System

Abstract

An object of the system according to the embodiment is to enable even an employee who is not familiar with legal affairs to quickly respond to related regulations and risks.SOLUTION: A system includes a business content input part, an analysis part, a regulation presentation part, a risk presentation part, a countermeasure proposal part, a document creation part, a transmission part, and a cooperation part. The business contents input part inputs business contents. The analysis unit analyzes the business contents input by the business contents input unit. The regulation presentation unit presents a related regulation based on the business contents analyzed by the analysis unit. The risk presentation unit presents a risk based on the work content analyzed by the analysis unit. The handling method proposing section proposes a handling method for the risk presented by the risk presenting section. The document creation unit creates a document based on the contents presented by the regulation presentation unit and the risk presentation unit. The sending unit sends the document created by the document creation unit. The cooperation part cooperates the document created by the document creation part with an in-house system.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] With conventional technology, it is difficult for employees who are not familiar with legal matters to respond to relevant laws and regulations and risks, which can lead to reduced work efficiency.

[0005] The system according to the embodiment aims to enable even employees who are not familiar with legal matters to quickly respond to relevant laws and regulations and risks. [Means for solving the problem]

[0006] The system according to the embodiment includes a business content input unit, an analysis unit, a legal content presentation unit, a risk presentation unit, a solution proposal unit, a document creation unit, a sending unit, and a linking unit. The business content input unit inputs business content. The analysis unit analyzes the business content input by the business content input unit. The legal content presentation unit presents relevant laws and regulations based on the business content analyzed by the analysis unit. The risk presentation unit presents risks based on the business content analyzed by the analysis unit. The solution proposal unit proposes solutions to risks presented by the risk presentation unit. The document creation unit creates documents based on the content presented by the legal content presentation unit and the risk presentation unit. The sending unit sends the documents created by the document creation unit. The linking unit links the documents created by the document creation unit with an internal system. [Effects of the Invention]

[0007] The system according to the embodiment can enable even employees who are not familiar with legal affairs to quickly respond to relevant laws and regulations and risks. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The legal advice system according to an embodiment of the present invention is a system in which, by inputting the details of a business operation, a generation AI presents relevant laws and regulations, risks, and ways to deal with them, and automatically creates and sends documents, materials, emails, etc. with necessary wording, etc. This enables even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, reducing problems related to security and copyright, etc., and contributing to improving the work efficiency of employees.

[0029] A legal advice system according to an embodiment includes a business content input unit, an analysis unit, a legal provision presentation unit, a risk presentation unit, a solution proposal unit, a document creation unit, a sending unit, and a linking unit. The business content input unit inputs business content. For example, a user inputs the business content in text format. The business content input unit can also input the business content using voice input. For example, the voice input can be converted into text using voice recognition technology. The analysis unit analyzes the business content input by the business content input unit. For example, a generation AI analyzes the business content using text analysis technology. The analysis unit can also refer to the user's past input history to automatically complete similar business content. For example, it automatically completes related laws and risks based on business content input in the past. The legal provision presentation unit presents related laws and regulations based on the business content analyzed by the analysis unit. For example, the generation AI searches a database for laws and regulations related to the business content and presents them. The legal provision presentation unit can also refer to past precedents and similar cases to present specific cases. For example, it presents precedents based on related laws and regulations. The risk presentation unit presents risks based on the business content analyzed by the analysis unit. For example, the generation AI searches a database for risks related to the business content and presents them. The risk presentation unit can also simultaneously present other laws, regulations, and risks related to the user's business content in response to the presented laws, regulations, and risks. For example, it presents a list of related risks. The countermeasure proposal unit proposes countermeasures for the risks presented by the risk presentation unit. For example, the generation AI searches a database for and proposes countermeasures for risks. The countermeasure proposal unit can also refer to past success stories and failure stories to present specific action plans. For example, it presents specific countermeasures based on success stories. The document creation unit creates documents based on the content presented by the laws, regulations presentation unit and risk presentation unit. For example, the generation AI automatically creates documents by adding necessary wording based on the presented laws, regulations, and risks. The document creation unit can also select the optimal format by referring to similar documents and materials from the past. For example, it selects the optimal format based on past documents. The sending unit sends the documents created by the document creation unit.For example, the generation AI sends the documents it creates via email. The sending unit can also refer to the recipient's past responses and select the optimal timing for sending. For example, it selects the optimal timing for sending based on past responses. The linking unit links the documents created by the document creation unit with an internal system. For example, the generation AI links with an internal project management system and automatically handles necessary legal matters according to the progress of the project. The linking unit can also refer to past data and select the optimal linking method. For example, it selects the optimal linking method based on past data. As a result, the legal advice system according to the embodiment allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks. For example, by simply entering the details of the work, the generation AI can present relevant laws and risks and automatically create and send the necessary documents. Furthermore, linking with internal systems and databases makes it possible to automate legal matters according to the progress of the project.

[0030] The analysis unit can reference the user's past input history and automatically complete similar work content. For example, when a user inputs work content, the analysis unit uses a generation AI to analyze the past input history and automatically suggest similar work content. For example, based on work content related to "launching a new product" that was previously input, it can automatically complete related laws and risks. The analysis unit can also save past input history in cloud storage and refer to it as needed. For example, it can save input history from the past year and suggest similar work content. This makes it possible to reference the user's past input history and automatically complete similar work content.

[0031] The analysis unit can suggest relevant laws, regulations and risks in real time when a business operation content is input, allowing the user to select from them. For example, when a user inputs business operation content, the analysis unit uses a generation AI to suggest relevant laws, regulations and risks in real time. For example, when a business operation content related to "launching a new product" is input, the generation AI suggests "Product Safety Act" and "Consumer Protection Act." The analysis unit also allows the user to select from the suggested laws, regulations and risks in real time. For example, the user can make a selection using a drop-down menu or check box. This allows relevant laws, regulations and risks to be suggested in real time when a business operation content is input, allowing the user to select from them.

