system

Abstract

The system according to this embodiment aims to enable users to obtain quick and accurate guidelines for legal questions and issues. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a review unit, and a provision unit. The reception unit receives legal questions and problems from users. The analysis unit analyzes the information received by the reception unit and obtains information from relevant legal databases. The generation unit creates initial guidelines based on the information obtained by the analysis unit. The review unit reviews the guidelines created by the generation unit and makes corrections or additions. The provision unit provides the user with the guidelines corrected by the review unit.

Description

Technical Field

[0006] ,

[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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to obtain quick and accurate guidelines for questions and problems related to laws.

[0005] The system according to the embodiment aims to enable a user to obtain quick and accurate guidelines for questions and problems related to laws.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a review unit, and a provision unit. The reception unit receives legal questions and problems from users. The analysis unit analyzes the information received by the reception unit and retrieves information from relevant legal databases. The generation unit creates initial guidelines based on the information retrieved by the analysis unit. The review unit reviews the guidelines created by the generation unit and makes corrections or additions. The provision unit provides the user with the guidelines corrected by the review unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to obtain quick and accurate guidelines for legal questions and issues. [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, etc. The communication I / F manages communication between a plurality of 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 guideline provision system according to an embodiment of the present invention is a system in which a generative AI and a legal expert cooperate to provide guidelines for addressing legal regulations and intellectual property rights issues. In this guideline provision system, the user inputs a legal question or problem, the generative AI analyzes the question or problem, and retrieves information from relevant legal databases. Based on the retrieved information, the generative AI creates an initial guideline. Subsequently, a legal expert reviews the guideline created by the generative AI and makes revisions or additions as necessary. Finally, the revised guideline is provided to the user. This system enables a quick and accurate response to legal issues and provides guidelines that are easy for users with limited legal knowledge to understand. For example, the user inputs a legal question or problem. In this case, the user can input specific situations and backgrounds. For example, the user may input a question such as, "How should I respond if I suspect copyright infringement?" This information is input to the generative AI. Next, the generative AI analyzes the input information and retrieves information from relevant legal databases. The generative AI analyzes a large amount of legal data and identifies information related to the user's question or problem. For example, it retrieves information on copyright infringement from a database on copyright law. Based on the retrieved information, the generative AI creates an initial guideline. The generative AI generates initial guidelines for user questions and problems based on the acquired information. For example, it creates initial guidelines on how to respond in cases of suspected copyright infringement. Subsequently, legal professionals review the guidelines generated by the generative AI and make revisions or additions as needed. The legal professionals review the guidelines generated by the generative AI and evaluate their accuracy and applicability. They make revisions or additions as needed to complete the final guidelines. Finally, the revised guidelines are provided to the user. The user receives the guidelines created collaboratively by the generative AI and legal professionals and can understand how to respond to legal issues. This system enables quick and accurate responses to legal issues and provides guidelines that are easy to understand even for users with limited legal knowledge.This allows the guideline provision system to provide users with quick and accurate guidelines for their legal questions and issues.

[0029] The guideline provision system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a review unit, and a provision unit. The reception unit receives legal questions and problems from users. The reception unit can, for example, receive specific situations and backgrounds entered by the user. The analysis unit analyzes the information received by the reception unit and obtains information from relevant legal databases. The analysis unit obtains information from legal databases using, for example, a generation AI. The generation unit creates initial guidelines based on the information obtained by the analysis unit. The generation unit creates initial guidelines using, for example, a generation AI. The review unit reviews the guidelines created by the generation unit and makes corrections or additions. The review unit, for example, has a legal expert review the guidelines created by the generation AI and makes corrections or additions as necessary. The provision unit provides the user with the guidelines corrected by the review unit. The provision unit provides the user with the corrected guidelines. As a result, the guideline provision system can provide users with quick and accurate guidelines for their legal questions and problems.

[0030] The reception department receives legal questions and issues from users. Specifically, it can receive specific situations and background information entered by users. For example, users can enter detailed information about their legal issues through web forms or mobile apps. This includes the circumstances of the issue, information about those involved, and relevant documents and evidence. The reception department centrally manages this information and stores it in a database for use by subsequent analysis and generation departments. Furthermore, the reception department has a function to automatically classify user input and assign it to the appropriate category. For example, classifying it into categories such as labor issues, contract issues, and family law ensures efficient processing. The reception department can also automatically send confirmation messages to users regarding their input, prompting them to review the input and provide additional information. This allows the reception department to collect information from users accurately and quickly, improving the overall processing efficiency of the system.

[0031] The analysis unit analyzes information received by the reception unit and retrieves information from relevant legal databases. Specifically, it uses generative AI to retrieve information from legal databases. First, the analysis unit analyzes the information provided by the user using natural language processing technology to identify the core of the problem and the relevant legal provisions. For example, if a user enters a problem related to employment contracts, the analysis unit identifies the Labor Standards Act and relevant precedents and extracts the necessary information. Generative AI has the ability to quickly search large legal databases and extract relevant information. Furthermore, the analysis unit refers to similar past cases and precedents to find the best solution to the user's problem. This allows the analysis unit to provide users with quick and accurate information regarding their problems. The analysis unit also organizes the retrieved information and converts it into a format that is easy for the generation unit to use. This includes summarizing relevant legal provisions and extracting key points. In this way, the analysis unit supports the generation unit in efficiently creating guidelines.

[0032] The generation unit creates initial guidelines based on information obtained by the analysis unit. Specifically, it uses a generation AI to create the initial guidelines. Based on the information provided by the analysis unit, the generation unit generates guidelines that include specific advice and procedures for the user's problem. The generation AI uses natural language generation technology to create guidelines in a format that is easy for the user to understand. For example, in the case of an issue concerning an employment contract, the generation unit provides specific advice on how to revise the contract and on rights and obligations under labor standards law. The generation unit also analyzes the user's input in detail to create customized guidelines tailored to the user's situation and address individual needs. This allows the generation unit to provide users with specific and practical guidelines. Furthermore, the generation unit conducts internal reviews and tests to ensure the quality of the generated guidelines and makes corrections as needed. This allows the generation unit to quickly provide high-quality guidelines.

