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 reception part, an analysis part, a presentation part, a creation part, and a transmission part. The reception part inputs business contents. The analysis unit analyzes the business contents input by the reception unit and specifies related regulations and risks. The presentation unit presents a coping method based on the regulation or risk specified by the analysis unit. The creation part automatically creates a document, a material and a mail to which necessary words are added on the basis of the coping method presented by the presentation part. The transmission unit automatically transmits the document, the material, and the mail created by the creation unit.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 reception unit, an analysis unit, a presentation unit, a creation unit, and a sending unit. The reception unit inputs the business content. The analysis unit analyzes the business content input by the reception unit and identifies relevant laws and regulations and risks. The presentation unit presents countermeasures based on the laws and regulations and risks identified by the analysis unit. The creation unit automatically creates documents, materials, and emails with necessary wording added based on the countermeasures presented by the presentation unit. The sending unit automatically sends the documents, materials, and emails created by the creation unit. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A legal advice system according to an embodiment of the present invention is a system in which, when a user inputs work content, a generation AI presents relevant laws and regulations, risks, and countermeasures, and automatically creates and sends documents, materials, emails, etc., adding necessary wording. The legal advice system allows even employees who are not familiar with legal matters to quickly address relevant laws and risks, thereby contributing to reducing security and copyright-related issues and improving employee work efficiency. For example, an employee inputs work content into the legal advice system. For example, the legal advice system inputs work content, such as starting a new project or creating a contract. This information is then input into the generation AI. The legal advice system then uses the generation AI to analyze the input work content and identify relevant laws and risks. For example, the generation AI identifies risks related to security laws and copyright. Based on this information, the generation AI presents necessary countermeasures. The legal advice system then uses the generation AI to automatically create documents, materials, emails, etc., adding necessary wording, etc., based on the presented countermeasures. For example, the generation AI creates draft contracts and risk management guidelines. Next, the legal advice system uses generative AI to automatically send the created documents, materials, emails, etc. For example, the generative AI sends them to relevant parties via email or uploads them to the company's internal system. This allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, thereby reducing issues related to security and copyright, and contributing to improved employee work efficiency. Furthermore, the legal advice system can be customized for each company by utilizing generative AI and integrating with internal systems and databases. This allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, thereby reducing issues related to security and copyright, and contributing to improved employee work efficiency. For example, employees input their work details into the legal advice system. For example, the legal advice system inputs work details such as starting a new project or creating a contract. This information is input into the generative AI. The legal advice system then uses generative AI to analyze the input work details and identify relevant laws and risks.For example, the generative AI identifies risks related to security laws and copyrights. Based on this information, the generative AI proposes necessary solutions. The legal advice system then uses the generative AI to automatically create documents, materials, emails, etc., adding necessary wording based on the proposed solutions. For example, the generative AI creates draft contracts and risk management guidelines. The legal advice system then uses the generative AI to automatically send the created documents, materials, emails, etc. For example, the generative AI sends them to relevant parties via email or uploads them to an internal system. This allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, reducing issues related to security and copyrights and improving employee work efficiency. Furthermore, the legal advice system can be customized for each company by utilizing the generative AI and integrating it with internal systems and databases.

[0029] A legal advice system according to an embodiment includes a reception unit, an analysis unit, a presentation unit, a creation unit, and a sending unit. The reception unit inputs business details. Examples of business details include, but are not limited to, legal work, accounting work, and sales work. The reception unit receives business details, such as, for example, an employee's input of business details, such as starting a new project or creating a contract. The analysis unit uses a generation AI to analyze the business details input by the reception unit and identify relevant laws and risks. The analysis may be performed using, for example, text analysis, data mining, risk assessment, or other methods, but is not limited to these examples. For example, the generation AI analyzes the input business details and identifies risks related to security laws and copyright. The presentation unit uses the generation AI to present solutions based on the laws and risks identified by the analysis unit. Examples of solutions include, but are not limited to, legal procedures, risk avoidance measures, and improvement measures. For example, the generation AI presents necessary solutions based on the identified laws and risks. The creation unit uses the generation AI to automatically create documents, materials, emails, etc., adding necessary wording, etc., based on the countermeasures presented by the presentation unit. Examples of documents, materials, emails, etc. include, but are not limited to, contracts, reports, and notification emails. For example, the generation AI creates contract drafts and risk countermeasure guidelines. The sending unit uses the generation AI to automatically send the documents, materials, emails, etc. created by the creation unit. Sending can be performed, for example, by email, uploading to an internal system, or mail, but is not limited to these examples. For example, the generation AI sends the documents to relevant parties by email or uploads them to an internal system. As a result, the legal advice system according to the embodiment automates processes from inputting business details to identifying relevant laws and regulations and risks, presenting countermeasures, creating documents, and sending them, allowing even employees who are not familiar with legal matters to quickly respond to relevant laws and regulations and risks.

[0030] Furthermore, the legal advice system includes a reception unit that can be specialized for each company by linking with internal systems and databases. The reception unit can be specialized for each company by linking with internal systems and databases. Examples of linking with internal systems and databases include, but are not limited to, ERP systems and CRM databases. For example, the reception unit can link with an ERP system to streamline the input of business details. The reception unit can also link with a CRM database to specialize business details based on customer information. This allows the system to meet the specific needs of each company by linking with internal systems and databases.

[0031] Furthermore, the legal advice system includes a reception unit that provides input assistance by referring to the user's past input history when entering business content. The reception unit provides input assistance by referring to the user's past input history using a generation AI when entering business content. The past input history includes, but is not limited to, a past input database, a log file, etc. For example, the reception unit automatically displays business content that the user has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice or text) that the user has used in the past. The reception unit can also predict and suggest business content to be used in a specific time period based on the user's past input history. This makes it possible to improve input efficiency and accuracy by referring to the past input history. The input assistance is realized, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (such as LLM) or a multimodal generation AI.

[0032] Furthermore, the legal advice system includes a reception unit that customizes input fields according to the type of work of the user when the work content is input. The reception unit customizes the input fields according to the type of work of the user using a generation AI when the work content is input. The type of work is identified by, for example, selecting a work category or analyzing keywords in the work content, but is not limited to these examples. For example, when a user inputs legal-related work, the reception unit can prioritize displaying legal-related input fields. Furthermore, when a user inputs security-related work, the reception unit can prioritize displaying security-related input fields. Furthermore, when a user inputs copyright-related work, the reception unit can prioritize displaying copyright-related input fields. This customization of input fields according to the type of work improves input efficiency and accuracy. The customization of input fields is achieved using, for example, a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0033] Furthermore, the legal advice system includes a reception unit that provides an appropriate input means according to the user's input method when inputting business content. The reception unit uses a generation AI to provide the optimal input means according to the user's input method (voice, text, image, etc.) when inputting business content. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, when a user inputs business content by voice, the reception unit converts it into text using voice recognition technology. Furthermore, when a user inputs business content by text, the reception unit can also provide an input assistance function. Furthermore, when a user inputs business content by image, the reception unit can also convert it into text using image recognition technology. This provides the optimal input means according to the user's input method, thereby improving input efficiency and accuracy. The input means is provided using, for example, a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0034] Furthermore, the legal advice system includes a reception unit that prioritizes input of highly relevant information based on the user's geographical location information when the user inputs the business content. The reception unit uses a generation AI to prioritize input of highly relevant information based on the user's geographical location information when the user inputs the business content. The geographical location information is obtained, for example, using GPS data, an IP address, or other methods, but is not limited to these examples. For example, if the user is in a specific region, the reception unit can prioritize displaying laws and risks related to that region. Also, if the user is in a specific country, the reception unit can prioritize displaying laws and risks related to that country. Also, if the user is in a specific city, the reception unit can prioritize displaying laws and risks related to that city. This prioritizes input of highly relevant information based on the geographical location information, thereby improving input efficiency and accuracy. The prioritized input of highly relevant information is achieved, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0035] The legal advice system further includes a reception unit that analyzes the user's social media activity and inputs related information when the user inputs the business content. The reception unit uses a generation AI to analyze the user's social media activity and inputs related information when the user inputs the business content. The social media activity is analyzed by, for example, analyzing data such as the content of posts, the number of followers, and the number of likes, but is not limited to, such examples. For example, the reception unit automatically inputs the business content shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related business content. The reception unit can also suggest related business content based on the activity of the user's friends on social media. This improves the efficiency and accuracy of input by inputting related information based on the analysis of social media activity. The analysis of social media activity is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0036] Furthermore, the legal advice system includes a reception unit that customizes the input method by reflecting the user's past feedback when entering the business content. The reception unit uses a generation AI to customize the input method by reflecting the user's past feedback when entering the business content. The past feedback is provided by, for example, but not limited to, a method of referencing data such as the user's evaluation comments and survey results. For example, the reception unit improves the input interface based on feedback provided by the user in the past. The reception unit can also suggest an optimal input method based on the user's past feedback. The reception unit can also customize input items by reflecting the user's past feedback. This customization of the input method by reflecting the past feedback improves the efficiency and accuracy of input. The customization of the input method is achieved by, for example, a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples.