[0032] The work content input unit can automatically convert the work content from voice input into text using voice recognition technology. For example, in the work content input unit, a user inputs the work content by voice, and the generation AI automatically converts it into text using voice recognition technology. For example, if a user inputs "work content related to the launch of a new product" by voice, the generation AI converts that content into text. The work content input unit can also perform highly accurate voice recognition using voice recognition software. For example, the voice recognition software automatically analyzes the voice and saves it as text. This allows automatic conversion from voice input to text.

[0033] The work content input unit can automatically insert related visual data when work content is input. For example, when a user inputs work content, the work content input unit allows the generation AI to automatically insert related visual data. For example, if "work content related to the launch of a new product" is input, the generation AI will automatically insert product images and market analysis charts. The work content input unit can also allow the user to select the type of visual data and the insertion method. For example, visual data such as image files, graphs, and charts can be selected. This allows related visual data to be automatically inserted.

[0034] The legal rule presentation unit can refer to past precedents and similar cases and present specific examples when presenting related laws and risks. For example, when the generation AI presents related laws and risks, the legal rule presentation unit refers to past precedents and similar cases and presents specific examples. For example, for the task content related to "launching a new product," the generation AI presents past precedents related to product safety laws. The legal rule presentation unit can also allow the user to check detailed information about precedents and similar cases. For example, it displays detailed information about related precedents. This makes it possible to refer to past precedents and similar cases and present specific examples.

[0035] The legal regulations presentation unit can provide customized advice specific to the user's business operations for the presented laws and risks. For example, the legal regulations presentation unit provides customized advice specific to the user's business operations for the presented laws and risks by the generation AI. For example, for business operations related to "launching a new product," the generation AI proposes specific countermeasures based on the Product Safety Act. The legal regulations presentation unit can also analyze detailed information about the business operations to provide advice based on the user's business operations. For example, it provides customized advice based on detailed information about the business operations. This makes it possible to provide customized advice specific to the user's business operations.

[0036] The legal regulations presentation unit can visually present relevant laws and risks using interactive charts and graphs. For example, the legal regulations presentation unit uses a generation AI to visually present relevant laws and risks using interactive charts and graphs. For example, the legal regulations and risks related to "launching a new product" can be displayed in a chart, allowing the user to click to view more detailed information. The legal regulations presentation unit can also visually show the relationship between laws and risks using interactive charts and graphs. For example, the relationship between laws and risks can be displayed in a graph. This makes it possible to visually present relevant laws and risks.

[0037] The legal regulations presentation unit can simultaneously present other laws, regulations, and risks related to the user's business content in addition to the presented laws, regulations, and risks. For example, the legal regulations presentation unit simultaneously presents other laws, regulations, and risks related to the user's business content in addition to the laws, regulations, and risks presented by the generation AI. For example, for business content related to "launching a new product," the generation AI presents consumer protection laws and environmental regulations in addition to product safety laws. The legal regulations presentation unit can also allow the user to check a list of related laws, regulations, and risks. For example, it displays a list of related laws, regulations, and risks. This allows other laws, regulations, and risks related to the user's business content to be simultaneously presented.

[0038] The solution proposal unit can refer to past success stories and failure stories for the solution it proposes and present a specific action plan. For example, the solution proposal unit can refer to past success stories and failure stories for the solution proposed by the generation AI and present a specific action plan. For example, in response to the "risk of litigation due to product defects," the generation AI presents specific quality control procedures based on past success stories. The solution proposal unit can also allow the user to check detailed information about success stories and failure stories. For example, it can display detailed information about related success stories and failure stories. This makes it possible to refer to past success stories and failure stories and present a specific action plan.

[0039] The solution proposal unit can provide customized advice specific to the user's work content for the proposed solution. For example, the solution proposal unit provides customized advice specific to the user's work content for the solution proposed by the generation AI. For example, in response to the "risk of litigation due to product defects," the generation AI proposes specific countermeasures according to the type of product and the market. The solution proposal unit can also analyze detailed information about the work content in order to provide advice based on the user's work content. For example, it provides customized advice based on detailed information about the work content. This makes it possible to provide customized advice specific to the user's work content.

[0040] The solution proposal unit can visually present the proposed solution using an interactive simulation. For example, the solution proposal unit visually presents the solution proposed by the generation AI using an interactive simulation. For example, it simulates a solution to the "risk of litigation due to product defects" so that the user can visually understand the specific solution. The solution proposal unit can also visually show the effect of the solution using an interactive simulation. For example, it displays the effect of the solution in a simulation. This allows the proposed solution to be visually understood.

[0041] The solution proposal unit can simultaneously present other solutions related to the user's work content in response to the proposed solution. For example, the solution proposal unit simultaneously presents other solutions related to the user's work content in response to the solution proposed by the generation AI. For example, in addition to solutions for the "risk of litigation due to product defects," the solution proposal unit can also present specific quality control procedures and risk management methods. The solution proposal unit can also allow the user to check a list of related solutions. For example, it can display a list of related solutions. This allows other solutions related to the user's work content to be simultaneously presented.

[0042] The document creation unit can refer to similar documents and materials from the past and select the optimal format for the documents and materials to be automatically created. For example, the document creation unit can refer to similar documents and materials from the past and select the optimal format for the documents and materials to be automatically created by the generation AI. For example, when creating a document related to the "launch of a new product," the optimal format can be selected based on similar documents from the past. The document creation unit can also allow the user to check detailed information about similar documents and materials. For example, detailed information about related similar documents and materials can be displayed. This allows the user to refer to similar documents and materials from the past and select the optimal format.

[0043] The document creation unit can add customized content specific to the user's business operations to automatically created documents and materials. For example, the document creation unit adds customized content specific to the user's business operations to documents and materials automatically created by the generation AI. For example, detailed product information and market analysis are added to documents related to the "launch of a new product." The document creation unit can also analyze detailed information about the business operations to provide additional information based on the user's business operations. For example, customized content is added based on detailed information about the business operations. This allows customized content specific to the user's business operations to be added.

