System

Abstract

An object of the system according to the embodiment is to efficiently organize information necessary for business planning and proposals and automatically generate an entire assembly drawing.SOLUTION: A system includes an information input unit, an analysis unit, and a diagram generation unit. The information input unit inputs information necessary for the user to plan or propose a business. The analysis unit analyzes the information input by the information input unit. The drawing generation part automatically generates the whole assembly drawing on the basis of a result analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently organize the information necessary for business planning and proposals and automatically generate an overall layout diagram.

[0005] The system according to the embodiment aims to efficiently organize information necessary for business planning and proposals and automatically generate an overall layout diagram. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, an analysis unit, and a diagram generation unit. The information input unit inputs information necessary for a user to plan or propose a business. The analysis unit analyzes the information input by the information input unit. The diagram generation unit automatically generates an overall layout diagram based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize the information necessary for business planning and proposals and automatically generate an overall layout diagram. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The automatic generation system according to an embodiment of the present invention is a system that, by inputting the necessary information when planning or proposing a business targeting a corporation, automatically generates diagrams and slides that allow a grasp of the overall structure, including the characters (corporations and organizations) involved and their relationships (such as monetization points and contract types), the way data is handled and its flow, and a list of any remaining points that need to be clarified in the future. As a result, the automatic generation system can automatically generate diagrams and slides that allow a grasp of the overall structure when planning or proposing a business targeting a corporation, simply by inputting the necessary information.

[0029] The automatic generation system according to the embodiment includes an information input unit, an analysis unit, and a diagram generation unit. The information input unit allows a user to input information necessary for business planning and proposals. For example, the user inputs company names and contract details. The information input unit also allows a user to input information regarding data flow. The information input unit also allows a user to input unsorted points. For example, the user inputs undetermined portions of the contract type. The analysis unit analyzes the information input by the information input unit. For example, the analysis unit analyzes the input company names and contract details to identify the parties (corporate companies and organizations) and their relationships (e.g., monetization points and contract type). The analysis unit can also analyze the data flow and identify how the data is handled and how it flows. The analysis unit can also analyze unsorted points and list unsorted points that need to be clarified. For example, the analysis unit lists undetermined portions of the contract type and undetermined portions of the data storage method. The diagram generation unit automatically generates an overall layout diagram based on the results of the analysis by the analysis unit. For example, the diagram generation unit automatically generates an overall layout diagram that includes the relationships between characters, the flow of data, and points of uncertainty. The diagram generation unit can also output in Google Slides format, visually expressing the diagram using shapes, arrows, icons, and the like. Furthermore, the diagram generation unit can automatically generate slides that include, in addition to the overall layout diagram, an overview, a summary, a list of concerns and issues that need clarification, and proposed solutions. As a result, the automatic generation system according to the embodiment can automatically generate an overall layout diagram simply by inputting the information required for business planning and proposals by the user. For example, the system can be used in situations such as service planning, system development contract proposals, seminar planning, and exhibition planning.

[0030] The information input unit allows the generation AI to automatically complete related information input by the user, encouraging detailed input. For example, when a user inputs a company name or contract details, the generation AI automatically completes that company's past transaction history and related industry information, encouraging detailed input. For example, the unit presents Company A's past contract history and industry trend information. Furthermore, when a user inputs information about data flow, the generation AI automatically completes related data formats and protocols, encouraging detailed input. For example, the unit suggests data collection methods and analysis techniques. Furthermore, when a user inputs unsorted points, the generation AI searches for similar past cases and automatically completes solutions to the unsorted points. For example, the unit presents solutions to undetermined parts of the contract form. This allows the generation AI to automatically complete related information input by the user, encouraging detailed input.

[0031] The information input unit allows the generation AI to search for similar past cases based on the input information and provide reference information. For example, based on the contract type input by the user, the information input unit allows the generation AI to search for similar past contract cases and provide reference contract templates and success stories. For example, it may present past cases similar to the contract type between Company A and Company B. The information input unit also allows the generation AI to search for past data management cases based on the data holding method and flow input by the user and provide reference data management methods and tools. For example, it may show the flow from data collection to analysis. The information input unit also allows the generation AI to search for past solutions to unsorted points based on the unsorted points input by the user and provide reference solutions and countermeasures. For example, it may present solutions to undecided parts of the data storage method. This allows the generation AI to search for similar past cases based on the input information and provide reference information.

[0032] The information input unit can enable a user to intuitively input information using voice input or image input. The information input unit, for example, enables a user to input information necessary for business planning and proposals using voice input. For example, it uses voice recognition technology to convert the user's utterances into text data. The information input unit also enables a user to input information necessary for business planning and proposals using image input. For example, an image of a contract or data flow diagram is uploaded, and the generation AI analyzes the content. The information input unit also combines voice input and image input to provide an interface that allows a user to intuitively input information. For example, related images are uploaded while providing explanations via voice. This enables a user to intuitively input information using voice input or image input.

[0033] The information input unit can provide templates based on business models from different industries, allowing users to select and input them. For example, the information input unit can provide templates based on business models from different industries, allowing users to select and input them. For example, business model templates for the IT industry and manufacturing industry can be prepared. Furthermore, the information input unit can have the generative AI automatically complete the necessary information based on the template selected by the user and prompt for detailed input. For example, the information input unit can suggest how data related to the selected template should be held and how it should flow. Furthermore, the information input unit can customize business model templates from different industries, allowing users to select the template that is best suited to their business. For example, the information input unit can provide a function to add or delete items from the template. This allows the information input unit to provide templates based on business models from different industries, allowing users to select and input them.

[0034] The analysis unit analyzes the characters' past transaction history and relationships, and is able to grasp detailed relationships. For example, the generation AI analyzes the characters' past transaction history to grasp detailed relationships. For example, it analyzes the past transaction details and contract history between Company A and Company B. When analyzing the characters' relationships, the generation AI also performs a risk assessment based on the past transaction history and presents potential risks. For example, it analyzes the history of past troubles and contract violations. The analysis unit also evaluates the strength and reliability of the relationship based on the characters' past transaction history. For example, it calculates the strength of the relationship based on the number of transactions and contract amounts. This allows the generation AI to analyze the characters' past transaction history and relationships, and grasp detailed relationships.

[0035] The analysis unit allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks. For example, the analysis unit allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks. For example, the analysis unit evaluates contract risks and data leakage risks based on the relationship between Company A and Company B. The analysis unit also allows the generation AI to perform a risk assessment based on the relationship analysis results and propose risk avoidance measures. For example, the analysis unit proposes a review of contract content and data protection measures. The analysis unit also allows the generation AI to perform a risk assessment based on the relationship analysis results and calculate a risk score. For example, the analysis unit calculates a risk score based on the strength and reliability of the relationship and identifies high-risk relationships. This allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks.