[0033] The review team reviews the guidelines created by the generation team and makes revisions or additions. Specifically, legal experts review the guidelines created by the generation AI and make revisions or additions as needed. The review team thoroughly checks the content of the generated guidelines and evaluates their legal accuracy and applicability. For example, in the case of guidelines concerning employment contracts, legal experts check whether the advice provided is based on the latest laws and precedents and make revisions as necessary. The review team can also provide additional advice and information tailored to the user's specific situation. This allows the review team to improve the quality of the generated guidelines and provide users with reliable information. Furthermore, the review team revises the text and adjusts the layout to make the content of the guidelines easier for users to understand. This allows the review team to provide users with clear and practical guidelines.

[0034] The service provider will provide users with the revised guidelines, as reviewed by the service provider. Specifically, the service provider will provide users with the revised guidelines. To make the guidelines easily accessible to users, the service provider will distribute the guidelines through websites and mobile apps. For example, users can log in to their accounts to download or view the revised guidelines. The service provider also has a function to notify users of the availability of the guidelines. For example, when the guidelines are revised and ready to be provided, users can be notified via email or push notification. This allows the service provider to provide users with guidelines quickly and reliably. Furthermore, the service provider can collect feedback from users and use it to improve the entire system. For example, users can leave ratings and comments on the provided guidelines, and the service provider can use this feedback to improve the content and delivery method of the guidelines. This allows the service provider to continue providing high-quality guidelines that meet user needs.

[0035] The analysis unit can retrieve information from legal databases using generative AI. For example, the analysis unit retrieves information from legal databases using generative AI. The generative AI retrieves information from legal databases using, for example, text generation AI (e.g., LLM). The generative AI analyzes large amounts of legal data and identifies information relevant to the user's questions or problems. This allows for the rapid retrieval of relevant legal information using the generative AI.

[0036] The generation unit can create initial guidelines using a generation AI. For example, the generation unit creates initial guidelines using a generation AI. The generation AI generates initial guidelines for user questions and problems based on the acquired information. For example, the generation AI creates initial guidelines using a text generation AI (e.g., LLM). This allows for the rapid creation of initial guidelines using a generation AI.

[0037] The review department allows legal professionals to review the guidelines generated by the AI ​​and make revisions or additions. For example, the review department allows legal professionals to review the guidelines generated by the AI ​​and evaluate their accuracy and applicability. Legal professionals make revisions or additions as needed to complete the final guidelines. This improves the accuracy and applicability of the guidelines through legal professional review. Some or all of the above processes in the review department may be performed using AI, for example, or without AI.

[0038] The service provider can provide users with revised guidelines. The service provider can provide users with revised guidelines, for example. The service provider can provide the guidelines through, for example, a web application or a mobile application. The service provider can also send the guidelines by email, for example. By providing users with revised guidelines, users can obtain accurate information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI.

[0039] The reception desk can receive specific circumstances and background information entered by the user. For example, the reception desk can receive specific circumstances and background information entered by the user. For example, the reception desk can receive details of the incident and information about those involved entered by the user. By receiving the user's specific circumstances and background information, it is possible to provide more appropriate guidelines. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0040] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk may prioritize suggesting question formats that the user has frequently used in the past. For example, the reception desk may suggest a reception method suitable for a specific time of day based on the user's past question history. For example, the reception desk may analyze the content of the user's past questions and automatically suggest related questions. In this way, the reception desk can provide the optimal reception method by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0041] The reception desk can filter inquiries based on the urgency of the user's current legal problem. For example, the reception desk may prioritize and respond quickly to high-urgency legal problems. For example, it may process low-urgency legal problems in the normal order of receipt. For example, it may receive moderately urgent legal problems at an appropriate time and process them quickly. This allows for a rapid response by filtering based on the urgency of the legal problem. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0042] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving legal issues related to that region. For example, the reception desk will automatically suggest region-specific legal issues based on the user's location. For example, if the user is on the move, the reception desk will receive the most relevant legal issues based on the user's current location. This allows the reception desk to prioritize receiving questions that are highly relevant by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0043] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can automatically suggest relevant legal issues from the user's social media posts. For example, the reception desk can analyze the user's social media activity and suggest the most appropriate question format. For example, the reception desk can prioritize questions based on the user's social media follower count and influence. This allows the reception desk to accept relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the legal data during the analysis. For example, the analysis unit performs a detailed analysis on legal data of high importance. For example, the analysis unit performs a concise analysis on legal data of low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on legal data of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the legal data, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can apply different analysis algorithms depending on the legal category during analysis. For example, the analysis unit applies a copyright law-specific algorithm for analysis related to copyright law. For example, the analysis unit applies a patent law-specific algorithm for analysis related to patent law. For example, the analysis unit applies a trademark law-specific algorithm for analysis related to trademark law. By applying different analysis algorithms depending on the legal category, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0046] The analysis unit can determine the priority of analysis based on the submission date of legal data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent legal data. For example, the analysis unit may analyze older legal data with normal priority. For example, the analysis unit may analyze legal data submitted at a moderate time with appropriate priority. This allows for prioritization of the analysis of the latest information by determining the priority of analysis based on the submission date of legal data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the legal data during analysis. For example, the analysis unit prioritizes analyzing the legal data most relevant to the user's question. For example, the analysis unit analyzes less relevant legal data in the normal order. For example, the analysis unit analyzes moderately relevant legal data in an appropriate order. In this way, by adjusting the order of analysis based on the relevance of the legal data, highly relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0048] The generation unit can adjust the level of detail of the guidelines based on the importance of the legal issue during guideline generation. For example, the generation unit provides detailed guidelines for highly important legal issues. For example, the generation unit provides concise guidelines for less important legal issues. For example, the generation unit provides guidelines with a moderate level of detail for legal issues of moderate importance. In this way, appropriate guidelines can be provided by adjusting the level of detail of the guidelines based on the importance of the legal issue. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0049] The generation unit can apply different generation algorithms depending on the legal category when generating guidelines. For example, the generation unit applies a generation algorithm specifically for copyright law to guidelines concerning copyright law. For example, the generation unit applies a generation algorithm specifically for patent law to guidelines concerning patent law. For example, the generation unit applies a generation algorithm specifically for trademark law to guidelines concerning trademark law. By applying different generation algorithms depending on the legal category, more accurate guidelines can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0050] The generation unit can determine the priority of guidelines based on when the legal issues were submitted during guideline generation. For example, the generation unit provides guidelines preferentially for the most recent legal issues. For example, the generation unit provides guidelines with normal priority for older legal issues. For example, the generation unit provides guidelines with appropriate priority for legal issues that were submitted at a moderate time. This ensures that the most up-to-date information is provided preferentially by determining the priority of guidelines based on when the legal issues were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0051] The generation unit can adjust the order of guidelines based on the relevance of the legal issues when generating them. For example, the generation unit prioritizes reflecting the legal issues most relevant to the user's question in the guidelines. For example, the generation unit reflects less relevant legal issues in the guidelines in the normal order. For example, the generation unit reflects moderately relevant legal issues in the guidelines in an appropriate order. This allows for the priority provision of highly relevant information by adjusting the order of guidelines based on the relevance of the legal issues. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0052] The review unit can adjust the level of detail of a review based on the importance of the legal issue. For example, the review unit provides a detailed review for highly important legal issues. For example, the review unit provides a concise review for less important legal issues. For example, the review unit provides a review with a moderate level of detail for legal issues of moderate importance. This allows for the provision of appropriate reviews by adjusting the level of detail based on the importance of the legal issue. Some or all of the above processing in the review unit may be performed using AI, for example, or without AI.

