system

Abstract

The system according to this embodiment aims to enable small and medium-sized business owners and sole proprietors to respond quickly to legal questions and reduce legal costs. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a contract analysis unit, and an information provision unit. The reception unit receives legal questions from users. The analysis unit analyzes the questions received by the reception unit. The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. The contract analysis unit analyzes contracts uploaded by users. The information provision unit provides the latest information on legal revisions.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for small and medium-sized enterprise operators and individual business owners to quickly respond to legal questions and the cost of lawyers is high.

[0005] The system according to the embodiment aims to enable small and medium-sized enterprise operators and individual business owners to quickly respond to legal questions and reduce the cost of lawyers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a contract analysis unit, and an information provision unit. The reception unit receives legal questions from users. The analysis unit analyzes the questions received by the reception unit. The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. The contract analysis unit analyzes contracts uploaded by users. The information provision unit provides the latest information on legal revisions. [Effects of the Invention]

[0007] The system according to this embodiment allows small and medium-sized business owners and sole proprietors to receive prompt responses to legal questions and reduce legal costs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The legal support agent system according to an embodiment of the present invention is a system that provides a 24 / 7 legal support agent utilizing natural language processing and machine learning for small and medium-sized enterprise (SME) managers and sole proprietors. This legal support agent system aims to reduce business risks while lowering legal costs by providing contract review, immediate responses to legal questions, and the latest legal amendment information. For example, users input legal questions from their smartphones or personal computers. For example, natural language processing technology is used to analyze the input questions and generate appropriate answers. Furthermore, machine learning models are used to analyze a database of past case precedents and provide highly relevant advice. This mechanism allows users to receive legal support quickly and at low cost. The system also includes a contract review function, where AI analyzes user-uploaded contracts and identifies potential risks. This makes it easier for users to understand the content of contracts and identify risks in advance. Additionally, it provides the latest legal amendment information, allowing users to stay up-to-date with the latest legal situation. This agent is offered on a subscription basis, starting from several thousand yen per month. This makes legal support easily accessible to SMEs and sole proprietors. Furthermore, because it is accessible 24 hours a day, 365 days a year, legal support can be received at any time. This system allows small and medium-sized enterprises (SMEs) and sole proprietors to mitigate legal risks and foster a healthy business environment. In addition, it aims to democratize access to legal knowledge and create a society where many companies feel more comfortable with the law. As a result, the legal support agent system can provide legal support to SME managers and sole proprietors quickly and at a low cost.

[0029] The legal support agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a contract analysis unit, and an information provision unit. The reception unit receives legal questions from users. The reception unit can, for example, receive legal questions entered by users from a smartphone or personal computer. The reception unit can also, for example, handle cases where users ask legal questions using voice input. The reception unit can also, for example, handle cases where users ask legal questions in chat format. The analysis unit analyzes the questions received by the reception unit. The analysis unit analyzes the entered questions using, for example, natural language processing technology. The analysis unit can also analyze the entered questions using, for example, a machine learning model. The analysis unit can also, for example, refer to a database of past case precedents to analyze the relevance of the entered questions. The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. The generation unit generates appropriate answers using, for example, natural language generation technology. The generation unit can also generate appropriate answers using, for example, a machine learning model. The generation unit can also generate appropriate answers by, for example, referring to a database of past case precedents. The contract analysis unit analyzes contracts uploaded by users. The contract analysis unit analyzes the content of contracts using, for example, natural language processing technology. The contract analysis unit can also analyze the content of contracts using, for example, machine learning models. The contract analysis unit can also analyze the clauses of contracts and point out potential risks. The information provision unit provides the latest legal amendment information. The information provision unit can, for example, automatically collect legal amendment information and provide it to users. The information provision unit can also, for example, periodically update legal amendment information and notify users. The information provision unit can also, for example, provide information on amendments to specific laws. As a result, the legal support agent system according to the embodiment can provide quick and appropriate answers to users' legal questions and mitigate business risks by providing contract analysis and the latest legal amendment information.

[0030] The reception department receives legal questions from users. For example, it can receive legal questions entered by users via smartphones or personal computers. Specifically, it provides an interface for users to enter questions through a dedicated application or website. This allows users to easily enter and submit legal questions. The reception department can also handle cases where users ask legal questions using voice input. Using speech recognition technology, it converts the user's voice into text, accurately understanding the question. This allows users to ask questions by voice without using their hands, improving convenience. The reception department can also handle cases where users ask legal questions via chat. Using a chatbot, it engages in real-time dialogue with users and receives questions. The chatbot collects question content while providing appropriate responses to user input. This allows users to ask questions in a natural conversational format, enabling smooth communication. Furthermore, the reception department can centrally manage user questions and quickly hand them over to the analysis and generation departments. This provides a foundation for providing quick and appropriate answers to user questions.

[0031] The analysis unit analyzes the questions received by the reception unit. The analysis unit analyzes the input questions using, for example, natural language processing technology. Specifically, it performs morphological and grammatical analysis to accurately grasp the intent and content of the questions. The analysis unit can also analyze the input questions using, for example, machine learning models. Machine learning models can learn from large amounts of legal data and analyze question patterns and relationships with high accuracy. The analysis unit can also analyze the relationships of the input questions by referring to, for example, a database of past case precedents. The database of past case precedents contains precedents and interpretations for similar legal issues, and by referring to this, it can derive appropriate answers to questions. Furthermore, depending on the content of the question, the analysis unit can identify relevant laws and articles and clarify the basis for the answer. As a result, the analysis unit can provide accurate and reliable analysis results for the user's questions.

[0032] The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. The generation unit generates appropriate answers using, for example, natural language generation technology. Specifically, it generates answers to the user's questions in natural language based on the analysis results. The generation unit can also generate appropriate answers using, for example, machine learning models. Machine learning models have the ability to learn from large amounts of legal documents and answer data and generate the optimal answers to questions. The generation unit can also generate appropriate answers by, for example, referring to a database of past case law. A database of past case law contains precedents and interpretations for similar legal issues, and by referring to this, it can derive appropriate answers to questions. Furthermore, the generation unit can generate answers that include explanations of technical terms and concrete examples in order to make the content of the answer easy for the user to understand. In this way, the generation unit can provide accurate and easy-to-understand answers to the user's questions.

[0033] The contract analysis unit analyzes contracts uploaded by users. The unit analyzes the content of contracts using, for example, natural language processing technology. Specifically, it analyzes the clauses and wording of contracts to accurately understand their content. The contract analysis unit can also analyze contract content using, for example, machine learning models. These models can learn from large amounts of contract data and analyze patterns in contract clauses and wording with high accuracy. The contract analysis unit can also analyze contract clauses and point out potential risks. Specifically, it analyzes contract clauses to identify legal risks and unfavorable conditions and warns the user. Furthermore, the contract analysis unit can provide analysis results with explanations of technical terms and concrete examples to make the contract content easy for users to understand. This allows the contract analysis unit to provide users with information to accurately understand the contract content and avoid potential risks.

[0034] The Information Provision Department provides the latest information on legal amendments. For example, the Information Provision Department automatically collects and provides information on legal amendments to users. Specifically, it uses a dedicated crawler to collect the latest information on legal amendments from government and legal agency websites. The Information Provision Department can also, for example, periodically update information on legal amendments and notify users. It sets up a regular update schedule and provides a mechanism to automatically notify users of the latest information on legal amendments. The Information Provision Department can also, for example, provide information on amendments to specific laws. By prioritizing the collection and provision of information on amendments to specific laws that users are interested in, it provides information that meets the user's needs. Furthermore, in order to make the content of the legal amendments easy for users to understand, the Information Provision Department can provide information along with explanations of the background and impact of the amendments. In this way, the Information Provision Department can help users accurately grasp the latest information on legal amendments and take appropriate action.