[0036] The analysis unit analyzes relationships that include participants from different industries and regions, and is able to grasp those relationships from a global perspective. For example, the analysis unit analyzes relationships that include participants from different industries, and is able to grasp those relationships from a global perspective. For example, the analysis unit analyzes the relationships between companies in the IT industry and the manufacturing industry. The analysis unit also analyzes relationships that include participants from different regions, and is able to grasp those relationships from a global perspective. For example, the analysis unit analyzes the relationships between companies in Asia and Europe. The analysis unit also analyzes relationships that include participants from different industries and regions, and is able to discover business opportunities from a global perspective. For example, the analysis unit evaluates the possibility of cooperation between companies in different industries and regions. This makes it possible to analyze relationships that include participants from different industries and regions, and is able to grasp those relationships from a global perspective.

[0037] The analysis unit can display the relationship analysis results as a visual map, allowing the user to intuitively understand. The analysis unit, for example, displays the relationship analysis results as a visual map, allowing the user to intuitively understand. For example, the relationships between companies are represented by nodes and edges. The analysis unit also uses the visual map to visually display the strength and reliability of the relationships. For example, the strength of the relationships is indicated by color or thickness. The analysis unit also displays the relationship analysis results as a visual map, allowing the user to access detailed information by clicking or zooming. For example, detailed information about each company is displayed in a pop-up. In this way, the relationship analysis results can be displayed as a visual map, allowing the user to intuitively understand.

[0038] The analysis unit can evaluate data security risks and propose countermeasures when analyzing how data is held and how it flows. For example, the generation AI analyzes how data is held and how it flows to evaluate the data security risks. For example, it analyzes data transfer paths and storage locations to identify potential security risks. The analysis unit also has the generation AI propose countermeasures based on the results of the data security risk assessment. For example, it proposes the introduction of encryption technology and strengthening access control. The analysis unit also refers to past security incidents when the generation AI analyzes how data is held and how it flows to assess security risks and propose countermeasures. For example, it performs risk assessments based on past data leak cases. This allows the generation AI to evaluate data security risks and propose countermeasures when analyzing how data is held and how it flows.

[0039] When analyzing the data flow, the analysis unit allows the generation AI to evaluate the quality and reliability of the data and suggest areas for improvement. For example, the analysis unit allows the generation AI to analyze the data flow and evaluate the quality and reliability of the data. For example, it analyzes the data collection method and analysis technique and evaluates the quality and reliability. Furthermore, the analysis unit allows the generation AI to suggest areas for improvement based on the results of evaluating the data quality and reliability. For example, it may suggest revising the data collection method or improving the analysis technique. Furthermore, when the generation AI analyzes the data flow and evaluates the quality and reliability, the analysis unit refers to past data quality issues and suggests areas for improvement. For example, it performs a quality assessment based on past cases of data inconsistency. This allows the generation AI to evaluate the quality and reliability of the data and suggest areas for improvement when analyzing the data flow.

[0040] The analysis unit can analyze data flows that correspond to different data formats and protocols and ensure compatibility. For example, the analysis unit allows the generation AI to analyze data flows that correspond to different data formats and protocols and ensure compatibility. For example, it analyzes different data formats such as CSV, JSON, and XML. The analysis unit also allows the generation AI to propose data conversion methods to support different data formats and protocols. For example, it proposes data format conversion tools and protocol bridges. The analysis unit also allows the generation AI to analyze data flows that correspond to different data formats and protocols and propose best practices to ensure compatibility. For example, it proposes standardization of data formats and unification of protocols. This allows the generation AI to analyze data flows that correspond to different data formats and protocols and ensure compatibility.

[0041] The analysis unit can visualize the data flow using 3D models and animations to enable users to intuitively understand it. For example, the analysis unit uses a generative AI to visualize the data flow using 3D models to enable users to intuitively understand it. For example, the flow of data collection, analysis, and utilization is displayed using a 3D model. The analysis unit also visualizes the data flow using animations to enable users to intuitively understand the movement of data. For example, the data transfer and processing process is shown using animations. The analysis unit also develops tools that visualize the data flow using 3D models and animations to enable users to easily understand the data flow. For example, it provides a function for building a data flow using drag and drop. This allows the data flow to be visualized using 3D models and animations to enable users to intuitively understand it.

[0042] When listing unsorted points, the analysis unit can refer to similar past cases and propose solutions to the unsorted points. For example, when the generation AI lists unsorted points, the analysis unit can refer to similar past cases and propose solutions to the unsorted points. For example, it can propose solutions based on past cases where the contract type was not determined. Furthermore, when listing unsorted points, the analysis unit causes the generation AI to search for similar past cases and propose solutions. For example, it can propose solutions for parts of the data storage method that are not yet determined. Furthermore, the analysis unit builds a system where, when the generation AI lists unsorted points, it refers to similar past cases and proposes solutions. For example, it can propose solutions based on past cases where unsorted points were resolved. In this way, when listing unsorted points, it can refer to similar past cases and propose solutions to the unsorted points.

[0043] The analysis unit can prioritize the unsorted points and present countermeasures according to their importance. For example, the generation AI in the analysis unit prioritizes the unsorted points and presents countermeasures according to their importance. For example, it proposes countermeasures that give top priority to resolving the undetermined parts of the contract type. Furthermore, when prioritizing the unsorted points, the generation AI evaluates their importance and presents countermeasures. For example, it proposes countermeasures that give top priority to resolving the undetermined parts of the data storage method. Furthermore, the analysis unit builds a system in which the generation AI prioritizes the unsorted points and presents countermeasures according to their importance. For example, it scores the importance of the unsorted points and presents countermeasures. This makes it possible to prioritize the unsorted points and present countermeasures according to their importance.

[0044] The analysis unit can compare unsorted points from different industries and fields and find common solutions. For example, the analysis unit allows the generation AI to compare unsorted points from different industries and fields and find common solutions. For example, it compares unsorted points from the IT industry and the manufacturing industry and proposes a common solution. The analysis unit also allows the generation AI to find common solutions when comparing unsorted points from different industries and fields. For example, it proposes a common solution for undetermined parts of data storage methods. The analysis unit also builds a system where the generation AI compares unsorted points from different industries and fields and finds common solutions. For example, it compares unsorted points from different industries and fields and proposes a common solution. This makes it possible to compare unsorted points from different industries and fields and find a common solution.

[0045] The analysis unit can display the unsorted points as an interactive checklist, allowing the user to manage progress. For example, the analysis unit causes the generation AI to display the unsorted points as an interactive checklist, allowing the user to manage progress. For example, the analysis unit displays the unsorted points in checklist format and manages the resolution status. Furthermore, when the analysis unit displays the unsorted points as an interactive checklist, the generation AI updates the progress status in real time. For example, the analysis unit automatically updates the resolution status of the unsorted points. Furthermore, the analysis unit constructs a system in which the generation AI displays the unsorted points as an interactive checklist, allowing the user to manage progress. For example, the analysis unit visually displays the resolution status of the unsorted points. This allows the generation AI to display the unsorted points as an interactive checklist, allowing the user to manage progress.

