System

Abstract

An object of the system according to the embodiment is to efficiently organize information necessary for business planning and proposals and provide the information in a visually easy-to-understand format.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. A reception part inputs information necessary for business planning and proposal. The analysis unit analyzes the information input by the reception unit, and lists characters and their relationships, how to hold and flow data, and unexamined points. The generation unit generates a business model diagram and a check list based on the information obtained by the analysis unit. The providing unit provides the output generated by the generating unit in a presentation format.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 technologies have had the problem of making it difficult to efficiently organize the information necessary for business planning and proposals and provide it in a visually easy-to-understand format.

[0005] The system according to the embodiment aims to efficiently organize information necessary for business planning and proposals and provide it in a visually easy-to-understand format. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information necessary for business planning and proposals. The analysis unit analyzes the information input by the reception unit and lists the characters and their relationships, the data handling and flow, and any unconsidered points. The generation unit generates a business model diagram and a checklist based on the information obtained by the analysis unit. The provision unit provides the output generated by the generation unit in a presentation format. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize information necessary for business planning and proposals and provide it in a visually easy-to-understand format. [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) A business planning and proposal system according to an embodiment of the present invention automatically generates diagrams and checklists that provide an overview of the overall structure of a business plan or proposal, including the characters (corporations and organizations) and their relationships (e.g., monetization points and contract types), data ownership and flow, and unconsiderated points. The business planning and proposal system allows users to input the information necessary for a business plan or proposal, and the generation AI analyzes the input information to list the characters and their relationships, data ownership and flow, and unconsiderated points. The generation AI automatically generates business model diagrams and checklists. For example, the business planning and proposal system may generate a business model diagram that shows the structure of the relationships between the characters, and use text, shapes, arrows, icons, and other elements to represent the positions and contractual relationships of each character, such as the data subjects, the data handling methods and content, and monetization points. Furthermore, the business planning and proposal system may generate income and expenditure structures and plans, lists of concerns and unresolved issues, proposed solutions, and proposed service terms. This allows users to grasp the overall picture of their business plan or proposal. For example, a business planning proposal system provides generated output in a presentation format. This allows users to use the generated diagrams and other images. For example, when planning an event such as a seminar or exhibition, a presentation can be made using the generated diagrams. This system allows users to efficiently create complex business plans and proposals. For example, when proposing a system development contract, the generated business model diagram and checklist can be used to clearly communicate the proposal content. In addition, since multiple input methods and multiple types of output documents can be generated, flexible responses can be made to meet user needs. This allows the business planning proposal system to efficiently grasp the overall picture of the business plan or proposal and provide it in a presentation format. For example, users can quickly and accurately create business plans and proposals and clearly communicate the proposal content.Furthermore, users can use the generated diagram images to make presentations at events such as seminars and exhibitions, thereby enabling users to efficiently carry out complex business plans and proposals.

[0029] A business planning and proposal system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information necessary for business planning and proposals. Examples of information necessary for business planning and proposals include, but are not limited to, market research data, competitive analysis, and financial data. The reception unit can input information using, for example, web-based chat or voice input during a conference. The analysis unit analyzes the information input by the reception unit and lists the participants and their relationships, the data handling and flow, and any unconsidered points. The analysis unit analyzes the input information using, for example, natural language processing or a machine learning algorithm. Examples of natural language processing include morphological analysis, grammatical analysis, and semantic analysis. Examples of machine learning algorithms include K-means and hierarchical clustering. The generation unit generates a business model diagram or a checklist based on the information obtained by the analysis unit. The generation unit generates, for example, a business model diagram that depicts a structure that shows the relationships between the participants. The generation unit can also express the positions and contractual relationships of each character, such as the subject of the data being handled, the data handling method and content, and monetization points using text, shapes, arrows, and icons. Furthermore, the generation unit can generate a revenue and expenditure structure and plan, a list of concerns and unresolved issues, proposed solutions, and proposed service terms. The provision unit provides the output generated by the generation unit in a presentation format. The provision unit can provide the output in the form of, for example, a slideshow or video presentation. This allows the business planning and proposal system according to the embodiment to efficiently grasp the overall picture of a business plan or proposal and provide it in a presentation format. For example, users can quickly and accurately create business plans and proposals and clearly communicate the content of their proposals. Furthermore, users can use the generated diagrams and images to make presentations at event planning events such as seminars and exhibitions. This allows users to efficiently create complex business plans and proposals.

