system

Abstract

The system according to this embodiment aims to evaluate and analyze legal risks in real time and to warn of future legal risks in advance. [Solution] The system according to this embodiment comprises a reception unit, an evaluation unit, an evolution unit, and a warning unit. The reception unit receives input for legal consultation matters. The evaluation unit evaluates and analyzes legal risks based on the information entered by the reception unit. The evolution unit continuously evolves through dialogue with legal personnel based on the evaluation results obtained by the evaluation unit. The warning unit provides advance warnings about future legal risks based on the information evolved by the evolution 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. <00000l0>

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 the evaluation of legal risks and preventive legal responses are not performed in real time, and it takes time to consult a legal professional.

[0005] The system according to the embodiment aims to evaluate and analyze legal risks in real time and give a warning of future legal risks in advance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, an evolution unit, and a warning unit. The reception unit receives input for legal consultation matters. The evaluation unit evaluates and analyzes legal risks based on the information entered by the reception unit. The evolution unit continuously evolves through dialogue with legal personnel based on the evaluation results obtained by the evaluation unit. The warning unit provides advance warnings about future legal risks based on the information evolved by the evolution unit. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate and analyze legal risks in real time and provide advance warnings of future legal risks. [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 multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The legal assistant system according to an embodiment of the present invention is a system that learns from past cases and the latest legal revisions and evaluates and analyzes legal risks in real time. When a user inputs a legal consultation matter, the generating AI evaluates and analyzes the legal risks based on past cases, the latest legal revisions, company-specific rules, and industry-specific regulations. Furthermore, the generating AI continuously evolves through dialogue with legal personnel and incorporates predictive analysis functions to warn of future legal risks in advance. This allows users to easily seek legal advice without the need to consult with legal personnel. For example, if a user inputs a question such as, "Is this contract legally sound?", the generating AI refers to similar past cases, considers the latest legal revisions, and determines whether the contract contains any legal risks. The generating AI learns new rules and regulations through dialogue with legal personnel and can reflect them in future legal consultations. The generating AI also predicts future legal revisions and industry trends and issues warnings if the contract contains future legal risks. This allows users, even those with no legal knowledge, to quickly determine whether a matter is legal by using the generating AI. Furthermore, it improves the efficiency of legal staff's work and reduces legal risks. As a result, the legal assistant system can handle everything from evaluating and analyzing legal risks to evolving and issuing warnings, simply by having the user input legal consultation details.

[0029] The legal assistant system according to this embodiment comprises a reception unit, an evaluation unit, an evolution unit, and a warning unit. The reception unit receives input from the user regarding legal consultation matters. Legal consultation matters include, but are not limited to, contract review, legal advice, and litigation support. The reception unit allows the user to input questions such as, "Is this contract legally sound?" The evaluation unit evaluates and analyzes legal risks based on the information entered by the reception unit. The evaluation unit evaluates and analyzes legal risks based on, for example, past cases, the latest legal amendments, company-specific rules, and industry-specific regulations. The evaluation unit determines whether there are any legal risks in the content of the contract, for example, by referring to similar past cases and considering the latest legal amendments. The evolution unit continuously evolves through dialogue with legal personnel based on the evaluation results obtained by the evaluation unit. The evolution unit can learn new rules and regulations through dialogue with legal personnel and reflect them in future legal consultations. The warning unit provides advance warnings of future legal risks based on the information evolved by the evolution unit. The warning function, for example, predicts future legal changes and industry trends, and issues warnings if the contract contains future legal risks. This allows the legal assistant system to handle everything from legal risk assessment and analysis to improvement and warnings, simply by the user inputting legal consultation details.

[0030] The reception desk receives user input for legal consultations. These consultations include, but are not limited to, contract review, legal advice, and litigation support. For example, users can input questions such as, "Is this contract legally sound?" Specifically, the reception desk provides an interface for receiving user-inputted legal consultations. This interface is designed to allow users to easily input questions and consultation details and includes text input fields and multiple-choice question items. The information entered by users is stored in the system's database and used for subsequent processing. Furthermore, the reception desk has a function to automatically classify the user-inputted information and assign it to the appropriate category. For example, questions regarding contract review are classified into the "Contract Category," and questions regarding legal advice are classified into the "Advice Category." This allows subsequent evaluation and development departments to process the information efficiently. The reception desk also includes a checking function to verify the accuracy and completeness of the information entered by users. For example, if there are unclear or incomplete points in the input, a message prompting the user to enter additional information is displayed. This allows the reception desk to assist users in entering accurate and complete information, improving the overall accuracy and reliability of the system.

[0031] The evaluation department assesses and analyzes legal risks based on information entered by the reception department. For example, the evaluation department assesses and analyzes legal risks based on past cases, recent legal amendments, company-specific rules, and industry-specific regulations. Specifically, the evaluation department analyzes the legal consultation matters entered by users and collects relevant legal information. This includes information sources such as legal databases, case law databases, and industry guidelines. The evaluation department extracts relevant data from these information sources and evaluates legal risks in light of the user's consultation content. For example, in the case of a consultation regarding contract review, the evaluation department analyzes the contract clauses, refers to similar past cases and recent legal amendments, and determines whether there are any legal risks in the contract's content. Furthermore, the evaluation department utilizes AI technology to assess legal risks. Specifically, it uses natural language processing technology to analyze the user's consultation content and automatically extracts relevant legal information. In addition, it uses machine learning algorithms to learn from past case data and improve the accuracy of risk assessments for new consultation content. This allows the evaluation department to provide quick and accurate legal risk assessments for user consultations.