[0046] When automatically generating an overall layout diagram, the diagram generation unit can refer to past success stories and propose the optimal layout. In the diagram generation unit, for example, the generation AI refers to past success stories and automatically generates the overall layout diagram. For example, it proposes the optimal layout based on past successful business models. In addition, when automatically generating the overall layout diagram, the generation AI analyzes past success stories and proposes the optimal layout. For example, it extracts elements of the success stories and reflects them in the layout diagram. In addition, the diagram generation unit builds a system in which the generation AI refers to past success stories and automatically generates the overall layout diagram. For example, it proposes the optimal layout based on a database of success stories. In this way, when automatically generating the overall layout diagram, it is possible to refer to past success stories and propose the optimal layout.

[0047] The diagram generation unit can analyze the elements included in the seating diagram in detail and clarify the interrelationships between each element. For example, the generation AI in the diagram generation unit analyzes the elements included in the seating diagram in detail and clarify the interrelationships between each element. For example, it analyzes in detail the relationship between Company A and Company B and the flow of data. Furthermore, when the diagram generation unit analyzes the elements included in the seating diagram in detail, the generation AI clarifies the interrelationships. For example, it analyzes the details of contract types and data flows and shows the interrelationships. Furthermore, the diagram generation unit builds a system in which the generation AI analyzes the elements included in the seating diagram in detail and clarifies the interrelationships between each element. For example, it visually displays the relationships between elements. This allows the elements included in the seating diagram to be analyzed in detail and clarifies the interrelationships between each element.

[0048] The diagram generation unit can generate a seating diagram from different perspectives (e.g., technical perspective, business perspective) and provide multiple variations. In the diagram generation unit, for example, a generation AI generates a seating diagram from different perspectives and provides multiple variations. For example, the diagram generation unit generates seating diagrams from a technical perspective and a business perspective. Furthermore, when the diagram generation unit generates a seating diagram from different perspectives, the generation AI provides multiple variations. For example, the diagram generation unit generates seating diagrams from a marketing perspective or a legal perspective. Furthermore, the diagram generation unit constructs a system in which the generation AI generates a seating diagram from different perspectives and provides multiple variations. For example, the system allows a user to select a perspective to generate a seating diagram. This makes it possible to generate a seating diagram from different perspectives and provide multiple variations.

[0049] The diagram generation unit can provide the seating diagram in an interactive format, allowing users to freely add and modify elements. For example, the diagram generation unit can provide the seating diagram in an interactive format using a generation AI, allowing users to freely add and modify elements. For example, a function for adding and modifying elements using drag and drop can be provided. When the diagram generation unit provides the seating diagram in an interactive format, the generation AI reflects user operations in real time. For example, the addition or modification of elements can be immediately reflected in the seating diagram. The diagram generation unit can also build a system in which the generation AI provides the seating diagram in an interactive format, allowing users to freely add and modify elements. For example, the diagram generation unit can allow users to save and share customized seating diagrams. This allows the seating diagram to be provided in an interactive format, allowing users to freely add and modify elements.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The information input section allows the generation AI to automatically complete related information input by the user, encouraging detailed input. For example, when a user inputs a company name and contract details, the generation AI automatically completes that company's past transaction history and related industry information, encouraging detailed input. For example, it may present Company A's past contract history and industry trend information. Furthermore, when a user inputs information about data flow, the information input section automatically completes related data formats and protocols, encouraging detailed input. For example, it may suggest data collection methods and analysis techniques. Furthermore, when a user inputs unsorted points in the information input section, the generation AI searches for similar past cases and automatically completes solutions to the unsorted points. For example, it may present solutions to undetermined parts of the contract form. This allows the generation AI to automatically complete related information input by the user, encouraging detailed input.

[0052] The information input unit can use voice input or image input to enable a user to intuitively input information. For example, it can enable a user to input information necessary for business planning or proposals using voice input. For example, it can use voice recognition technology to convert the user's utterances into text data. The information input unit can also enable a user to input information necessary for business planning or proposals using image input. For example, an image of a contract or data flow diagram can be uploaded, and the generation AI can analyze the content. The information input unit can also combine voice input and image input to provide an interface that allows a user to intuitively input information. For example, it can upload related images while providing a voice explanation. This allows a user to intuitively input information using voice input or image input.

[0053] The information input unit can provide templates based on business models from different industries, allowing users to select and input them. For example, business model templates for the IT industry and manufacturing industry can be provided. The information input unit can also use a generative AI to automatically complete the necessary information based on the template selected by the user and prompt for detailed input. For example, it can suggest how data related to the selected template should be held and how it should flow. The information input unit can also customize business model templates from different industries, allowing users to select the template that best suits their business. For example, it can provide a function to add or delete items from a template. This allows templates based on business models from different industries to be provided, allowing users to select and input them.

[0054] The analysis unit analyzes the characters' past transaction history and relationships to understand detailed relationships. For example, the generation AI analyzes the characters' past transaction history to understand the relationships in detail. For example, it analyzes the past transaction details and contract history between Company A and Company B. When the analysis unit analyzes the characters' relationships, the generation AI performs a risk assessment based on the past transaction history and presents potential risks. For example, it analyzes the history of past troubles and contract violations. The analysis unit also evaluates the strength and reliability of the relationship based on the characters' past transaction history. For example, it calculates the strength of the relationship based on the number of transactions and contract amount. This allows the generation AI to analyze the characters' past transaction history and relationships to understand detailed relationships.

[0055] The analysis unit allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks. For example, the generation AI performs a risk assessment based on the relationship analysis results and presents potential risks. For example, it evaluates contract risks and data leakage risks based on the relationship between Company A and Company B. The analysis unit also allows the generation AI to perform a risk assessment based on the relationship analysis results and propose risk avoidance measures. For example, it proposes a review of contract terms and data protection measures. The analysis unit also allows the generation AI to perform a risk assessment based on the relationship analysis results and calculate a risk score. For example, it calculates a risk score based on the strength and reliability of the relationship and identifies high-risk relationships. This allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks.