[0030] The reception unit can input information using a web-based chat or voice input during a meeting. Examples of web-based chat include Slack (registered trademark) and Microsoft Teams (registered trademark). For example, a user can input information to the reception unit using a web-based chat tool. The reception unit can also input information using voice input during a meeting. Voice input includes, for example, voice recognition software and microphone settings. For example, the reception unit can input information using voice input during a meeting. This allows users to input information in a variety of ways. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice-input data to a generation AI and have the generation AI convert the voice data into text data.

[0031] The analysis unit can analyze the input information using natural language processing or a machine learning algorithm. Natural language processing includes, for example, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit analyzes the input information using, for example, morphological analysis. The analysis unit can also analyze the input information using grammatical analysis. The analysis unit can also analyze the input information using semantic analysis. Machine learning algorithms include, for example, K-means and hierarchical clustering. The analysis unit analyzes the input information using, for example, K-means. The analysis unit can also analyze the input information using hierarchical clustering. This improves the analysis accuracy of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the input information to a generation AI and cause the generation AI to output the analysis results.

[0032] The generation unit can generate a business model diagram that expresses a structure that shows the relationships between the characters. The structure includes, for example, an organization chart and a role assignment diagram. The generation unit, for example, uses an organization chart to express a structure that shows the relationships between the characters. The generation unit can also use a role assignment diagram to express a structure that shows the relationships between the characters. This makes it possible to generate a business model diagram. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data regarding the relationships between the characters into the generation AI and cause the generation AI to generate a business model diagram.

[0033] The generation unit can represent the subject of the data being handled, the respective positions and contractual relationships of the characters, the data handling method and content, and monetization points using text, shapes, arrows, and icons. Examples of the text, shapes, arrows, and icons include text editors and graphic software. The generation unit can represent the positions and contractual relationships of the subject of the data being handled and the characters using, for example, a text editor. The generation unit can also represent the data handling method and content and monetization points using graphic software. This allows for visual representation of the data handling method and monetization points. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input information regarding data handling into the generation AI and have the generation AI execute a visual representation.

[0034] The generation unit can generate an income and expenditure structure and plan, items of concerns and unresolved matters, proposed countermeasures for those, and proposed service terms. The income and expenditure structure and plan include, for example, an income and expenditure forecast and a budget plan. The generation unit can, for example, generate the income and expenditure structure and plan using the income and expenditure forecast. The generation unit can also generate the income and expenditure structure and plan using the budget plan. The concerns and unresolved matters include, for example, risk factors and unresolved issues. The generation unit can, for example, generate concerns by listing risk factors. The generation unit can also generate unresolved matters by listing unresolved issues. The proposed countermeasures include, for example, a risk management plan and solutions to problems. The generation unit can, for example, generate countermeasures using the risk management plan. The generation unit can also generate countermeasures using solutions to problems. The proposed service terms include, for example, terms of use and privacy policy. The generation unit can, for example, generate draft service terms using terms of use. The generation unit can also generate draft service terms using a privacy policy. This makes it possible to automatically generate countermeasures for the income and expenditure structure and the concerns. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input information about income and expenditure structure and concerns into the generation AI, and cause the generation AI to generate countermeasure proposals and service terms proposals.

[0035] The providing unit can provide the generated output in a presentation format. Examples of presentation formats include a slide show and a video presentation. The providing unit can provide the generated output in, for example, a slide show format. The providing unit can also provide the generated output in a video presentation format. This allows the generated output to be provided in a presentation format. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the generated output to a generation AI and cause the generation AI to provide the output in a presentation format.

[0036] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can preferentially suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. The reception unit can also analyze the user's past input history and select the most efficient input method. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0037] The reception unit can filter the input information based on the user's current project or area of ​​interest. For example, the reception unit can preferentially input only information related to the user's current project. The reception unit can also filter and input highly relevant information based on the user's area of ​​interest. The reception unit can also filter and input optimal information by referring to the user's past project history. This makes it possible to filter information based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or can be performed without using AI. For example, the reception unit can input data related to the user's project or area of ​​interest to the generation AI and have the generation AI perform the filtering.

[0038] The reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user desires voice input, the reception unit performs the input using voice recognition technology. Furthermore, if the user desires text input, the reception unit can provide a text box for input. Furthermore, if the user desires image input, the reception unit can perform the input using image recognition technology. This makes it possible to select the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data related to the user's input method to the generation AI and have the generation AI select the optimal input means.