[0032] The Evolution Department continuously evolves through dialogue with legal professionals based on evaluation results obtained by the Evaluation Department. For example, the Evolution Department can learn new rules and regulations through dialogue with legal professionals and reflect them in subsequent legal consultations. Specifically, the Evolution Department presents the legal risk assessment results provided by the Evaluation Department to legal professionals and collects feedback from them. This feedback includes the accuracy of the assessment results, additional legal information, and practical insights. Based on this feedback, the Evolution Department updates the system's knowledge base and reflects it in subsequent legal consultations. For example, it learns information on new legal revisions and industry trends to improve the system's evaluation accuracy. In addition, the Evolution Department understands areas for improvement in system usability and functionality through dialogue with legal professionals and continuously improves the system. As a result, the Evolution Department keeps the system's knowledge base constantly up-to-date and can provide users with high-quality legal assistant services. Furthermore, the Evolution Department is equipped with a function that uses AI technology to analyze the content of dialogues with legal professionals and automatically learns from it. This allows the evolution department to efficiently incorporate the expertise of legal personnel and accelerate the evolution of the system.

[0033] The warning unit proactively warns of future legal risks based on information evolved by the evolution unit. For example, the warning unit predicts future legal changes and industry trends, and issues warnings if the content of a contract contains future legal risks. Specifically, the warning unit predicts future legal risks based on new rules, regulations, and industry trends learned by the evolution unit. For example, based on the latest legal change information learned by the evolution unit, if a clause in a contract may involve future legal risks, the warning unit will issue a warning to the user. The warning unit also utilizes AI technology to predict future legal risks. Specifically, it uses machine learning algorithms to analyze past data and predict future legal changes and industry trends. This allows the warning unit to issue early warnings to users and prevent legal risks before they occur. Furthermore, the warning unit also has the function of suggesting specific countermeasures to users. For example, it can present proposed revisions to contracts to comply with future legal changes or specific action plans to respond to industry trends. In this way, the warning unit helps users take appropriate measures against future legal risks and minimizes them.

[0034] The legal assistant system according to this embodiment further includes a learning unit that learns new rules and regulations through dialogue with legal personnel. The learning unit learns new rules and regulations through dialogue with legal personnel. For example, the learning unit can learn new rules and regulations through dialogue with legal personnel and reflect them in subsequent legal consultations. For example, the learning unit can learn specific laws and industry regulations to improve the accuracy of the system. As a result, the legal assistant system improves its accuracy by learning new rules and regulations through dialogue with legal personnel.

[0035] The legal assistant system according to this embodiment further includes a prediction unit that predicts future legal revisions and industry trends. The prediction unit predicts future legal revisions and industry trends. For example, the prediction unit predicts future legal revisions and industry trends and issues a warning if there are future legal risks in the content of a contract. For example, the prediction unit predicts revisions to specific laws and industry trends and warns of legal risks in advance. In this way, the legal assistant system can warn of future legal risks in advance by predicting future legal revisions and industry trends.

[0036] The evaluation department can assess and analyze legal risks based on past cases, the latest legal revisions, company-specific rules, and industry-specific regulations. For example, the evaluation department can refer to past cases and consider the latest legal revisions to determine whether there are any legal risks in the content of a contract. The evaluation department can also assess and analyze legal risks based on company-specific rules and industry-specific regulations. The evaluation department can also assess and analyze legal risks based on past cases and the latest legal revisions. This allows the evaluation department to conduct highly accurate assessments by assessing and analyzing legal risks based on past cases, the latest legal revisions, company-specific rules, and industry-specific regulations.

[0037] The warning section can predict future legal changes and industry trends and issue warnings about legal risks in advance. For example, the warning section can predict future legal changes and industry trends and issue warnings if there are future legal risks in the content of a contract. For example, the warning section can predict changes in specific laws or industry trends and issue warnings about legal risks in advance. For example, the warning section can predict future legal changes and industry trends and issue warnings about legal risks in advance. This enables preventative legal measures by predicting future legal changes and industry trends and issuing warnings about legal risks in advance.

[0038] The reception desk can analyze a user's past legal consultation history and suggest the optimal input format. For example, the reception desk can automatically display as suggestions legal consultation topics that the user has frequently entered in the past. For example, the reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest legal consultation topics that will be used during specific time periods based on the user's past legal consultation history. In this way, the reception desk improves input efficiency by analyzing the user's past legal consultation history and suggesting the optimal input format.

[0039] The reception system can filter input content based on the user's industry and company characteristics when legal consultations are entered. For example, the reception system can prioritize displaying highly relevant legal consultations based on the characteristics of the user's industry. The reception system can also filter legal consultations related to specific rules or regulations based on the characteristics of the user's company. The reception system can also simplify input content by excluding unnecessary information based on the user's industry and company characteristics. As a result, the reception system can prioritize obtaining highly relevant information by filtering input content based on the user's industry and company characteristics.

[0040] The reception desk can prioritize retrieving highly relevant information when users input legal consultation requests, taking into account their geographical location. For example, if a user is in a specific region, the reception desk will prioritize displaying legal consultation requests related to that region. The reception desk can also prioritize retrieving information related to region-specific rules and regulations based on the user's geographical location. The reception desk can also prioritize collaboration with local legal professionals, taking into account the user's geographical location. As a result, the reception desk can respond to region-specific legal consultations by prioritizing the retrieval of highly relevant information based on the user's geographical location.

[0041] The reception desk can analyze the user's social media activity and obtain relevant information when they input legal consultation details. For example, the reception desk can extract keywords related to the legal consultation details from the user's social media activity and complete the input content. For example, the reception desk can also analyze the user's social media activity and suggest relevant legal consultation details. For example, the reception desk can automatically complete the input content of the legal consultation details based on the user's social media activity. In this way, the reception desk can complete the input content of the legal consultation details by analyzing the user's social media activity and obtaining relevant information.

[0042] The evaluation unit can adjust the level of detail in its legal risk assessment based on the success rate of similar past cases. For example, if the success rate of similar past cases is high, the evaluation unit will perform a detailed assessment. For example, if the success rate of similar past cases is low, the evaluation unit can also perform a simplified assessment. The evaluation unit can also dynamically adjust the level of detail in its assessment based on the success rate of similar past cases. This improves the accuracy of the assessment by allowing the evaluation unit to adjust the level of detail based on the success rate of similar past cases.