[0056] The analysis unit analyzes relationships that include participants from different industries and regions, and can grasp these relationships from a global perspective. For example, it analyzes relationships that include participants from different industries, and can grasp these relationships from a global perspective. For example, it analyzes the relationship between companies in the IT industry and the manufacturing industry. The analysis unit also analyzes relationships that include participants from different regions, and can grasp these relationships from a global perspective. For example, it analyzes the relationship between companies in Asia and Europe. The analysis unit also analyzes relationships that include participants from different industries and regions, and can discover business opportunities from a global perspective. For example, it evaluates the possibility of cooperation between companies in different industries and regions. This makes it possible to analyze relationships that include participants from different industries and regions, and can grasp these relationships from a global perspective.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information input section allows the user to input the information necessary for business planning and proposals. For example, the user can input the company name and contract details. The information input section also allows the user to input information about data flow and unsorted points. For example, the user can input the undetermined parts of the contract type. Step 2: The analysis unit analyzes the information input by the information input unit. For example, it analyzes company names and contract details to understand the parties involved (corporations and organizations) and their relationships (monetization points, contract type, etc.). It can also analyze data flow to understand how the data being handled is held and how it flows. It can also analyze unsorted points and list those that need to be clarified in the future. For example, it can list undetermined parts of contract types and data storage methods. Step 3: The diagram generation unit automatically generates an overall diagram based on the results of the analysis by the analysis unit. For example, it automatically generates an overall diagram that includes the relationships between characters, the flow of data, and points of uncertainty. It also outputs the diagram in Google Slides format, allowing for visual representation using shapes, arrows, icons, and more. In addition to the overall diagram, it can also automatically generate slides that include an overview, summary, points of concern and points of uncertainty that need to be clarified, and proposed solutions.

[0059] (Example 2) The automatic generation system according to an embodiment of the present invention is a system that, by inputting the necessary information when planning or proposing a business targeting a corporation, automatically generates diagrams and slides that allow a grasp of the overall structure, including the characters (corporations and organizations) involved and their relationships (such as monetization points and contract types), the way data is handled and its flow, and a list of any remaining points that need to be clarified in the future. As a result, the automatic generation system can automatically generate diagrams and slides that allow a grasp of the overall structure when planning or proposing a business targeting a corporation, simply by inputting the necessary information.

[0060] The automatic generation system according to the embodiment includes an information input unit, an analysis unit, and a diagram generation unit. The information input unit allows a user to input information necessary for business planning and proposals. For example, the user inputs company names and contract details. The information input unit also allows a user to input information regarding data flow. The information input unit also allows a user to input unsorted points. For example, the user inputs undetermined portions of the contract type. The analysis unit analyzes the information input by the information input unit. For example, the analysis unit analyzes the input company names and contract details to identify the parties (corporate companies and organizations) and their relationships (e.g., monetization points and contract type). The analysis unit can also analyze the data flow and identify how the data is handled and how it flows. The analysis unit can also analyze unsorted points and list unsorted points that need to be clarified. For example, the analysis unit lists undetermined portions of the contract type and undetermined portions of the data storage method. The diagram generation unit automatically generates an overall layout diagram based on the results of the analysis by the analysis unit. For example, the diagram generation unit automatically generates an overall layout diagram that includes the relationships between characters, the flow of data, and points of uncertainty. The diagram generation unit can also output in Google Slides format, visually expressing the diagram using shapes, arrows, icons, and the like. Furthermore, the diagram generation unit can automatically generate slides that include, in addition to the overall layout diagram, an overview, a summary, a list of concerns and issues that need clarification, and proposed solutions. As a result, the automatic generation system according to the embodiment can automatically generate an overall layout diagram simply by inputting the information required for business planning and proposals by the user. For example, the system can be used in situations such as service planning, system development contract proposals, seminar planning, and exhibition planning.

[0061] The information input unit allows the generation AI to automatically complete related information input by the user, encouraging detailed input. For example, when a user inputs a company name or contract details, the generation AI automatically completes that company's past transaction history and related industry information, encouraging detailed input. For example, the unit presents Company A's past contract history and industry trend information. Furthermore, when a user inputs information about data flow, the generation AI automatically completes related data formats and protocols, encouraging detailed input. For example, the unit suggests data collection methods and analysis techniques. Furthermore, when a user inputs unsorted points, the generation AI searches for similar past cases and automatically completes solutions to the unsorted points. For example, the unit presents solutions to undetermined parts of the contract form. This allows the generation AI to automatically complete related information input by the user, encouraging detailed input.

[0062] The information input unit allows the generation AI to search for similar past cases based on the input information and provide reference information. For example, based on the contract type input by the user, the information input unit allows the generation AI to search for similar past contract cases and provide reference contract templates and success stories. For example, it may present past cases similar to the contract type between Company A and Company B. The information input unit also allows the generation AI to search for past data management cases based on the data holding method and flow input by the user and provide reference data management methods and tools. For example, it may show the flow from data collection to analysis. The information input unit also allows the generation AI to search for past solutions to unsorted points based on the unsorted points input by the user and provide reference solutions and countermeasures. For example, it may present solutions to undecided parts of the data storage method. This allows the generation AI to search for similar past cases based on the input information and provide reference information.

[0063] The information input unit can use an emotion estimation function to analyze the user's emotional state and suggest the optimal input method. For example, the information input unit can analyze the user's emotional state in real time when inputting information, and if the user is feeling stressed or anxious, the generation AI can suggest an input method that will help them relax. For example, the information input unit can collect information in the form of simple questions. The information input unit can also analyze the user's emotional state, and if the user's emotions are strong, the generation AI can ask additional questions to encourage more detailed input. For example, the information input unit can ask for specific contract details or data details. The information input unit can also customize the input method based on the user's emotional state, allowing the user to provide information in the most comfortable way. For example, the color or design of the interface can be changed. This allows the generation AI to analyze the user's emotional state and suggest the optimal input method.

[0064] The information input unit can enable a user to intuitively input information using voice input or image input. The information input unit, for example, enables a user to input information necessary for business planning and proposals using voice input. For example, it uses voice recognition technology to convert the user's utterances into text data. The information input unit also enables a user to input information necessary for business planning and proposals using image input. For example, an image of a contract or data flow diagram is uploaded, and the generation AI analyzes the content. The information input unit also combines voice input and image input to provide an interface that allows a user to intuitively input information. For example, related images are uploaded while providing explanations via voice. This enables a user to intuitively input information using voice input or image input.

[0065] The information input unit can provide templates based on business models from different industries, allowing users to select and input them. For example, the information input unit can provide templates based on business models from different industries, allowing users to select and input them. For example, business model templates for the IT industry and manufacturing industry can be prepared. Furthermore, the information input unit can have the generative AI automatically complete the necessary information based on the template selected by the user and prompt for detailed input. For example, the information input unit can suggest how data related to the selected template should be held and how it should flow. Furthermore, the information input unit can customize business model templates from different industries, allowing users to select the template that is best suited to their business. For example, the information input unit can provide a function to add or delete items from the template. This allows the information input unit to provide templates based on business models from different industries, allowing users to select and input them.