[0039] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, when the user is in a specific area, the reception unit prioritizes inputting information related to that area. The reception unit can also filter and input optimal information based on the user's geographical location information. Furthermore, when the user is moving, the reception unit can also input highly relevant information based on the user's current location. This makes it possible to prioritize inputting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to filter highly relevant information.

[0040] The reception unit can analyze the user's social media activities and input related information at the time of input. The reception unit can input related information based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity history and input optimal information. The reception unit can also input related information by referring to the activities of the user's friends on social media. This makes it possible to input related information based on the user's social media activities. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related information.

[0041] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input procedure by reflecting the user's past feedback. The reception unit can also customize the input method by referring to the user's past feedback. This makes it possible to customize the input method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. This makes it possible to adjust the level of detail of the analysis according to the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a business-specific analysis algorithm to business information. The analysis unit can also apply a technology-specific analysis algorithm to technical information. The analysis unit can also select and apply an optimal analysis algorithm depending on the category of information. This makes it possible to apply an optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the category of information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the user's past analysis results. The analysis unit can also optimize analysis parameters by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the time of information submission. This makes it possible to determine the priority of analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the time of information submission to the generation AI and have the generation AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of information. This makes it possible to adjust the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the relevance of information to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results that avoid technical terminology. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis results according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the user's level of expertise to the generation AI and have the generation AI use technical terminology.

[0048] The generation unit can adjust the level of detail of the generation based on the importance of the information during generation. For example, the generation unit generates detailed output for information with high importance. The generation unit can also generate simplified output for information with low importance. The generation unit can also dynamically adjust the level of detail of the generation according to the importance of the information. This makes it possible to adjust the level of detail of the generation according to the importance of the information. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0049] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit can apply a business-specific generation algorithm to business information. The generation unit can also apply a technology-specific generation algorithm to technical information. The generation unit can also select and apply an optimal generation algorithm depending on the category of information. This makes it possible to apply an optimal generation algorithm depending on the category of information. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the category of information to the generation AI and cause the generation AI to apply the generation algorithm.

[0050] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can improve accuracy by adjusting the generation algorithm based on the user's past generation results. The generation unit can also optimize generation parameters by referring to the user's past generation results. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. This makes it possible to improve the accuracy of generation based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0051] The generation unit can determine the generation priority based on the time of information submission at the time of generation. For example, the generation unit generates the most recent information with priority. The generation unit can also generate information that was submitted earlier later. The generation unit can also dynamically adjust the generation priority based on the time of information submission. This makes it possible to determine the generation priority based on the time of information submission. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the time of information submission to the generation AI and have the generation AI determine the generation priority.

[0052] The generation unit can adjust the order of generation based on the relevance of information during generation. For example, the generation unit prioritizes the generation of highly relevant information. The generation unit can also postpone the generation of less relevant information. The generation unit can also dynamically adjust the order of generation based on the relevance of information. This makes it possible to adjust the order of generation based on the relevance of information. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the relevance of information to the generation AI and cause the generation AI to adjust the order of generation.

[0053] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit generates output that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can generate output that avoids technical terminology. Furthermore, the generation unit can dynamically adjust the use of technical terminology in the generation according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the user's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0054] The providing unit can select the optimal display method by referring to the user's past operation history when providing the display method. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. The providing unit can also analyze the user's past operation history and select the most efficient display method. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0055] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit preferentially displays only information related to the user's current task. The providing unit can also customize and display highly relevant information based on the user's current task. The providing unit can also customize and display optimal information by referring to the user's past task history. This makes it possible to customize the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data related to the user's task into a generating AI and cause the generating AI to customize the display content.

[0056] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generation AI and cause the generation AI to select the optimal display method.

[0057] The providing unit can make the display content multilingual according to the user's language setting when providing the display content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes it possible to make the display content multilingual based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0058] At the time of providing, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0059] The providing unit can customize the display method by reflecting the user's past feedback when providing the display. The providing unit can, for example, suggest an optimal display method based on feedback provided by the user in the past. The providing unit can also improve the display procedure by reflecting the user's past feedback. The providing unit can also customize the display method by referring to the user's past feedback. This makes it possible to customize the display method based on the user's past feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the display method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can analyze the user's past input history and select the optimal input method. For example, it can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and select the most efficient input method. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0062] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This makes it possible to adjust the level of detail of the analysis according to the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0063] During generation, the generation unit can apply different generation algorithms depending on the category of information. For example, a business-specific generation algorithm is applied to business information. The generation unit can also apply a technology-specific generation algorithm to technical information. Furthermore, the generation unit can select and apply an optimal generation algorithm depending on the category of information. This makes it possible to apply an optimal generation algorithm depending on the category of information. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the category of information to the generation AI and cause the generation AI to apply the generation algorithm.