[0053] The review department can determine the priority of reviews based on when the legal issues were submitted. For example, the review department may prioritize the review of the most recent legal issues. For example, it may prioritize the review of older legal issues. For example, it may prioritize the review of legal issues submitted at a moderate time. This allows for prioritization of the review of the most recent information by determining the priority of reviews based on when the legal issues were submitted. Some or all of the above processing in the review department may be performed using AI, for example, or not using AI.

[0054] The review unit can adjust the order of reviews based on the relevance of the legal issues during the review process. For example, the review unit may prioritize reviewing legal issues that are most relevant to the user's question. For example, the review unit may review less relevant legal issues in the normal order. For example, the review unit may review moderately relevant legal issues in an appropriate order. This allows for prioritizing the review of highly relevant information by adjusting the order of reviews based on the relevance of the legal issues. Some or all of the above processing in the review unit may be performed using AI, for example, or not using AI.

[0055] The service provider can select the most suitable service method when providing guidelines by referring to the user's past legal problem history. For example, the service provider may prioritize using service methods previously used by the user. For example, the service provider may propose the most suitable service method based on the user's past legal problem history. For example, the service provider may analyze the user's past usage history and select the most efficient service method. This allows the service provider to select the most suitable service method by referring to the user's past legal problem history. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI.

[0056] The service provider can select the optimal delivery method when providing guidelines, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will use a delivery method adapted to the screen size. For example, if the user is using a tablet, the service provider will use a delivery method optimized for a larger screen. For example, if the user is using a personal computer, the service provider will provide detailed information. This allows the service provider to select the optimal delivery method by taking into account the user's device information. Some or all of the above processing by the service provider may be performed using AI, for example, or without using AI.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can automatically search for relevant past court precedents based on the user's input and present them to the user. For example, if a user asks a question about a specific legal issue, the reception desk will search for relevant past court precedents and present them to the user. This allows the user to understand more specific ways of responding by referring to past precedents. The reception desk can also automatically search for relevant legal documents and guidelines based on the user's input and present them to the user. For example, if a user asks a question about a specific legal document or guideline, the reception desk will search for that document or guideline and present it to the user. This allows the user to understand more specific ways of responding by referring to relevant legal documents and guidelines. Furthermore, the reception desk can also automatically search for and present the opinions and explanations of relevant legal experts based on the user's input. For example, if a user asks a question about a specific legal issue, the reception desk will search for opinions and explanations of relevant legal experts and present them to the user. This allows the user to understand more specific ways of responding by referring to the opinions and explanations of legal experts.

[0059] The analysis unit can evaluate the reliability of the information it retrieves from relevant legal databases based on user input. For example, the analysis unit checks the source and update date of the information retrieved from legal databases and prioritizes the retrieval of highly reliable information. This improves the reliability of the guidelines provided to users. The analysis unit can also retrieve information from multiple legal databases and check the degree of consistency to evaluate the reliability of the retrieved information. For example, the analysis unit checks whether the information retrieved from multiple legal databases is consistent and prioritizes the use of consistent information. This improves the reliability of the guidelines provided to users. Furthermore, the analysis unit can consult with legal experts to evaluate the reliability of the retrieved information. For example, the analysis unit requests legal experts to review the retrieved information and selects highly reliable information. This improves the reliability of the guidelines provided to users.

[0060] The generation unit can create guidelines based on user input, adjusting the presentation of the guidelines according to the user's level of understanding. For example, for users with limited legal knowledge, the generation unit uses a simple and easy-to-understand presentation. This makes the guidelines easier for users with limited legal knowledge to understand. The generation unit can also provide detailed information to users with extensive legal knowledge. For example, for users with extensive legal knowledge, the generation unit provides guidelines that include specialized terminology and detailed explanations. This makes the guidelines easier for users with extensive legal knowledge to understand. Furthermore, the generation unit can adjust the method of providing the guidelines according to the user's level of understanding. For example, for users with limited legal knowledge, the generation unit provides guidelines using diagrams and videos. This makes the guidelines easier for users with limited legal knowledge to understand.