[0037] Furthermore, the legal advice system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the business content during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the business content during analysis using a generation AI. The importance of the business content is evaluated based on criteria such as, but not limited to, the scope of impact of the business and urgency of the business. For example, the analysis unit performs a detailed analysis for important business content. The analysis unit can also perform a standard analysis for general business content. The analysis unit can also perform a simplified analysis for simple business content. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the business content. The adjustment of the level of detail of the analysis is realized, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0038] Furthermore, the legal advice system includes an analysis unit that applies different analysis algorithms depending on the category of the business content during analysis. The analysis unit uses a generation AI to apply different analysis algorithms depending on the category of the business content during analysis. Examples of analysis algorithms include, but are not limited to, text analysis algorithms and data mining algorithms. For example, the analysis unit applies an analysis algorithm specialized for legal matters when the business content is legal-related. Furthermore, the analysis unit can also apply an analysis algorithm specialized for security when the business content is security-related. Furthermore, the analysis unit can also apply an analysis algorithm specialized for copyright when the business content is copyright-related. This improves the accuracy of the analysis by applying an analysis algorithm depending on the category of the business content. The application of the analysis algorithm is realized, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0039] Furthermore, the legal advice system includes an analysis unit that improves the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit uses a generation AI to improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The past analysis results are obtained by, for example, referring to data such as past analysis reports and databases, but this is not limited to these examples. For example, the analysis unit improves the accuracy of the current analysis based on the analysis results previously performed by the user. The analysis unit can also propose an optimal analysis method based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. This enables more accurate analysis by improving the accuracy of the analysis by referring to the past analysis results. The improvement in analysis accuracy is achieved, for example, using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but this is not limited to these examples.

[0040] Furthermore, the legal advice system includes an analysis unit that determines the priority of analysis based on the submission date of the business content during analysis. The analysis unit determines the priority of analysis based on the submission date of the business content during analysis using a generation AI. The submission date is obtained, for example, by a submission deadline, schedule data, or other methods, but is not limited to these examples. For example, the analysis unit prioritizes analysis of urgent business content. The analysis unit can also prioritize analysis of business content with an upcoming submission deadline. The analysis unit can also postpone analysis of business content with a distant submission deadline. This enables efficient analysis by determining the priority of analysis based on the submission date. The determination of the priority of analysis is realized, for example, by using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0041] Furthermore, the legal advice system includes an analysis unit that adjusts the order of analysis based on the relevance of the business content during analysis. The analysis unit adjusts the order of analysis based on the relevance of the business content during analysis using a generation AI. The relevance of the business content is evaluated based on criteria such as, but not limited to, interdependence of the business content and related laws and regulations. For example, the analysis unit prioritizes analysis of highly relevant business content. The analysis unit can also postpone analysis of less relevant business content. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the business content. This enables efficient analysis by adjusting the order of analysis based on the relevance of the business content. The adjustment of the order of analysis is realized, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0042] Furthermore, the legal advice system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit uses a generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The level of expertise is evaluated based on, for example, but not limited to, criteria such as qualifications and past work experience. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. In this way, analysis results that are easy to understand are provided by adjusting the use of technical terms according to the user's level of expertise. The adjustment of the use of technical terms is achieved, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0043] The legal advice system further includes a presentation unit that adjusts the level of detail of the solution based on the importance of the law or risk when presenting the solution. The presentation unit uses a generation AI to adjust the level of detail of the solution based on the importance of the law or risk when presenting the solution. The importance of the law or risk is evaluated based on criteria such as, but not limited to, the scope of impact and the severity of penalties. For example, the presentation unit presents detailed solutions for important laws or risks. The presentation unit can also present standard solutions for general laws or risks. The presentation unit can also present simplified solutions for simple laws or risks. This enables efficient information provision by adjusting the level of detail of the solution based on the importance of the law or risk. The adjustment of the level of detail of the solution is achieved, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0044] Furthermore, the legal advice system includes a presentation unit that applies different presentation algorithms depending on the category of laws and risks when presenting information. The presentation unit uses a generation AI to apply different presentation algorithms depending on the category of laws and risks when presenting information. Examples of presentation algorithms include, but are not limited to, text analysis algorithms and data mining algorithms. For example, the presentation unit applies a presentation algorithm specialized for legal matters when the laws and risks are legal. Furthermore, the presentation unit can apply a presentation algorithm specialized for security when the laws and risks are security-related. Furthermore, the presentation unit can apply a presentation algorithm specialized for copyright when the laws and risks are copyright-related. This enables efficient information provision by applying a presentation algorithm depending on the category of laws and risks. The application of the presentation algorithm is realized, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0045] Furthermore, the legal advice system includes a presentation unit that, when presenting a solution, improves the accuracy of the solution by referring to the user's past presentation results. The presentation unit uses a generation AI to improve the accuracy of the solution by referring to the user's past presentation results. The past presentation results are obtained by, for example, referring to data such as past presentation reports and databases, but is not limited to these examples. For example, the presentation unit improves the accuracy of the current solution based on the user's past presentation results. The presentation unit can also propose an optimal solution from the user's past presentation results. The presentation unit can also adjust the presentation algorithm by referring to the user's past presentation results. This improves the accuracy of the solution by referring to the past presentation results, thereby providing a more accurate solution. The improvement in the accuracy of the solution is achieved, for example, by using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0046] Furthermore, the legal advice system includes a presentation unit that, at the time of presentation, determines the priority of countermeasures based on the submission dates of the laws and regulations and risks. The presentation unit, using a generation AI, determines the priority of countermeasures based on the submission dates of the laws and regulations and risks at the time of presentation. The submission dates are acquired, for example, by a submission deadline, schedule data, or other methods, but are not limited to these examples. For example, the presentation unit prioritizes presenting countermeasures for urgent laws and regulations or risks. The presentation unit can also prioritize presenting countermeasures for laws and regulations with upcoming submission deadlines. The presentation unit can also postpone presenting countermeasures for laws and risks with distant submission deadlines. This enables efficient information provision by determining the priority of countermeasures based on the submission dates. The priority of countermeasures is determined, for example, by a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0047] The legal advice system further includes a presentation unit that adjusts the order of countermeasures based on the relevance of laws and risks when presenting the countermeasures. The presentation unit adjusts the order of countermeasures based on the relevance of laws and risks when presenting the countermeasures using a generation AI. The relevance of laws and risks is evaluated based on, for example, but not limited to, criteria such as interdependence of laws and risks and associated risks. For example, the presentation unit prioritizes presenting countermeasures for highly relevant laws and risks. The presentation unit can also present countermeasures for less relevant laws and risks later. The presentation unit can also dynamically adjust the order of countermeasures based on the relevance of laws and risks. This enables efficient information provision by adjusting the order of countermeasures based on the relevance of laws and risks. The adjustment of the order of countermeasures is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0048] Furthermore, the legal advice system includes a presentation unit that adjusts the use of technical terms in the solution depending on the user's level of expertise when presenting the solution. The presentation unit uses a generation AI to adjust the use of technical terms in the solution depending on the user's level of expertise when presenting the solution. The level of expertise is evaluated based on, for example, but not limited to, criteria such as qualifications and past work experience. For example, if the user has technical expertise, the presentation unit presents a solution that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the presentation unit can present a solution in simple language. Furthermore, the presentation unit can adjust the way the solution is expressed depending on the user's level of expertise. In this way, by adjusting the use of technical terms depending on the user's level of expertise, a solution that is easy to understand is provided. The adjustment of the use of technical terms is realized, for example, using a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0049] Furthermore, the legal advice system includes a creation unit that adjusts the level of detail of documents, materials, emails, etc. based on the importance of the countermeasures during creation. The creation unit uses a generation AI to adjust the level of detail of documents, materials, emails, etc. based on the importance of the countermeasures during creation. The importance of the countermeasures is evaluated based on criteria such as, but not limited to, the scope of impact and the severity of penalties. For example, the creation unit creates detailed documents for important countermeasures. The creation unit can also create standard documents for general countermeasures. The creation unit can also create simplified documents for simple countermeasures. This enables efficient information provision by adjusting the level of detail according to the importance of the countermeasures. The adjustment of the level of detail is achieved, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0050] Furthermore, the legal advice system includes a creation unit that applies different creation algorithms depending on the category of the solution during creation. The creation unit uses a generation AI to apply different creation algorithms depending on the category of the solution during creation. Examples of creation algorithms include, but are not limited to, text generation algorithms and data mining algorithms. For example, the creation unit applies a creation algorithm specialized for legal matters in the case of a legal-related solution. Furthermore, the creation unit can also apply a creation algorithm specialized for security in the case of a security-related solution. Furthermore, the creation unit can also apply a creation algorithm specialized for copyright in the case of a copyright-related solution. This enables efficient information provision by applying a creation algorithm depending on the category of the solution. The application of the creation algorithm is realized, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0051] Furthermore, the legal advice system includes a creation unit that, during creation, improves the accuracy of creation by referring to the user's past creation results. The creation unit uses a generation AI to improve the accuracy of creation by referring to the user's past creation results. The past creation results are obtained by, for example, referring to data such as past creation reports and databases, but is not limited to these examples. For example, the creation unit improves the accuracy of current creation based on the user's past creation results. The creation unit can also suggest an optimal creation method based on the user's past creation results. The creation unit can also adjust the creation algorithm by referring to the user's past creation results. This improves the accuracy of creation by referring to the past creation results, thereby providing more accurate documents. The improvement in creation accuracy is achieved, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0052] Furthermore, the legal advice system includes a creation unit that, at the time of creation, determines the priority of documents, materials, emails, etc. based on the submission time of the solution. The creation unit uses a generation AI to determine the priority of documents, materials, emails, etc. based on the submission time of the solution. The submission time is obtained, for example, by a submission deadline, schedule data, or other method, but is not limited to these examples. For example, the creation unit prioritizes document creation for urgent solutions. Furthermore, the creation unit can also prioritize document creation for solutions with an upcoming submission deadline. Furthermore, the creation unit can postpone document creation for solutions with a distant submission deadline. This enables efficient information provision by determining the priority based on the submission time. The priority determination is realized, for example, by using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0053] Furthermore, the legal advice system includes a creation unit that adjusts the order of documents, materials, emails, etc. based on the relevance of the countermeasures during creation. The creation unit adjusts the order of documents, materials, emails, etc. based on the relevance of the countermeasures during creation using a generation AI. The relevance of the countermeasures is evaluated based on, for example, interdependence of the countermeasures, associated risks, etc., but is not limited to these examples. For example, the creation unit creates documents with priority given to highly relevant countermeasures. The creation unit can also create documents while leaving less relevant countermeasures for later creation. The creation unit can also dynamically adjust the order in which documents are created based on the relevance of the countermeasures. This enables efficient information provision by adjusting the order based on the relevance of the countermeasures. The order adjustment is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0054] The legal advice system further includes a creation unit that adjusts the use of technical terms in documents, materials, emails, etc., according to the user's level of expertise during creation. The creation unit uses a generation AI to adjust the use of technical terms in documents, materials, emails, etc., according to the user's level of expertise during creation. The level of expertise is evaluated based on, for example, but not limited to, criteria such as qualifications and past work experience. For example, the creation unit creates documents that use a lot of technical terms if the user has technical knowledge. The creation unit can also create documents in simple language if the user does not have technical knowledge. The creation unit can also adjust the way the documents are expressed according to the user's level of expertise. This allows for the adjustment of the use of technical terms according to the user's level of expertise, thereby providing documents that are easy to understand. The adjustment of the use of technical terms is achieved, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, for example.