[0043] The evaluation department can apply different evaluation algorithms depending on the characteristics of the company and the industry when assessing legal risks. For example, the evaluation department can apply a specific evaluation algorithm based on the characteristics of the company. The evaluation department can also apply different evaluation algorithms based on the characteristics of the industry. For example, the evaluation department can select the optimal evaluation algorithm depending on the characteristics of the company and the industry. As a result, the evaluation department can improve the accuracy of its evaluations by applying different evaluation algorithms depending on the characteristics of the company and the industry.

[0044] The evaluation department can prioritize evaluations based on the timing of user submissions when assessing legal risks. For example, the evaluation department can prioritize evaluations based on when the user submitted the information. The evaluation department can also prioritize evaluations of legal consultations submitted earlier. For example, the evaluation department can dynamically adjust evaluation priorities based on submission timing. This allows the evaluation department to conduct appropriate evaluations according to the timing of user submissions by prioritizing evaluations based on the submission timing.

[0045] The assessment unit can improve the accuracy of its assessments by referring to relevant legal literature when evaluating legal risks. For example, the assessment unit can improve the accuracy of its assessments by referring to relevant legal literature. The assessment unit can also adjust the level of detail in its assessments based on legal literature, for example. The assessment unit can also improve the accuracy of its assessments by dynamically referencing relevant legal literature, for example. This allows the assessment unit to perform more accurate assessments by improving the accuracy of its assessments by referring to relevant legal literature.

[0046] The evolution unit can analyze the history of conversations with legal personnel during evolution to select the optimal evolution method. For example, the evolution unit can analyze the history of conversations with legal personnel and select the optimal evolution method. The evolution unit can also optimize the evolution process based on the conversation history. For example, the evolution unit can refer to the history of conversations with legal personnel and dynamically adjust the evolution method. As a result, the evolution unit improves the accuracy of evolution by analyzing the history of conversations with legal personnel and selecting the optimal evolution method.

[0047] The evolution unit can customize the means of evolution based on the characteristics of the company and the industry during the evolution process. For example, the evolution unit can customize the means of evolution based on the characteristics of the company. The evolution unit can also adjust the means of evolution based on the characteristics of the industry. For example, the evolution unit can optimize the means of evolution according to the characteristics of the company and the industry. As a result, the evolution unit improves the accuracy of evolution by customizing the means of evolution based on the characteristics of the company and the industry.

[0048] The evolution unit can select the optimal evolution method during evolution, taking into account the user's geographical location information. For example, the evolution unit selects the optimal evolution method based on the user's geographical location information. The evolution unit can also optimize the evolution process, for example, by taking geographical location information into account. The evolution unit can also adjust the means of evolution according to the user's geographical location information. As a result, by selecting the optimal evolution method while taking the user's geographical location information into account, the evolution unit enables region-specific evolution.

[0049] The evolution unit can analyze the user's social media activity during evolution and propose evolutionary methods. For example, the evolution unit can analyze the user's social media activity and propose evolutionary methods. The evolution unit can also optimize the evolutionary process based on social media activity. For example, the evolution unit can refer to the user's social media activity and dynamically adjust the evolutionary method. This improves the accuracy of evolution by allowing the evolution unit to analyze the user's social media activity and propose evolutionary methods.

[0050] The warning unit can adjust the level of detail of a warning by referring to past warning history when an warning is issued. For example, the warning unit can refer to past warning history and display a more detailed warning. The warning unit can also adjust the level of detail of a warning based on past warning history. For example, the warning unit can dynamically refer to past warning history to improve the accuracy of warnings. As a result, the warning unit improves the accuracy of warnings by adjusting the level of detail of warnings by referring to past warning history.

[0051] The warning unit can apply different warning algorithms depending on the characteristics of the company or industry when a warning is issued. For example, the warning unit can apply a specific warning algorithm based on the characteristics of the company. The warning unit can also apply different warning algorithms based on the characteristics of the industry. For example, the warning unit can select the optimal warning algorithm depending on the characteristics of the company or industry. As a result, the accuracy of warnings is improved by the warning unit applying different warning algorithms depending on the characteristics of the company or industry.

[0052] The warning system can adjust the importance of a warning based on when the user submits it. For example, the warning system can adjust the importance of a warning based on when the user submits it. For example, the warning system can set a higher importance for legal consultation matters that are submitted earlier. For example, the warning system can dynamically adjust the importance of a warning based on when it is submitted. This allows the warning system to provide appropriate warnings based on when the user submits the information.

[0053] The warning unit can improve the accuracy of its warnings by referring to relevant legal documents when issuing a warning. For example, the warning unit can improve the accuracy of its warnings by referring to relevant legal documents. The warning unit can also adjust the level of detail of its warnings based on legal documents. For example, the warning unit can dynamically refer to relevant legal documents to improve the accuracy of its warnings. This allows the warning unit to issue more accurate warnings by improving the accuracy of its warnings by referring to relevant legal documents.

[0054] The learning unit can optimize the learning algorithm by referring to past training data during training. For example, the learning unit can optimize the learning algorithm by referring to past training data. The learning unit can also adjust the parameters of the learning algorithm based on the training data. Furthermore, the learning unit can dynamically refer to past training data to improve the accuracy of the learning algorithm. This allows the learning unit to improve the accuracy of learning by optimizing the learning algorithm by referring to past training data.

[0055] The learning unit can weight the learning data based on the submission date of legal consultation matters during the learning process. For example, the learning unit can weight the learning data based on the submission date of legal consultation matters. The learning unit can also, for example, set a higher weight for legal consultation matters that are submitted earlier. The learning unit can also, for example, dynamically adjust the weighting of the learning data based on the submission date. This allows the learning unit to perform appropriate learning according to the submission date of legal consultation matters by weighting the learning data based on the submission date.

[0056] The prediction unit can optimize its prediction algorithm by referring to past prediction data during the prediction process. For example, the prediction unit can optimize the prediction algorithm by referring to past prediction data. The prediction unit can also adjust the parameters of the prediction algorithm based on the prediction data. For example, the prediction unit can dynamically refer to past prediction data to improve the accuracy of the prediction algorithm. As a result, the prediction unit improves the accuracy of its predictions by optimizing the prediction algorithm by referring to past prediction data.