[0061] The review team can take user feedback into consideration when reviewing guidelines created by the generation AI. For example, the review team can collect user feedback on the provided guidelines and use it to improve them. This allows them to provide guidelines that are better suited to user needs. The review team can also adjust the wording and content of the guidelines based on user feedback. For example, if a user finds the content of the guidelines difficult to understand, the review team can simplify the wording. This makes the guidelines easier for users to understand. Furthermore, the review team can adjust how the guidelines are delivered based on user feedback. For example, if a user is dissatisfied with how the guidelines are delivered, the review team can improve the delivery method. This makes the guidelines easier for users to receive.

[0062] The service provider can select the most suitable delivery method for users when providing guidelines, depending on the user's device and environment. For example, if the user is using a smartphone, the service provider will use a delivery method optimized for the screen size. This makes the guidelines easier for users to view on their smartphones. Similarly, if the user is using a tablet, the service provider can use a delivery method optimized for larger screens. This also makes the guidelines easier for users to view on their tablets. Furthermore, if the user is using a personal computer, the service provider can provide more detailed information. This also makes the guidelines easier for users to view on their personal computers.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives legal questions and issues from users. For example, it can receive specific situations and background information entered by the user. Step 2: The analysis unit analyzes the information received by the reception unit and retrieves information from relevant legal databases. For example, it may use a generation AI to retrieve information from legal databases. Step 3: The generation unit creates initial guidelines based on the information obtained by the analysis unit. For example, the generation AI is used to create the initial guidelines. Step 4: The review team reviews the guidelines created by the generation team and makes revisions or additions. For example, legal experts review the guidelines created by the generation AI and make revisions or additions as needed. Step 5: The providing department provides the user with the revised guidelines from the review department. For example, they provide the user with the revised guidelines.

[0065] (Example of form 2) The guideline provision system according to an embodiment of the present invention is a system in which a generative AI and a legal expert cooperate to provide guidelines for addressing legal regulations and intellectual property rights issues. In this guideline provision system, the user inputs a legal question or problem, the generative AI analyzes the question or problem, and retrieves information from relevant legal databases. Based on the retrieved information, the generative AI creates an initial guideline. Subsequently, a legal expert reviews the guideline created by the generative AI and makes revisions or additions as necessary. Finally, the revised guideline is provided to the user. This system enables a quick and accurate response to legal issues and provides guidelines that are easy for users with limited legal knowledge to understand. For example, the user inputs a legal question or problem. In this case, the user can input specific situations and backgrounds. For example, the user may input a question such as, "How should I respond if I suspect copyright infringement?" This information is input to the generative AI. Next, the generative AI analyzes the input information and retrieves information from relevant legal databases. The generative AI analyzes a large amount of legal data and identifies information related to the user's question or problem. For example, it retrieves information on copyright infringement from a database on copyright law. Based on the retrieved information, the generative AI creates an initial guideline. The generative AI generates initial guidelines for user questions and problems based on the acquired information. For example, it creates initial guidelines on how to respond in cases of suspected copyright infringement. Subsequently, legal professionals review the guidelines generated by the generative AI and make revisions or additions as needed. The legal professionals review the guidelines generated by the generative AI and evaluate their accuracy and applicability. They make revisions or additions as needed to complete the final guidelines. Finally, the revised guidelines are provided to the user. The user receives the guidelines created collaboratively by the generative AI and legal professionals and can understand how to respond to legal issues. This system enables quick and accurate responses to legal issues and provides guidelines that are easy to understand even for users with limited legal knowledge.This allows the guideline provision system to provide users with quick and accurate guidelines for their legal questions and issues.

[0066] The guideline provision system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a review unit, and a provision unit. The reception unit receives legal questions and problems from users. The reception unit can, for example, receive specific situations and backgrounds entered by the user. The analysis unit analyzes the information received by the reception unit and obtains information from relevant legal databases. The analysis unit obtains information from legal databases using, for example, a generation AI. The generation unit creates initial guidelines based on the information obtained by the analysis unit. The generation unit creates initial guidelines using, for example, a generation AI. The review unit reviews the guidelines created by the generation unit and makes corrections or additions. The review unit, for example, has a legal expert review the guidelines created by the generation AI and makes corrections or additions as necessary. The provision unit provides the user with the guidelines corrected by the review unit. The provision unit provides the user with the corrected guidelines. As a result, the guideline provision system can provide users with quick and accurate guidelines for their legal questions and problems.

[0067] The reception department receives legal questions and issues from users. Specifically, it can receive specific situations and background information entered by users. For example, users can enter detailed information about their legal issues through web forms or mobile apps. This includes the circumstances of the issue, information about those involved, and relevant documents and evidence. The reception department centrally manages this information and stores it in a database for use by subsequent analysis and generation departments. Furthermore, the reception department has a function to automatically classify user input and assign it to the appropriate category. For example, classifying it into categories such as labor issues, contract issues, and family law ensures efficient processing. The reception department can also automatically send confirmation messages to users regarding their input, prompting them to review the input and provide additional information. This allows the reception department to collect information from users accurately and quickly, improving the overall processing efficiency of the system.

[0068] The analysis unit analyzes information received by the reception unit and retrieves information from relevant legal databases. Specifically, it uses generative AI to retrieve information from legal databases. First, the analysis unit analyzes the information provided by the user using natural language processing technology to identify the core of the problem and the relevant legal provisions. For example, if a user enters a problem related to employment contracts, the analysis unit identifies the Labor Standards Act and relevant precedents and extracts the necessary information. Generative AI has the ability to quickly search large legal databases and extract relevant information. Furthermore, the analysis unit refers to similar past cases and precedents to find the best solution to the user's problem. This allows the analysis unit to provide users with quick and accurate information regarding their problems. The analysis unit also organizes the retrieved information and converts it into a format that is easy for the generation unit to use. This includes summarizing relevant legal provisions and extracting key points. In this way, the analysis unit supports the generation unit in efficiently creating guidelines.