[0035] The legal support agent system includes a case law analysis unit that analyzes a database of past case precedents. The case law analysis unit, for example, analyzes the database of past case precedents and provides highly relevant advice. For example, the case law analysis unit can analyze precedents from a specific court. For example, the case law analysis unit can analyze precedents from a specific period. For example, the case law analysis unit can analyze the content of precedents and provide relevant legal advice. This allows the system to provide highly relevant advice by analyzing the database of past case precedents.

[0036] The legal support agent system includes a history analysis unit that analyzes the user's past consultation history. The history analysis unit, for example, analyzes the user's past consultation history and provides highly relevant advice. For example, the history analysis unit can analyze consultation history for a specific period. For example, the history analysis unit can analyze specific types of consultation history. For example, the history analysis unit can analyze the content of the consultation history and provide relevant legal advice. This allows for the provision of more personalized advice by analyzing the user's past consultation history.

[0037] The legal support agent system includes a personalization unit that provides personalized advice to users. The personalization unit can, for example, provide personalized advice based on the user's past consultation history. It can also provide personalized advice based on the user's business situation. Furthermore, it can provide personalized advice according to the user's specific needs. Finally, it can analyze the user's past consultation history to provide optimal advice. This allows for more appropriate legal support by providing personalized advice to users.

[0038] The contract analysis unit can analyze contracts uploaded by users and identify potential risks. For example, the contract analysis unit can analyze the clauses of a contract and identify potential risks. It can also analyze the content of a contract and identify legal risks. Furthermore, it can identify risks related to the clauses of a contract. By identifying potential risks in a contract, the contract analysis unit makes it easier for users to understand the contract's content and identify risks in advance.

[0039] The information provision department can provide the latest information on legal amendments. For example, the information provision department can automatically collect and provide information on legal amendments to users. The information provision department can also periodically update information on legal amendments and notify users. The information provision department can also provide information on amendments to specific laws. The information provision department can provide information on legal amendments to ensure that users are always up-to-date with the latest legal situation. By providing the latest information on legal amendments, users can always stay informed about the latest legal situation.

[0040] The reception desk can analyze a user's past question history and select the most suitable reception method. For example, the reception desk can automatically display frequently asked questions as suggestions. For example, the reception desk can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest questions that might be asked at a specific time based on the user's past question history. In this way, by analyzing the user's past question history, the reception desk can select the most suitable reception method and efficiently handle questions.

[0041] The reception desk can filter questions based on the user's current business situation and areas of interest. For example, the reception desk can prioritize receiving relevant legal questions based on the user's business situation. The reception desk can also filter relevant questions based on the user's areas of interest and receive appropriate questions. For example, the reception desk can suggest appropriate legal questions based on the user's business growth stage. This allows for the reception of more relevant questions by filtering questions based on the user's business situation and areas of interest.

[0042] The reception desk can prioritize receiving inquiries by considering the user's geographical location. For example, the reception desk can prioritize legal inquiries related to the user's location. The reception desk can also prioritize legal inquiries based on the user's business area. The reception desk can also suggest appropriate legal inquiries based on the user's geographical circumstances. This allows the reception desk to prioritize receiving inquiries that are highly relevant by considering the user's geographical location.

[0043] The reception desk can analyze a user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can analyze a user's social media posts and suggest relevant legal questions. The reception desk can also accept appropriate legal questions based on a user's social media activity history. The reception desk can also prioritize accepting relevant legal questions based on a user's areas of interest on social media. This allows the reception desk to accept relevant questions by analyzing a user's social media activity.

[0044] The analysis unit can adjust the level of detail in its analysis based on the importance of the question. For example, it can perform a detailed analysis and provide a comprehensive answer for high-importance questions. For example, it can perform a concise analysis and provide a quick answer for low-importance questions. The analysis unit can also adjust the priority of the analysis according to the importance of the question. This allows for more appropriate analysis by adjusting the level of detail based on the importance of the question.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the question when analyzing it. For example, the analysis unit can apply a contract analysis algorithm to questions about contracts. For example, the analysis unit can apply a legal amendment information analysis algorithm to questions about legal amendments. For example, the analysis unit can apply a case law analysis algorithm to questions about case law. By applying different analysis algorithms depending on the category of the question, more appropriate analysis can be performed.

[0046] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, the analysis unit can prioritize the analysis of urgent questions based on the time of day they were submitted. The analysis unit can also prioritize the analysis of important questions based on the date they were submitted. The analysis unit can also adjust the priority of analysis according to when the questions were submitted. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on when the questions were submitted.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the questions. For example, the analysis unit can prioritize the analysis of the most relevant questions based on their relevance. The analysis unit can also adjust the order of analysis according to the relevance of the questions. For example, the analysis unit can prioritize the analysis of questions of high importance based on their relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on the relevance of the questions.

[0048] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit will provide a detailed answer for a high-importance question. For example, the generation unit can also provide a concise answer for a low-importance question. The generation unit can adjust the level of detail in the answer according to the importance of the question. This allows for the provision of more appropriate answers by adjusting the level of detail in the answer based on the importance of the question.

[0049] The generation unit can apply different answer algorithms depending on the question category when generating answers. For example, for questions about contracts, the generation unit can apply a contract analysis algorithm. For example, for questions about legal amendments, the generation unit can apply a legal amendment information analysis algorithm. For example, for questions about case law, the generation unit can apply a case law analysis algorithm. By applying different answer algorithms depending on the question category, it is possible to provide more appropriate answers.

[0050] The generation unit can determine the priority of answers based on when the questions were submitted. For example, the generation unit can prioritize generating answers to urgent questions based on the time of day the questions were submitted. The generation unit can also prioritize generating answers to important questions based on the date the questions were submitted. The generation unit can also adjust the priority of answers according to when the questions were submitted. This allows for the provision of answers in a more appropriate order by determining the priority of answers based on when the questions were submitted.

[0051] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can prioritize generating answers to the most relevant questions based on the relevance of the questions. The generation unit can also adjust the order of answers according to the relevance of the questions. For example, the generation unit can prioritize generating answers to high-priority questions based on the relevance of the questions. By adjusting the order of answers based on the relevance of the questions, it is possible to provide answers in a more appropriate order.

[0052] The contract analysis unit can adjust the level of detail of its analysis based on the importance of the contract. For example, it can perform a detailed analysis of highly important contracts to provide comprehensive information. For example, it can perform a concise analysis of less important contracts to provide information quickly. The contract analysis unit can also adjust the priority of the analysis according to the importance of the contract. This allows for more appropriate analysis by adjusting the level of detail based on the importance of the contract.

[0053] The contract analysis unit can apply different analysis algorithms depending on the category of the contract during the analysis process. For example, the contract analysis unit can apply a sales contract analysis algorithm to a sales contract. For example, it can apply an employment contract analysis algorithm to an employment contract. For example, it can apply a tax contract analysis algorithm to a tax contract. By applying different analysis algorithms depending on the category of the contract, more appropriate analysis can be performed.

[0054] The contract analysis unit can determine the priority of contract analysis based on the submission date of the contract. For example, the contract analysis unit can prioritize the analysis of urgent contracts based on the time of submission. The contract analysis unit can also prioritize the analysis of important contracts based on the submission date. The contract analysis unit can also adjust the priority of analysis according to the submission date of the contract. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on the submission date of the contract.

[0055] The contract analysis unit can adjust the order of analysis based on the relevance of the contracts during the analysis process. For example, the contract analysis unit can prioritize the analysis of the most relevant contracts based on their relevance. The contract analysis unit can also adjust the order of analysis according to the relevance of the contracts. For example, the contract analysis unit can prioritize the analysis of contracts of high importance based on their relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on the relevance of the contracts.