[0055] Furthermore, the legal advice system includes a sending unit that adjusts the level of detail of the sending based on the importance of the documents, materials, emails, etc. at the time of sending. The sending unit adjusts the level of detail of the sending based on the importance of the documents, materials, emails, etc. at the time of sending using a generation AI. The importance of the documents, materials, emails, etc. is evaluated based on criteria such as, but not limited to, the scope of impact and the severity of penalties. For example, the sending unit provides a detailed sending method for important documents. The sending unit can also provide a standard sending method for general documents. The sending unit can also provide a simplified sending method for simple documents. This enables efficient information provision by adjusting the level of detail of the sending based on the importance of the documents, materials, emails, etc. The adjustment of the level of detail is realized, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0056] Furthermore, the legal advice system includes a sending unit that applies different sending algorithms depending on the category of document, material, email, etc., when sending. The sending unit uses a generation AI to apply different sending algorithms depending on the category of document, material, email, etc., when sending. Examples of sending algorithms include, but are not limited to, text analysis algorithms and data mining algorithms. For example, the sending unit applies a sending algorithm specialized for legal matters to legal-related documents. Furthermore, the sending unit can also apply a sending algorithm specialized for security-related documents. Furthermore, the sending unit can also apply a sending algorithm specialized for copyright-related documents. This enables efficient information provision by applying a sending algorithm depending on the category of document, material, email, etc. The application of a sending algorithm is realized, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0057] Furthermore, the legal advice system includes a sending unit that, at the time of sending, improves the accuracy of sending by referring to the user's past sending results. The sending unit uses a generation AI to improve the accuracy of sending by referring to the user's past sending results. The past sending results are obtained by, for example, referring to data such as past sending reports and databases, but is not limited to these examples. For example, the sending unit improves the accuracy of current sending based on the user's past sending results. The sending unit can also propose an optimal sending method based on the user's past sending results. The sending unit can also adjust the sending algorithm by referring to the user's past sending results. This enables more accurate sending by improving the accuracy of sending by referring to the past sending results. The improvement in sending accuracy is achieved, for example, by using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0058] Furthermore, the legal advice system includes a sending unit that determines the priority of sending documents, materials, emails, etc. based on the submission dates of the documents, materials, emails, etc. at the time of sending. The sending unit uses a generation AI to determine the priority of sending documents, materials, emails, etc. based on the submission dates of the documents, materials, emails, etc. at the time of sending. The submission dates are acquired, for example, by a submission deadline, schedule data, or other methods, but are not limited to these examples. For example, the sending unit prioritizes sending urgent documents. The sending unit can also prioritize sending documents with an approaching submission deadline. The sending unit can also postpone sending documents with a distant submission deadline. This enables efficient information provision by determining the priority of sending documents based on the submission dates. The priority is determined, for example, by a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0059] Furthermore, the legal advice system includes a sending unit that adjusts the sending order based on the relevance of documents, materials, emails, etc. at the time of sending. The sending unit adjusts the sending order based on the relevance of documents, materials, emails, etc. at the time of sending using a generation AI. The relevance of documents, materials, emails, etc. is evaluated based on, for example, but not limited to, criteria such as interdependence of documents and associated risks. For example, the sending unit prioritizes sending highly relevant documents. The sending unit can also postpone sending less relevant documents. The sending unit can also dynamically adjust the sending order based on the relevance of documents. This enables efficient information provision by adjusting the sending order based on the relevance of documents, materials, emails, etc. The order adjustment is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, for example.

[0060] The legal advice system further includes a sending unit that adjusts the use of technical terminology in the sending according to the user's level of expertise at the time of sending. The sending unit adjusts the use of technical terminology in the sending according to the user's level of expertise at the time of sending using a generation AI. The level of expertise is evaluated based on, for example, but not limited to, criteria such as qualifications and past work experience. For example, if the user has technical expertise, the sending unit provides a sending method that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the sending unit can also provide a sending method in simple language. Furthermore, the sending unit can adjust the expression of the sending method according to the user's level of expertise. This enables the sending to be easy to understand by adjusting the use of technical terminology according to the user's level of expertise. The adjustment of the use of technical terminology is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The legal advice system may further include a schedule analysis unit that analyzes a user's work schedule and provides legal advice at the optimal time. For example, the schedule analysis unit may link with the user's calendar and task management system to provide legal advice before important meetings or deadlines. The schedule analysis unit may also provide advice outside of peak work hours. Furthermore, the schedule analysis unit may suspend the provision of advice if the user is on vacation. This allows legal advice to be provided at the optimal time based on the user's work schedule, thereby improving work efficiency and reducing stress.