[0069] The generation unit creates initial guidelines based on information obtained by the analysis unit. Specifically, it uses a generation AI to create the initial guidelines. Based on the information provided by the analysis unit, the generation unit generates guidelines that include specific advice and procedures for the user's problem. The generation AI uses natural language generation technology to create guidelines in a format that is easy for the user to understand. For example, in the case of an issue concerning an employment contract, the generation unit provides specific advice on how to revise the contract and on rights and obligations under labor standards law. The generation unit also analyzes the user's input in detail to create customized guidelines tailored to the user's situation and address individual needs. This allows the generation unit to provide users with specific and practical guidelines. Furthermore, the generation unit conducts internal reviews and tests to ensure the quality of the generated guidelines and makes corrections as needed. This allows the generation unit to quickly provide high-quality guidelines.

[0070] The review team reviews the guidelines created by the generation team and makes revisions or additions. Specifically, legal experts review the guidelines created by the generation AI and make revisions or additions as needed. The review team thoroughly checks the content of the generated guidelines and evaluates their legal accuracy and applicability. For example, in the case of guidelines concerning employment contracts, legal experts check whether the advice provided is based on the latest laws and precedents and make revisions as necessary. The review team can also provide additional advice and information tailored to the user's specific situation. This allows the review team to improve the quality of the generated guidelines and provide users with reliable information. Furthermore, the review team revises the text and adjusts the layout to make the content of the guidelines easier for users to understand. This allows the review team to provide users with clear and practical guidelines.

[0071] The service provider will provide users with the revised guidelines, as reviewed by the service provider. Specifically, the service provider will provide users with the revised guidelines. To make the guidelines easily accessible to users, the service provider will distribute the guidelines through websites and mobile apps. For example, users can log in to their accounts to download or view the revised guidelines. The service provider also has a function to notify users of the availability of the guidelines. For example, when the guidelines are revised and ready to be provided, users can be notified via email or push notification. This allows the service provider to provide users with guidelines quickly and reliably. Furthermore, the service provider can collect feedback from users and use it to improve the entire system. For example, users can leave ratings and comments on the provided guidelines, and the service provider can use this feedback to improve the content and delivery method of the guidelines. This allows the service provider to continue providing high-quality guidelines that meet user needs.

[0072] The analysis unit can retrieve information from legal databases using generative AI. For example, the analysis unit retrieves information from legal databases using generative AI. The generative AI retrieves information from legal databases using, for example, text generation AI (e.g., LLM). The generative AI analyzes large amounts of legal data and identifies information relevant to the user's questions or problems. This allows for the rapid retrieval of relevant legal information using the generative AI.

[0073] The generation unit can create initial guidelines using a generation AI. For example, the generation unit creates initial guidelines using a generation AI. The generation AI generates initial guidelines for user questions and problems based on the acquired information. For example, the generation AI creates initial guidelines using a text generation AI (e.g., LLM). This allows for the rapid creation of initial guidelines using a generation AI.

[0074] The review department allows legal professionals to review the guidelines generated by the AI ​​and make revisions or additions. For example, the review department allows legal professionals to review the guidelines generated by the AI ​​and evaluate their accuracy and applicability. Legal professionals make revisions or additions as needed to complete the final guidelines. This improves the accuracy and applicability of the guidelines through legal professional review. Some or all of the above processes in the review department may be performed using AI, for example, or without AI.

[0075] The service provider can provide users with revised guidelines. The service provider can provide users with revised guidelines, for example. The service provider can provide the guidelines through, for example, a web application or a mobile application. The service provider can also send the guidelines by email, for example. By providing users with revised guidelines, users can obtain accurate information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI.

[0076] The reception desk can receive specific circumstances and background information entered by the user. For example, the reception desk can receive specific circumstances and background information entered by the user. For example, the reception desk can receive details of the incident and information about those involved entered by the user. By receiving the user's specific circumstances and background information, it is possible to provide more appropriate guidelines. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0077] 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 respond immediately. For example, if the user is relaxed, the reception desk will receive the question at the normal time. For example, if the user is in a hurry, the reception desk will prioritize receiving the question and process it quickly. This allows for a more appropriate response by adjusting the timing of question reception 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.

[0078] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk may prioritize suggesting question formats that the user has frequently used in the past. For example, the reception desk may suggest a reception method suitable for a specific time of day based on the user's past question history. For example, the reception desk may analyze the content of the user's past questions and automatically suggest related questions. In this way, the reception desk can provide the optimal reception method by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0079] The reception desk can filter inquiries based on the urgency of the user's current legal problem. For example, the reception desk may prioritize and respond quickly to high-urgency legal problems. For example, it may process low-urgency legal problems in the normal order of receipt. For example, it may receive moderately urgent legal problems at an appropriate time and process them quickly. This allows for a rapid response by filtering based on the urgency of the legal problem. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0080] The reception desk can estimate the user's emotions and determine the priority of questions to answer based on the estimated emotions. For example, if the user is stressed, the reception desk will set the question priority higher. If the user is relaxed, the reception desk will set the question priority to normal. If the user is in a hurry, the reception desk will set the question priority to highest priority. This allows for more appropriate responses by determining the priority of questions based on 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.