[0066] The information input unit can use the emotion estimation function to provide real-time feedback on the emotions felt when the user makes an input, thereby providing an interface that elicits positive emotions. For example, the information input unit can analyze the emotions felt when the user makes an input in real time and provide feedback to elicit positive emotions. For example, it can display encouraging messages or positive feedback. The information input unit can also use the emotion estimation function to analyze the emotional state of the user when making an input, thereby providing an interface that elicits positive emotions. For example, it can change the color or design of the interface. The information input unit can also customize the input method using a generation AI based on the user's emotional state, making suggestions to elicit positive emotions. For example, it can display appropriate encouragement or praise according to the input content. This makes it possible to provide real-time feedback on the emotions felt when the user makes an input, thereby providing an interface that elicits positive emotions.

[0067] The analysis unit analyzes the characters' past transaction history and relationships, and is able to grasp detailed relationships. For example, the generation AI analyzes the characters' past transaction history to grasp detailed relationships. For example, it analyzes the past transaction details and contract history between Company A and Company B. When analyzing the characters' relationships, the generation AI also performs a risk assessment based on the past transaction history and presents potential risks. For example, it analyzes the history of past troubles and contract violations. The analysis unit also evaluates the strength and reliability of the relationship based on the characters' past transaction history. For example, it calculates the strength of the relationship based on the number of transactions and contract amounts. This allows the generation AI to analyze the characters' past transaction history and relationships, and grasp detailed relationships.

[0068] The analysis unit allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks. For example, the analysis unit allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks. For example, the analysis unit evaluates contract risks and data leakage risks based on the relationship between Company A and Company B. The analysis unit also allows the generation AI to perform a risk assessment based on the relationship analysis results and propose risk avoidance measures. For example, the analysis unit proposes a review of contract content and data protection measures. The analysis unit also allows the generation AI to perform a risk assessment based on the relationship analysis results and calculate a risk score. For example, the analysis unit calculates a risk score based on the strength and reliability of the relationship and identifies high-risk relationships. This allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks.

[0069] The analysis unit can use the emotion estimation function to analyze the emotional relationships between characters and evaluate the likelihood of business success. For example, the analysis unit uses the emotion estimation function to analyze the emotional relationships between characters and evaluate the likelihood of business success. For example, the analysis unit analyzes the emotional relationships between companies A and B and evaluates the possibility of cooperation. When analyzing the emotional relationships between characters, the generative AI calculates an emotion score and evaluates the likelihood of business success. For example, relationships with strong positive emotions are prioritized in evaluation. The analysis unit also uses the emotion estimation function to monitor the emotional relationships between characters in real time and evaluate the likelihood of business success. For example, the analysis unit analyzes fluctuations in the emotion score and tracks changes in the relationships. This makes it possible to analyze the emotional relationships between characters and evaluate the likelihood of business success.

[0070] The analysis unit analyzes relationships that include participants from different industries and regions, and is able to grasp those relationships from a global perspective. For example, the analysis unit analyzes relationships that include participants from different industries, and is able to grasp those relationships from a global perspective. For example, the analysis unit analyzes the relationships between companies in the IT industry and the manufacturing industry. The analysis unit also analyzes relationships that include participants from different regions, and is able to grasp those relationships from a global perspective. For example, the analysis unit analyzes the relationships between companies in Asia and Europe. The analysis unit also analyzes relationships that include participants from different industries and regions, and is able to discover business opportunities from a global perspective. For example, the analysis unit evaluates the possibility of cooperation between companies in different industries and regions. This makes it possible to analyze relationships that include participants from different industries and regions, and is able to grasp those relationships from a global perspective.

[0071] The analysis unit can display the relationship analysis results as a visual map, allowing the user to intuitively understand. The analysis unit, for example, displays the relationship analysis results as a visual map, allowing the user to intuitively understand. For example, the relationships between companies are represented by nodes and edges. The analysis unit also uses the visual map to visually display the strength and reliability of the relationships. For example, the strength of the relationships is indicated by color or thickness. The analysis unit also displays the relationship analysis results as a visual map, allowing the user to access detailed information by clicking or zooming. For example, detailed information about each company is displayed in a pop-up. In this way, the relationship analysis results can be displayed as a visual map, allowing the user to intuitively understand.

[0072] The analysis unit can use the emotion estimation function to monitor the emotional relationships between characters in real time and track changes in the relationships. For example, the analysis unit uses the emotion estimation function to monitor the emotional relationships between characters in real time and track changes in the relationships. For example, the analysis unit analyzes fluctuations in the emotion scores between companies A and B. The analysis unit also builds a system that monitors the emotional relationships between characters in real time and tracks changes in the relationships. For example, the analysis unit displays fluctuations in the emotion scores in a graph. The analysis unit also uses the emotion estimation function to monitor the emotional relationships between characters in real time and evaluate the possibility of business success. For example, the strength of the relationship is evaluated based on fluctuations in the emotion scores. This makes it possible to monitor the emotional relationships between characters in real time and track changes in the relationships.

[0073] The analysis unit can evaluate data security risks and propose countermeasures when analyzing how data is held and how it flows. For example, the generation AI analyzes how data is held and how it flows to evaluate the data security risks. For example, it analyzes data transfer paths and storage locations to identify potential security risks. The analysis unit also has the generation AI propose countermeasures based on the results of the data security risk assessment. For example, it proposes the introduction of encryption technology and strengthening access control. The analysis unit also refers to past security incidents when the generation AI analyzes how data is held and how it flows to assess security risks and propose countermeasures. For example, it performs risk assessments based on past data leak cases. This allows the generation AI to evaluate data security risks and propose countermeasures when analyzing how data is held and how it flows.

[0074] When analyzing the data flow, the analysis unit allows the generation AI to evaluate the quality and reliability of the data and suggest areas for improvement. For example, the analysis unit allows the generation AI to analyze the data flow and evaluate the quality and reliability of the data. For example, it analyzes the data collection method and analysis technique and evaluates the quality and reliability. Furthermore, the analysis unit allows the generation AI to suggest areas for improvement based on the results of evaluating the data quality and reliability. For example, it may suggest revising the data collection method or improving the analysis technique. Furthermore, when the generation AI analyzes the data flow and evaluates the quality and reliability, the analysis unit refers to past data quality issues and suggests areas for improvement. For example, it performs a quality assessment based on past cases of data inconsistency. This allows the generation AI to evaluate the quality and reliability of the data and suggest areas for improvement when analyzing the data flow.

[0075] The analysis unit can use the emotion estimation function to analyze the emotions of people involved in the data flow and optimize the data flow. For example, the analysis unit uses the emotion estimation function to analyze the emotions of people involved in the data flow and optimize the data flow. For example, the analysis unit analyzes the emotional state of data collectors and suggests ways to reduce stress. Furthermore, when analyzing the emotions of people involved in the data flow, the generative AI calculates an emotion score and optimizes the data flow. For example, the analysis unit prioritizes the adoption of data flows with strong positive emotions. Furthermore, the analysis unit uses the emotion estimation function to monitor the emotions of people involved in the data flow in real time and optimize the data flow. For example, the analysis unit analyzes fluctuations in the emotion score and identifies areas for improvement in the data flow. This makes it possible to analyze the emotions of people involved in the data flow and optimize the data flow.