[0056] The Information Provision Department can adjust the level of detail provided when providing information on legal amendments based on the importance of the information. For example, the Information Provision Department can provide detailed information for legal amendments of high importance. For example, the Information Provision Department can also provide concise information for legal amendments of low importance. The Information Provision Department can also adjust the priority of provision according to the importance of the legal amendment information. In this way, by adjusting the level of detail provided based on the importance of the information, more appropriate information can be provided.

[0057] The information provision department can apply different provision algorithms depending on the category of information when providing information on legal amendments. For example, the information provision department can apply a labor law amendment information provision algorithm to information on labor law amendments. For example, the information provision department can also apply a tax law amendment information provision algorithm to information on tax law amendments. For example, the information provision department can also apply a commercial law amendment information provision algorithm to information on commercial law amendments. By applying different provision algorithms depending on the category of information, more appropriate information can be provided.

[0058] The Information Provision Department can determine the priority of information provision based on the timing of its submission when providing information on legal amendments. For example, the Information Provision Department may prioritize providing information of high urgency based on the time of submission of the legal amendment information. The Information Provision Department may also prioritize providing information of high importance based on the date of submission of the legal amendment information. The Information Provision Department may also adjust the priority of information provision according to the timing of its submission of the legal amendment information. This allows for information to be provided in a more appropriate order by determining the priority of information provision based on the timing of its submission.

[0059] The Information Provision Department can adjust the order in which information is provided based on its relevance when providing information on legal amendments. For example, the Information Provision Department can prioritize providing the most relevant information based on the relevance of the legal amendment information. The Information Provision Department can also adjust the order in which information is provided according to the relevance of the legal amendment information. For example, the Information Provision Department can also prioritize providing information of high importance based on the relevance of the legal amendment information. By adjusting the order in which information is provided based on its relevance, information can be provided in a more appropriate order.

[0060] The case law analysis unit can adjust the level of detail in its analysis based on the importance of each case. For example, it can perform a detailed analysis of highly important cases to provide comprehensive information. For example, it can perform a concise analysis of less important cases to provide information quickly. The case law analysis unit can also adjust the priority of its analysis according to the importance of each case. This allows for more appropriate analysis by adjusting the level of detail based on the importance of each case.

[0061] The case law analysis unit can apply different analysis algorithms depending on the category of the case law during the analysis. For example, the case law analysis unit can apply the labor case law analysis algorithm to labor case law. For example, the case law analysis unit can also apply the commercial law case law analysis algorithm to commercial law case law. For example, the case law analysis unit can also apply the tax law case law analysis algorithm to tax law case law. By applying different analysis algorithms depending on the category of the case law, more appropriate analysis can be performed.

[0062] The case law analysis unit can determine the priority of case analysis based on when the case law was submitted. For example, the case law analysis unit can prioritize the analysis of urgent cases based on the time of day the case law was submitted. The case law analysis unit can also prioritize the analysis of important cases based on the date the case law was submitted. For example, the case law analysis unit can adjust the priority of analysis according to when the case law was submitted. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on when the case law was submitted.

[0063] The case law analysis unit can adjust the order of analysis based on the relevance of the cases during the analysis process. For example, the case law analysis unit can prioritize the analysis of the most relevant cases based on their relevance. The case law analysis unit can also adjust the order of analysis according to the relevance of the cases. For example, the case law analysis unit can prioritize the analysis of cases of high importance based on their relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on the relevance of the cases.

[0064] The historical analysis unit can adjust the level of detail of its analysis based on the importance of the historical events. For example, it can perform a detailed analysis of high-importance historical events to provide comprehensive information. For example, it can perform a concise analysis of low-importance historical events to provide information quickly. The historical analysis unit can also adjust the priority of the analysis according to the importance of the historical events. This allows for more appropriate analysis by adjusting the level of detail based on the importance of the historical events.

[0065] The history analysis unit can apply different analysis algorithms depending on the category of history during the analysis. For example, the history analysis unit can apply the contract history analysis algorithm to the history of contracts. For example, the history analysis unit can apply the legal amendment history analysis algorithm to the history of legal amendments. For example, the history analysis unit can apply the case law history analysis algorithm to the history of case law. By applying different analysis algorithms depending on the category of history, more appropriate analysis can be performed.

[0066] The history analysis department can determine the priority of analysis based on when the history was submitted. For example, the history analysis department can prioritize analyzing history with high urgency based on the time of day it was submitted. The history analysis department can also prioritize analyzing history with high importance based on the date it was submitted. The history analysis department can also adjust the priority of analysis according to when the history was submitted. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on when the history was submitted.

[0067] The history analysis unit can adjust the order of analysis based on the relevance of the history during the analysis process. For example, the history analysis unit can prioritize the analysis of the most relevant history based on its relevance. The history analysis unit can also adjust the order of analysis according to the relevance of the history. For example, the history analysis unit can prioritize the analysis of history of high importance based on its relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on the relevance of the history.

[0068] The personalization unit can provide optimal advice by referring to the user's past consultation history when offering advice. For example, the personalization unit can provide highly relevant advice based on the user's past consultation history. For example, the personalization unit can predict and provide optimal advice based on the user's past consultation history. For example, the personalization unit can analyze the user's past consultation history and provide the most effective advice. This allows for the provision of more appropriate advice by referring to the user's past consultation history.

[0069] The personalization function can customize the content of advice based on the user's current business situation when providing advice. For example, the personalization function can provide optimal advice according to the user's current business situation. The personalization function can also provide appropriate advice based on the user's business growth stage. For example, the personalization function can provide customized advice according to the user's current business situation. This allows for the provision of more appropriate advice by customizing the content of advice based on the user's current business situation.

[0070] The personalization function can provide optimal advice by considering the user's geographical location when offering advice. For example, the personalization function can provide advice related to the user's location. It can also provide appropriate advice based on the user's business area. Furthermore, it can provide optimal advice based on the user's geographical circumstances. This allows for the provision of more appropriate advice by considering the user's geographical location.

[0071] The personalization unit can analyze the user's social media activity and adjust the content of the advice provided. For example, the personalization unit can analyze the user's social media posts and provide relevant advice. For example, the personalization unit can also provide appropriate advice based on the user's social media activity history. For example, the personalization unit can also provide relevant advice based on the user's social media interests. This allows for the provision of more appropriate advice by analyzing the user's social media activity.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The legal support agent system includes a history analysis unit that analyzes a user's past consultation history. For example, it analyzes a user's past consultation history to provide highly relevant advice. It can analyze consultation history for a specific period, and it can also analyze specific types of consultation history. It can also analyze the content of the consultation history to provide relevant legal advice. This allows for more personalized advice to be provided by analyzing a user's past consultation history.

[0074] The legal support agent system can filter questions based on the user's current business situation and areas of interest. For example, it can prioritize receiving legal questions relevant to the user's business situation. It can also filter relevant questions based on the user's areas of interest and receive appropriate questions. It can even suggest appropriate legal questions according to the user's business growth stage. This allows for receiving more relevant questions by filtering questions based on the user's business situation and areas of interest.

[0075] The legal support agent system can analyze a user's past question history and select the most appropriate method of handling inquiries. For example, it can automatically display frequently asked questions as suggestions. It can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Based on the user's past question history, it can also predict and suggest questions that might be asked at specific times of the day. In this way, by analyzing the user's past question history, the system can select the most appropriate method of handling inquiries and process them efficiently.

[0076] The legal support agent system can prioritize receiving highly relevant questions by considering the user's geographical location. For example, it can prioritize legal questions related to the user's location. It can also prioritize relevant legal questions based on the user's business area. It can even suggest appropriate legal questions based on the user's geographical circumstances. In this way, by considering the user's geographical location, it can prioritize receiving highly relevant questions.