[0081] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving legal issues related to that region. For example, the reception desk will automatically suggest region-specific legal issues based on the user's location. For example, if the user is on the move, the reception desk will receive the most relevant legal issues based on the user's current location. This allows the reception desk to prioritize receiving questions that are highly relevant by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0082] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can automatically suggest relevant legal issues from the user's social media posts. For example, the reception desk can analyze the user's social media activity and suggest the most appropriate question format. For example, the reception desk can prioritize questions based on the user's social media follower count and influence. This allows the reception desk to accept relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will use a simple and easy-to-understand presentation. For example, if the user is relaxed, the analysis unit will provide detailed analysis results. For example, if the user is in a hurry, the analysis unit will provide concise analysis results that get straight to the point. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the legal data during the analysis. For example, the analysis unit performs a detailed analysis on legal data of high importance. For example, the analysis unit performs a concise analysis on legal data of low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on legal data of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the legal data, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0085] The analysis unit can apply different analysis algorithms depending on the legal category during analysis. For example, the analysis unit applies a copyright law-specific algorithm for analysis related to copyright law. For example, the analysis unit applies a patent law-specific algorithm for analysis related to patent law. For example, the analysis unit applies a trademark law-specific algorithm for analysis related to trademark law. By applying different analysis algorithms depending on the legal category, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit will provide a detailed analysis result. For example, if the user is stressed, the analysis unit will provide a simple and easy-to-understand analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The analysis unit can determine the priority of analysis based on the submission date of legal data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent legal data. For example, the analysis unit may analyze older legal data with normal priority. For example, the analysis unit may analyze legal data submitted at a moderate time with appropriate priority. This allows for prioritization of the analysis of the latest information by determining the priority of analysis based on the submission date of legal data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the legal data during analysis. For example, the analysis unit prioritizes analyzing the legal data most relevant to the user's question. For example, the analysis unit analyzes less relevant legal data in the normal order. For example, the analysis unit analyzes moderately relevant legal data in an appropriate order. In this way, by adjusting the order of analysis based on the relevance of the legal data, highly relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0089] The generation unit can estimate the user's emotions and adjust the way the guidelines are presented based on those emotions. For example, if the user is stressed, the generation unit provides simple and easy-to-understand guidelines. If the user is relaxed, the generation unit provides detailed guidelines. If the user is in a hurry, the generation unit provides concise and to-the-point guidelines. By adjusting the way the guidelines are presented based on the user's emotions, more appropriate guidelines can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The generation unit can adjust the level of detail of the guidelines based on the importance of the legal issue during guideline generation. For example, the generation unit provides detailed guidelines for highly important legal issues. For example, the generation unit provides concise guidelines for less important legal issues. For example, the generation unit provides guidelines with a moderate level of detail for legal issues of moderate importance. In this way, appropriate guidelines can be provided by adjusting the level of detail of the guidelines based on the importance of the legal issue. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0091] The generation unit can apply different generation algorithms depending on the legal category when generating guidelines. For example, the generation unit applies a generation algorithm specifically for copyright law to guidelines concerning copyright law. For example, the generation unit applies a generation algorithm specifically for patent law to guidelines concerning patent law. For example, the generation unit applies a generation algorithm specifically for trademark law to guidelines concerning trademark law. By applying different generation algorithms depending on the legal category, more accurate guidelines can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0092] The generation unit can estimate the user's emotions and adjust the length of the guidelines based on the estimated emotions. For example, if the user is in a hurry, the generation unit will provide short, concise guidelines. For example, if the user is relaxed, the generation unit will provide detailed guidelines. For example, if the user is stressed, the generation unit will provide simple and easy-to-understand guidelines. By adjusting the length of the guidelines based on the user's emotions, more appropriate guidelines can be provided. 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 generation unit can determine the priority of guidelines based on when the legal issues were submitted during guideline generation. For example, the generation unit provides guidelines preferentially for the most recent legal issues. For example, the generation unit provides guidelines with normal priority for older legal issues. For example, the generation unit provides guidelines with appropriate priority for legal issues that were submitted at a moderate time. This ensures that the most up-to-date information is provided preferentially by determining the priority of guidelines based on when the legal issues were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0094] The generation unit can adjust the order of guidelines based on the relevance of the legal issues when generating them. For example, the generation unit prioritizes reflecting the legal issues most relevant to the user's question in the guidelines. For example, the generation unit reflects less relevant legal issues in the guidelines in the normal order. For example, the generation unit reflects moderately relevant legal issues in the guidelines in an appropriate order. This allows for the priority provision of highly relevant information by adjusting the order of guidelines based on the relevance of the legal issues. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0095] The review function can estimate the user's emotions and adjust the way the review is expressed based on those emotions. For example, if the user is stressed, the review function will provide a simple and easy-to-understand review. If the user is relaxed, the review function will provide a detailed review. If the user is in a hurry, the review function will provide a concise review that gets straight to the point. This allows for the provision of more appropriate reviews by adjusting the way the review is expressed based on 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.

[0096] The review unit can adjust the level of detail of a review based on the importance of the legal issue. For example, the review unit provides a detailed review for highly important legal issues. For example, the review unit provides a concise review for less important legal issues. For example, the review unit provides a review with a moderate level of detail for legal issues of moderate importance. This allows for the provision of appropriate reviews by adjusting the level of detail based on the importance of the legal issue. Some or all of the above processing in the review unit may be performed using AI, for example, or without AI.

[0097] The review function can estimate the user's emotions and adjust the length of the review based on those emotions. For example, if the user is in a hurry, the review function will provide a short, to-the-point review. If the user is relaxed, the review function will provide a detailed review. If the user is stressed, the review function will provide a simple and easy-to-understand review. By adjusting the length of the review based on the user's emotions, a more appropriate review 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.

[0098] The review department can determine the priority of reviews based on when the legal issues were submitted. For example, the review department may prioritize the review of the most recent legal issues. For example, it may prioritize the review of older legal issues. For example, it may prioritize the review of legal issues submitted at a moderate time. This allows for prioritization of the review of the most recent information by determining the priority of reviews based on when the legal issues were submitted. Some or all of the above processing in the review department may be performed using AI, for example, or not using AI.

[0099] The review unit can adjust the order of reviews based on the relevance of the legal issues during the review process. For example, the review unit may prioritize reviewing legal issues that are most relevant to the user's question. For example, the review unit may review less relevant legal issues in the normal order. For example, the review unit may review moderately relevant legal issues in an appropriate order. This allows for prioritizing the review of highly relevant information by adjusting the order of reviews based on the relevance of the legal issues. Some or all of the above processing in the review unit may be performed using AI, for example, or not using AI.