[0064] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and select the most efficient display method. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into the generating AI and cause the generating AI to select the optimal display method.

[0065] The providing unit can customize the display content according to the user's current task when providing the display content. For example, the providing unit can prioritize displaying only information related to the user's current task. The providing unit can also customize and display highly relevant information based on the user's current task. Furthermore, the providing unit can also customize and display optimal information by referring to the user's past task history. This allows the display content to be customized according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data related to the user's task into the generating AI and cause the generating AI to customize the display content.

[0066] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information to the generation AI and cause the generation AI to select the optimal display method.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The receptionist inputs the information necessary for the business plan or proposal. Information necessary for the business plan or proposal includes market research data, competitive analysis, financial data, etc. The receptionist can input information using web-based chat or voice input during a meeting. Step 2: The analysis unit analyzes the information input by the reception unit and lists the characters and their relationships, the data handling and flow, and any unexamined points. The analysis unit analyzes the input information using natural language processing and machine learning algorithms. Natural language processing includes morphological analysis, grammatical analysis, and semantic analysis, while machine learning algorithms include K-means and hierarchical clustering. Step 3: The generation unit generates a business model diagram and checklist based on the information obtained by the analysis unit. The generation unit generates a business model diagram that shows the structure of the parties involved, and can express the parties' positions and contractual relationships, data handling methods and content, and monetization points using text, shapes, arrows, and icons. It can also generate income and expenditure structures and plans, points of concern, bullet points for unresolved issues, proposed solutions, and proposed service terms. Step 4: The providing unit provides the output generated by the generating unit in a presentation format, such as a slide show or video presentation.

[0069] (Example 2) A business planning and proposal system according to an embodiment of the present invention automatically generates diagrams and checklists that provide an overview of the overall structure of a business plan or proposal, including the characters (corporations and organizations) and their relationships (e.g., monetization points and contract types), data ownership and flow, and unconsiderated points. The business planning and proposal system allows users to input the information necessary for a business plan or proposal, and the generation AI analyzes the input information to list the characters and their relationships, data ownership and flow, and unconsiderated points. The generation AI automatically generates business model diagrams and checklists. For example, the business planning and proposal system may generate a business model diagram that shows the structure of the relationships between the characters, and use text, shapes, arrows, icons, and other elements to represent the positions and contractual relationships of each character, such as the data subjects, the data handling methods and content, and monetization points. Furthermore, the business planning and proposal system may generate income and expenditure structures and plans, lists of concerns and unresolved issues, proposed solutions, and proposed service terms. This allows users to grasp the overall picture of their business plan or proposal. For example, a business planning proposal system provides generated output in a presentation format. This allows users to use the generated diagrams and other images. For example, when planning an event such as a seminar or exhibition, a presentation can be made using the generated diagrams. This system allows users to efficiently create complex business plans and proposals. For example, when proposing a system development contract, the generated business model diagram and checklist can be used to clearly communicate the proposal content. In addition, since multiple input methods and multiple types of output documents can be generated, flexible responses can be made to meet user needs. This allows the business planning proposal system to efficiently grasp the overall picture of the business plan or proposal and provide it in a presentation format. For example, users can quickly and accurately create business plans and proposals and clearly communicate the proposal content.Furthermore, users can use the generated diagram images to make presentations at events such as seminars and exhibitions, thereby enabling users to efficiently carry out complex business plans and proposals.