[0076] The analysis unit can analyze data flows that correspond to different data formats and protocols and ensure compatibility. For example, the analysis unit allows the generation AI to analyze data flows that correspond to different data formats and protocols and ensure compatibility. For example, it analyzes different data formats such as CSV, JSON, and XML. The analysis unit also allows the generation AI to propose data conversion methods to support different data formats and protocols. For example, it proposes data format conversion tools and protocol bridges. The analysis unit also allows the generation AI to analyze data flows that correspond to different data formats and protocols and propose best practices to ensure compatibility. For example, it proposes standardization of data formats and unification of protocols. This allows the generation AI to analyze data flows that correspond to different data formats and protocols and ensure compatibility.

[0077] The analysis unit can visualize the data flow using 3D models and animations to enable users to intuitively understand it. For example, the analysis unit uses a generative AI to visualize the data flow using 3D models to enable users to intuitively understand it. For example, the flow of data collection, analysis, and utilization is displayed using a 3D model. The analysis unit also visualizes the data flow using animations to enable users to intuitively understand the movement of data. For example, the data transfer and processing process is shown using animations. The analysis unit also develops tools that visualize the data flow using 3D models and animations to enable users to easily understand the data flow. For example, it provides a function for building a data flow using drag and drop. This allows the data flow to be visualized using 3D models and animations to enable users to intuitively understand it.

[0078] The analysis unit uses the emotion estimation function to monitor the emotions of people involved in the data flow in real time, and can promote improvements to the data flow. The analysis unit, for example, uses the emotion estimation function to monitor the emotions of people involved in the data flow in real time, and promotes improvements to the data flow. For example, the analysis unit analyzes the emotional state of a data collector and suggests ways to reduce stress. The analysis unit also monitors the emotions of people involved in the data flow in real time, and identifies areas for improvement in the data flow. For example, the analysis unit analyzes fluctuations in emotion scores and optimizes the data flow. The analysis unit also uses the emotion estimation function to build a system that monitors the emotions of people involved in the data flow in real time, and promotes improvements to the data flow. For example, the analysis unit identifies areas for improvement in the data flow based on fluctuations in emotion scores. This makes it possible to monitor the emotions of people involved in the data flow in real time, and promote improvements to the data flow.

[0079] When listing unsorted points, the analysis unit can refer to similar past cases and propose solutions to the unsorted points. For example, when the generation AI lists unsorted points, the analysis unit can refer to similar past cases and propose solutions to the unsorted points. For example, it can propose solutions based on past cases where the contract type was not determined. Furthermore, when listing unsorted points, the analysis unit causes the generation AI to search for similar past cases and propose solutions. For example, it can propose solutions for parts of the data storage method that are not yet determined. Furthermore, the analysis unit builds a system where, when the generation AI lists unsorted points, it refers to similar past cases and proposes solutions. For example, it can propose solutions based on past cases where unsorted points were resolved. In this way, when listing unsorted points, it can refer to similar past cases and propose solutions to the unsorted points.

[0080] The analysis unit can prioritize the unsorted points and present countermeasures according to their importance. For example, the generation AI in the analysis unit prioritizes the unsorted points and presents countermeasures according to their importance. For example, it proposes countermeasures that give top priority to resolving the undetermined parts of the contract type. Furthermore, when prioritizing the unsorted points, the generation AI evaluates their importance and presents countermeasures. For example, it proposes countermeasures that give top priority to resolving the undetermined parts of the data storage method. Furthermore, the analysis unit builds a system in which the generation AI prioritizes the unsorted points and presents countermeasures according to their importance. For example, it scores the importance of the unsorted points and presents countermeasures. This makes it possible to prioritize the unsorted points and present countermeasures according to their importance.

[0081] The analysis unit can use the emotion estimation function to analyze the emotions of the parties involved regarding the unsorted points and evaluate the acceptability of a solution. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of the parties involved regarding the unsorted points and evaluate the acceptability of a solution. For example, the analysis unit analyzes the emotions of the parties involved regarding the undetermined parts of the contract form. Furthermore, when the analysis unit analyzes the emotions of the parties involved regarding the unsorted points, the generation AI calculates an emotion score and evaluates the acceptability of a solution. For example, the analysis unit preferentially proposes solutions with strong positive emotions. Furthermore, the analysis unit uses the emotion estimation function to monitor the emotions of the parties involved regarding the unsorted points in real time and evaluate the acceptability of a solution. For example, the analysis unit analyzes fluctuations in the emotion score and evaluates the acceptability of a solution. In this way, the emotions of the parties involved regarding the unsorted points can be analyzed and the acceptability of a solution can be evaluated.

[0082] The analysis unit can compare unsorted points from different industries and fields and find common solutions. For example, the analysis unit allows the generation AI to compare unsorted points from different industries and fields and find common solutions. For example, it compares unsorted points from the IT industry and the manufacturing industry and proposes a common solution. The analysis unit also allows the generation AI to find common solutions when comparing unsorted points from different industries and fields. For example, it proposes a common solution for undetermined parts of data storage methods. The analysis unit also builds a system where the generation AI compares unsorted points from different industries and fields and finds common solutions. For example, it compares unsorted points from different industries and fields and proposes a common solution. This makes it possible to compare unsorted points from different industries and fields and find a common solution.

[0083] The analysis unit can display the unsorted points as an interactive checklist, allowing the user to manage progress. For example, the analysis unit causes the generation AI to display the unsorted points as an interactive checklist, allowing the user to manage progress. For example, the analysis unit displays the unsorted points in checklist format and manages the resolution status. Furthermore, when the analysis unit displays the unsorted points as an interactive checklist, the generation AI updates the progress status in real time. For example, the analysis unit automatically updates the resolution status of the unsorted points. Furthermore, the analysis unit constructs a system in which the generation AI displays the unsorted points as an interactive checklist, allowing the user to manage progress. For example, the analysis unit visually displays the resolution status of the unsorted points. This allows the generation AI to display the unsorted points as an interactive checklist, allowing the user to manage progress.

[0084] The analysis unit can use the emotion estimation function to monitor the emotions of the parties regarding the unsorted points in real time and optimize a solution. The analysis unit, for example, uses the emotion estimation function to monitor the emotions of the parties regarding the unsorted points in real time and optimize a solution. For example, the analysis unit analyzes the emotions of the parties regarding the undetermined parts of the contract form. The analysis unit also builds a system that monitors the emotions of the parties regarding the unsorted points in real time and optimizes a solution. For example, the analysis unit analyzes fluctuations in the emotion score and optimizes a solution. The analysis unit also uses the emotion estimation function to monitor the emotions of the parties regarding the unsorted points in real time and optimizes a solution. For example, the analysis unit optimizes a solution based on fluctuations in the emotion score. In this way, the emotions of the parties regarding the unsorted points can be monitored in real time and a solution can be optimized.