[0057] The prediction unit can weight the prediction data based on the submission timing of legal consultation matters during the prediction process. For example, the prediction unit can weight the prediction data based on the submission timing of legal consultation matters. The prediction unit can also, for example, set a higher weight for prediction data for legal consultation matters submitted earlier. The prediction unit can also, for example, dynamically adjust the weighting of the prediction data based on the submission timing. As a result, by weighting the prediction data based on the submission timing of legal consultation matters, the prediction unit can make appropriate predictions according to the submission timing.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The legal assistant system can analyze a user's past legal consultation history and suggest the optimal input format. For example, it can automatically display legal consultation topics that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest legal consultation topics that will be used during specific time periods based on the user's past legal consultation history. In this way, by analyzing the user's past legal consultation history and suggesting the optimal input format, input efficiency is improved.

[0060] The legal assistant system can filter input content based on the user's industry and company characteristics when entering legal consultation matters. For example, it can prioritize displaying highly relevant legal consultation matters based on the characteristics of the user's industry. It can also filter legal consultation matters related to specific rules and regulations based on the characteristics of the user's company. Furthermore, it can simplify input content by excluding unnecessary information based on the user's industry and company characteristics. In this way, by filtering input content based on the user's industry and company characteristics, highly relevant information can be prioritized.

[0061] The legal assistant system prioritizes retrieving highly relevant information when users input legal consultation requests, taking into account their geographical location. For example, if a user is in a specific region, legal consultation requests related to that region can be displayed preferentially. It can also prioritize retrieving information related to region-specific rules and regulations based on the user's geographical location. Furthermore, it can prioritize collaboration with local legal professionals, taking the user's geographical location into consideration. This allows the system to address region-specific legal consultations by prioritizing the retrieval of highly relevant information based on the user's geographical location.

[0062] The legal assistant system can analyze the user's social media activity and retrieve relevant information when they input legal consultation matters. For example, it can extract keywords related to the legal consultation matter from the user's social media activity and complete the input content. It can also analyze the user's social media activity and suggest relevant legal consultation matters. Furthermore, it can automatically complete the input content of the legal consultation matter based on the user's social media activity. In this way, by analyzing the user's social media activity and retrieving relevant information, the system can complete the input content of the legal consultation matter.

[0063] The legal assistant system can adjust the level of detail in its legal risk assessment based on the success rate of similar past cases. For example, if the success rate of similar past cases is high, a detailed assessment can be performed. Conversely, if the success rate of similar past cases is low, a simplified assessment can be performed. Furthermore, the level of detail in the assessment can be dynamically adjusted based on the success rate of similar past cases. This improves the accuracy of the assessment by adjusting the level of detail based on the success rate of similar past cases.

[0064] The legal assistant system can apply different evaluation algorithms to assess legal risks depending on the characteristics of the company and the industry. For example, a specific evaluation algorithm can be applied based on the characteristics of the company. Alternatively, a different evaluation algorithm can be applied based on the characteristics of the industry. Furthermore, the system can select the optimal evaluation algorithm according to the characteristics of the company and the industry. This improves the accuracy of the evaluation by applying different evaluation algorithms according to the characteristics of the company and the industry.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives the user's legal consultation request. Legal consultation requests include, but are not limited to, contract review, legal advice, and litigation support. Users can enter questions such as, "Is this contract legally sound?" Step 2: The evaluation department assesses and analyzes legal risks based on the information entered by the reception department. The evaluation department assesses and analyzes legal risks based on past cases, the latest legal amendments, company-specific rules, and industry-specific regulations. For example, it refers to similar past cases and takes into account the latest legal amendments to determine whether there are any legal risks in the content of the contract. Step 3: The Evolution Department continuously evolves through dialogue with legal personnel based on the evaluation results obtained by the Evaluation Department. The Evolution Department learns new rules and regulations through dialogue with legal personnel and can reflect them in future legal consultations. Step 4: The warning section proactively warns of future legal risks based on the information evolved by the evolution section. The warning section predicts future legal changes and industry trends and issues warnings if the contract contains future legal risks.

[0067] (Example of form 2) The legal assistant system according to an embodiment of the present invention is a system that learns from past cases and the latest legal revisions and evaluates and analyzes legal risks in real time. When a user inputs a legal consultation matter, the generating AI evaluates and analyzes the legal risks based on past cases, the latest legal revisions, company-specific rules, and industry-specific regulations. Furthermore, the generating AI continuously evolves through dialogue with legal personnel and incorporates predictive analysis functions to warn of future legal risks in advance. This allows users to easily seek legal advice without the need to consult with legal personnel. For example, if a user inputs a question such as, "Is this contract legally sound?", the generating AI refers to similar past cases, considers the latest legal revisions, and determines whether the contract contains any legal risks. The generating AI learns new rules and regulations through dialogue with legal personnel and can reflect them in future legal consultations. The generating AI also predicts future legal revisions and industry trends and issues warnings if the contract contains future legal risks. This allows users, even those with no legal knowledge, to quickly determine whether a matter is legal by using the generating AI. Furthermore, it improves the efficiency of legal staff's work and reduces legal risks. As a result, the legal assistant system can handle everything from evaluating and analyzing legal risks to evolving and issuing warnings, simply by having the user input legal consultation details.