[0063] The legal advice system may further include a news provider that provides relevant legal news and the latest legal amendments based on the user's business operations. For example, if the user is drafting a contract, the news provider may provide information on recent amendments to contract law. If the user is working in security-related fields, the news provider may also provide news on the latest security laws. Furthermore, if the user is working in copyright-related fields, the news provider may also provide the latest copyright law case law information. This allows the user to always keep up with the latest legal information and take appropriate action.

[0064] The legal advice system may further include a training provider that provides relevant legal training and educational content based on the user's work. For example, if the user is starting a new project, the training provider may provide legal training on project management. Also, if the user is drafting a contract, the training provider may provide educational content on contract law. Furthermore, if the user is performing security-related work, the training provider may provide training on security laws and regulations. This allows the user to acquire the necessary legal knowledge and improve the quality of their work.

[0065] The legal advice system may further include a resource introduction unit that introduces relevant legal resources and experts based on the user's business. For example, if the user is drafting a contract, the resource introduction unit may introduce a lawyer who is knowledgeable in contract law. If the user is working on security-related matters, the resource introduction unit may also introduce a security expert. If the user is working on copyright-related matters, the resource introduction unit may also introduce an expert who is knowledgeable in copyright law. This allows the user to access the necessary legal resources and experts and receive appropriate support.

[0066] The legal advice system may further include a template provider that provides relevant legal templates based on the user's business operations. For example, when a user creates a contract, the template provider may provide a standard contract template. When a user creates a security policy, the template provider may provide a security policy template. When a user creates a copyright-related document, the template provider may provide a copyright document template. This allows the user to create documents efficiently and improve the quality of their work.

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

[0068] Step 1: The reception department inputs the details of the work. The work includes legal work, accounting work, sales work, etc. For example, an employee inputs the details of the work, such as starting a new project or creating a contract. Step 2: The analysis unit uses the generation AI to analyze the business details entered by the reception unit and identify relevant laws and regulations and risks. The analysis is performed using methods such as text analysis, data mining, and risk assessment. For example, the generation AI analyzes the entered business details and identifies risks related to security laws and copyright. Step 3: The presentation unit uses the generation AI to present countermeasures based on the laws and regulations and risks identified by the analysis unit. Countermeasures include legal procedures, risk avoidance measures, and improvement measures. For example, the generation AI presents the necessary countermeasures based on the identified laws and regulations and risks. Step 4: The creation unit uses the generation AI to automatically create documents, materials, emails, etc., adding necessary wording based on the solutions presented by the presentation unit. Documents, materials, emails, etc. include contracts, reports, notification emails, etc. For example, the generation AI creates draft contracts and risk management guidelines. Step 5: The sending department uses the generation AI to automatically send the documents, materials, emails, etc. created by the creation department. Sending is done by email, uploading to an internal system, mailing, etc. For example, the generation AI sends them to the relevant parties by email or uploads them to an internal system.

[0069] (Example 2) A legal advice system according to an embodiment of the present invention is a system in which, when a user inputs work content, a generation AI presents relevant laws and regulations, risks, and countermeasures, and automatically creates and sends documents, materials, emails, etc., adding necessary wording. The legal advice system allows even employees who are not familiar with legal matters to quickly address relevant laws and risks, thereby contributing to reducing security and copyright-related issues and improving employee work efficiency. For example, an employee inputs work content into the legal advice system. For example, the legal advice system inputs work content, such as starting a new project or creating a contract. This information is then input into the generation AI. The legal advice system then uses the generation AI to analyze the input work content and identify relevant laws and risks. For example, the generation AI identifies risks related to security laws and copyright. Based on this information, the generation AI presents necessary countermeasures. The legal advice system then uses the generation AI to automatically create documents, materials, emails, etc., adding necessary wording, etc., based on the presented countermeasures. For example, the generation AI creates draft contracts and risk management guidelines. Next, the legal advice system uses generative AI to automatically send the created documents, materials, emails, etc. For example, the generative AI sends them to relevant parties via email or uploads them to the company's internal system. This allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, thereby reducing issues related to security and copyright, and contributing to improved employee work efficiency. Furthermore, the legal advice system can be customized for each company by utilizing generative AI and integrating with internal systems and databases. This allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, thereby reducing issues related to security and copyright, and contributing to improved employee work efficiency. For example, employees input their work details into the legal advice system. For example, the legal advice system inputs work details such as starting a new project or creating a contract. This information is input into the generative AI. The legal advice system then uses generative AI to analyze the input work details and identify relevant laws and risks.For example, the generative AI identifies risks related to security laws and copyrights. Based on this information, the generative AI proposes necessary solutions. The legal advice system then uses the generative AI to automatically create documents, materials, emails, etc., adding necessary wording based on the proposed solutions. For example, the generative AI creates draft contracts and risk management guidelines. The legal advice system then uses the generative AI to automatically send the created documents, materials, emails, etc. For example, the generative AI sends them to relevant parties via email or uploads them to an internal system. This allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, reducing issues related to security and copyrights and improving employee work efficiency. Furthermore, the legal advice system can be customized for each company by utilizing the generative AI and integrating it with internal systems and databases.

[0070] A legal advice system according to an embodiment includes a reception unit, an analysis unit, a presentation unit, a creation unit, and a sending unit. The reception unit inputs business details. Examples of business details include, but are not limited to, legal work, accounting work, and sales work. The reception unit receives business details, such as, for example, an employee's input of business details, such as starting a new project or creating a contract. The analysis unit uses a generation AI to analyze the business details input by the reception unit and identify relevant laws and risks. The analysis may be performed using, for example, text analysis, data mining, risk assessment, or other methods, but is not limited to these examples. For example, the generation AI analyzes the input business details and identifies risks related to security laws and copyright. The presentation unit uses the generation AI to present solutions based on the laws and risks identified by the analysis unit. Examples of solutions include, but are not limited to, legal procedures, risk avoidance measures, and improvement measures. For example, the generation AI presents necessary solutions based on the identified laws and risks. The creation unit uses the generation AI to automatically create documents, materials, emails, etc., adding necessary wording, etc., based on the countermeasures presented by the presentation unit. Examples of documents, materials, emails, etc. include, but are not limited to, contracts, reports, and notification emails. For example, the generation AI creates contract drafts and risk countermeasure guidelines. The sending unit uses the generation AI to automatically send the documents, materials, emails, etc. created by the creation unit. Sending can be performed, for example, by email, uploading to an internal system, or mail, but is not limited to these examples. For example, the generation AI sends the documents to relevant parties by email or uploads them to an internal system. As a result, the legal advice system according to the embodiment automates processes from inputting business details to identifying relevant laws and regulations and risks, presenting countermeasures, creating documents, and sending them, allowing even employees who are not familiar with legal matters to quickly respond to relevant laws and regulations and risks.

[0071] Furthermore, the legal advice system includes a reception unit that can be specialized for each company by linking with internal systems and databases. The reception unit can be specialized for each company by linking with internal systems and databases. Examples of linking with internal systems and databases include, but are not limited to, ERP systems and CRM databases. For example, the reception unit can link with an ERP system to streamline the input of business details. The reception unit can also link with a CRM database to specialize business details based on customer information. This allows the system to meet the specific needs of each company by linking with internal systems and databases.