[0085] When automatically generating an overall layout diagram, the diagram generation unit can refer to past success stories and propose the optimal layout. In the diagram generation unit, for example, the generation AI refers to past success stories and automatically generates the overall layout diagram. For example, it proposes the optimal layout based on past successful business models. In addition, when automatically generating the overall layout diagram, the generation AI analyzes past success stories and proposes the optimal layout. For example, it extracts elements of the success stories and reflects them in the layout diagram. In addition, the diagram generation unit builds a system in which the generation AI refers to past success stories and automatically generates the overall layout diagram. For example, it proposes the optimal layout based on a database of success stories. In this way, when automatically generating the overall layout diagram, it is possible to refer to past success stories and propose the optimal layout.

[0086] The diagram generation unit can analyze the elements included in the seating diagram in detail and clarify the interrelationships between each element. For example, the generation AI in the diagram generation unit analyzes the elements included in the seating diagram in detail and clarify the interrelationships between each element. For example, it analyzes in detail the relationship between Company A and Company B and the flow of data. Furthermore, when the diagram generation unit analyzes the elements included in the seating diagram in detail, the generation AI clarifies the interrelationships. For example, it analyzes the details of contract types and data flows and shows the interrelationships. Furthermore, the diagram generation unit builds a system in which the generation AI analyzes the elements included in the seating diagram in detail and clarifies the interrelationships between each element. For example, it visually displays the relationships between elements. This allows the elements included in the seating diagram to be analyzed in detail and clarifies the interrelationships between each element.

[0087] The diagram generation unit can use the emotion estimation function to analyze the user's emotions regarding the seating diagram and generate a diagram that is visually easy to understand. The diagram generation unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the seating diagram and generate a diagram that is visually easy to understand. For example, the diagram design is adjusted according to the user's emotional state. Furthermore, when the diagram generation unit analyzes the user's emotions regarding the seating diagram, the generation AI calculates an emotion score and generates a diagram that is visually easy to understand. For example, a design that indicates a strong positive emotion is adopted. Furthermore, the diagram generation unit uses the emotion estimation function to monitor the user's emotions regarding the seating diagram in real time and generate a diagram that is visually easy to understand. For example, the diagram design is adjusted based on fluctuations in the emotion score. In this way, the user's emotions regarding the seating diagram can be analyzed and a diagram that is visually easy to understand can be generated.

[0088] The diagram generation unit can generate a seating diagram from different perspectives (e.g., technical perspective, business perspective) and provide multiple variations. In the diagram generation unit, for example, a generation AI generates a seating diagram from different perspectives and provides multiple variations. For example, the diagram generation unit generates seating diagrams from a technical perspective and a business perspective. Furthermore, when the diagram generation unit generates a seating diagram from different perspectives, the generation AI provides multiple variations. For example, the diagram generation unit generates seating diagrams from a marketing perspective or a legal perspective. Furthermore, the diagram generation unit constructs a system in which the generation AI generates a seating diagram from different perspectives and provides multiple variations. For example, the system allows a user to select a perspective to generate a seating diagram. This makes it possible to generate a seating diagram from different perspectives and provide multiple variations.

[0089] The diagram generation unit can provide the seating diagram in an interactive format, allowing users to freely add and modify elements. For example, the diagram generation unit can provide the seating diagram in an interactive format using a generation AI, allowing users to freely add and modify elements. For example, a function for adding and modifying elements using drag and drop can be provided. When the diagram generation unit provides the seating diagram in an interactive format, the generation AI reflects user operations in real time. For example, the addition or modification of elements can be immediately reflected in the seating diagram. The diagram generation unit can also build a system in which the generation AI provides the seating diagram in an interactive format, allowing users to freely add and modify elements. For example, the diagram generation unit can allow users to save and share customized seating diagrams. This allows the seating diagram to be provided in an interactive format, allowing users to freely add and modify elements.

[0090] The diagram generation unit can use the emotion estimation function to monitor the user's emotion regarding the seating chart in real time and continuously generate optimal diagrams. The diagram generation unit, for example, uses the emotion estimation function to monitor the user's emotion regarding the seating chart in real time and continuously generate optimal diagrams. For example, the diagram design is adjusted according to the user's emotional state. The diagram generation unit also builds a system that monitors the user's emotion regarding the seating chart in real time and continuously generates optimal diagrams. For example, the diagram design is adjusted based on fluctuations in the emotion score. The diagram generation unit also uses the emotion estimation function to monitor the user's emotion regarding the seating chart in real time and continuously generate optimal diagrams. For example, the diagram design is adjusted based on fluctuations in the emotion score. In this way, the user's emotion regarding the seating chart can be monitored in real time and optimal diagrams can be continuously generated.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The information input section allows the generation AI to automatically complete related information input by the user, encouraging detailed input. For example, when a user inputs a company name and contract details, the generation AI automatically completes that company's past transaction history and related industry information, encouraging detailed input. For example, it may present Company A's past contract history and industry trend information. Furthermore, when a user inputs information about data flow, the information input section automatically completes related data formats and protocols, encouraging detailed input. For example, it may suggest data collection methods and analysis techniques. Furthermore, when a user inputs unsorted points in the information input section, the generation AI searches for similar past cases and automatically completes solutions to the unsorted points. For example, it may present solutions to undetermined parts of the contract form. This allows the generation AI to automatically complete related information input by the user, encouraging detailed input.

[0093] The information input unit can use its emotion estimation function to analyze the user's emotional state and suggest the optimal input method. For example, it can analyze the user's emotional state in real time when inputting information, and if the user is feeling stressed or anxious, the generation AI can suggest an input method that will help them relax. For example, it can collect information in the form of simple questions. The information input unit can also analyze the user's emotional state, and if the user's emotions are strong, the generation AI can ask additional questions to encourage more detailed input. For example, it can ask for specific contract details or data details. The information input unit can also customize the input method based on the user's emotional state, allowing the user to provide information in the most comfortable way. For example, it can change the color or design of the interface. This allows the generation AI to analyze the user's emotional state and suggest the optimal input method.

[0094] The information input unit can use voice input or image input to enable a user to intuitively input information. For example, it can enable a user to input information necessary for business planning or proposals using voice input. For example, it can use voice recognition technology to convert the user's utterances into text data. The information input unit can also enable a user to input information necessary for business planning or proposals using image input. For example, an image of a contract or data flow diagram can be uploaded, and the generation AI can analyze the content. The information input unit can also combine voice input and image input to provide an interface that allows a user to intuitively input information. For example, it can upload related images while providing a voice explanation. This allows a user to intuitively input information using voice input or image input.

