and the flow of trade. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide users with generated diagrams to improve the communication and sharing process. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Based on the analysis results, incorrect configurations are detected and corrected. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze past input data and propose the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter data, the system filters the input based on their current project status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input data, the system prioritizes inputting information that is highly relevant based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Prioritize analysis based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the representation of the generated diagrams based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Adjust the level of detail in the diagram based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is Apply different generation algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the generated diagram based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is Prioritize the diagrams based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Adjust the order of the diagrams based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the provided diagrams are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing diagrams, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the instructions for the diagrams provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing diagrams, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing diagrams, multilingual display is provided according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where you input information about companies, contracts, trade flows, financial flows, data handled, and the information you want to realize. The analysis unit analyzes the information received by the reception unit and organizes the structure of the business model, A generation unit generates a visual diagram of the structure organized by the analysis unit, The system includes a providing unit that provides the diagram generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned analysis unit, We calculate the optimal team structure based on successful business models, and then organize the team structure based on those results. The system according to feature 1.

3. The generating unit is Generate diagrams showing inter-company relationships and the flow of trade. The system according to feature 1.

4. The aforementioned supply unit is, Provide users with generated diagrams to improve the communication and sharing process. The system according to feature 1.

5. The aforementioned analysis unit, Based on the analysis results, incorrect configurations are detected and corrected. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze past input data and propose the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When users enter data, the system filters the input based on their current project status and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When users input data, the system prioritizes inputting information that is highly relevant based on their geographical location. The system according to feature 1.

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