[0044] The document creation unit can simultaneously create other documents and materials related to the user's work content in addition to automatically created documents and materials. For example, the document creation unit simultaneously creates other documents and materials related to the user's work content in addition to documents and materials automatically created by the generation AI. For example, in addition to documents related to the "launch of a new product," the document creation unit can also simultaneously create a market analysis report for the product and quality control guidelines. The document creation unit can also allow the user to check a list of related documents and materials. For example, it can display a list of related documents and materials. This allows other documents and materials related to the user's work content to be simultaneously created.

[0045] The sending unit can refer to the recipient's past responses to documents and materials to be automatically sent and select the optimal timing to send them. For example, the sending unit can refer to the recipient's past responses to documents and materials to be automatically sent by the generation AI and select the optimal timing to send them. For example, when sending a notification email regarding the "launch of a new product," the optimal timing to send it is selected based on past responses. The sending unit can also adjust the timing of sending based on the recipient's business hours and past response data. For example, the notification email is sent according to the recipient's business hours. This makes it possible to refer to the recipient's past responses and select the optimal timing to send it.

[0046] The sending unit can add customized content specific to the user's work content to automatically sent documents and materials. For example, the sending unit adds customized content specific to the user's work content to documents and materials automatically sent by the generation AI. For example, detailed product information and market analysis are added to a notification email regarding the "release of a new product." The sending unit can also analyze detailed information about the work content to provide additional information based on the user's work content. For example, customized content is added based on detailed information about the work content. This makes it possible to add customized content specific to the user's work content.

[0047] The sending unit can visually display automatically sent documents and materials using an interactive dashboard. For example, the sending unit visually presents documents and materials automatically sent by the generation AI using an interactive dashboard. For example, a notification email regarding "new product launch" can be displayed on the dashboard so that the user can click to view more detailed information. The sending unit can also visually display the contents of the documents and materials using the interactive dashboard. For example, the contents of the notification email can be displayed on the dashboard. This makes it possible to visually present automatically sent documents and materials.

[0048] The sending unit can simultaneously send other documents and materials related to the user's work content in addition to the automatically sent documents and materials. For example, the sending unit simultaneously sends other documents and materials related to the user's work content in addition to the documents and materials automatically sent by the generation AI. For example, in addition to a notification email about the "launch of a new product," the sending unit can also simultaneously send a market analysis report for the product and quality control guidelines. The sending unit can also allow the user to check a list of related documents and materials. For example, the sending unit can display a list of related documents and materials. This allows other documents and materials related to the user's work content to be simultaneously sent.

[0049] When linking with an internal system or database, the linking unit can refer to past data and select the optimal linking method. For example, when the generation AI links with an internal system or database, the linking unit refers to past data and selects the optimal linking method. For example, when linking with a project management system, the linking unit selects the optimal linking method based on past project data. The linking unit can also store past data in cloud storage and reference it as needed. For example, it can store data from the past year and select the optimal linking method. This makes it possible to refer to past data and select the optimal linking method.

[0050] The collaboration unit can provide a customized collaboration method specialized for the user's work content when collaborating with in-house systems and databases. For example, the collaboration unit provides a customized collaboration method specialized for the user's work content when collaborating with in-house systems and databases by the generation AI. For example, when collaborating with a project management system, it provides a collaboration method according to the progress status of a specific project. The collaboration unit can also analyze detailed information about the work content in order to provide a collaboration method based on the user's work content. For example, it provides a customized collaboration method based on detailed information about the work content. This makes it possible to provide a customized collaboration method specialized for the user's work content.

[0051] The linking unit can visually link with internal systems and databases using an interactive interface. For example, the linking unit uses an interactive interface in which the generation AI visually presents linkage with internal systems and databases. For example, the linkage with a project management system can be displayed in the interface, allowing the user to click to check detailed information. The linking unit can also visually show the content of the linkage using an interactive interface. For example, the content of the linkage can be displayed in the interface. This allows linkage with internal systems and databases to be visually performed.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The legal advice system can also include an education module that provides relevant educational content based on the user's job description. For example, if a user enters a job description related to "launching a new product," the generative AI can provide educational videos and online courses on product safety and consumer protection laws. The education module can also provide quizzes and tests to check the user's understanding. For example, it can assess the user's understanding through a quiz on legal regulations. This allows the user to deepen their knowledge of the laws and risks related to their job description.

[0054] The analysis unit may also include a market data provider that provides relevant market data based on the user's business operations. For example, if a user enters a business operation related to "launching a new product," the generation AI will provide information on market trends and competitors. The market data provider may also provide detailed data on specific market segments that interest the user. For example, it may provide market data related to specific regions or age groups. This allows the user to quickly obtain market information related to their business operations.

[0055] When presenting relevant laws and risks, the legal regulations presentation unit can provide a customized checklist based on the user's work content. For example, if a user enters work content related to "launching a new product," the generation AI will provide a checklist based on the Product Safety Act and Consumer Protection Act. The legal regulations presentation unit can also display checklist items in a format that is easy for users to check. For example, it can display them in checkbox format, allowing users to proceed while checking the items. This allows users to efficiently check the laws and risks related to their work content.

[0056] The document creation unit can add visual designs based on the user's work content to automatically created documents and materials. For example, when a user creates a document related to the "launch of a new product," the generation AI can automatically insert product images and logos. The document creation unit can also provide visual design templates for the user to select from. For example, the user can choose from multiple design templates to improve the appearance of the document. This allows the user to add visual designs based on the user's work content.

[0057] The integration unit can provide automated workflows based on the user's work content by linking with internal systems and databases. For example, when a user enters work content related to "launching a new product," the generative AI will link with the project management system and automate the workflow from product development to market launch. The integration unit can also monitor the progress of the workflow in real time and notify the user. For example, it can send a notification when a specific task is completed. This makes it possible to provide automated workflows based on the user's work content.