[0077] The legal support agent system can analyze a user's social media activity and receive relevant questions. For example, it can analyze a user's social media posts and suggest relevant legal questions. It can also receive appropriate legal questions based on the user's social media activity history. It can also prioritize receiving relevant legal questions based on the user's areas of interest on social media. In this way, it can receive relevant questions by analyzing the user's social media activity.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The reception desk receives legal questions from users. For example, it can accept legal questions entered by users from their smartphones or personal computers. It can also handle legal questions via voice input or chat. Step 2: The analysis unit analyzes the questions received by the reception unit. For example, it can analyze the input questions using natural language processing technology or machine learning models. It can also analyze the relevance of the input questions by referring to a database of past case precedents. Step 3: The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. For example, appropriate answers can be generated using natural language generation technology or machine learning models. Alternatively, appropriate answers can be generated by referring to a database of past case precedents. Step 4: The contract analysis unit analyzes the contract uploaded by the user. For example, it can analyze the content of the contract using natural language processing technology or machine learning models. It can also analyze the clauses of the contract and point out potential risks. Step 5: The information provision department provides the latest information on legal amendments. For example, it can automatically collect and provide information on legal amendments to users. It can also periodically update information on legal amendments and notify users.

[0080] (Example of form 2) The legal support agent system according to an embodiment of the present invention is a system that provides a 24 / 7 legal support agent utilizing natural language processing and machine learning for small and medium-sized enterprise (SME) managers and sole proprietors. This legal support agent system aims to reduce business risks while lowering legal costs by providing contract review, immediate responses to legal questions, and the latest legal amendment information. For example, users input legal questions from their smartphones or personal computers. For example, natural language processing technology is used to analyze the input questions and generate appropriate answers. Furthermore, machine learning models are used to analyze a database of past case precedents and provide highly relevant advice. This mechanism allows users to receive legal support quickly and at low cost. The system also includes a contract review function, where AI analyzes user-uploaded contracts and identifies potential risks. This makes it easier for users to understand the content of contracts and identify risks in advance. Additionally, it provides the latest legal amendment information, allowing users to stay up-to-date with the latest legal situation. This agent is offered on a subscription basis, starting from several thousand yen per month. This makes legal support easily accessible to SMEs and sole proprietors. Furthermore, because it is accessible 24 hours a day, 365 days a year, legal support can be received at any time. This system allows small and medium-sized enterprises (SMEs) and sole proprietors to mitigate legal risks and foster a healthy business environment. In addition, it aims to democratize access to legal knowledge and create a society where many companies feel more comfortable with the law. As a result, the legal support agent system can provide legal support to SME managers and sole proprietors quickly and at a low cost.

[0081] The legal support agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a contract analysis unit, and an information provision unit. The reception unit receives legal questions from users. The reception unit can, for example, receive legal questions entered by users from a smartphone or personal computer. The reception unit can also, for example, handle cases where users ask legal questions using voice input. The reception unit can also, for example, handle cases where users ask legal questions in chat format. The analysis unit analyzes the questions received by the reception unit. The analysis unit analyzes the entered questions using, for example, natural language processing technology. The analysis unit can also analyze the entered questions using, for example, a machine learning model. The analysis unit can also, for example, refer to a database of past case precedents to analyze the relevance of the entered questions. The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. The generation unit generates appropriate answers using, for example, natural language generation technology. The generation unit can also generate appropriate answers using, for example, a machine learning model. The generation unit can also generate appropriate answers by, for example, referring to a database of past case precedents. The contract analysis unit analyzes contracts uploaded by users. The contract analysis unit analyzes the content of contracts using, for example, natural language processing technology. The contract analysis unit can also analyze the content of contracts using, for example, machine learning models. The contract analysis unit can also analyze the clauses of contracts and point out potential risks. The information provision unit provides the latest legal amendment information. The information provision unit can, for example, automatically collect legal amendment information and provide it to users. The information provision unit can also, for example, periodically update legal amendment information and notify users. The information provision unit can also, for example, provide information on amendments to specific laws. As a result, the legal support agent system according to the embodiment can provide quick and appropriate answers to users' legal questions and mitigate business risks by providing contract analysis and the latest legal amendment information.

[0082] The reception department receives legal questions from users. For example, it can receive legal questions entered by users via smartphones or personal computers. Specifically, it provides an interface for users to enter questions through a dedicated application or website. This allows users to easily enter and submit legal questions. The reception department can also handle cases where users ask legal questions using voice input. Using speech recognition technology, it converts the user's voice into text, accurately understanding the question. This allows users to ask questions by voice without using their hands, improving convenience. The reception department can also handle cases where users ask legal questions via chat. Using a chatbot, it engages in real-time dialogue with users and receives questions. The chatbot collects question content while providing appropriate responses to user input. This allows users to ask questions in a natural conversational format, enabling smooth communication. Furthermore, the reception department can centrally manage user questions and quickly hand them over to the analysis and generation departments. This provides a foundation for providing quick and appropriate answers to user questions.

[0083] The analysis unit analyzes the questions received by the reception unit. The analysis unit analyzes the input questions using, for example, natural language processing technology. Specifically, it performs morphological and grammatical analysis to accurately grasp the intent and content of the questions. The analysis unit can also analyze the input questions using, for example, machine learning models. Machine learning models can learn from large amounts of legal data and analyze question patterns and relationships with high accuracy. The analysis unit can also analyze the relationships of the input questions by referring to, for example, a database of past case precedents. The database of past case precedents contains precedents and interpretations for similar legal issues, and by referring to this, it can derive appropriate answers to questions. Furthermore, depending on the content of the question, the analysis unit can identify relevant laws and articles and clarify the basis for the answer. As a result, the analysis unit can provide accurate and reliable analysis results for the user's questions.

[0084] The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. The generation unit generates appropriate answers using, for example, natural language generation technology. Specifically, it generates answers to the user's questions in natural language based on the analysis results. The generation unit can also generate appropriate answers using, for example, machine learning models. Machine learning models have the ability to learn from large amounts of legal documents and answer data and generate the optimal answers to questions. The generation unit can also generate appropriate answers by, for example, referring to a database of past case law. A database of past case law contains precedents and interpretations for similar legal issues, and by referring to this, it can derive appropriate answers to questions. Furthermore, the generation unit can generate answers that include explanations of technical terms and concrete examples in order to make the content of the answer easy for the user to understand. In this way, the generation unit can provide accurate and easy-to-understand answers to the user's questions.

[0085] The contract analysis unit analyzes contracts uploaded by users. The unit analyzes the content of contracts using, for example, natural language processing technology. Specifically, it analyzes the clauses and wording of contracts to accurately understand their content. The contract analysis unit can also analyze contract content using, for example, machine learning models. These models can learn from large amounts of contract data and analyze patterns in contract clauses and wording with high accuracy. The contract analysis unit can also analyze contract clauses and point out potential risks. Specifically, it analyzes contract clauses to identify legal risks and unfavorable conditions and warns the user. Furthermore, the contract analysis unit can provide analysis results with explanations of technical terms and concrete examples to make the contract content easy for users to understand. This allows the contract analysis unit to provide users with information to accurately understand the contract content and avoid potential risks.

[0086] The Information Provision Department provides the latest information on legal amendments. For example, the Information Provision Department automatically collects and provides information on legal amendments to users. Specifically, it uses a dedicated crawler to collect the latest information on legal amendments from government and legal agency websites. The Information Provision Department can also, for example, periodically update information on legal amendments and notify users. It sets up a regular update schedule and provides a mechanism to automatically notify users of the latest information on legal amendments. The Information Provision Department can also, for example, provide information on amendments to specific laws. By prioritizing the collection and provision of information on amendments to specific laws that users are interested in, it provides information that meets the user's needs. Furthermore, in order to make the content of the legal amendments easy for users to understand, the Information Provision Department can provide information along with explanations of the background and impact of the amendments. In this way, the Information Provision Department can help users accurately grasp the latest information on legal amendments and take appropriate action.