[0068] The legal assistant system according to this embodiment comprises a reception unit, an evaluation unit, an evolution unit, and a warning unit. The reception unit receives input from the user regarding legal consultation matters. Legal consultation matters include, but are not limited to, contract review, legal advice, and litigation support. The reception unit allows the user to input questions such as, "Is this contract legally sound?" The evaluation unit evaluates and analyzes legal risks based on the information entered by the reception unit. The evaluation unit evaluates and analyzes legal risks based on, for example, past cases, the latest legal amendments, company-specific rules, and industry-specific regulations. The evaluation unit determines whether there are any legal risks in the content of the contract, for example, by referring to similar past cases and considering the latest legal amendments. The evolution unit continuously evolves through dialogue with legal personnel based on the evaluation results obtained by the evaluation unit. The evolution unit can learn new rules and regulations through dialogue with legal personnel and reflect them in future legal consultations. The warning unit provides advance warnings of future legal risks based on the information evolved by the evolution unit. The warning function, for example, predicts future legal changes and industry trends, and issues warnings if the contract contains future legal risks. This allows the legal assistant system to handle everything from legal risk assessment and analysis to improvement and warnings, simply by the user inputting legal consultation details.

[0069] The reception desk receives user input for legal consultations. These consultations include, but are not limited to, contract review, legal advice, and litigation support. For example, users can input questions such as, "Is this contract legally sound?" Specifically, the reception desk provides an interface for receiving user-inputted legal consultations. This interface is designed to allow users to easily input questions and consultation details and includes text input fields and multiple-choice question items. The information entered by users is stored in the system's database and used for subsequent processing. Furthermore, the reception desk has a function to automatically classify the user-inputted information and assign it to the appropriate category. For example, questions regarding contract review are classified into the "Contract Category," and questions regarding legal advice are classified into the "Advice Category." This allows subsequent evaluation and development departments to process the information efficiently. The reception desk also includes a checking function to verify the accuracy and completeness of the information entered by users. For example, if there are unclear or incomplete points in the input, a message prompting the user to enter additional information is displayed. This allows the reception desk to assist users in entering accurate and complete information, improving the overall accuracy and reliability of the system.

[0070] The evaluation department assesses and analyzes legal risks based on information entered by the reception department. For example, the evaluation department assesses and analyzes legal risks based on past cases, recent legal amendments, company-specific rules, and industry-specific regulations. Specifically, the evaluation department analyzes the legal consultation matters entered by users and collects relevant legal information. This includes information sources such as legal databases, case law databases, and industry guidelines. The evaluation department extracts relevant data from these information sources and evaluates legal risks in light of the user's consultation content. For example, in the case of a consultation regarding contract review, the evaluation department analyzes the contract clauses, refers to similar past cases and recent legal amendments, and determines whether there are any legal risks in the contract's content. Furthermore, the evaluation department utilizes AI technology to assess legal risks. Specifically, it uses natural language processing technology to analyze the user's consultation content and automatically extracts relevant legal information. In addition, it uses machine learning algorithms to learn from past case data and improve the accuracy of risk assessments for new consultation content. This allows the evaluation department to provide quick and accurate legal risk assessments for user consultations.

[0071] The Evolution Department continuously evolves through dialogue with legal professionals based on evaluation results obtained by the Evaluation Department. For example, the Evolution Department can learn new rules and regulations through dialogue with legal professionals and reflect them in subsequent legal consultations. Specifically, the Evolution Department presents the legal risk assessment results provided by the Evaluation Department to legal professionals and collects feedback from them. This feedback includes the accuracy of the assessment results, additional legal information, and practical insights. Based on this feedback, the Evolution Department updates the system's knowledge base and reflects it in subsequent legal consultations. For example, it learns information on new legal revisions and industry trends to improve the system's evaluation accuracy. In addition, the Evolution Department understands areas for improvement in system usability and functionality through dialogue with legal professionals and continuously improves the system. As a result, the Evolution Department keeps the system's knowledge base constantly up-to-date and can provide users with high-quality legal assistant services. Furthermore, the Evolution Department is equipped with a function that uses AI technology to analyze the content of dialogues with legal professionals and automatically learns from it. This allows the evolution department to efficiently incorporate the expertise of legal personnel and accelerate the evolution of the system.

[0072] The warning unit proactively warns of future legal risks based on information evolved by the evolution unit. For example, the warning unit predicts future legal changes and industry trends, and issues warnings if the content of a contract contains future legal risks. Specifically, the warning unit predicts future legal risks based on new rules, regulations, and industry trends learned by the evolution unit. For example, based on the latest legal change information learned by the evolution unit, if a clause in a contract may involve future legal risks, the warning unit will issue a warning to the user. The warning unit also utilizes AI technology to predict future legal risks. Specifically, it uses machine learning algorithms to analyze past data and predict future legal changes and industry trends. This allows the warning unit to issue early warnings to users and prevent legal risks before they occur. Furthermore, the warning unit also has the function of suggesting specific countermeasures to users. For example, it can present proposed revisions to contracts to comply with future legal changes or specific action plans to respond to industry trends. In this way, the warning unit helps users take appropriate measures against future legal risks and minimizes them.

[0073] The legal assistant system according to this embodiment further includes a learning unit that learns new rules and regulations through dialogue with legal personnel. The learning unit learns new rules and regulations through dialogue with legal personnel. For example, the learning unit can learn new rules and regulations through dialogue with legal personnel and reflect them in subsequent legal consultations. For example, the learning unit can learn specific laws and industry regulations to improve the accuracy of the system. As a result, the legal assistant system improves its accuracy by learning new rules and regulations through dialogue with legal personnel.

[0074] The legal assistant system according to this embodiment further includes a prediction unit that predicts future legal revisions and industry trends. The prediction unit predicts future legal revisions and industry trends. For example, the prediction unit predicts future legal revisions and industry trends and issues a warning if there are future legal risks in the content of a contract. For example, the prediction unit predicts revisions to specific laws and industry trends and warns of legal risks in advance. In this way, the legal assistant system can warn of future legal risks in advance by predicting future legal revisions and industry trends.

[0075] The evaluation department can assess and analyze legal risks based on past cases, the latest legal revisions, company-specific rules, and industry-specific regulations. For example, the evaluation department can refer to past cases and consider the latest legal revisions to determine whether there are any legal risks in the content of a contract. The evaluation department can also assess and analyze legal risks based on company-specific rules and industry-specific regulations. The evaluation department can also assess and analyze legal risks based on past cases and the latest legal revisions. This allows the evaluation department to conduct highly accurate assessments by assessing and analyzing legal risks based on past cases, the latest legal revisions, company-specific rules, and industry-specific regulations.