[0058] The legal advice system can also include a news provider that provides a news feed on relevant laws and risks based on the user's work. For example, if a user enters a work description related to "launching a new product," the generation AI will provide news on the latest legal amendments and risks. The news provider can also filter and provide news on specific laws and risks that interest the user. For example, it can provide news related to a specific industry or region. This allows users to quickly obtain the latest information related to their work.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The business content input unit inputs business content. For example, the user inputs the business content in text format. Alternatively, the business content can be input using voice input. The voice input is converted into text using voice recognition technology. Step 2: The analysis unit analyzes the work content entered by the work content input unit. For example, the generation AI can analyze the work content using text analysis technology. It can also refer to the user's past input history and automatically complete similar work content. Step 3: The legal provision presentation unit presents relevant laws and regulations based on the business content analyzed by the analysis unit. For example, the generation AI searches a database for laws and regulations related to the business content and presents them. It can also refer to past precedents and similar cases to present specific examples. Step 4: The risk presentation unit presents risks based on the business content analyzed by the analysis unit. For example, the generation AI searches a database for risks related to the business content and presents them. In addition, other laws and risks related to the user's business content can also be presented at the same time as the presented laws and risks. Step 5: The solution proposal unit proposes solutions to the risks presented by the risk presentation unit. For example, the generation AI searches a database for solutions to risks and proposes them. It can also refer to past successes and failures to present specific action plans. Step 6: The document creation section creates documents based on the content presented by the legal regulations presentation section and risk presentation section. For example, the generation AI automatically creates documents by adding necessary wording based on the presented legal regulations and risks. It can also refer to similar documents and materials from the past to select the optimal format. Step 7: The sending unit sends the document created by the document creation unit. For example, the document created by the generation AI is sent by email. It can also refer to the recipient's past responses and select the optimal timing for sending. Step 8: The Collaboration Department connects the documents created by the Document Creation Department to internal systems. For example, the generation AI connects with an internal project management system and automatically handles the necessary legal matters according to the project's progress. It can also refer to past data to select the optimal collaboration method.

[0061] (Example 2) The legal advice system according to an embodiment of the present invention is a system in which, by inputting the details of a business operation, a generation AI presents relevant laws and regulations, risks, and ways to deal with them, and automatically creates and sends documents, materials, emails, etc. with necessary wording, etc. This enables even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, reducing problems related to security and copyright, etc., and contributing to improving the work efficiency of employees.

[0062] A legal advice system according to an embodiment includes a business content input unit, an analysis unit, a legal provision presentation unit, a risk presentation unit, a solution proposal unit, a document creation unit, a sending unit, and a linking unit. The business content input unit inputs business content. For example, a user inputs the business content in text format. The business content input unit can also input the business content using voice input. For example, the voice input can be converted into text using voice recognition technology. The analysis unit analyzes the business content input by the business content input unit. For example, a generation AI analyzes the business content using text analysis technology. The analysis unit can also refer to the user's past input history to automatically complete similar business content. For example, it automatically completes related laws and risks based on business content input in the past. The legal provision presentation unit presents related laws and regulations based on the business content analyzed by the analysis unit. For example, the generation AI searches a database for laws and regulations related to the business content and presents them. The legal provision presentation unit can also refer to past precedents and similar cases to present specific cases. For example, it presents precedents based on related laws and regulations. The risk presentation unit presents risks based on the business content analyzed by the analysis unit. For example, the generation AI searches a database for risks related to the business content and presents them. The risk presentation unit can also simultaneously present other laws, regulations, and risks related to the user's business content in response to the presented laws, regulations, and risks. For example, it presents a list of related risks. The countermeasure proposal unit proposes countermeasures for the risks presented by the risk presentation unit. For example, the generation AI searches a database for and proposes countermeasures for risks. The countermeasure proposal unit can also refer to past success stories and failure stories to present specific action plans. For example, it presents specific countermeasures based on success stories. The document creation unit creates documents based on the content presented by the laws, regulations presentation unit and risk presentation unit. For example, the generation AI automatically creates documents by adding necessary wording based on the presented laws, regulations, and risks. The document creation unit can also select the optimal format by referring to similar documents and materials from the past. For example, it selects the optimal format based on past documents. The sending unit sends the documents created by the document creation unit.For example, the generation AI sends the documents it creates via email. The sending unit can also refer to the recipient's past responses and select the optimal timing for sending. For example, it selects the optimal timing for sending based on past responses. The linking unit links the documents created by the document creation unit with an internal system. For example, the generation AI links with an internal project management system and automatically handles necessary legal matters according to the progress of the project. The linking unit can also refer to past data and select the optimal linking method. For example, it selects the optimal linking method based on past data. As a result, the legal advice system according to the embodiment allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks. For example, by simply entering the details of the work, the generation AI can present relevant laws and risks and automatically create and send the necessary documents. Furthermore, linking with internal systems and databases makes it possible to automate legal matters according to the progress of the project.

[0063] The analysis unit can reference the user's past input history and automatically complete similar work content. For example, when a user inputs work content, the analysis unit uses a generation AI to analyze the past input history and automatically suggest similar work content. For example, based on work content related to "launching a new product" that was previously input, it can automatically complete related laws and risks. The analysis unit can also save past input history in cloud storage and refer to it as needed. For example, it can save input history from the past year and suggest similar work content. This makes it possible to reference the user's past input history and automatically complete similar work content.

[0064] The analysis unit can suggest relevant laws, regulations and risks in real time when a business operation content is input, allowing the user to select from them. For example, when a user inputs business operation content, the analysis unit uses a generation AI to suggest relevant laws, regulations and risks in real time. For example, when a business operation content related to "launching a new product" is input, the generation AI suggests "Product Safety Act" and "Consumer Protection Act." The analysis unit also allows the user to select from the suggested laws, regulations and risks in real time. For example, the user can make a selection using a drop-down menu or check box. This allows relevant laws, regulations and risks to be suggested in real time when a business operation content is input, allowing the user to select from them.