[0072] The legal advice system further includes a reception unit that estimates a user's emotions and adjusts the input interface for the business content based on the estimated user emotions. The reception unit estimates the user's emotions using a generation AI and adjusts the input interface for the business content based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, and the like, but are not limited to these examples. For example, if the user is feeling stressed, the reception unit may provide a simple interface and minimize input steps. If the user is relaxed, the reception unit may provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit may prioritize voice input to enable quick input of the business content. This adjusts the input interface according to the user's emotions, reducing user stress and enabling efficient input. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] Furthermore, the legal advice system includes a reception unit that provides input assistance by referring to the user's past input history when entering business content. The reception unit provides input assistance by referring to the user's past input history using a generation AI when entering business content. The past input history includes, but is not limited to, a past input database, a log file, etc. For example, the reception unit automatically displays business content that the user has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice or text) that the user has used in the past. The reception unit can also predict and suggest business content to be used in a specific time period based on the user's past input history. This makes it possible to improve input efficiency and accuracy by referring to the past input history. The input assistance is realized, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (such as LLM) or a multimodal generation AI.

[0074] Furthermore, the legal advice system includes a reception unit that customizes input fields according to the type of work of the user when the work content is input. The reception unit customizes the input fields according to the type of work of the user using a generation AI when the work content is input. The type of work is identified by, for example, selecting a work category or analyzing keywords in the work content, but is not limited to these examples. For example, when a user inputs legal-related work, the reception unit can prioritize displaying legal-related input fields. Furthermore, when a user inputs security-related work, the reception unit can prioritize displaying security-related input fields. Furthermore, when a user inputs copyright-related work, the reception unit can prioritize displaying copyright-related input fields. This customization of input fields according to the type of work improves input efficiency and accuracy. The customization of input fields is achieved using, for example, a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] Furthermore, the legal advice system includes a reception unit that provides an appropriate input means according to the user's input method when inputting business content. The reception unit uses a generation AI to provide the optimal input means according to the user's input method (voice, text, image, etc.) when inputting business content. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, when a user inputs business content by voice, the reception unit converts it into text using voice recognition technology. Furthermore, when a user inputs business content by text, the reception unit can also provide an input assistance function. Furthermore, when a user inputs business content by image, the reception unit can also convert it into text using image recognition technology. This provides the optimal input means according to the user's input method, thereby improving input efficiency and accuracy. The input means is provided using, for example, a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The legal advice system further includes a reception unit that estimates a user's emotions and prioritizes input content based on the estimated user emotions. The reception unit estimates the user's emotions using a generation AI and prioritizes the input content based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, and other methods, but are not limited to these examples. For example, if the user is nervous, the reception unit may prioritize displaying important input items. If the user is relaxed, the reception unit may also display detailed input items. If the user is in a hurry, the reception unit may also display a minimum number of input items. This enables efficient input by prioritizing input content according to the user's emotions. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] Furthermore, the legal advice system includes a reception unit that prioritizes input of highly relevant information based on the user's geographical location information when the user inputs the business content. The reception unit uses a generation AI to prioritize input of highly relevant information based on the user's geographical location information when the user inputs the business content. The geographical location information is obtained, for example, using GPS data, an IP address, or other methods, but is not limited to these examples. For example, if the user is in a specific region, the reception unit can prioritize displaying laws and risks related to that region. Also, if the user is in a specific country, the reception unit can prioritize displaying laws and risks related to that country. Also, if the user is in a specific city, the reception unit can prioritize displaying laws and risks related to that city. This prioritizes input of highly relevant information based on the geographical location information, thereby improving input efficiency and accuracy. The prioritized input of highly relevant information is achieved, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The legal advice system further includes a reception unit that analyzes the user's social media activity and inputs related information when the user inputs the business content. The reception unit uses a generation AI to analyze the user's social media activity and inputs related information when the user inputs the business content. The social media activity is analyzed by, for example, analyzing data such as the content of posts, the number of followers, and the number of likes, but is not limited to, such examples. For example, the reception unit automatically inputs the business content shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related business content. The reception unit can also suggest related business content based on the activity of the user's friends on social media. This improves the efficiency and accuracy of input by inputting related information based on the analysis of social media activity. The analysis of social media activity is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] Furthermore, the legal advice system includes a reception unit that customizes the input method by reflecting the user's past feedback when entering the business content. The reception unit uses a generation AI to customize the input method by reflecting the user's past feedback when entering the business content. The past feedback is provided by, for example, but not limited to, a method of referencing data such as the user's evaluation comments and survey results. For example, the reception unit improves the input interface based on feedback provided by the user in the past. The reception unit can also suggest an optimal input method based on the user's past feedback. The reception unit can also customize input items by reflecting the user's past feedback. This customization of the input method by reflecting the past feedback improves the efficiency and accuracy of input. The customization of the input method is achieved by, for example, a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples.

[0080] The legal advice system further includes an analysis unit that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using a generation AI and adjusts the display method of the analysis results based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, and other methods, but are not limited to these examples. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the analysis results according to the user's emotions, enabling efficient information provision. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] Furthermore, the legal advice system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the business content during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the business content during analysis using a generation AI. The importance of the business content is evaluated based on criteria such as, but not limited to, the scope of impact of the business and urgency of the business. For example, the analysis unit performs a detailed analysis for important business content. The analysis unit can also perform a standard analysis for general business content. The analysis unit can also perform a simplified analysis for simple business content. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the business content. The adjustment of the level of detail of the analysis is realized, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] Furthermore, the legal advice system includes an analysis unit that applies different analysis algorithms depending on the category of the business content during analysis. The analysis unit uses a generation AI to apply different analysis algorithms depending on the category of the business content during analysis. Examples of analysis algorithms include, but are not limited to, text analysis algorithms and data mining algorithms. For example, the analysis unit applies an analysis algorithm specialized for legal matters when the business content is legal-related. Furthermore, the analysis unit can also apply an analysis algorithm specialized for security when the business content is security-related. Furthermore, the analysis unit can also apply an analysis algorithm specialized for copyright when the business content is copyright-related. This improves the accuracy of the analysis by applying an analysis algorithm depending on the category of the business content. The application of the analysis algorithm is realized, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] Furthermore, the legal advice system includes an analysis unit that improves the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit uses a generation AI to improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The past analysis results are obtained by, for example, referring to data such as past analysis reports and databases, but this is not limited to these examples. For example, the analysis unit improves the accuracy of the current analysis based on the analysis results previously performed by the user. The analysis unit can also propose an optimal analysis method based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. This enables more accurate analysis by improving the accuracy of the analysis by referring to the past analysis results. The improvement in analysis accuracy is achieved, for example, using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but this is not limited to these examples.