[0076] The warning section can predict future legal changes and industry trends and issue warnings about legal risks in advance. For example, the warning section can predict future legal changes and industry trends and issue warnings if there are future legal risks in the content of a contract. For example, the warning section can predict changes in specific laws or industry trends and issue warnings about legal risks in advance. For example, the warning section can predict future legal changes and industry trends and issue warnings about legal risks in advance. This enables preventative legal measures by predicting future legal changes and industry trends and issuing warnings about legal risks in advance.

[0077] The reception desk can estimate the user's emotions and adjust the input method for legal consultations based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of legal consultations. In this way, the reception desk improves user convenience by adjusting the input method for legal consultations according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The reception desk can analyze a user's past legal consultation history and suggest the optimal input format. For example, the reception desk can automatically display as suggestions legal consultation topics that the user has frequently entered in the past. For example, the reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest legal consultation topics that will be used during specific time periods based on the user's past legal consultation history. In this way, the reception desk improves input efficiency by analyzing the user's past legal consultation history and suggesting the optimal input format.

[0079] The reception system can filter input content based on the user's industry and company characteristics when legal consultations are entered. For example, the reception system can prioritize displaying highly relevant legal consultations based on the characteristics of the user's industry. The reception system can also filter legal consultations related to specific rules or regulations based on the characteristics of the user's company. The reception system can also simplify input content by excluding unnecessary information based on the user's industry and company characteristics. As a result, the reception system can prioritize obtaining highly relevant information by filtering input content based on the user's industry and company characteristics.

[0080] The reception desk can estimate the user's emotions and determine the priority of the legal consultations entered based on the estimated emotions. For example, if the user feels urgency, the reception desk will prioritize processing the entered legal consultations. For example, if the user is relaxed, the reception desk can process the legal consultations with normal priority. For example, if the user is feeling anxious, the reception desk can prioritize processing high-priority legal consultations. In this way, the reception desk can prioritize important legal consultations by determining the priority of legal consultations 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk can prioritize retrieving highly relevant information when users input legal consultation requests, taking into account their geographical location. For example, if a user is in a specific region, the reception desk will prioritize displaying legal consultation requests related to that region. The reception desk can also prioritize retrieving information related to region-specific rules and regulations based on the user's geographical location. The reception desk can also prioritize collaboration with local legal professionals, taking into account the user's geographical location. As a result, the reception desk can respond to region-specific legal consultations by prioritizing the retrieval of highly relevant information based on the user's geographical location.

[0082] The reception desk can analyze the user's social media activity and obtain relevant information when they input legal consultation details. For example, the reception desk can extract keywords related to the legal consultation details from the user's social media activity and complete the input content. For example, the reception desk can also analyze the user's social media activity and suggest relevant legal consultation details. For example, the reception desk can automatically complete the input content of the legal consultation details based on the user's social media activity. In this way, the reception desk can complete the input content of the legal consultation details by analyzing the user's social media activity and obtaining relevant information.

[0083] The evaluation unit can estimate the user's emotions and adjust the legal risk assessment method based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can provide a detailed assessment method to reassure them. For example, if the user is relaxed, the evaluation unit can apply a standard assessment method. For example, if the user is in a hurry, the evaluation unit can provide a rapid assessment method to quickly assess the legal risks. This allows the evaluation unit to provide an assessment that is appropriate for the user by adjusting the legal risk assessment method 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.

[0084] The evaluation unit can adjust the level of detail in its legal risk assessment based on the success rate of similar past cases. For example, if the success rate of similar past cases is high, the evaluation unit will perform a detailed assessment. For example, if the success rate of similar past cases is low, the evaluation unit can also perform a simplified assessment. The evaluation unit can also dynamically adjust the level of detail in its assessment based on the success rate of similar past cases. This improves the accuracy of the assessment by allowing the evaluation unit to adjust the level of detail based on the success rate of similar past cases.

[0085] The evaluation department can apply different evaluation algorithms depending on the characteristics of the company and the industry when assessing legal risks. For example, the evaluation department can apply a specific evaluation algorithm based on the characteristics of the company. The evaluation department can also apply different evaluation algorithms based on the characteristics of the industry. For example, the evaluation department can select the optimal evaluation algorithm depending on the characteristics of the company and the industry. As a result, the evaluation department can improve the accuracy of its evaluations by applying different evaluation algorithms depending on the characteristics of the company and the industry.

[0086] The evaluation unit can estimate the user's emotions and adjust how the legal risk assessment results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the evaluation unit can display detailed assessment results to provide reassurance. For example, if the user is relaxed, the evaluation unit can display normal assessment results. For example, if the user is in a hurry, the evaluation unit can display concise assessment results to provide information quickly. In this way, the evaluation unit can provide information tailored to the user by adjusting how the legal risk assessment results are displayed 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The evaluation department can prioritize evaluations based on the timing of user submissions when assessing legal risks. For example, the evaluation department can prioritize evaluations based on when the user submitted the information. The evaluation department can also prioritize evaluations of legal consultations submitted earlier. For example, the evaluation department can dynamically adjust evaluation priorities based on submission timing. This allows the evaluation department to conduct appropriate evaluations according to the timing of user submissions by prioritizing evaluations based on the submission timing.

[0088] The assessment unit can improve the accuracy of its assessments by referring to relevant legal literature when evaluating legal risks. For example, the assessment unit can improve the accuracy of its assessments by referring to relevant legal literature. The assessment unit can also adjust the level of detail in its assessments based on legal literature, for example. The assessment unit can also improve the accuracy of its assessments by dynamically referencing relevant legal literature, for example. This allows the assessment unit to perform more accurate assessments by improving the accuracy of its assessments by referring to relevant legal literature.