[0070] A business planning and proposal system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information necessary for business planning and proposals. Examples of information necessary for business planning and proposals include, but are not limited to, market research data, competitive analysis, and financial data. The reception unit can input information using, for example, web-based chat or voice input during a conference. The analysis unit analyzes the information input by the reception unit and lists the participants and their relationships, the data handling and flow, and any unconsidered points. The analysis unit analyzes the input information using, for example, natural language processing or a machine learning algorithm. Examples of natural language processing include morphological analysis, grammatical analysis, and semantic analysis. Examples of machine learning algorithms include K-means and hierarchical clustering. The generation unit generates a business model diagram or a checklist based on the information obtained by the analysis unit. The generation unit generates, for example, a business model diagram that depicts a structure that shows the relationships between the participants. The generation unit can also express the positions and contractual relationships of each character, such as the subject of the data being handled, the data handling method and content, and monetization points using text, shapes, arrows, and icons. Furthermore, the generation unit can generate a revenue and expenditure structure and plan, a list of concerns and unresolved issues, proposed solutions, and proposed service terms. The provision unit provides the output generated by the generation unit in a presentation format. The provision unit can provide the output in the form of, for example, a slideshow or video presentation. This allows the business planning and proposal system according to the embodiment to efficiently grasp the overall picture of a business plan or proposal and provide it in a presentation format. For example, users can quickly and accurately create business plans and proposals and clearly communicate the content of their proposals. Furthermore, users can use the generated diagrams and images to make presentations at event planning events such as seminars and exhibitions. This allows users to efficiently create complex business plans and proposals.

[0071] The reception unit can input information using a web-based chat or voice input during a meeting. Examples of web-based chat include Slack and Microsoft Teams. For example, a user can input information to the reception unit using a web-based chat tool. The reception unit can also input information using voice input during a meeting. Voice input includes, for example, voice recognition software and microphone settings. For example, the reception unit can input information to the user using voice input during a meeting. This allows users to input information in a variety of ways. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice-input data to a generation AI and have the generation AI convert the voice data into text data.

[0072] The analysis unit can analyze the input information using natural language processing or a machine learning algorithm. Natural language processing includes, for example, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit analyzes the input information using, for example, morphological analysis. The analysis unit can also analyze the input information using grammatical analysis. The analysis unit can also analyze the input information using semantic analysis. Machine learning algorithms include, for example, K-means and hierarchical clustering. The analysis unit analyzes the input information using, for example, K-means. The analysis unit can also analyze the input information using hierarchical clustering. This improves the analysis accuracy of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the input information to a generation AI and cause the generation AI to output the analysis results.

[0073] The generation unit can generate a business model diagram that expresses a structure that shows the relationships between the characters. The structure includes, for example, an organization chart and a role assignment diagram. The generation unit, for example, uses an organization chart to express a structure that shows the relationships between the characters. The generation unit can also use a role assignment diagram to express a structure that shows the relationships between the characters. This makes it possible to generate a business model diagram. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data regarding the relationships between the characters into the generation AI and cause the generation AI to generate a business model diagram.

[0074] The generation unit can represent the subject of the data being handled, the respective positions and contractual relationships of the characters, the data handling method and content, and monetization points using text, shapes, arrows, and icons. Examples of the text, shapes, arrows, and icons include text editors and graphic software. The generation unit can represent the positions and contractual relationships of the subject of the data being handled and the characters using, for example, a text editor. The generation unit can also represent the data handling method and content and monetization points using graphic software. This allows for visual representation of the data handling method and monetization points. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input information regarding data handling into the generation AI and have the generation AI execute a visual representation.

[0075] The generation unit can generate an income and expenditure structure and plan, items of concerns and unresolved matters, proposed countermeasures for those, and proposed service terms. The income and expenditure structure and plan include, for example, an income and expenditure forecast and a budget plan. The generation unit can, for example, generate the income and expenditure structure and plan using the income and expenditure forecast. The generation unit can also generate the income and expenditure structure and plan using the budget plan. The concerns and unresolved matters include, for example, risk factors and unresolved issues. The generation unit can, for example, generate concerns by listing risk factors. The generation unit can also generate unresolved matters by listing unresolved issues. The proposed countermeasures include, for example, a risk management plan and solutions to problems. The generation unit can, for example, generate countermeasures using the risk management plan. The generation unit can also generate countermeasures using solutions to problems. The proposed service terms include, for example, terms of use and privacy policy. The generation unit can, for example, generate draft service terms using terms of use. The generation unit can also generate draft service terms using a privacy policy. This makes it possible to automatically generate countermeasures for the income and expenditure structure and the concerns. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input information about income and expenditure structure and concerns into the generation AI, and cause the generation AI to generate countermeasure proposals and service terms proposals.