[0095] The information input unit can provide templates based on business models from different industries, allowing users to select and input them. For example, business model templates for the IT industry and manufacturing industry can be provided. The information input unit can also use a generative AI to automatically complete the necessary information based on the template selected by the user and prompt for detailed input. For example, it can suggest how data related to the selected template should be held and how it should flow. The information input unit can also customize business model templates from different industries, allowing users to select the template that best suits their business. For example, it can provide a function to add or delete items from a template. This allows templates based on business models from different industries to be provided, allowing users to select and input them.

[0096] The information input unit can use the emotion estimation function to provide real-time feedback on the emotions felt when the user makes input, thereby providing an interface that elicits positive emotions. For example, the emotion estimated by the emotion estimation function can be analyzed in real time to provide feedback designed to elicit positive emotions. For example, encouraging messages or positive feedback can be displayed. The information input unit can also use the emotion estimation function to analyze the emotional state of the user when making input, thereby providing an interface that elicits positive emotions. For example, the color or design of the interface can be changed. The information input unit can also customize the input method using a generation AI based on the user's emotional state, making suggestions to elicit positive emotions. For example, appropriate encouragement or praise can be displayed according to the input content. This makes it possible to provide real-time feedback on the emotions felt when the user makes input, thereby providing an interface that elicits positive emotions.

[0097] The analysis unit analyzes the characters' past transaction history and relationships to understand detailed relationships. For example, the generation AI analyzes the characters' past transaction history to understand the relationships in detail. For example, it analyzes the past transaction details and contract history between Company A and Company B. When the analysis unit analyzes the characters' relationships, the generation AI performs a risk assessment based on the past transaction history and presents potential risks. For example, it analyzes the history of past troubles and contract violations. The analysis unit also evaluates the strength and reliability of the relationship based on the characters' past transaction history. For example, it calculates the strength of the relationship based on the number of transactions and contract amount. This allows the generation AI to analyze the characters' past transaction history and relationships to understand detailed relationships.

[0098] The analysis unit allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks. For example, the generation AI performs a risk assessment based on the relationship analysis results and presents potential risks. For example, it evaluates contract risks and data leakage risks based on the relationship between Company A and Company B. The analysis unit also allows the generation AI to perform a risk assessment based on the relationship analysis results and propose risk avoidance measures. For example, it proposes a review of contract terms and data protection measures. The analysis unit also allows the generation AI to perform a risk assessment based on the relationship analysis results and calculate a risk score. For example, it calculates a risk score based on the strength and reliability of the relationship and identifies high-risk relationships. This allows the generation AI to perform a risk assessment based on the relationship analysis results and present potential risks.

[0099] The analysis unit can use the emotion estimation function to analyze the emotional relationships between characters and evaluate the likelihood of business success. For example, the emotion estimation function can be used to analyze the emotional relationships between characters and evaluate the likelihood of business success. For example, the emotional relationship between Company A and Company B can be analyzed to evaluate the possibility of cooperation. Furthermore, when the analysis unit analyzes the emotional relationships between characters, the generative AI calculates an emotion score and evaluates the likelihood of business success. For example, relationships with strong positive emotions can be prioritized in evaluation. Furthermore, the analysis unit can use the emotion estimation function to monitor the emotional relationships between characters in real time and evaluate the likelihood of business success. For example, the analysis unit can analyze fluctuations in the emotion score and track changes in the relationship. This makes it possible to analyze the emotional relationships between characters and evaluate the likelihood of business success.

[0100] The analysis unit analyzes relationships that include participants from different industries and regions, and can grasp these relationships from a global perspective. For example, it analyzes relationships that include participants from different industries, and can grasp these relationships from a global perspective. For example, it analyzes the relationship between companies in the IT industry and the manufacturing industry. The analysis unit also analyzes relationships that include participants from different regions, and can grasp these relationships from a global perspective. For example, it analyzes the relationship between companies in Asia and Europe. The analysis unit also analyzes relationships that include participants from different industries and regions, and can discover business opportunities from a global perspective. For example, it evaluates the possibility of cooperation between companies in different industries and regions. This makes it possible to analyze relationships that include participants from different industries and regions, and can grasp these relationships from a global perspective.

[0101] The analysis unit can use the emotion estimation function to monitor the emotional relationships between characters in real time and track changes in the relationships. For example, the emotion estimation function can be used to monitor the emotional relationships between characters in real time and track changes in the relationships. For example, the analysis unit analyzes fluctuations in the emotion scores between companies A and B. The analysis unit also builds a system that monitors the emotional relationships between characters in real time and tracks changes in the relationships. For example, it displays fluctuations in the emotion scores in a graph. The analysis unit also uses the emotion estimation function to monitor the emotional relationships between characters in real time and evaluate the possibility of business success. For example, it evaluates the strength of the relationships based on fluctuations in the emotion scores. This makes it possible to monitor the emotional relationships between characters in real time and track changes in the relationships.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The information input section allows the user to input the information necessary for business planning and proposals. For example, the user can input the company name and contract details. The information input section also allows the user to input information about data flow and unsorted points. For example, the user can input the undetermined parts of the contract type. Step 2: The analysis unit analyzes the information input by the information input unit. For example, it analyzes company names and contract details to understand the parties involved (corporations and organizations) and their relationships (monetization points, contract type, etc.). It can also analyze data flow to understand how the data being handled is held and how it flows. It can also analyze unsorted points and list those that need to be clarified in the future. For example, it can list undetermined parts of contract types and data storage methods. Step 3: The diagram generation unit automatically generates an overall diagram based on the results of the analysis by the analysis unit. For example, it automatically generates an overall diagram that includes the relationships between characters, the flow of data, and points of uncertainty. It also outputs the diagram in Google Slides format, allowing for visual representation using shapes, arrows, icons, and more. In addition to the overall diagram, it can also automatically generate slides that include an overview, summary, points of concern and points of uncertainty that need to be clarified, and proposed solutions.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information input section where users input information necessary for business planning and proposals; an analysis unit that analyzes the information input by the information input unit; A diagram generating unit that automatically generates an overall assembly diagram based on the results of the analysis by the analysis unit. A system characterized by:

2. The information input unit The AI ​​automatically complements the information the user inputs with related information, prompting further input.

2. The system of claim 1.

3. The information input unit Based on the input information, the generative AI searches for similar past cases and provides reference information.

2. The system of claim 1.

4. The information input unit Analyze the user's emotional state and suggest the optimal input method 2. The system of claim 1.

5. The information input unit Using voice input and image input, the user can intuitively input information.

2. The system of claim 1.

6. The information input unit Provide templates based on business models from different industries, allowing users to select and input.

2. The system of claim 1.

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