[0087] The legal support agent system includes a case law analysis unit that analyzes a database of past case precedents. The case law analysis unit, for example, analyzes the database of past case precedents and provides highly relevant advice. For example, the case law analysis unit can analyze precedents from a specific court. For example, the case law analysis unit can analyze precedents from a specific period. For example, the case law analysis unit can analyze the content of precedents and provide relevant legal advice. This allows the system to provide highly relevant advice by analyzing the database of past case precedents.

[0088] The legal support agent system includes a history analysis unit that analyzes the user's past consultation history. The history analysis unit, for example, analyzes the user's past consultation history and provides highly relevant advice. For example, the history analysis unit can analyze consultation history for a specific period. For example, the history analysis unit can analyze specific types of consultation history. For example, the history analysis unit can analyze the content of the consultation history and provide relevant legal advice. This allows for the provision of more personalized advice by analyzing the user's past consultation history.

[0089] The legal support agent system includes a personalization unit that provides personalized advice to users. The personalization unit can, for example, provide personalized advice based on the user's past consultation history. It can also provide personalized advice based on the user's business situation. Furthermore, it can provide personalized advice according to the user's specific needs. Finally, it can analyze the user's past consultation history to provide optimal advice. This allows for more appropriate legal support by providing personalized advice to users.

[0090] The contract analysis unit can analyze contracts uploaded by users and identify potential risks. For example, the contract analysis unit can analyze the clauses of a contract and identify potential risks. It can also analyze the content of a contract and identify legal risks. Furthermore, it can identify risks related to the clauses of a contract. By identifying potential risks in a contract, the contract analysis unit makes it easier for users to understand the contract's content and identify risks in advance.

[0091] The information provision department can provide the latest information on legal amendments. For example, the information provision department can automatically collect and provide information on legal amendments to users. The information provision department can also periodically update information on legal amendments and notify users. The information provision department can also provide information on amendments to specific laws. The information provision department can provide information on legal amendments to ensure that users are always up-to-date with the latest legal situation. By providing the latest information on legal amendments, users can always stay informed about the latest legal situation.

[0092] The reception desk can estimate the user's emotions and adjust the timing of question reception based on the estimated emotions. For example, if the user is stressed, the reception desk will quickly receive the question and prioritize immediate response. For example, if the user is relaxed, the reception desk can guide the user through the interface carefully to receive detailed questions. For example, if the user is in a hurry, the reception desk can receive concise questions and provide quick answers. This allows for question reception at a more appropriate time by adjusting the timing of question reception according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The reception desk can analyze a user's past question history and select the most suitable reception method. For example, the reception desk can automatically display frequently asked questions as suggestions. For example, the reception desk can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest questions that might be asked at a specific time based on the user's past question history. In this way, by analyzing the user's past question history, the reception desk can select the most suitable reception method and efficiently handle questions.

[0094] The reception desk can filter questions based on the user's current business situation and areas of interest. For example, the reception desk can prioritize receiving relevant legal questions based on the user's business situation. The reception desk can also filter relevant questions based on the user's areas of interest and receive appropriate questions. For example, the reception desk can suggest appropriate legal questions based on the user's business growth stage. This allows for the reception of more relevant questions by filtering questions based on the user's business situation and areas of interest.

[0095] The reception desk can estimate the user's emotions and determine the priority of questions to accept based on the estimated emotions. For example, if the user is feeling anxious, the reception desk will prioritize urgent questions. If the user is relaxed, the reception desk may also prioritize detailed questions. If the user is in a hurry, the reception desk may also prioritize concise questions. This allows questions to be accepted in a more appropriate order by prioritizing them according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The reception desk can prioritize receiving inquiries by considering the user's geographical location. For example, the reception desk can prioritize legal inquiries related to the user's location. The reception desk can also prioritize legal inquiries based on the user's business area. The reception desk can also suggest appropriate legal inquiries based on the user's geographical circumstances. This allows the reception desk to prioritize receiving inquiries that are highly relevant by considering the user's geographical location.

[0097] The reception desk can analyze a user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can analyze a user's social media posts and suggest relevant legal questions. The reception desk can also accept appropriate legal questions based on a user's social media activity history. The reception desk can also prioritize accepting relevant legal questions based on a user's areas of interest on social media. This allows the reception desk to accept relevant questions by analyzing a user's social media activity.

[0098] The analysis unit can estimate the user's emotions and adjust the question analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can perform a detailed analysis and provide a reassuring answer. For example, if the user is relaxed, the analysis unit can perform a concise analysis and provide a quick answer. For example, if the user is in a hurry, the analysis unit can perform a concise analysis and provide a quick answer. In this way, by adjusting the question analysis method according to the user's emotions, more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The analysis unit can adjust the level of detail in its analysis based on the importance of the question. For example, it can perform a detailed analysis and provide a comprehensive answer for high-importance questions. For example, it can perform a concise analysis and provide a quick answer for low-importance questions. The analysis unit can also adjust the priority of the analysis according to the importance of the question. This allows for more appropriate analysis by adjusting the level of detail based on the importance of the question.

[0100] The analysis unit can apply different analysis algorithms depending on the category of the question when analyzing it. For example, the analysis unit can apply a contract analysis algorithm to questions about contracts. For example, the analysis unit can apply a legal amendment information analysis algorithm to questions about legal amendments. For example, the analysis unit can apply a case law analysis algorithm to questions about case law. By applying different analysis algorithms depending on the category of the question, more appropriate analysis can be performed.

[0101] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize analyzing urgent questions. For example, if the user is relaxed, the analysis unit may prioritize analyzing detailed questions. For example, if the user is in a hurry, the analysis unit may prioritize analyzing concise questions. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, the analysis unit can prioritize the analysis of urgent questions based on the time of day they were submitted. The analysis unit can also prioritize the analysis of important questions based on the date they were submitted. The analysis unit can also adjust the priority of analysis according to when the questions were submitted. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on when the questions were submitted.

[0103] The analysis unit can adjust the order of analysis based on the relevance of the questions. For example, the analysis unit can prioritize the analysis of the most relevant questions based on their relevance. The analysis unit can also adjust the order of analysis according to the relevance of the questions. For example, the analysis unit can prioritize the analysis of questions of high importance based on their relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on the relevance of the questions.

[0104] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will provide a response expressed in a way that provides reassurance. For example, if the user is relaxed, the generation unit can also provide a response expressed in a concise and clear way. For example, if the user is in a hurry, the generation unit can provide a response expressed in a way that gets straight to the point. In this way, by adjusting the way the response is expressed according to the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit will provide a detailed answer for a high-importance question. For example, the generation unit can also provide a concise answer for a low-importance question. The generation unit can adjust the level of detail in the answer according to the importance of the question. This allows for the provision of more appropriate answers by adjusting the level of detail in the answer based on the importance of the question.

[0106] The generation unit can apply different answer algorithms depending on the question category when generating answers. For example, for questions about contracts, the generation unit can apply a contract analysis algorithm. For example, for questions about legal amendments, the generation unit can apply a legal amendment information analysis algorithm. For example, for questions about case law, the generation unit can apply a case law analysis algorithm. By applying different answer algorithms depending on the question category, it is possible to provide more appropriate answers.

[0107] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will provide a detailed response. For example, if the user is relaxed, the generation unit may provide a concise response. For example, if the user is in a hurry, the generation unit may provide a short, to-the-point response. By adjusting the length of the response according to the user's emotions, it is possible to provide a more appropriate response. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The generation unit can determine the priority of answers based on when the questions were submitted. For example, the generation unit can prioritize generating answers to urgent questions based on the time of day the questions were submitted. The generation unit can also prioritize generating answers to important questions based on the date the questions were submitted. The generation unit can also adjust the priority of answers according to when the questions were submitted. This allows for the provision of answers in a more appropriate order by determining the priority of answers based on when the questions were submitted.