[0089] The evolution unit can estimate the user's emotions and adjust the evolution method based on the estimated emotions. For example, if the user is feeling anxious, the evolution unit can provide a detailed evolution method to reassure them. For example, if the user is relaxed, the evolution unit can apply a normal evolution method. For example, if the user is in a hurry, the evolution unit can provide a rapid evolution method to expedite the evolution process. In this way, the evolution unit can adjust the evolution method according to the user's emotions, enabling evolution that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The evolution unit can analyze the history of conversations with legal personnel during evolution to select the optimal evolution method. For example, the evolution unit can analyze the history of conversations with legal personnel and select the optimal evolution method. The evolution unit can also optimize the evolution process based on the conversation history. For example, the evolution unit can refer to the history of conversations with legal personnel and dynamically adjust the evolution method. As a result, the evolution unit improves the accuracy of evolution by analyzing the history of conversations with legal personnel and selecting the optimal evolution method.

[0091] The evolution unit can customize the means of evolution based on the characteristics of the company and the industry during the evolution process. For example, the evolution unit can customize the means of evolution based on the characteristics of the company. The evolution unit can also adjust the means of evolution based on the characteristics of the industry. For example, the evolution unit can optimize the means of evolution according to the characteristics of the company and the industry. As a result, the evolution unit improves the accuracy of evolution by customizing the means of evolution based on the characteristics of the company and the industry.

[0092] The evolution unit can estimate the user's emotions and determine evolutionary priorities based on those estimated emotions. For example, if the user is feeling anxious, the evolution unit will set a higher evolutionary priority. If the user is relaxed, the evolution unit can also perform evolutionary priorities at a normal level. If the user is in a hurry, the evolution unit can also set a rapid evolutionary priority. This allows the evolution unit to prioritize important evolutionary steps by determining the priority of evolutionary priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The evolution unit can select the optimal evolution method during evolution, taking into account the user's geographical location information. For example, the evolution unit selects the optimal evolution method based on the user's geographical location information. The evolution unit can also optimize the evolution process, for example, by taking geographical location information into account. The evolution unit can also adjust the means of evolution according to the user's geographical location information. As a result, by selecting the optimal evolution method while taking the user's geographical location information into account, the evolution unit enables region-specific evolution.

[0094] The evolution unit can analyze the user's social media activity during evolution and propose evolutionary methods. For example, the evolution unit can analyze the user's social media activity and propose evolutionary methods. The evolution unit can also optimize the evolutionary process based on social media activity. For example, the evolution unit can refer to the user's social media activity and dynamically adjust the evolutionary method. This improves the accuracy of evolution by allowing the evolution unit to analyze the user's social media activity and propose evolutionary methods.

[0095] The warning unit can estimate the user's emotions and adjust how warnings are displayed based on those emotions. For example, if the user is feeling anxious, the warning unit may display a detailed warning to provide reassurance. If the user is relaxed, the warning unit may display a normal warning. If the user is in a hurry, the warning unit may display a concise warning to provide information quickly. This allows the warning unit to provide user-appropriate warnings by adjusting how warnings are displayed according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The warning unit can adjust the level of detail of a warning by referring to past warning history when an warning is issued. For example, the warning unit can refer to past warning history and display a more detailed warning. The warning unit can also adjust the level of detail of a warning based on past warning history. For example, the warning unit can dynamically refer to past warning history to improve the accuracy of warnings. As a result, the warning unit improves the accuracy of warnings by adjusting the level of detail of warnings by referring to past warning history.

[0097] The warning unit can apply different warning algorithms depending on the characteristics of the company or industry when a warning is issued. For example, the warning unit can apply a specific warning algorithm based on the characteristics of the company. The warning unit can also apply different warning algorithms based on the characteristics of the industry. For example, the warning unit can select the optimal warning algorithm depending on the characteristics of the company or industry. As a result, the accuracy of warnings is improved by the warning unit applying different warning algorithms depending on the characteristics of the company or industry.

[0098] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is feeling anxious, the alert unit will set a higher priority for the alert. For example, if the user is relaxed, the alert unit can issue an alert with a normal priority. For example, if the user is in a hurry, the alert unit can also set a faster priority for the alert. In this way, the alert unit can prioritize important alerts by determining the priority of alerts according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The warning system can adjust the importance of a warning based on when the user submits it. For example, the warning system can adjust the importance of a warning based on when the user submits it. For example, the warning system can set a higher importance for legal consultation matters that are submitted earlier. For example, the warning system can dynamically adjust the importance of a warning based on when it is submitted. This allows the warning system to provide appropriate warnings based on when the user submits the information.

[0100] The warning unit can improve the accuracy of its warnings by referring to relevant legal documents when issuing a warning. For example, the warning unit can improve the accuracy of its warnings by referring to relevant legal documents. The warning unit can also adjust the level of detail of its warnings based on legal documents. For example, the warning unit can dynamically refer to relevant legal documents to improve the accuracy of its warnings. This allows the warning unit to issue more accurate warnings by improving the accuracy of its warnings by referring to relevant legal documents.

[0101] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is feeling anxious, the learning unit can select detailed training data to provide reassurance. For example, if the user is relaxed, the learning unit can select normal training data. For example, if the user is in a hurry, the learning unit can quickly select training data to expedite the learning process. This allows the learning unit to select training data according to the user's emotions, enabling user-appropriate learning. 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.

[0102] The learning unit can optimize the learning algorithm by referring to past training data during training. For example, the learning unit can optimize the learning algorithm by referring to past training data. The learning unit can also adjust the parameters of the learning algorithm based on the training data. Furthermore, the learning unit can dynamically refer to past training data to improve the accuracy of the learning algorithm. This allows the learning unit to improve the accuracy of learning by optimizing the learning algorithm by referring to past training data.