[0076] The providing unit can provide the generated output in a presentation format. Examples of presentation formats include a slide show and a video presentation. The providing unit can provide the generated output in, for example, a slide show format. The providing unit can also provide the generated output in a video presentation format. This allows the generated output to be provided in a presentation format. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the generated output to a generation AI and cause the generation AI to provide the output in a presentation format.

[0077] The reception unit can analyze the user's emotions and adjust the timing of input based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of input to provide a relaxing environment. Furthermore, if the user is relaxed, the reception unit can also accelerate the timing of input to efficiently collect information. Furthermore, if the user is in a hurry, the reception unit can immediately time the input to quickly collect information. This allows the timing of input to be adjusted according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of input.

[0078] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can preferentially suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. The reception unit can also analyze the user's past input history and select the most efficient input method. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0079] The reception unit can filter the input information based on the user's current project or area of ​​interest. For example, the reception unit can preferentially input only information related to the user's current project. The reception unit can also filter and input highly relevant information based on the user's area of ​​interest. The reception unit can also filter and input optimal information by referring to the user's past project history. This makes it possible to filter information based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or can be performed without using AI. For example, the reception unit can input data related to the user's project or area of ​​interest to the generation AI and have the generation AI perform the filtering.

[0080] The reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user desires voice input, the reception unit performs the input using voice recognition technology. Furthermore, if the user desires text input, the reception unit can provide a text box for input. Furthermore, if the user desires image input, the reception unit can perform the input using image recognition technology. This makes it possible to select the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data related to the user's input method to the generation AI and have the generation AI select the optimal input means.

[0081] The reception unit can analyze the user's emotions and determine the priority of information to be input based on the analyzed user's emotions. For example, when the user is feeling stressed, the reception unit postpones less important information and prioritizes input of more important information. Furthermore, when the user is relaxed, the reception unit can input all information equally. Furthermore, when the user is in a hurry, the reception unit can prioritize input of the most important information. This allows the priority of information to be input to be determined according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the information to be input.

[0082] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, when the user is in a specific area, the reception unit prioritizes inputting information related to that area. The reception unit can also filter and input optimal information based on the user's geographical location information. Furthermore, when the user is moving, the reception unit can also input highly relevant information based on the user's current location. This makes it possible to prioritize inputting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to filter highly relevant information.

[0083] The reception unit can analyze the user's social media activities and input related information at the time of input. The reception unit can input related information based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity history and input optimal information. The reception unit can also input related information by referring to the activities of the user's friends on social media. This makes it possible to input related information based on the user's social media activities. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related information.

[0084] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input procedure by reflecting the user's past feedback. The reception unit can also customize the input method by referring to the user's past feedback. This makes it possible to customize the input method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0085] The analysis unit can analyze the user's emotions and adjust the way the analysis is presented based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows the way the analysis is presented to be adjusted according to the user's emotions. The user's emotions are estimated using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. This makes it possible to adjust the level of detail of the analysis according to the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0087] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a business-specific analysis algorithm to business information. The analysis unit can also apply a technology-specific analysis algorithm to technical information. The analysis unit can also select and apply an optimal analysis algorithm depending on the category of information. This makes it possible to apply an optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the category of information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the user's past analysis results. The analysis unit can also optimize analysis parameters by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0089] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. The user's emotions are estimated using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0090] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the time of information submission. This makes it possible to determine the priority of analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the time of information submission to the generation AI and have the generation AI determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of information. This makes it possible to adjust the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the relevance of information to the generation AI and cause the generation AI to adjust the order of analysis.

[0092] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results that avoid technical terminology. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis results according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the user's level of expertise to the generation AI and have the generation AI use technical terminology.

[0093] The generation unit can analyze the user's emotions and adjust the expression method of the generated output based on the analyzed user's emotions. For example, if the user is relaxed, the generation unit can generate an output that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate an output that emphasizes the shortest route. If the user is excited, the generation unit can also generate an output that adds visually stimulating effects. This allows the expression method of the output to be adjusted according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression method of the output.

[0094] The generation unit can adjust the level of detail of the generation based on the importance of the information during generation. For example, the generation unit generates detailed output for information with high importance. The generation unit can also generate simplified output for information with low importance. The generation unit can also dynamically adjust the level of detail of the generation according to the importance of the information. This makes it possible to adjust the level of detail of the generation according to the importance of the information. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0095] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit can apply a business-specific generation algorithm to business information. The generation unit can also apply a technology-specific generation algorithm to technical information. The generation unit can also select and apply an optimal generation algorithm depending on the category of information. This makes it possible to apply an optimal generation algorithm depending on the category of information. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the category of information to the generation AI and cause the generation AI to apply the generation algorithm.