[0109] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can prioritize generating answers to the most relevant questions based on the relevance of the questions. The generation unit can also adjust the order of answers according to the relevance of the questions. For example, the generation unit can prioritize generating answers to high-priority questions based on the relevance of the questions. By adjusting the order of answers based on the relevance of the questions, it is possible to provide answers in a more appropriate order.

[0110] The contract analysis unit can estimate the user's emotions and adjust the contract analysis method based on the estimated emotions. For example, if the user is feeling anxious, the contract analysis unit can perform a detailed analysis and provide reassuring information. For example, if the user is relaxed, the contract analysis unit can perform a concise analysis and provide information quickly. For example, if the user is in a hurry, the contract analysis unit can perform a concise analysis and provide information quickly. In this way, by adjusting the contract analysis method according to the user's emotions, more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The contract analysis unit can adjust the level of detail of its analysis based on the importance of the contract. For example, it can perform a detailed analysis of highly important contracts to provide comprehensive information. For example, it can perform a concise analysis of less important contracts to provide information quickly. The contract analysis unit can also adjust the priority of the analysis according to the importance of the contract. This allows for more appropriate analysis by adjusting the level of detail based on the importance of the contract.

[0112] The contract analysis unit can apply different analysis algorithms depending on the category of the contract during the analysis process. For example, the contract analysis unit can apply a sales contract analysis algorithm to a sales contract. For example, it can apply an employment contract analysis algorithm to an employment contract. For example, it can apply a tax contract analysis algorithm to a tax contract. By applying different analysis algorithms depending on the category of the contract, more appropriate analysis can be performed.

[0113] The contract analysis unit can estimate the user's emotions and adjust the display method of the contract analysis results based on the estimated emotions. For example, if the user is feeling anxious, the contract analysis unit can provide the analysis results in a reassuring display method. For example, if the user is relaxed, the contract analysis unit can provide the analysis results in a concise and clear display method. For example, if the user is in a hurry, the contract analysis unit can provide the analysis results in a concise display method that gets straight to the point. By adjusting the display method of the contract analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The contract analysis unit can determine the priority of contract analysis based on the submission date of the contract. For example, the contract analysis unit can prioritize the analysis of urgent contracts based on the time of submission. The contract analysis unit can also prioritize the analysis of important contracts based on the submission date. The contract analysis unit can also adjust the priority of analysis according to the submission date of the contract. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on the submission date of the contract.

[0115] The contract analysis unit can adjust the order of analysis based on the relevance of the contracts during the analysis process. For example, the contract analysis unit can prioritize the analysis of the most relevant contracts based on their relevance. The contract analysis unit can also adjust the order of analysis according to the relevance of the contracts. For example, the contract analysis unit can prioritize the analysis of contracts of high importance based on their relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on the relevance of the contracts.

[0116] The information provision department can estimate the user's emotions and adjust the method of providing legal amendment information based on the estimated emotions. For example, if the user is feeling anxious, the information provision department can provide legal amendment information in a reassuring manner. For example, if the user is relaxed, the information provision department can also provide legal amendment information in a concise and clear manner. For example, if the user is in a hurry, the information provision department can also provide legal amendment information in a concise and to-the-point manner. In this way, by adjusting the method of providing legal amendment information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The Information Provision Department can adjust the level of detail provided when providing information on legal amendments based on the importance of the information. For example, the Information Provision Department can provide detailed information for legal amendments of high importance. For example, the Information Provision Department can also provide concise information for legal amendments of low importance. The Information Provision Department can also adjust the priority of provision according to the importance of the legal amendment information. In this way, by adjusting the level of detail provided based on the importance of the information, more appropriate information can be provided.

[0118] The information provision department can apply different provision algorithms depending on the category of information when providing information on legal amendments. For example, the information provision department can apply a labor law amendment information provision algorithm to information on labor law amendments. For example, the information provision department can also apply a tax law amendment information provision algorithm to information on tax law amendments. For example, the information provision department can also apply a commercial law amendment information provision algorithm to information on commercial law amendments. By applying different provision algorithms depending on the category of information, more appropriate information can be provided.

[0119] The information provision unit can estimate the user's emotions and adjust the order in which legal amendment information is provided based on the estimated emotions. For example, if the user is feeling anxious, the information provision unit will prioritize providing information that provides reassurance. For example, if the user is relaxed, the information provision unit can also prioritize providing concise and clear information. For example, if the user is in a hurry, the information provision unit can also prioritize providing information that gets straight to the point. In this way, by adjusting the order in which legal amendment information is provided according to the user's emotions, information can be provided in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0120] The Information Provision Department can determine the priority of information provision based on the timing of its submission when providing information on legal amendments. For example, the Information Provision Department may prioritize providing information of high urgency based on the time of submission of the legal amendment information. The Information Provision Department may also prioritize providing information of high importance based on the date of submission of the legal amendment information. The Information Provision Department may also adjust the priority of information provision according to the timing of its submission of the legal amendment information. This allows for information to be provided in a more appropriate order by determining the priority of information provision based on the timing of its submission.

[0121] The Information Provision Department can adjust the order in which information is provided based on its relevance when providing information on legal amendments. For example, the Information Provision Department can prioritize providing the most relevant information based on the relevance of the legal amendment information. The Information Provision Department can also adjust the order in which information is provided according to the relevance of the legal amendment information. For example, the Information Provision Department can also prioritize providing information of high importance based on the relevance of the legal amendment information. By adjusting the order in which information is provided based on its relevance, information can be provided in a more appropriate order.

[0122] The case law analysis unit can estimate the user's emotions and adjust the case law analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the case law analysis unit can perform a detailed analysis and provide reassuring information. For example, if the user is relaxed, the case law analysis unit can perform a concise analysis and provide information quickly. For example, if the user is in a hurry, the case law analysis unit can perform a concise analysis and provide information quickly. In this way, by adjusting the case law analysis method according to the user's emotions, more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The case law analysis unit can adjust the level of detail in its analysis based on the importance of each case. For example, it can perform a detailed analysis of highly important cases to provide comprehensive information. For example, it can perform a concise analysis of less important cases to provide information quickly. The case law analysis unit can also adjust the priority of its analysis according to the importance of each case. This allows for more appropriate analysis by adjusting the level of detail based on the importance of each case.

[0124] The case law analysis unit can apply different analysis algorithms depending on the category of the case law during the analysis. For example, the case law analysis unit can apply the labor case law analysis algorithm to labor case law. For example, the case law analysis unit can also apply the commercial law case law analysis algorithm to commercial law case law. For example, the case law analysis unit can also apply the tax law case law analysis algorithm to tax law case law. By applying different analysis algorithms depending on the category of the case law, more appropriate analysis can be performed.

[0125] The case law analysis unit can estimate the user's emotions and adjust the display method of the case law analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the case law analysis unit can provide the analysis results in a reassuring display method. For example, if the user is relaxed, the case law analysis unit can also provide the analysis results in a concise and clear display method. For example, if the user is in a hurry, the case law analysis unit can provide the analysis results in a concise display method that gets straight to the point. In this way, by adjusting the display method of the case law analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The case law analysis unit can determine the priority of case analysis based on when the case law was submitted. For example, the case law analysis unit can prioritize the analysis of urgent cases based on the time of day the case law was submitted. The case law analysis unit can also prioritize the analysis of important cases based on the date the case law was submitted. For example, the case law analysis unit can adjust the priority of analysis according to when the case law was submitted. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on when the case law was submitted.

[0127] The case law analysis unit can adjust the order of analysis based on the relevance of the cases during the analysis process. For example, the case law analysis unit can prioritize the analysis of the most relevant cases based on their relevance. The case law analysis unit can also adjust the order of analysis according to the relevance of the cases. For example, the case law analysis unit can prioritize the analysis of cases of high importance based on their relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on the relevance of the cases.