[0065] The analysis unit can use the emotion estimation function to analyze the emotions of the user regarding the work content entered by the user and provide advice to reduce stress and anxiety. For example, when the user enters work content, the generation AI uses the emotion estimation function to analyze the user's emotions and provide advice to reduce stress and anxiety. For example, when the user enters work content related to "launching a new product," the generation AI analyzes the user's stress level and provides advice to relax. The analysis unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, when the user enters work content, the generation AI monitors the user's emotions and displays positive messages. This allows the analysis of the user's emotions and provides advice to reduce stress and anxiety.

[0066] The work content input unit can automatically convert the work content from voice input into text using voice recognition technology. For example, in the work content input unit, a user inputs the work content by voice, and the generation AI automatically converts it into text using voice recognition technology. For example, if a user inputs "work content related to the launch of a new product" by voice, the generation AI converts that content into text. The work content input unit can also perform highly accurate voice recognition using voice recognition software. For example, the voice recognition software automatically analyzes the voice and saves it as text. This allows automatic conversion from voice input to text.

[0067] The work content input unit can automatically insert related visual data when work content is input. For example, when a user inputs work content, the work content input unit allows the generation AI to automatically insert related visual data. For example, if "work content related to the launch of a new product" is input, the generation AI will automatically insert product images and market analysis charts. The work content input unit can also allow the user to select the type of visual data and the insertion method. For example, visual data such as image files, graphs, and charts can be selected. This allows related visual data to be automatically inserted.

[0068] The analysis unit can use the emotion estimation function to analyze the emotions regarding the work content entered by the user and provide an interface for eliciting positive emotions. For example, when the user enters work content, the generation AI uses the emotion estimation function to analyze the user's emotions and provides an interface for eliciting positive emotions. For example, when the user enters "work content related to the launch of a new product," the generation AI displays a positive message. The analysis unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, when the user enters work content, the generation AI monitors the user's emotions and displays a positive message. This makes it possible to analyze the user's emotions and provide an interface for eliciting positive emotions.

[0069] The legal rule presentation unit can refer to past precedents and similar cases and present specific examples when presenting related laws and risks. For example, when the generation AI presents related laws and risks, the legal rule presentation unit refers to past precedents and similar cases and presents specific examples. For example, for the task content related to "launching a new product," the generation AI presents past precedents related to product safety laws. The legal rule presentation unit can also allow the user to check detailed information about precedents and similar cases. For example, it displays detailed information about related precedents. This makes it possible to refer to past precedents and similar cases and present specific examples.

[0070] The legal regulations presentation unit can provide customized advice specific to the user's business operations for the presented laws and risks. For example, the legal regulations presentation unit provides customized advice specific to the user's business operations for the presented laws and risks by the generation AI. For example, for business operations related to "launching a new product," the generation AI proposes specific countermeasures based on the Product Safety Act. The legal regulations presentation unit can also analyze detailed information about the business operations to provide advice based on the user's business operations. For example, it provides customized advice based on detailed information about the business operations. This makes it possible to provide customized advice specific to the user's business operations.

[0071] The legal regulations presentation unit can visually present relevant laws and risks using interactive charts and graphs. For example, the legal regulations presentation unit uses a generation AI to visually present relevant laws and risks using interactive charts and graphs. For example, the legal regulations and risks related to "launching a new product" can be displayed in a chart, allowing the user to click to view more detailed information. The legal regulations presentation unit can also visually show the relationship between laws and risks using interactive charts and graphs. For example, the relationship between laws and risks can be displayed in a graph. This makes it possible to visually present relevant laws and risks.

[0072] The legal regulations presentation unit can simultaneously present other laws, regulations, and risks related to the user's business content in addition to the presented laws, regulations, and risks. For example, the legal regulations presentation unit simultaneously presents other laws, regulations, and risks related to the user's business content in addition to the laws, regulations, and risks presented by the generation AI. For example, for business content related to "launching a new product," the generation AI presents consumer protection laws and environmental regulations in addition to product safety laws. The legal regulations presentation unit can also allow the user to check a list of related laws, regulations, and risks. For example, it displays a list of related laws, regulations, and risks. This allows other laws, regulations, and risks related to the user's business content to be simultaneously presented.

[0073] The legal rule presentation unit can use the emotion estimation function to analyze the user's emotions regarding the presented laws and regulations or risks, and provide additional information to elicit positive emotions. For example, the generation AI in the legal rule presentation unit can use the emotion estimation function to analyze the user's emotions regarding the presented laws and regulations or risks, and provide additional information to elicit positive emotions. For example, if a user feels anxious about laws and regulations regarding the "launch of a new product," the generation AI can present success stories. The legal rule presentation unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if a user feels anxious about laws and regulations or risks, the generation AI can display a positive message. This allows the user's emotions to be analyzed and additional information to elicit positive emotions to be provided.

[0074] The solution proposal unit can refer to past success stories and failure stories for the solution it proposes and present a specific action plan. For example, the solution proposal unit can refer to past success stories and failure stories for the solution proposed by the generation AI and present a specific action plan. For example, in response to the "risk of litigation due to product defects," the generation AI presents specific quality control procedures based on past success stories. The solution proposal unit can also allow the user to check detailed information about success stories and failure stories. For example, it can display detailed information about related success stories and failure stories. This makes it possible to refer to past success stories and failure stories and present a specific action plan.

[0075] The solution proposal unit can provide customized advice specific to the user's work content for the proposed solution. For example, the solution proposal unit provides customized advice specific to the user's work content for the solution proposed by the generation AI. For example, in response to the "risk of litigation due to product defects," the generation AI proposes specific countermeasures according to the type of product and the market. The solution proposal unit can also analyze detailed information about the work content in order to provide advice based on the user's work content. For example, it provides customized advice based on detailed information about the work content. This makes it possible to provide customized advice specific to the user's work content.