[0096] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can improve accuracy by adjusting the generation algorithm based on the user's past generation results. The generation unit can also optimize generation parameters by referring to the user's past generation results. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. This makes it possible to improve the accuracy of generation based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0097] The generation unit can analyze the user's emotions and adjust the length of the generated output based on the analyzed user's emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point output. If the user is relaxed, the generation unit can generate a longer output with detailed explanations. If the user is excited, the generation unit can generate an output with visually stimulating effects. This allows the length of the output to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the output.

[0098] The generation unit can determine the generation priority based on the time of information submission at the time of generation. For example, the generation unit generates the most recent information with priority. The generation unit can also generate information that was submitted earlier later. The generation unit can also dynamically adjust the generation priority based on the time of information submission. This makes it possible to determine the generation priority based on the time of information submission. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the time of information submission to the generation AI and have the generation AI determine the generation priority.

[0099] The generation unit can adjust the order of generation based on the relevance of information during generation. For example, the generation unit prioritizes the generation of highly relevant information. The generation unit can also postpone the generation of less relevant information. The generation unit can also dynamically adjust the order of generation based on the relevance of information. This makes it possible to adjust the order of generation based on the relevance of information. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the relevance of information to the generation AI and cause the generation AI to adjust the order of generation.

[0100] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit generates output that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can generate output that avoids technical terminology. Furthermore, the generation unit can dynamically adjust the use of technical terminology in the generation according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the user's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0101] The providing unit can analyze the user's emotions and adjust the display method of the output to be provided based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows the display method of the output to be adjusted according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0102] The providing unit can select the optimal display method by referring to the user's past operation history when providing the display method. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. The providing unit can also analyze the user's past operation history and select the most efficient display method. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0103] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit preferentially displays only information related to the user's current task. The providing unit can also customize and display highly relevant information based on the user's current task. The providing unit can also customize and display optimal information by referring to the user's past task history. This makes it possible to customize the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data related to the user's task into a generating AI and cause the generating AI to customize the display content.

[0104] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generation AI and cause the generation AI to select the optimal display method.

[0105] The providing unit can analyze the user's emotions and adjust the operation procedures of the output to be provided based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can provide quick and concise operation procedures. This allows the output operation procedures to be adjusted according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the operation procedures.

[0106] The providing unit can make the display content multilingual according to the user's language setting when providing the display content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes it possible to make the display content multilingual based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0107] At the time of providing, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0108] The providing unit can customize the display method by reflecting the user's past feedback when providing the display. The providing unit can, for example, suggest an optimal display method based on feedback provided by the user in the past. The providing unit can also improve the display procedure by reflecting the user's past feedback. The providing unit can also customize the display method by referring to the user's past feedback. This makes it possible to customize the display method based on the user's past feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the display method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input information necessary for business planning and proposals using the reception device 38 of the smart device 14. For example, the analysis unit analyzes the input information using the specific processing unit 290 of the data processing device 12 and lists the characters and their relationships, the data ownership and flow, and unconsidered points. For example, the generation unit generates a business model diagram or checklist using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the generated output in a presentation format using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input information necessary for business planning and proposals using the microphone 238 of the smart glasses 214. For example, the analysis unit analyzes the input information using the specific processing unit 290 of the data processing device 12 and lists the characters and their relationships, the data handling and flow, and unconsidered points. For example, the generation unit generates a business model diagram or checklist using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the generated output in a presentation format using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can input information necessary for business planning and proposals using the microphone 238 of the headset terminal 314. For example, the analysis unit analyzes the input information using the specific processing unit 290 of the data processing device 12 and lists the characters and their relationships, the way data is held and flowed, and any unconsidered points. For example, the generation unit generates a business model diagram or a checklist using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the generated output in a presentation format using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input information necessary for business planning and proposals using the microphone 238 of the robot 414. For example, the analysis unit analyzes the input information using the specific processing unit 290 of the data processing device 12 and lists the characters and their relationships, the data handling and flow, and any unconsidered points. For example, the generation unit generates a business model diagram or a checklist using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the generated output in a presentation format using the speaker 240 of the robot 414.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The reception unit can analyze the user's past input history and select the optimal input method. For example, it can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and select the most efficient input method. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0111] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This makes it possible to adjust the level of detail of the analysis according to the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0112] During generation, the generation unit can apply different generation algorithms depending on the category of information. For example, a business-specific generation algorithm is applied to business information. The generation unit can also apply a technology-specific generation algorithm to technical information. Furthermore, the generation unit can select and apply an optimal generation algorithm depending on the category of information. This makes it possible to apply an optimal generation algorithm depending on the category of information. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data regarding the category of information to the generation AI and cause the generation AI to apply the generation algorithm.