[0128] The history analysis unit can estimate the user's emotions and adjust the analysis method of past consultation history based on the estimated user emotions. For example, if the user is feeling anxious, the history analysis unit can perform a detailed analysis and provide reassuring information. For example, if the user is relaxed, the history analysis unit can perform a concise analysis and provide information quickly. For example, if the user is in a hurry, the history analysis unit can perform a concise analysis and provide information quickly. In this way, by adjusting the analysis method of past consultation history according to the user's emotions, more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The historical analysis unit can adjust the level of detail of its analysis based on the importance of the historical events. For example, it can perform a detailed analysis of high-importance historical events to provide comprehensive information. For example, it can perform a concise analysis of low-importance historical events to provide information quickly. The historical analysis unit can also adjust the priority of the analysis according to the importance of the historical events. This allows for more appropriate analysis by adjusting the level of detail based on the importance of the historical events.

[0130] The history analysis unit can apply different analysis algorithms depending on the category of history during the analysis. For example, the history analysis unit can apply the contract history analysis algorithm to the history of contracts. For example, the history analysis unit can apply the legal amendment history analysis algorithm to the history of legal amendments. For example, the history analysis unit can apply the case law history analysis algorithm to the history of case law. By applying different analysis algorithms depending on the category of history, more appropriate analysis can be performed.

[0131] The history analysis unit can estimate the user's emotions and adjust the display method of the history analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the history analysis unit can provide the analysis results in a reassuring display method. For example, if the user is relaxed, the history analysis unit can also provide the analysis results in a concise and clear display method. For example, if the user is in a hurry, the history analysis unit can provide the analysis results in a concise display method. In this way, by adjusting the display method of the history analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The history analysis department can determine the priority of analysis based on when the history was submitted. For example, the history analysis department can prioritize analyzing history with high urgency based on the time of day it was submitted. The history analysis department can also prioritize analyzing history with high importance based on the date it was submitted. The history analysis department can also adjust the priority of analysis according to when the history was submitted. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on when the history was submitted.

[0133] The history analysis unit can adjust the order of analysis based on the relevance of the history during the analysis process. For example, the history analysis unit can prioritize the analysis of the most relevant history based on its relevance. The history analysis unit can also adjust the order of analysis according to the relevance of the history. For example, the history analysis unit can prioritize the analysis of history of high importance based on its relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on the relevance of the history.

[0134] The personalization unit can estimate the user's emotions and adjust the way advice is delivered based on those emotions. For example, if the user is feeling anxious, the personalization unit can provide advice in a reassuring way. If the user is relaxed, the personalization unit can also provide advice in a concise and clear way. If the user is in a hurry, the personalization unit can also provide advice in a to-the-point way. By adjusting the way advice is delivered according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0135] The personalization unit can provide optimal advice by referring to the user's past consultation history when offering advice. For example, the personalization unit can provide highly relevant advice based on the user's past consultation history. For example, the personalization unit can predict and provide optimal advice based on the user's past consultation history. For example, the personalization unit can analyze the user's past consultation history and provide the most effective advice. This allows for the provision of more appropriate advice by referring to the user's past consultation history.

[0136] The personalization function can customize the content of advice based on the user's current business situation when providing advice. For example, the personalization function can provide optimal advice according to the user's current business situation. The personalization function can also provide appropriate advice based on the user's business growth stage. For example, the personalization function can provide customized advice according to the user's current business situation. This allows for the provision of more appropriate advice by customizing the content of advice based on the user's current business situation.

[0137] The personalization unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is feeling anxious, the personalization unit will prioritize providing urgent advice. For example, if the user is relaxed, the personalization unit can also prioritize providing detailed advice. For example, if the user is in a hurry, the personalization unit can also prioritize providing concise advice. This allows advice to be delivered in a more appropriate order by prioritizing it according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0138] The personalization function can provide optimal advice by considering the user's geographical location when offering advice. For example, the personalization function can provide advice related to the user's location. It can also provide appropriate advice based on the user's business area. Furthermore, it can provide optimal advice based on the user's geographical circumstances. This allows for the provision of more appropriate advice by considering the user's geographical location.

[0139] The personalization unit can analyze the user's social media activity and adjust the content of the advice provided. For example, the personalization unit can analyze the user's social media posts and provide relevant advice. For example, the personalization unit can also provide appropriate advice based on the user's social media activity history. For example, the personalization unit can also provide relevant advice based on the user's social media interests. This allows for the provision of more appropriate advice by analyzing the user's social media activity.

[0140] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0141] The legal support agent system can estimate the user's emotions and adjust the timing of question acceptance based on those emotions. For example, if the user is stressed, the system can accept the question quickly and prioritize immediate response. If the user is relaxed, the system can guide them through the interface carefully to accept detailed questions. If the user is in a hurry, the system can accept concise questions and provide quick answers. This allows for more appropriate question acceptance by adjusting the timing according to the user's emotions.

[0142] The legal support agent system includes a history analysis unit that analyzes a user's past consultation history. For example, it analyzes a user's past consultation history to provide highly relevant advice. It can analyze consultation history for a specific period, and it can also analyze specific types of consultation history. It can also analyze the content of the consultation history to provide relevant legal advice. This allows for more personalized advice to be provided by analyzing a user's past consultation history.

[0143] The legal support agent system can estimate the user's emotions and adjust how it analyzes questions based on those emotions. For example, if the user is feeling anxious, it can perform a detailed analysis and provide reassuring answers. If the user is relaxed, it can perform a concise analysis and provide quick answers. If the user is in a hurry, it can perform a to-the-point analysis and provide quick answers. In this way, by adjusting how it analyzes questions according to the user's emotions, it can perform more appropriate analysis.

[0144] The legal support agent system can filter questions based on the user's current business situation and areas of interest. For example, it can prioritize receiving legal questions relevant to the user's business situation. It can also filter relevant questions based on the user's areas of interest and receive appropriate questions. It can even suggest appropriate legal questions according to the user's business growth stage. This allows for receiving more relevant questions by filtering questions based on the user's business situation and areas of interest.

[0145] The legal support agent system can estimate the user's emotions and adjust the way it expresses its responses based on those emotions. For example, if the user is feeling anxious, it can provide a reassuring response. If the user is relaxed, it can provide a concise and clear response. If the user is in a hurry, it can provide a to-the-point response. By adjusting the way it expresses its responses according to the user's emotions, it can provide more appropriate answers.

[0146] The legal support agent system can analyze a user's past question history and select the most appropriate method of handling inquiries. For example, it can automatically display frequently asked questions as suggestions. It can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Based on the user's past question history, it can also predict and suggest questions that might be asked at specific times of the day. In this way, by analyzing the user's past question history, the system can select the most appropriate method of handling inquiries and process them efficiently.

[0147] The legal support agent system can estimate the user's emotions and adjust its contract analysis method based on those emotions. For example, if the user is feeling anxious, it can perform a detailed analysis and provide reassuring information. If the user is relaxed, it can perform a concise analysis and provide information quickly. If the user is in a hurry, it can perform a concise analysis and provide information quickly. In this way, by adjusting the contract analysis method according to the user's emotions, a more appropriate analysis can be performed.

[0148] The legal support agent system can prioritize receiving highly relevant questions by considering the user's geographical location. For example, it can prioritize legal questions related to the user's location. It can also prioritize relevant legal questions based on the user's business area. It can even suggest appropriate legal questions based on the user's geographical circumstances. In this way, by considering the user's geographical location, it can prioritize receiving highly relevant questions.

[0149] The legal support agent system can estimate the user's emotions and adjust how legal change information is delivered based on those emotions. For example, if the user is feeling anxious, the system can provide legal change information in a reassuring manner. If the user is relaxed, the system can provide legal change information in a concise and clear manner. If the user is in a hurry, the system can provide legal change information in a concise and to-the-point manner. By adjusting how legal change information is delivered according to the user's emotions, the system can provide more appropriate information.