[0084] The legal advice system further includes an analysis unit that estimates a user's emotions and prioritizes analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using a generation AI and prioritizes the analysis results based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, and other methods, but are not limited to these examples. For example, if the user is nervous, the analysis unit prioritizes displaying important analysis results. If the user is relaxed, the analysis unit can also display detailed analysis results. If the user is in a hurry, the analysis unit can also display analysis results that focus on the main points. This enables efficient information provision by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] Furthermore, the legal advice system includes an analysis unit that determines the priority of analysis based on the submission date of the business content during analysis. The analysis unit determines the priority of analysis based on the submission date of the business content during analysis using a generation AI. The submission date is obtained, for example, by a submission deadline, schedule data, or other methods, but is not limited to these examples. For example, the analysis unit prioritizes analysis of urgent business content. The analysis unit can also prioritize analysis of business content with an upcoming submission deadline. The analysis unit can also postpone analysis of business content with a distant submission deadline. This enables efficient analysis by determining the priority of analysis based on the submission date. The determination of the priority of analysis is realized, for example, by using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] Furthermore, the legal advice system includes an analysis unit that adjusts the order of analysis based on the relevance of the business content during analysis. The analysis unit adjusts the order of analysis based on the relevance of the business content during analysis using a generation AI. The relevance of the business content is evaluated based on criteria such as, but not limited to, interdependence of the business content and related laws and regulations. For example, the analysis unit prioritizes analysis of highly relevant business content. The analysis unit can also postpone analysis of less relevant business content. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the business content. This enables efficient analysis by adjusting the order of analysis based on the relevance of the business content. The adjustment of the order of analysis is realized, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] Furthermore, the legal advice system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit uses a generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The level of expertise is evaluated based on, for example, but not limited to, criteria such as qualifications and past work experience. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. In this way, analysis results that are easy to understand are provided by adjusting the use of technical terms according to the user's level of expertise. The adjustment of the use of technical terms is achieved, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] The legal advice system further includes a presentation unit that estimates the user's emotions and adjusts the presentation method of the solution based on the estimated user emotions. The presentation unit estimates the user's emotions using a generation AI and adjusts the presentation method of the solution based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, and the like, but are not limited to these examples. For example, if the user is nervous, the presentation unit provides a simple, highly visible presentation method. Furthermore, if the user is relaxed, the presentation unit can provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a presentation method that focuses on the main points. This improves visibility by adjusting the presentation method of the solution based on the user's emotions, enabling efficient information provision. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The legal advice system further includes a presentation unit that adjusts the level of detail of the solution based on the importance of the law or risk when presenting the solution. The presentation unit uses a generation AI to adjust the level of detail of the solution based on the importance of the law or risk when presenting the solution. The importance of the law or risk is evaluated based on criteria such as, but not limited to, the scope of impact and the severity of penalties. For example, the presentation unit presents detailed solutions for important laws or risks. The presentation unit can also present standard solutions for general laws or risks. The presentation unit can also present simplified solutions for simple laws or risks. This enables efficient information provision by adjusting the level of detail of the solution based on the importance of the law or risk. The adjustment of the level of detail of the solution is achieved, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] Furthermore, the legal advice system includes a presentation unit that applies different presentation algorithms depending on the category of laws and risks when presenting information. The presentation unit uses a generation AI to apply different presentation algorithms depending on the category of laws and risks when presenting information. Examples of presentation algorithms include, but are not limited to, text analysis algorithms and data mining algorithms. For example, the presentation unit applies a presentation algorithm specialized for legal matters when the laws and risks are legal. Furthermore, the presentation unit can apply a presentation algorithm specialized for security when the laws and risks are security-related. Furthermore, the presentation unit can apply a presentation algorithm specialized for copyright when the laws and risks are copyright-related. This enables efficient information provision by applying a presentation algorithm depending on the category of laws and risks. The application of the presentation algorithm is realized, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] Furthermore, the legal advice system includes a presentation unit that, when presenting a solution, improves the accuracy of the solution by referring to the user's past presentation results. The presentation unit uses a generation AI to improve the accuracy of the solution by referring to the user's past presentation results. The past presentation results are obtained by, for example, referring to data such as past presentation reports and databases, but is not limited to these examples. For example, the presentation unit improves the accuracy of the current solution based on the user's past presentation results. The presentation unit can also propose an optimal solution from the user's past presentation results. The presentation unit can also adjust the presentation algorithm by referring to the user's past presentation results. This improves the accuracy of the solution by referring to the past presentation results, thereby providing a more accurate solution. The improvement in the accuracy of the solution is achieved, for example, by using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The legal advice system further includes a presentation unit that estimates the user's emotions and prioritizes countermeasures based on the estimated user emotions. The presentation unit estimates the user's emotions using a generation AI and prioritizes countermeasures based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, and the like, but are not limited to these examples. For example, if the user is nervous, the presentation unit prioritizes important countermeasures. If the user is relaxed, the presentation unit can also present detailed countermeasures. If the user is in a hurry, the presentation unit can also present countermeasures that focus on the main points. This enables efficient information provision by prioritizing countermeasures based on the user's emotions. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] Furthermore, the legal advice system includes a presentation unit that, at the time of presentation, determines the priority of countermeasures based on the submission dates of the laws and regulations and risks. The presentation unit, using a generation AI, determines the priority of countermeasures based on the submission dates of the laws and regulations and risks at the time of presentation. The submission dates are acquired, for example, by a submission deadline, schedule data, or other methods, but are not limited to these examples. For example, the presentation unit prioritizes presenting countermeasures for urgent laws and regulations or risks. The presentation unit can also prioritize presenting countermeasures for laws and regulations with upcoming submission deadlines. The presentation unit can also postpone presenting countermeasures for laws and risks with distant submission deadlines. This enables efficient information provision by determining the priority of countermeasures based on the submission dates. The priority of countermeasures is determined, for example, by a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The legal advice system further includes a presentation unit that adjusts the order of countermeasures based on the relevance of laws and risks when presenting the countermeasures. The presentation unit adjusts the order of countermeasures based on the relevance of laws and risks when presenting the countermeasures using a generation AI. The relevance of laws and risks is evaluated based on, for example, but not limited to, criteria such as interdependence of laws and risks and associated risks. For example, the presentation unit prioritizes presenting countermeasures for highly relevant laws and risks. The presentation unit can also present countermeasures for less relevant laws and risks later. The presentation unit can also dynamically adjust the order of countermeasures based on the relevance of laws and risks. This enables efficient information provision by adjusting the order of countermeasures based on the relevance of laws and risks. The adjustment of the order of countermeasures is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] Furthermore, the legal advice system includes a presentation unit that adjusts the use of technical terms in the solution depending on the user's level of expertise when presenting the solution. The presentation unit uses a generation AI to adjust the use of technical terms in the solution depending on the user's level of expertise when presenting the solution. The level of expertise is evaluated based on, for example, but not limited to, criteria such as qualifications and past work experience. For example, if the user has technical expertise, the presentation unit presents a solution that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the presentation unit can present a solution in simple language. Furthermore, the presentation unit can adjust the way the solution is expressed depending on the user's level of expertise. In this way, by adjusting the use of technical terms depending on the user's level of expertise, a solution that is easy to understand is provided. The adjustment of the use of technical terms is realized, for example, using a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The legal advice system further includes a creation unit that estimates a user's emotions and adjusts the creation method of documents, materials, emails, etc. based on the estimated user emotions. The creation unit estimates the user's emotions using a generation AI and adjusts the creation method of documents, materials, emails, etc. based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to these examples. For example, if the user is nervous, the creation unit creates a simple, highly visible document. If the user is relaxed, the creation unit can also create a document containing detailed information. If the user is in a hurry, the creation unit can also create a document that focuses on the main points. This improves visibility by adjusting the creation method according to the user's emotions, enabling efficient information provision. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] Furthermore, the legal advice system includes a creation unit that adjusts the level of detail of documents, materials, emails, etc. based on the importance of the countermeasures during creation. The creation unit uses a generation AI to adjust the level of detail of documents, materials, emails, etc. based on the importance of the countermeasures during creation. The importance of the countermeasures is evaluated based on criteria such as, but not limited to, the scope of impact and the severity of penalties. For example, the creation unit creates detailed documents for important countermeasures. The creation unit can also create standard documents for general countermeasures. The creation unit can also create simplified documents for simple countermeasures. This enables efficient information provision by adjusting the level of detail according to the importance of the countermeasures. The adjustment of the level of detail is achieved, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] Furthermore, the legal advice system includes a creation unit that applies different creation algorithms depending on the category of the solution during creation. The creation unit uses a generation AI to apply different creation algorithms depending on the category of the solution during creation. Examples of creation algorithms include, but are not limited to, text generation algorithms and data mining algorithms. For example, the creation unit applies a creation algorithm specialized for legal matters in the case of a legal-related solution. Furthermore, the creation unit can also apply a creation algorithm specialized for security in the case of a security-related solution. Furthermore, the creation unit can also apply a creation algorithm specialized for copyright in the case of a copyright-related solution. This enables efficient information provision by applying a creation algorithm depending on the category of the solution. The application of the creation algorithm is realized, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] Furthermore, the legal advice system includes a creation unit that, during creation, improves the accuracy of creation by referring to the user's past creation results. The creation unit uses a generation AI to improve the accuracy of creation by referring to the user's past creation results. The past creation results are obtained by, for example, referring to data such as past creation reports and databases, but is not limited to these examples. For example, the creation unit improves the accuracy of current creation based on the user's past creation results. The creation unit can also suggest an optimal creation method based on the user's past creation results. The creation unit can also adjust the creation algorithm by referring to the user's past creation results. This improves the accuracy of creation by referring to the past creation results, thereby providing more accurate documents. The improvement in creation accuracy is achieved, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] The legal advice system further includes a creation unit that estimates a user's emotions and prioritizes documents, materials, emails, etc. based on the estimated user emotions. The creation unit uses a generation AI to estimate the user's emotions and prioritizes documents, materials, emails, etc. based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to these examples. For example, if the user is nervous, the creation unit prioritizes creating important documents. The creation unit can also create detailed documents if the user is relaxed. The creation unit can also prioritize creating documents that focus on the main points if the user is in a hurry. This enables efficient information provision by determining priorities based on the user's emotions. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] Furthermore, the legal advice system includes a creation unit that, at the time of creation, determines the priority of documents, materials, emails, etc. based on the submission time of the solution. The creation unit uses a generation AI to determine the priority of documents, materials, emails, etc. based on the submission time of the solution. The submission time is obtained, for example, by a submission deadline, schedule data, or other method, but is not limited to these examples. For example, the creation unit prioritizes document creation for urgent solutions. Furthermore, the creation unit can also prioritize document creation for solutions with an upcoming submission deadline. Furthermore, the creation unit can postpone document creation for solutions with a distant submission deadline. This enables efficient information provision by determining the priority based on the submission time. The priority determination is realized, for example, by using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] Furthermore, the legal advice system includes a creation unit that adjusts the order of documents, materials, emails, etc. based on the relevance of the countermeasures during creation. The creation unit adjusts the order of documents, materials, emails, etc. based on the relevance of the countermeasures during creation using a generation AI. The relevance of the countermeasures is evaluated based on, for example, interdependence of the countermeasures, associated risks, etc., but is not limited to these examples. For example, the creation unit creates documents with priority given to highly relevant countermeasures. The creation unit can also create documents while leaving less relevant countermeasures for later creation. The creation unit can also dynamically adjust the order in which documents are created based on the relevance of the countermeasures. This enables efficient information provision by adjusting the order based on the relevance of the countermeasures. The order adjustment is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] The legal advice system further includes a creation unit that adjusts the use of technical terms in documents, materials, emails, etc., according to the user's level of expertise during creation. The creation unit uses a generation AI to adjust the use of technical terms in documents, materials, emails, etc., according to the user's level of expertise during creation. The level of expertise is evaluated based on, for example, but not limited to, criteria such as qualifications and past work experience. For example, the creation unit creates documents that use a lot of technical terms if the user has technical knowledge. The creation unit can also create documents in simple language if the user does not have technical knowledge. The creation unit can also adjust the way the documents are expressed according to the user's level of expertise. This allows for the adjustment of the use of technical terms according to the user's level of expertise, thereby providing documents that are easy to understand. The adjustment of the use of technical terms is achieved, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, for example.