[0076] The solution suggestion unit can use the emotion estimation function to analyze the emotions the user has about the proposed solution and provide additional information to deepen understanding. For example, the solution suggestion unit uses the emotion estimation function to analyze the emotions the user has about the proposed solution and provide additional information to deepen understanding. For example, if the user feels anxious about the solution to the "risk of litigation due to product defects," the generation AI will provide additional explanation. The solution suggestion unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if the user feels anxious about the solution, the generation AI will provide information to reassure them. This makes it possible to analyze the user's emotions and provide additional information to deepen understanding.

[0077] The solution proposal unit can visually present the proposed solution using an interactive simulation. For example, the solution proposal unit visually presents the solution proposed by the generation AI using an interactive simulation. For example, it simulates a solution to the "risk of litigation due to product defects" so that the user can visually understand the specific solution. The solution proposal unit can also visually show the effect of the solution using an interactive simulation. For example, it displays the effect of the solution in a simulation. This allows the proposed solution to be visually understood.

[0078] The solution proposal unit can simultaneously present other solutions related to the user's work content in response to the proposed solution. For example, the solution proposal unit simultaneously presents other solutions related to the user's work content in response to the solution proposed by the generation AI. For example, in addition to solutions for the "risk of litigation due to product defects," the solution proposal unit can also present specific quality control procedures and risk management methods. The solution proposal unit can also allow the user to check a list of related solutions. For example, it can display a list of related solutions. This allows other solutions related to the user's work content to be simultaneously presented.

[0079] The solution suggestion unit can use the emotion estimation function to analyze the user's emotions toward the proposed solution and provide additional information to elicit positive emotions. For example, the solution suggestion unit uses the emotion estimation function to analyze the user's emotions toward the proposed solution and provide additional information to elicit positive emotions. For example, if a user feels anxious about the solution to the "risk of litigation due to product defects," the generation AI presents a success story. The solution suggestion unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if the user feels anxious about the solution, the generation AI displays a positive message. This allows the user's emotions to be analyzed and additional information to elicit positive emotions to be provided.

[0080] The document creation unit can refer to similar documents and materials from the past and select the optimal format for the documents and materials to be automatically created. For example, the document creation unit can refer to similar documents and materials from the past and select the optimal format for the documents and materials to be automatically created by the generation AI. For example, when creating a document related to the "launch of a new product," the optimal format can be selected based on similar documents from the past. The document creation unit can also allow the user to check detailed information about similar documents and materials. For example, detailed information about related similar documents and materials can be displayed. This allows the user to refer to similar documents and materials from the past and select the optimal format.

[0081] The document creation unit can add customized content specific to the user's business operations to automatically created documents and materials. For example, the document creation unit adds customized content specific to the user's business operations to documents and materials automatically created by the generation AI. For example, detailed product information and market analysis are added to documents related to the "launch of a new product." The document creation unit can also analyze detailed information about the business operations to provide additional information based on the user's business operations. For example, customized content is added based on detailed information about the business operations. This allows customized content specific to the user's business operations to be added.

[0082] The document creation unit can use the emotion estimation function to analyze the emotions a user feels toward automatically created documents and materials, and provide additional information to deepen understanding. For example, the document creation unit can use the emotion estimation function to have the generation AI analyze the emotions a user feels toward automatically created documents and materials, and provide additional information to deepen understanding. For example, if a user feels anxious about a document regarding the "launch of a new product," the generation AI can provide additional explanation. The document creation unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if a user feels anxious about a document or material, the generation AI can provide information to reassure the user. This allows the document creation unit to analyze the user's emotions and provide additional information to deepen understanding.

[0083] The document creation unit can simultaneously create other documents and materials related to the user's work content in addition to automatically created documents and materials. For example, the document creation unit simultaneously creates other documents and materials related to the user's work content in addition to documents and materials automatically created by the generation AI. For example, in addition to documents related to the "launch of a new product," the document creation unit can also simultaneously create a market analysis report for the product and quality control guidelines. The document creation unit can also allow the user to check a list of related documents and materials. For example, it can display a list of related documents and materials. This allows other documents and materials related to the user's work content to be simultaneously created.

[0084] The document creation unit can use the emotion estimation function to analyze the emotions a user feels toward automatically created documents and materials, and provide additional information to elicit positive emotions. For example, the document creation unit can use the emotion estimation function to analyze the emotions a user feels toward automatically created documents and materials, and provide additional information to elicit positive emotions. For example, if a user feels anxious about a document related to a "new product launch," the generation AI can present a success story. The document creation unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if a user feels anxious about a document or material, the generation AI can display a positive message. This allows the user's emotions to be analyzed and additional information to elicit positive emotions to be provided.

[0085] The sending unit can refer to the recipient's past responses to documents and materials to be automatically sent and select the optimal timing to send them. For example, the sending unit can refer to the recipient's past responses to documents and materials to be automatically sent by the generation AI and select the optimal timing to send them. For example, when sending a notification email regarding the "launch of a new product," the optimal timing to send it is selected based on past responses. The sending unit can also adjust the timing of sending based on the recipient's business hours and past response data. For example, the notification email is sent according to the recipient's business hours. This makes it possible to refer to the recipient's past responses and select the optimal timing to send it.

[0086] The sending unit can add customized content specific to the user's work content to automatically sent documents and materials. For example, the sending unit adds customized content specific to the user's work content to documents and materials automatically sent by the generation AI. For example, detailed product information and market analysis are added to a notification email regarding the "release of a new product." The sending unit can also analyze detailed information about the work content to provide additional information based on the user's work content. For example, customized content is added based on detailed information about the work content. This makes it possible to add customized content specific to the user's work content.