[0113] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and select the most efficient display method. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into the generating AI and cause the generating AI to select the optimal display method.

[0114] The reception unit can analyze the user's emotions and adjust the timing of input based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of input to provide a relaxing environment. Furthermore, if the user is relaxed, the reception unit can also speed up the timing of input to efficiently collect information. Furthermore, if the user is in a hurry, the reception unit can immediately time the input to quickly collect information. This allows the timing of input to be adjusted according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of input.

[0115] The analysis unit can analyze the user's emotions and adjust the way the analysis is presented based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows the way the analysis is presented to be adjusted according to the user's emotions. The user's emotions are estimated using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0116] The generation unit can analyze the user's emotions and adjust the expression method of the generated output based on the analyzed user's emotions. For example, if the user is relaxed, the generation unit can generate an output that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate an output that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an output that adds visually stimulating effects. This allows the expression method of the output to be adjusted according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression method of the output.

[0117] The providing unit can analyze the user's emotions and adjust the display method of the output to be provided based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows the display method of the output to be adjusted according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0118] The providing unit can customize the display content according to the user's current task when providing the display content. For example, the providing unit can prioritize displaying only information related to the user's current task. The providing unit can also customize and display highly relevant information based on the user's current task. Furthermore, the providing unit can also customize and display optimal information by referring to the user's past task history. This allows the display content to be customized according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data related to the user's task into the generating AI and cause the generating AI to customize the display content.

[0119] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information to the generation AI and cause the generation AI to select the optimal display method.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The receptionist inputs the information necessary for the business plan or proposal. Information necessary for the business plan or proposal includes market research data, competitive analysis, financial data, etc. The receptionist can input information using web-based chat or voice input during a meeting. Step 2: The analysis unit analyzes the information input by the reception unit and lists the characters and their relationships, the data handling and flow, and any unexamined points. The analysis unit analyzes the input information using natural language processing and machine learning algorithms. Natural language processing includes morphological analysis, grammatical analysis, and semantic analysis, while machine learning algorithms include K-means and hierarchical clustering. Step 3: The generation unit generates a business model diagram and checklist based on the information obtained by the analysis unit. The generation unit generates a business model diagram that shows the structure of the parties involved, and can express the parties' positions and contractual relationships, data handling methods and content, and monetization points using text, shapes, arrows, and icons. It can also generate income and expenditure structures and plans, points of concern, bullet points for unresolved issues, proposed solutions, and proposed service terms. Step 4: The providing unit provides the output generated by the generating unit in a presentation format, such as a slide show or video presentation.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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. A reception desk that inputs the information necessary for business planning and proposals, an analysis unit that analyzes the information input by the reception unit and lists the characters and their relationships, the way data is held and flowed, and any unexamined points; a generation unit that generates a business model diagram and a checklist based on the information obtained by the analysis unit; a providing unit that provides the output generated by the generating unit in a presentation format. A system characterized by:

2. The reception unit Enter information using web-based chat or voice input during meetings 2. The system of claim 1.

3. The analysis unit Analyze input information using natural language processing or machine learning algorithms 2. The system of claim 1.

4. The generation unit Generate a business model diagram that shows the relationships between the characters 2. The system of claim 1.

5. The generation unit The data subjects, the positions and contractual relationships of each person involved, the data handling methods and content, and monetization points are expressed using text, shapes, arrows, and icons.

2. The system of claim 1.

6. The generation unit Generate a financial structure and plan, a list of concerns and outstanding issues, proposals for resolving them, and a draft of terms of service 2. The system of claim 1.

7. The providing unit Present the generated output in a presentation format 2. The system of claim 1.

8. The reception unit Analyzes user emotions and adjusts input timing based on the analyzed user emotions 2. The system of claim 1.

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