[0104] The legal advice system further includes a sending unit that estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The sending unit estimates the user's emotions using a generation AI and adjusts the delivery method based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, and the like, but are not limited to these examples. For example, if the user is nervous, the sending unit provides a simple, highly visible delivery method. If the user is relaxed, the sending unit can also provide a delivery method that includes detailed information. If the user is in a hurry, the sending unit can also provide a delivery method that focuses on the main points. This improves visibility by adjusting the delivery method according to the user's emotions, enabling efficient information provision. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] Furthermore, the legal advice system includes a sending unit that adjusts the level of detail of the sending based on the importance of the documents, materials, emails, etc. at the time of sending. The sending unit adjusts the level of detail of the sending based on the importance of the documents, materials, emails, etc. at the time of sending using a generation AI. The importance of the documents, materials, emails, etc. is evaluated based on criteria such as, but not limited to, the scope of impact and the severity of penalties. For example, the sending unit provides a detailed sending method for important documents. The sending unit can also provide a standard sending method for general documents. The sending unit can also provide a simplified sending method for simple documents. This enables efficient information provision by adjusting the level of detail of the sending based on the importance of the documents, materials, emails, etc. The adjustment of the level of detail is realized, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] Furthermore, the legal advice system includes a sending unit that applies different sending algorithms depending on the category of document, material, email, etc., when sending. The sending unit uses a generation AI to apply different sending algorithms depending on the category of document, material, email, etc., when sending. Examples of sending algorithms include, but are not limited to, text analysis algorithms and data mining algorithms. For example, the sending unit applies a sending algorithm specialized for legal matters to legal-related documents. Furthermore, the sending unit can also apply a sending algorithm specialized for security-related documents. Furthermore, the sending unit can also apply a sending algorithm specialized for copyright-related documents. This enables efficient information provision by applying a sending algorithm depending on the category of document, material, email, etc. The application of a sending algorithm is realized, for example, by using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] Furthermore, the legal advice system includes a sending unit that, at the time of sending, improves the accuracy of sending by referring to the user's past sending results. The sending unit uses a generation AI to improve the accuracy of sending by referring to the user's past sending results. The past sending results are obtained by, for example, referring to data such as past sending reports and databases, but is not limited to these examples. For example, the sending unit improves the accuracy of current sending based on the user's past sending results. The sending unit can also propose an optimal sending method based on the user's past sending results. The sending unit can also adjust the sending algorithm by referring to the user's past sending results. This enables more accurate sending by improving the accuracy of sending by referring to the past sending results. The improvement in sending accuracy is achieved, for example, by using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] The legal advice system further includes a sending unit that estimates a user's emotions and determines a sending priority based on the estimated user emotions. The sending unit estimates the user's emotions using a generation AI and determines a sending priority based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but are not limited to these examples. For example, if the user is nervous, the sending unit may prioritize sending important documents. If the user is relaxed, the sending unit may also prioritize sending detailed documents. If the user is in a hurry, the sending unit may also prioritize sending documents that focus on the main points. This enables efficient information provision by determining the sending priority based on the user's emotions. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] Furthermore, the legal advice system includes a sending unit that determines the priority of sending documents, materials, emails, etc. based on the submission dates of the documents, materials, emails, etc. at the time of sending. The sending unit uses a generation AI to determine the priority of sending documents, materials, emails, etc. based on the submission dates of the documents, materials, emails, etc. at the time of sending. The submission dates are acquired, for example, by a submission deadline, schedule data, or other methods, but are not limited to these examples. For example, the sending unit prioritizes sending urgent documents. The sending unit can also prioritize sending documents with an approaching submission deadline. The sending unit can also postpone sending documents with a distant submission deadline. This enables efficient information provision by determining the priority of sending documents based on the submission dates. The priority is determined, for example, by a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] Furthermore, the legal advice system includes a sending unit that adjusts the sending order based on the relevance of documents, materials, emails, etc. at the time of sending. The sending unit adjusts the sending order based on the relevance of documents, materials, emails, etc. at the time of sending using a generation AI. The relevance of documents, materials, emails, etc. is evaluated based on, for example, but not limited to, criteria such as interdependence of documents and associated risks. For example, the sending unit prioritizes sending highly relevant documents. The sending unit can also postpone sending less relevant documents. The sending unit can also dynamically adjust the sending order based on the relevance of documents. This enables efficient information provision by adjusting the sending order based on the relevance of documents, materials, emails, etc. The order adjustment is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, for example.

[0111] The legal advice system further includes a sending unit that adjusts the use of technical terminology in the sending according to the user's level of expertise at the time of sending. The sending unit adjusts the use of technical terminology in the sending according to the user's level of expertise at the time of sending using a generation AI. The level of expertise is evaluated based on, for example, but not limited to, criteria such as qualifications and past work experience. For example, if the user has technical expertise, the sending unit provides a sending method that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the sending unit can also provide a sending method in simple language. Furthermore, the sending unit can adjust the expression of the sending method according to the user's level of expertise. This enables the sending to be easy to understand by adjusting the use of technical terminology according to the user's level of expertise. The adjustment of the use of technical terminology is realized, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, presentation unit, creation unit, and sending unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input business details using the reception device 38 of the smart device 14. For example, the analysis unit analyzes the business details using a generation AI by the specific processing unit 290 of the data processing device 12 and identifies relevant laws and regulations and risks. For example, the presentation unit presents countermeasures by the specific processing unit 290 of the data processing device 12. For example, the creation unit automatically creates documents, materials, emails, etc. with necessary wording, etc. added by the specific processing unit 290 of the data processing device 12. For example, the sending unit automatically sends documents, materials, emails, etc. created using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, presentation unit, creation unit, and sending unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the business details using the microphone 238 of the smart glasses 214. For example, the analysis unit analyzes the business details using a generation AI by the specific processing unit 290 of the data processing device 12 and identifies relevant laws and regulations and risks. For example, the presentation unit presents countermeasures by the specific processing unit 290 of the data processing device 12. For example, the creation unit automatically creates documents, materials, emails, etc. with necessary wording, etc. added by the specific processing unit 290 of the data processing device 12. For example, the sending unit automatically sends documents, materials, emails, etc. created using the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, presentation unit, creation unit, and sending unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the business details using the microphone 238 of the headset-type terminal 314. For example, the analysis unit analyzes the business details using a generation AI by the specific processing unit 290 of the data processing device 12 and identifies relevant laws and regulations and risks. For example, the presentation unit presents countermeasures by the specific processing unit 290 of the data processing device 12. For example, the creation unit automatically creates documents, materials, emails, etc. with necessary wording, etc. added by the specific processing unit 290 of the data processing device 12. For example, the sending unit automatically sends documents, materials, emails, etc. created using the communication I / F 44 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, analysis unit, presentation unit, creation unit, and sending unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the business details using the microphone 238 of the robot 414. For example, the analysis unit analyzes the business details using a generation AI by the specific processing unit 290 of the data processing device 12 and identifies relevant laws and regulations and risks. For example, the presentation unit presents countermeasures by the specific processing unit 290 of the data processing device 12. For example, the creation unit automatically creates documents, materials, emails, etc. with necessary wording, etc. added by the specific processing unit 290 of the data processing device 12. For example, the sending unit automatically sends documents, materials, emails, etc. created using the communication I / F 44 of the robot 414.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The legal advice system may further include a schedule analysis unit that analyzes a user's work schedule and provides legal advice at the optimal time. For example, the schedule analysis unit may link with the user's calendar and task management system to provide legal advice before important meetings or deadlines. The schedule analysis unit may also provide advice outside of peak work hours. Furthermore, the schedule analysis unit may suspend the provision of advice if the user is on vacation. This allows legal advice to be provided at the optimal time based on the user's work schedule, thereby improving work efficiency and reducing stress.