[0087] The sending unit can use the emotion estimation function to analyze the emotions the user feels toward the automatically sent documents or materials and provide additional information to deepen understanding. For example, the sending unit can have the generation AI use the emotion estimation function to analyze the emotions the user feels toward the automatically sent documents or materials and provide additional information to deepen understanding. For example, if the user feels anxious about a notification email regarding a "new product launch," the generation AI can provide additional explanation. The sending unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if the user feels anxious about a notification email, the generation AI can provide information to reassure the user. This makes it possible to analyze the user's emotions and provide additional information to deepen understanding.

[0088] The sending unit can visually display automatically sent documents and materials using an interactive dashboard. For example, the sending unit visually presents documents and materials automatically sent by the generation AI using an interactive dashboard. For example, a notification email regarding "new product launch" can be displayed on the dashboard so that the user can click to view more detailed information. The sending unit can also visually display the contents of the documents and materials using the interactive dashboard. For example, the contents of the notification email can be displayed on the dashboard. This makes it possible to visually present automatically sent documents and materials.

[0089] The sending unit can simultaneously send other documents and materials related to the user's work content in addition to the automatically sent documents and materials. For example, the sending unit simultaneously sends other documents and materials related to the user's work content in addition to the documents and materials automatically sent by the generation AI. For example, in addition to a notification email about the "launch of a new product," the sending unit can also simultaneously send a market analysis report for the product and quality control guidelines. The sending unit can also allow the user to check a list of related documents and materials. For example, the sending unit can display a list of related documents and materials. This allows other documents and materials related to the user's work content to be simultaneously sent.

[0090] The sending unit can use the emotion estimation function to analyze the emotions a user feels toward automatically sent documents or materials and provide additional information to elicit positive emotions. For example, the sending unit uses the emotion estimation function to analyze the emotions a user feels toward automatically sent documents or materials and provide additional information to elicit positive emotions. For example, if a user feels anxious about a notification email regarding a "new product launch," the generation AI presents a success story. The sending unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if a user feels anxious about a notification email, the generation AI displays a positive message. This allows the user's emotions to be analyzed and additional information to elicit positive emotions to be provided.

[0091] When linking with an internal system or database, the linking unit can refer to past data and select the optimal linking method. For example, when the generation AI links with an internal system or database, the linking unit refers to past data and selects the optimal linking method. For example, when linking with a project management system, the linking unit selects the optimal linking method based on past project data. The linking unit can also store past data in cloud storage and reference it as needed. For example, it can store data from the past year and select the optimal linking method. This makes it possible to refer to past data and select the optimal linking method.

[0092] The collaboration unit can provide a customized collaboration method specialized for the user's work content when collaborating with in-house systems and databases. For example, the collaboration unit provides a customized collaboration method specialized for the user's work content when collaborating with in-house systems and databases by the generation AI. For example, when collaborating with a project management system, it provides a collaboration method according to the progress status of a specific project. The collaboration unit can also analyze detailed information about the work content in order to provide a collaboration method based on the user's work content. For example, it provides a customized collaboration method based on detailed information about the work content. This makes it possible to provide a customized collaboration method specialized for the user's work content.

[0093] The linking unit can use the emotion estimation function to analyze the emotions a user has regarding linking with internal systems and databases, and provide additional information to deepen understanding. For example, the linking unit uses the emotion estimation function to have the generation AI analyze the emotions a user has regarding linking with internal systems and databases, and provide additional information to deepen understanding. For example, if the user feels anxious about linking with a project management system, the generation AI provides additional explanation. The linking unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if the user feels anxious about linking, the generation AI provides information to reassure them. This makes it possible to analyze the user's emotions and provide additional information to deepen understanding.

[0094] The linking unit can visually link with internal systems and databases using an interactive interface. For example, the linking unit uses an interactive interface in which the generation AI visually presents linkage with internal systems and databases. For example, the linkage with a project management system can be displayed in the interface, allowing the user to click to check detailed information. The linking unit can also visually show the content of the linkage using an interactive interface. For example, the content of the linkage can be displayed in the interface. This allows linkage with internal systems and databases to be visually performed.

[0095] The linking unit can use the emotion estimation function to analyze the emotions a user has toward linking with internal systems and databases, and provide additional information to elicit positive emotions. For example, the linking unit uses the emotion estimation function of the generation AI to analyze the emotions a user has toward linking with internal systems and databases, and provide additional information to elicit positive emotions. For example, if a user feels anxious about linking with a project management system, the generation AI presents success stories. The linking unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if a user feels anxious about linking, the generation AI displays a positive message. This makes it possible to analyze the user's emotions and provide additional information to elicit positive emotions.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The legal advice system can also include an education module that provides relevant educational content based on the user's job description. For example, if a user enters a job description related to "launching a new product," the generative AI can provide educational videos and online courses on product safety and consumer protection laws. The education module can also provide quizzes and tests to check the user's understanding. For example, it can assess the user's understanding through a quiz on legal regulations. This allows the user to deepen their knowledge of the laws and risks related to their job description.

[0098] The analysis unit may also include a market data provider that provides relevant market data based on the user's business operations. For example, if a user enters a business operation related to "launching a new product," the generation AI will provide information on market trends and competitors. The market data provider may also provide detailed data on specific market segments that interest the user. For example, it may provide market data related to specific regions or age groups. This allows the user to quickly obtain market information related to their business operations.

[0099] The analysis unit can use the emotion estimation function to analyze the emotions of the user regarding the work content entered by the user and provide advice to improve the user's motivation. For example, when a user enters work content related to "launching a new product," the generation AI analyzes the user's motivation level and provides an encouraging message. The analysis unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, when a user enters work content, the generation AI monitors the user's emotions and provides advice to improve motivation. This makes it possible to analyze the user's emotions and provide advice to improve motivation.

[0100] When presenting relevant laws and risks, the legal regulations presentation unit can provide a customized checklist based on the user's work content. For example, if a user enters work content related to "launching a new product," the generation AI will provide a checklist based on the Product Safety Act and Consumer Protection Act. The legal regulations presentation unit can also display checklist items in a format that is easy for users to check. For example, it can display them in checkbox format, allowing users to proceed while checking the items. This allows users to efficiently check the laws and risks related to their work content.