[0150] The legal support agent system can analyze a user's social media activity and receive relevant questions. For example, it can analyze a user's social media posts and suggest relevant legal questions. It can also receive appropriate legal questions based on the user's social media activity history. It can also prioritize receiving relevant legal questions based on the user's areas of interest on social media. In this way, it can receive relevant questions by analyzing the user's social media activity.

[0151] The following briefly describes the processing flow for example form 2.

[0152] Step 1: The reception desk receives legal questions from users. For example, it can accept legal questions entered by users from their smartphones or personal computers. It can also handle legal questions via voice input or chat. Step 2: The analysis unit analyzes the questions received by the reception unit. For example, it can analyze the input questions using natural language processing technology or machine learning models. It can also analyze the relevance of the input questions by referring to a database of past case precedents. Step 3: The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. For example, appropriate answers can be generated using natural language generation technology or machine learning models. Alternatively, appropriate answers can be generated by referring to a database of past case precedents. Step 4: The contract analysis unit analyzes the contract uploaded by the user. For example, it can analyze the content of the contract using natural language processing technology or machine learning models. It can also analyze the clauses of the contract and point out potential risks. Step 5: The information provision department provides the latest information on legal amendments. For example, it can automatically collect and provide information on legal amendments to users. It can also periodically update information on legal amendments and notify users.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0155] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, contract analysis unit, and information provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives legal questions from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received questions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates appropriate answers based on the analyzed questions. The contract analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes contracts uploaded by the user. The information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the latest legal amendment information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0158] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, contract analysis unit, and information provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives legal questions from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received questions. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate answers based on the analyzed questions. The contract analysis unit is implemented, for example, by the control unit 46A of the smart glasses 214 and analyzes contracts uploaded by the user. The information provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides the latest legal amendment information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0174] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0181] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0182] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0183] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0184] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0185] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0187] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0188] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, contract analysis unit, and information provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives legal questions from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received questions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates appropriate answers based on the analyzed questions. The contract analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes contracts uploaded by the user. The information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the latest legal amendment information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0189] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0190] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0191] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0192] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0193] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0194] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0195] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0196] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0197] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0198] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0199] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0200] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0201] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0202] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0203] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0204] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0205] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, contract analysis unit, and information provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives legal questions from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received questions. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates appropriate answers based on the analyzed questions. The contract analysis unit is implemented by, for example, the control unit 46A of the robot 414 and analyzes contracts uploaded by the user. The information provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the latest legal amendment information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0206] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0207] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0208] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0209] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0210] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0211] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0212] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0213] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0214] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0215] 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.

[0216] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0217] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0218] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0219] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0220] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0221] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0222] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0223] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0224] (Note 1) A reception desk that handles legal inquiries from users, An analysis unit that analyzes the questions received by the reception unit, A generation unit that generates an appropriate answer based on the question analyzed by the analysis unit, The contract analysis unit analyzes contracts uploaded by users, It includes an information provision department that provides the latest information on legal revisions. A system characterized by the following features. (Note 2) It includes a case law analysis unit that analyzes a database of past case precedents. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a history analysis unit that analyzes the user's past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a personalization section that provides personalized advice to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned contract analysis unit, Analyze user-uploaded contracts and identify potential risks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned information provision unit, Provides the latest information on legal amendments. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions, filtering is performed based on the user's current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the question analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing questions, adjust the level of detail based on the importance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing a question, different analysis algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing questions, we prioritize the analysis based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing questions, the order of analysis is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating answers, different answer algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned contract analysis unit, We estimate the user's emotions and adjust the contract analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned contract analysis unit, When analyzing contracts, adjust the level of detail based on the importance of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned contract analysis unit, When analyzing contracts, different analysis algorithms are applied depending on the category of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned contract analysis unit, The system estimates the user's emotions and adjusts how the contract analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned contract analysis unit, When analyzing contracts, the priority of the analysis is determined based on when the contracts were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned contract analysis unit, When analyzing contracts, the order of analysis is adjusted based on the relationships between the contracts. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned information provision unit, We estimate user sentiment and adjust the method of providing information on legal changes based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned information provision unit, When providing information on legal amendments, the level of detail provided will be adjusted based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned information provision unit, When providing information on legal amendments, different provision algorithms will be applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned information provision unit, The system estimates user sentiment and adjusts the order in which legal amendment information is provided based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned information provision unit, When providing information on legal amendments, the priority of information provision will be determined based on the timing of its submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned information provision unit, When providing information on legal amendments, the order in which the information is provided will be adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned case law analysis unit, We estimate the user's emotions and adjust the case analysis method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned case law analysis unit, When analyzing case law, adjust the level of detail of the analysis based on the importance of the case. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned case law analysis unit, When analyzing case law, different analysis algorithms are applied depending on the category of the case. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned case law analysis unit, The system estimates the user's emotions and adjusts how the case analysis results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned case law analysis unit, When analyzing case law, the priority of the analysis is determined based on when the case law was filed. The system described in Appendix 2, characterized by the features described herein. (Appendix 42) The case analysis unit adjusts the analysis order based on the relevance of cases during case analysis. The system according to Appendix 2, characterized in that. (Appendix 43) The history analysis unit estimates the user's emotion and adjusts the analysis method of the past consultation history based on the estimated user's emotion. The system according to Appendix 3, characterized in that. (Appendix 44) The history analysis unit adjusts the analysis detail level based on the importance of the history during history analysis. The system according to Appendix 3, characterized in that. (Appendix 45) The history analysis unit applies different analysis algorithms according to the category of the history during history analysis. The system according to Appendix 3, characterized in that. (Appendix 46) The history analysis unit estimates the user's emotion and adjusts the display method of the analysis result of the history based on the estimated user's emotion. The system according to Appendix 3, characterized in that. (Appendix 47) The history analysis unit determines the analysis priority based on the submission time of the history during history analysis. The system according to Appendix 3, characterized in that. (Appendix 48) The history analysis unit adjusts the analysis order based on the relevance of the history during history analysis. The system according to Appendix 3, characterized in that. (Appendix 49) The personalization unit estimates the user's emotion and adjusts the advice providing method based on the estimated user's emotion. The system according to Appendix 4, characterized in that. (Appendix 50) The personalization unit described above is When providing advice, we refer to the user's past consultation history to provide the most appropriate advice. The system described in Appendix 4, characterized by the features described herein. (Note 51) The personalization unit described above is When providing advice, customize the content of the advice based on the user's current business situation. The system described in Appendix 4, characterized by the features described herein. (Note 52) The personalization unit described above is It estimates the user's emotions and determines the priority of advice based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 53) The personalization unit described above is When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 4, characterized by the features described herein. (Note 54) The personalization unit described above is When providing advice, we analyze the user's social media activity and adjust the content of the advice accordingly. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0225] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles legal inquiries from users, An analysis unit that analyzes the questions received by the reception unit, A generation unit that generates an appropriate answer based on the question analyzed by the analysis unit, The contract analysis unit analyzes contracts uploaded by users, It includes an information provision department that provides the latest information on legal revisions. A system characterized by the following features.

2. It includes a case law analysis unit that analyzes a database of past case precedents. The system according to feature 1.

3. It includes a history analysis unit that analyzes the user's past consultation history. The system according to feature 1.

4. It includes a personalization section that provides personalized advice to the user. The system according to feature 1.

5. The aforementioned contract analysis unit, Analyze user-uploaded contracts and identify potential risks. The system according to feature 1.

6. The aforementioned information provision unit, Provides the latest information on legal amendments. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

9. The aforementioned reception unit is When receiving questions, filtering is performed based on the user's current business situation and areas of interest. The system according to feature 1.

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