[0114] The legal advice system may further include an advice adjustment unit that estimates the user's emotions and adjusts the content of the legal advice based on the estimated user emotions. For example, the advice adjustment unit may provide concise and specific advice when the user is stressed. Alternatively, the advice adjustment unit may provide detailed background information and options when the user is relaxed. Furthermore, the advice adjustment unit may provide advice that focuses on the most important points when the user is in a hurry. In this way, effective legal advice can be provided by adjusting the content of the advice according to the user's emotions.

[0115] The legal advice system may further include a news provider that provides relevant legal news and the latest legal amendments based on the user's business operations. For example, if the user is drafting a contract, the news provider may provide information on recent amendments to contract law. If the user is working in security-related fields, the news provider may also provide news on the latest security laws. Furthermore, if the user is working in copyright-related fields, the news provider may also provide the latest copyright law case law information. This allows the user to always keep up with the latest legal information and take appropriate action.

[0116] The legal advice system may further include a format adjustment unit that estimates the user's emotions and adjusts the format of the legal advice based on the estimated user emotions. For example, if the user is nervous, the format adjustment unit may provide a format that makes extensive use of visually easy-to-understand graphics and diagrams. Alternatively, if the user is relaxed, the format adjustment unit may provide a format that includes detailed text information. Furthermore, if the user is in a hurry, the format adjustment unit may provide a concise format with bullet points. This allows for the adjustment of the format according to the user's emotions, improving visibility and enabling efficient information provision.

[0117] The legal advice system may further include a training provider that provides relevant legal training and educational content based on the user's work. For example, if the user is starting a new project, the training provider may provide legal training on project management. Also, if the user is drafting a contract, the training provider may provide educational content on contract law. Furthermore, if the user is performing security-related work, the training provider may provide training on security laws and regulations. This allows the user to acquire the necessary legal knowledge and improve the quality of their work.

[0118] The legal advice system may further include a priority determination unit that estimates the user's emotions and determines the priority of legal advice based on the estimated user emotions. For example, if the user is nervous, the priority determination unit may provide the most important legal advice with priority. If the user is relaxed, the priority determination unit may also provide detailed legal advice with priority. Furthermore, if the user is in a hurry, the priority determination unit may also provide legal advice that focuses on the main points with priority. This enables efficient information provision by determining priorities according to the user's emotions.

[0119] The legal advice system may further include a resource introduction unit that introduces relevant legal resources and experts based on the user's business. For example, if the user is drafting a contract, the resource introduction unit may introduce a lawyer who is knowledgeable in contract law. If the user is working on security-related matters, the resource introduction unit may also introduce a security expert. If the user is working on copyright-related matters, the resource introduction unit may also introduce an expert who is knowledgeable in copyright law. This allows the user to access the necessary legal resources and experts and receive appropriate support.

[0120] The legal advice system may further include a notification adjustment unit that estimates the user's emotions and adjusts the notification method of legal advice based on the estimated user emotions. For example, if the user is nervous, the notification adjustment unit may provide a simple, highly visible notification method. If the user is relaxed, the notification adjustment unit may also provide a notification method that includes detailed information. If the user is in a hurry, the notification adjustment unit may also provide a notification method that focuses on the main points. This allows for adjustment of the notification method according to the user's emotions, improving visibility and enabling efficient information provision.

[0121] The legal advice system may further include a template provider that provides relevant legal templates based on the user's business operations. For example, when a user creates a contract, the template provider may provide a standard contract template. When a user creates a security policy, the template provider may provide a security policy template. When a user creates a copyright-related document, the template provider may provide a copyright document template. This allows the user to create documents efficiently and improve the quality of their work.

[0122] The legal advice system may further include a feedback adjustment unit that estimates the user's emotions and adjusts the method of providing legal advice feedback based on the estimated user emotions. For example, the feedback adjustment unit may provide simple and specific feedback when the user is nervous. Alternatively, the feedback adjustment unit may provide detailed feedback when the user is relaxed. Furthermore, the feedback adjustment unit may provide feedback that focuses on the main points when the user is in a hurry. In this way, effective feedback is provided by adjusting the feedback method according to the user's emotions.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The reception department inputs the details of the work. The work includes legal work, accounting work, sales work, etc. For example, an employee inputs the details of the work, such as starting a new project or creating a contract. Step 2: The analysis unit uses the generation AI to analyze the business details entered by the reception unit and identify relevant laws and regulations and risks. The analysis is performed using methods such as text analysis, data mining, and risk assessment. For example, the generation AI analyzes the entered business details and identifies risks related to security laws and copyright. Step 3: The presentation unit uses the generation AI to present countermeasures based on the laws and regulations and risks identified by the analysis unit. Countermeasures include legal procedures, risk avoidance measures, and improvement measures. For example, the generation AI presents the necessary countermeasures based on the identified laws and regulations and risks. Step 4: The creation unit uses the generation AI to automatically create documents, materials, emails, etc., adding necessary wording based on the solutions presented by the presentation unit. Documents, materials, emails, etc. include contracts, reports, notification emails, etc. For example, the generation AI creates draft contracts and risk management guidelines. Step 5: The sending department uses the generation AI to automatically send the documents, materials, emails, etc. created by the creation department. Sending is done by email, uploading to an internal system, mailing, etc. For example, the generation AI sends them to the relevant parties by email or uploads them to an internal system.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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 AI 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

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

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] 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 AI 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] 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.

[0174] 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.

[0175] 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 AI 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where work details are entered; an analysis unit that analyzes the business content input by the reception unit and identifies relevant laws and regulations and risks; a presentation unit that presents countermeasures based on the laws and regulations and risks identified by the analysis unit; a creating unit that automatically creates documents, materials, and emails with necessary wording added based on the countermeasures presented by the presenting unit; a sending unit that automatically sends the documents, materials, and emails created by the creation unit; Equipped with A system characterized by:

2. The reception unit By linking with in-house systems and databases, we can specialize for each company. The system of claim 1 .

3. The reception unit Estimates user emotions and adjusts the input interface for work content based on the estimated user emotions. The system of claim 1 .

4. The reception unit When entering business details, the system provides input assistance by referring to the user's past input history. The system of claim 1 .

5. The reception unit When entering work details, customize the input fields according to the type of work the user does. The system of claim 1 .

6. The reception unit When entering business details, provide appropriate input methods according to the user's input method. The system of claim 1 .

7. The reception unit Estimate the user's emotions and prioritize input content based on the estimated user emotions. The system of claim 1 .

8. The reception unit When entering work details, prioritize relevant information based on the user's geographic location. The system of claim 1 .

9. The reception unit When entering work details, analyze users' social media activity and prompt them to enter relevant information. The system of claim 1 .

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