[0100] The service provider can estimate the user's emotions and adjust the way the guidelines are delivered based on those emotions. For example, if the user is stressed, the service provider will use a simple and easy-to-understand delivery method. For example, if the user is relaxed, the service provider will use a detailed delivery method. For example, if the user is in a hurry, the service provider will use a concise and to-the-point delivery method. By adjusting the way the guidelines are delivered based on the user's emotions, more appropriate guidelines 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.

[0101] The service provider can select the most suitable service method when providing guidelines by referring to the user's past legal problem history. For example, the service provider may prioritize using service methods previously used by the user. For example, the service provider may propose the most suitable service method based on the user's past legal problem history. For example, the service provider may analyze the user's past usage history and select the most efficient service method. This allows the service provider to select the most suitable service method by referring to the user's past legal problem history. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI.

[0102] The service provider can estimate the user's emotions and adjust the guideline delivery procedure based on the estimated emotions. For example, if the user is stressed, the service provider will use a simple and easy-to-understand delivery procedure. For example, if the user is relaxed, the service provider will use a detailed delivery procedure. For example, if the user is in a hurry, the service provider will use a concise delivery procedure that gets straight to the point. This allows for the provision of more appropriate guidelines by adjusting the guideline delivery procedure based on 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.

[0103] The service provider can select the optimal delivery method when providing guidelines, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will use a delivery method adapted to the screen size. For example, if the user is using a tablet, the service provider will use a delivery method optimized for a larger screen. For example, if the user is using a personal computer, the service provider will provide detailed information. This allows the service provider to select the optimal delivery method by taking into account the user's device information. Some or all of the above processing by the service provider may be performed using AI, for example, or without using AI.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception desk can automatically search for relevant past court precedents based on the user's input and present them to the user. For example, if a user asks a question about a specific legal issue, the reception desk will search for relevant past court precedents and present them to the user. This allows the user to understand more specific ways of responding by referring to past precedents. The reception desk can also automatically search for relevant legal documents and guidelines based on the user's input and present them to the user. For example, if a user asks a question about a specific legal document or guideline, the reception desk will search for that document or guideline and present it to the user. This allows the user to understand more specific ways of responding by referring to relevant legal documents and guidelines. Furthermore, the reception desk can also automatically search for and present the opinions and explanations of relevant legal experts based on the user's input. For example, if a user asks a question about a specific legal issue, the reception desk will search for opinions and explanations of relevant legal experts and present them to the user. This allows the user to understand more specific ways of responding by referring to the opinions and explanations of legal experts.

[0106] The analysis unit can evaluate the reliability of the information it retrieves from relevant legal databases based on user input. For example, the analysis unit checks the source and update date of the information retrieved from legal databases and prioritizes the retrieval of highly reliable information. This improves the reliability of the guidelines provided to users. The analysis unit can also retrieve information from multiple legal databases and check the degree of consistency to evaluate the reliability of the retrieved information. For example, the analysis unit checks whether the information retrieved from multiple legal databases is consistent and prioritizes the use of consistent information. This improves the reliability of the guidelines provided to users. Furthermore, the analysis unit can consult with legal experts to evaluate the reliability of the retrieved information. For example, the analysis unit requests legal experts to review the retrieved information and selects highly reliable information. This improves the reliability of the guidelines provided to users.

[0107] The generation unit can create guidelines based on user input, adjusting the presentation of the guidelines according to the user's level of understanding. For example, for users with limited legal knowledge, the generation unit uses a simple and easy-to-understand presentation. This makes the guidelines easier for users with limited legal knowledge to understand. The generation unit can also provide detailed information to users with extensive legal knowledge. For example, for users with extensive legal knowledge, the generation unit provides guidelines that include specialized terminology and detailed explanations. This makes the guidelines easier for users with extensive legal knowledge to understand. Furthermore, the generation unit can adjust the method of providing the guidelines according to the user's level of understanding. For example, for users with limited legal knowledge, the generation unit provides guidelines using diagrams and videos. This makes the guidelines easier for users with limited legal knowledge to understand.

[0108] The review team can take user feedback into consideration when reviewing guidelines created by the generation AI. For example, the review team can collect user feedback on the provided guidelines and use it to improve them. This allows them to provide guidelines that are better suited to user needs. The review team can also adjust the wording and content of the guidelines based on user feedback. For example, if a user finds the content of the guidelines difficult to understand, the review team can simplify the wording. This makes the guidelines easier for users to understand. Furthermore, the review team can adjust how the guidelines are delivered based on user feedback. For example, if a user is dissatisfied with how the guidelines are delivered, the review team can improve the delivery method. This makes the guidelines easier for users to receive.

[0109] The service provider can select the most suitable delivery method for users when providing guidelines, depending on the user's device and environment. For example, if the user is using a smartphone, the service provider will use a delivery method optimized for the screen size. This makes the guidelines easier for users to view on their smartphones. Similarly, if the user is using a tablet, the service provider can use a delivery method optimized for larger screens. This also makes the guidelines easier for users to view on their tablets. Furthermore, if the user is using a personal computer, the service provider can provide more detailed information. This also makes the guidelines easier for users to view on their personal computers.

[0110] The reception desk can estimate the user's emotions and adjust the timing of question reception based on those estimates. For example, if the user is stressed, the reception desk will quickly receive the question and respond immediately. This helps reduce the user's stress. If the user is relaxed, the reception desk can receive the question at the normal time. This allows the user to ask questions in a relaxed state. Furthermore, if the user is in a hurry, the reception desk will prioritize receiving the question and process it quickly. This ensures that users receive prompt assistance even when they are in a hurry.

[0111] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, the analysis unit uses a simple and easy-to-understand presentation. This allows the user to understand the analysis results while reducing their stress. If the user is relaxed, the analysis unit can provide detailed results. This allows the user to understand the detailed results in a relaxed state. Furthermore, if the user is in a hurry, the analysis unit can provide concise results that get straight to the point. This allows the user to quickly understand the analysis results even when they are in a hurry.