[0101] The solution suggestion unit can use the emotion estimation function to analyze the user's emotions regarding the proposed solution and suggest relaxation methods to reduce the user's stress. For example, if the user feels anxious about the solution to the "risk of litigation due to product defects," the generation AI can suggest breathing exercises or a short meditation session for relaxation. The solution suggestion unit can also use the emotion estimation function to monitor the user's emotions in real time and suggest appropriate relaxation methods. For example, if the user feels anxious about the solution, the generation AI can provide relaxation music. This allows the system to analyze the user's emotions and suggest relaxation methods to reduce stress.

[0102] The document creation unit can add visual designs based on the user's work content to automatically created documents and materials. For example, when a user creates a document related to the "launch of a new product," the generation AI can automatically insert product images and logos. The document creation unit can also provide visual design templates for the user to select from. For example, the user can choose from multiple design templates to improve the appearance of the document. This allows the user to add visual designs based on the user's work content.

[0103] The sending unit can use the emotion estimation function to analyze the emotions the user feels toward the automatically sent documents and materials, and provide feedback to improve the user's satisfaction. For example, if the user feels anxious about a notification email regarding a "new product launch," the generation AI will provide positive feedback. The sending unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate feedback. For example, if the user feels anxious about a notification email, the generation AI will provide information to give the user a sense of security. This allows the system to analyze the user's emotions and provide feedback to improve satisfaction.

[0104] The integration unit can provide automated workflows based on the user's work content by linking with internal systems and databases. For example, when a user enters work content related to "launching a new product," the generative AI will link with the project management system and automate the workflow from product development to market launch. The integration unit can also monitor the progress of the workflow in real time and notify the user. For example, it can send a notification when a specific task is completed. This makes it possible to provide automated workflows based on the user's work content.

[0105] The analysis unit can use the emotion estimation function to analyze the emotions of the user regarding the work content entered by the user and provide customized advice based on the user's emotions. For example, when a user enters work content related to "launching a new product," the generation AI analyzes the user's emotions and provides specific advice to elicit positive emotions. The analysis unit can also use the emotion estimation function to monitor the user's emotions in real time and provide appropriate advice. For example, when a user enters work content, the generation AI monitors the user's emotions and provides advice based on the emotions. This makes it possible to analyze the user's emotions and provide customized advice.

[0106] The legal advice system can also include a news provider that provides a news feed on relevant laws and risks based on the user's work. For example, if a user enters a work description related to "launching a new product," the generation AI will provide news on the latest legal amendments and risks. The news provider can also filter and provide news on specific laws and risks that interest the user. For example, it can provide news related to a specific industry or region. This allows users to quickly obtain the latest information related to their work.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The business content input unit inputs business content. For example, the user inputs the business content in text format. Alternatively, the business content can be input using voice input. The voice input is converted into text using voice recognition technology. Step 2: The analysis unit analyzes the work content entered by the work content input unit. For example, the generation AI can analyze the work content using text analysis technology. It can also refer to the user's past input history and automatically complete similar work content. Step 3: The legal provision presentation unit presents relevant laws and regulations based on the business content analyzed by the analysis unit. For example, the generation AI searches a database for laws and regulations related to the business content and presents them. It can also refer to past precedents and similar cases to present specific examples. Step 4: The risk presentation unit presents risks based on the business content analyzed by the analysis unit. For example, the generation AI searches a database for risks related to the business content and presents them. In addition, other laws and risks related to the user's business content can also be presented at the same time as the presented laws and risks. Step 5: The solution proposal unit proposes solutions to the risks presented by the risk presentation unit. For example, the generation AI searches a database for solutions to risks and proposes them. It can also refer to past successes and failures to present specific action plans. Step 6: The document creation section creates documents based on the content presented by the legal regulations presentation section and risk presentation section. For example, the generation AI automatically creates documents by adding necessary wording based on the presented legal regulations and risks. It can also refer to similar documents and materials from the past to select the optimal format. Step 7: The sending unit sends the document created by the document creation unit. For example, the document created by the generation AI is sent by email. It can also refer to the recipient's past responses and select the optimal timing for sending. Step 8: The Collaboration Department connects the documents created by the Document Creation Department to internal systems. For example, the generation AI connects with an internal project management system and automatically handles the necessary legal matters according to the project's progress. It can also refer to past data to select the optimal collaboration method.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

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

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

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

[0125] 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.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[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 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.

[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 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).

[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] 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.

[0150] 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.

[0151] 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.

[0152] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 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 business content input unit for inputting business content; an analysis unit that analyzes the business content input by the business content input unit; a law presentation unit that presents relevant laws and regulations based on the business content analyzed by the analysis unit; a risk presentation unit that presents risks based on the business content analyzed by the analysis unit; a countermeasure suggestion unit that suggests countermeasures for the risks presented by the risk presentation unit; a document creation unit that creates documents based on the content presented by the legal regulation presentation unit and the risk presentation unit; a sending unit that sends the document created by the document creation unit; A system comprising: a linking unit that links the document created by the document creation unit with an in-house system.

2. The business content input unit Using voice recognition technology, the business content is automatically converted from voice input to text.

2. The system of claim 1.

3. The law presentation unit When presenting the relevant laws and regulations and the risks, refer to past precedents and similar cases and provide specific examples.

2. The system of claim 1.

4. The solution proposal unit Present a concrete action plan for the proposed solution, referencing past successes and failures.

2. The system of claim 1.

5. The document creation unit For documents and materials to be automatically created, refer to similar documents and materials from the past and select the optimal format.

2. The system of claim 1.

6. The sending unit Analyze users' feelings about automatically sent documents and materials and provide additional information to deepen their understanding 2. The system of claim 1.

7. The linking unit is Analyze users' sentiment towards integrating with internal systems and databases, and provide additional information to deepen understanding 2. The system of claim 1.

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