[0112] The generation unit can estimate the user's emotions and adjust the way the guidelines are presented based on those emotions. For example, if the user is stressed, the generation unit can provide simple and easy-to-understand guidelines, allowing the user to understand the guidelines while reducing stress. If the user is relaxed, the generation unit can provide detailed guidelines, allowing the user to understand the detailed guidelines in a relaxed state. Furthermore, if the user is in a hurry, the generation unit can provide concise guidelines that get straight to the point, allowing the user to quickly understand the guidelines even in a rushed situation.

[0113] The review function can estimate the user's emotions and adjust the way the review is presented based on those emotions. For example, if a user is stressed, the review function can provide a simple and easy-to-understand review, allowing the user to understand the review while reducing stress. If a user is relaxed, the review function can provide a detailed review, allowing the user to understand the detailed review in a relaxed state. Furthermore, if a user is in a hurry, the review function can provide a concise review that gets straight to the point, allowing the user to quickly understand the review even when they are in a hurry.

[0114] The service provider can estimate the user's emotions and adjust the way the guidelines are delivered based on those estimates. For example, if the user is stressed, the service provider can use a simple and easy-to-understand delivery method. This allows the user to receive the guidelines while reducing their stress. If the user is relaxed, the service provider can use a detailed delivery method. This allows the user to receive the guidelines in a relaxed state. Furthermore, if the user is in a hurry, the service provider can use a concise and to-the-point delivery method. This allows the user to receive the guidelines quickly even when they are in a hurry.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives legal questions and issues from users. For example, it can receive specific situations and background information entered by the user. Step 2: The analysis unit analyzes the information received by the reception unit and retrieves information from relevant legal databases. For example, it may use a generation AI to retrieve information from legal databases. Step 3: The generation unit creates initial guidelines based on the information obtained by the analysis unit. For example, the generation AI is used to create the initial guidelines. Step 4: The review team reviews the guidelines created by the generation team and makes revisions or additions. For example, legal experts review the guidelines created by the generation AI and make revisions or additions as needed. Step 5: The providing department provides the user with the revised guidelines from the review department. For example, they provide the user with the revised guidelines.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, review unit, and 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 reception device 38 of the smart device 14 and receives legal questions and problems from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and retrieves information from the relevant legal database. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates initial guidelines based on the information retrieved by the analysis unit. The review unit is implemented by the specific processing unit 290 of the data processing unit 12 and reviews the guidelines created by the generation unit and makes corrections or additions. The provision unit is implemented by the output device 40 of the smart device 14 and provides the user with the guidelines corrected by the review unit. 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.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, review unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives legal questions and issues from the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and retrieves information from the relevant legal database. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates initial guidelines based on the information retrieved by the analysis unit. The review unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and reviews the guidelines created by the generation unit and makes corrections or additions. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the user with the guidelines corrected by the review unit. 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.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, review unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives legal questions and problems 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 information received by the reception unit and retrieves information from the relevant legal database. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates initial guidelines based on the information retrieved by the analysis unit. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reviews the guidelines created by the generation unit and makes corrections or additions. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the user with the guidelines corrected by the review unit. 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.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, review unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives legal questions and problems from the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and retrieves information from the relevant legal database. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates initial guidelines based on the information retrieved by the analysis unit. The review unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and reviews the guidelines created by the generation unit and makes corrections or additions. The provision unit is implemented, for example, by the speaker 240 of the robot 414 and provides the user with the guidelines corrected by the review unit. 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A reception desk that handles legal questions and issues from users, An analysis unit analyzes the information received by the aforementioned reception unit and obtains information from relevant legal databases, A generation unit that creates initial guidelines based on the information obtained by the analysis unit, A review unit reviews the guidelines created by the generation unit and makes corrections or additions, The system includes a provisioning unit that provides the user with the guidelines revised by the reviewing unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Information is retrieved from legal databases using a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generative AI creates initial guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned review section, Legal experts review the guidelines generated by AI and make revisions or additions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide users with revised guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system accepts specific details and background information entered by the user. 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 a question, filtering is performed based on the urgency of the user's current legal issue. 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, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the legal data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the legal category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the timing of submission of legal data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the legal data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the user's emotions and adjust the wording of the guidelines 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 guidelines, adjust the level of detail based on the importance of the legal issue. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating guidelines, different generation algorithms are applied depending on the legal category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the user's emotions and adjusts the length of the guidelines based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating guidelines, prioritize the guidelines based on when the legal issues were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating guidelines, adjust the order of the guidelines based on the relevance of the legal issues. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned review section, It estimates user sentiment and adjusts the way reviews are written based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned review section, During the review process, adjust the level of detail based on the importance of the legal issues. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned review section, It estimates the user's sentiment and adjusts the length of the review based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned review section, During the review process, the priority of the review will be determined based on when the legal issues were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned review section, During the review process, the order of reviews will be adjusted based on the relevance of the legal issues. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We estimate user sentiment and adjust how guidelines are provided based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing guidelines, we will refer to the user's past legal history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, We estimate user sentiment and adjust the guideline delivery procedures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing guidelines, the optimal method of delivery will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 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 questions and issues from users, An analysis unit analyzes the information received by the aforementioned reception unit and obtains information from relevant legal databases, A generation unit that creates initial guidelines based on the information obtained by the analysis unit, A review unit reviews the guidelines created by the generation unit and makes corrections or additions, The system includes a provisioning unit that provides the user with the guidelines revised by the reviewing unit. A system characterized by the following features.

2. The aforementioned analysis unit, Information is retrieved from legal databases using a generation AI. The system according to feature 1.

3. The generating unit is Generative AI creates initial guidelines. The system according to feature 1.

4. The aforementioned review section, Legal experts review the guidelines generated by AI and make revisions or additions. The system according to feature 1.

5. The aforementioned supply unit is, Provide users with revised guidelines. The system according to feature 1.

6. The aforementioned reception unit is The system accepts specific details and background information entered by the user. 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 a question, filtering is performed based on the urgency of the user's current legal issue. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to ask based on those estimated emotions. The system according to feature 1.

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