[0103] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is feeling anxious, the learning unit can set a higher learning frequency to provide reassurance. If the user is relaxed, the learning unit can also perform learning at a normal frequency. If the user is in a hurry, the learning unit can also set a faster learning frequency. In this way, the learning unit can adjust the learning frequency according to the user's emotions, enabling learning that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The learning unit can weight the learning data based on the submission date of legal consultation matters during the learning process. For example, the learning unit can weight the learning data based on the submission date of legal consultation matters. The learning unit can also, for example, set a higher weight for legal consultation matters that are submitted earlier. The learning unit can also, for example, dynamically adjust the weighting of the learning data based on the submission date. This allows the learning unit to perform appropriate learning according to the submission date of legal consultation matters by weighting the learning data based on the submission date.

[0105] The prediction unit can estimate the user's emotions and adjust how the prediction is displayed based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit can display a detailed prediction to provide reassurance. For example, if the user is relaxed, the prediction unit can display a normal prediction. For example, if the user is in a hurry, the prediction unit can display a concise prediction to provide information quickly. In this way, the prediction unit can provide predictions that are appropriate for the user by adjusting how the prediction is displayed 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The prediction unit can optimize its prediction algorithm by referring to past prediction data during the prediction process. For example, the prediction unit can optimize the prediction algorithm by referring to past prediction data. The prediction unit can also adjust the parameters of the prediction algorithm based on the prediction data. For example, the prediction unit can dynamically refer to past prediction data to improve the accuracy of the prediction algorithm. As a result, the prediction unit improves the accuracy of its predictions by optimizing the prediction algorithm by referring to past prediction data.

[0107] The prediction unit can estimate the user's emotions and determine prediction priorities based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit will set a higher priority for that prediction. If the user is relaxed, the prediction unit can also perform predictions with a normal priority. If the user is in a hurry, the prediction unit can also set a rapid priority for that prediction. This allows the prediction unit to prioritize important predictions by determining the priority of predictions 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.

[0108] The prediction unit can weight the prediction data based on the submission timing of legal consultation matters during the prediction process. For example, the prediction unit can weight the prediction data based on the submission timing of legal consultation matters. The prediction unit can also, for example, set a higher weight for prediction data for legal consultation matters submitted earlier. The prediction unit can also, for example, dynamically adjust the weighting of the prediction data based on the submission timing. As a result, by weighting the prediction data based on the submission timing of legal consultation matters, the prediction unit can make appropriate predictions according to the submission timing.

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

[0110] The legal assistant system can estimate the user's emotions and adjust the input method for legal consultations based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of legal consultations. This improves user convenience by adjusting the input method for legal consultations according to the user's emotions.

[0111] The legal assistant system can analyze a user's past legal consultation history and suggest the optimal input format. For example, it can automatically display legal consultation topics that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest legal consultation topics that will be used during specific time periods based on the user's past legal consultation history. In this way, by analyzing the user's past legal consultation history and suggesting the optimal input format, input efficiency is improved.

[0112] The legal assistant system can filter input content based on the user's industry and company characteristics when entering legal consultation matters. For example, it can prioritize displaying highly relevant legal consultation matters based on the characteristics of the user's industry. It can also filter legal consultation matters related to specific rules and regulations based on the characteristics of the user's company. Furthermore, it can simplify input content by excluding unnecessary information based on the user's industry and company characteristics. In this way, by filtering input content based on the user's industry and company characteristics, highly relevant information can be prioritized.

[0113] The legal assistant system can estimate the user's emotions and prioritize the legal consultations entered based on those emotions. For example, if the user feels a sense of urgency, the system can prioritize the legal consultations entered. Conversely, if the user is relaxed, the system can process legal consultations with the usual priority. Furthermore, if the user is feeling anxious, the system can prioritize legal consultations of high importance. In this way, by prioritizing legal consultations according to the user's emotions, important legal consultations can be handled with priority.

[0114] The legal assistant system prioritizes retrieving highly relevant information when users input legal consultation requests, taking into account their geographical location. For example, if a user is in a specific region, legal consultation requests related to that region can be displayed preferentially. It can also prioritize retrieving information related to region-specific rules and regulations based on the user's geographical location. Furthermore, it can prioritize collaboration with local legal professionals, taking the user's geographical location into consideration. This allows the system to address region-specific legal consultations by prioritizing the retrieval of highly relevant information based on the user's geographical location.

[0115] The legal assistant system can analyze the user's social media activity and retrieve relevant information when they input legal consultation matters. For example, it can extract keywords related to the legal consultation matter from the user's social media activity and complete the input content. It can also analyze the user's social media activity and suggest relevant legal consultation matters. Furthermore, it can automatically complete the input content of the legal consultation matter based on the user's social media activity. In this way, by analyzing the user's social media activity and retrieving relevant information, the system can complete the input content of the legal consultation matter.

[0116] The legal assistant system can estimate the user's emotions and adjust the legal risk assessment method based on those emotions. For example, if the user is feeling anxious, it can provide a detailed assessment method to reassure them. If the user is relaxed, the standard assessment method can be applied. Furthermore, if the user is in a hurry, it can provide a rapid assessment method to quickly evaluate the legal risks. In this way, by adjusting the legal risk assessment method according to the user's emotions, it becomes possible to provide an assessment that is appropriate for the user.

[0117] The legal assistant system can adjust the level of detail in its legal risk assessment based on the success rate of similar past cases. For example, if the success rate of similar past cases is high, a detailed assessment can be performed. Conversely, if the success rate of similar past cases is low, a simplified assessment can be performed. Furthermore, the level of detail in the assessment can be dynamically adjusted based on the success rate of similar past cases. This improves the accuracy of the assessment by adjusting the level of detail based on the success rate of similar past cases.

[0118] The legal assistant system can apply different evaluation algorithms to assess legal risks depending on the characteristics of the company and the industry. For example, a specific evaluation algorithm can be applied based on the characteristics of the company. Alternatively, a different evaluation algorithm can be applied based on the characteristics of the industry. Furthermore, the system can select the optimal evaluation algorithm according to the characteristics of the company and the industry. This improves the accuracy of the evaluation by applying different evaluation algorithms according to the characteristics of the company and the industry.

[0119] The legal assistant system can estimate the user's emotions and adjust how legal risk assessment results are displayed based on those emotions. For example, if the user is feeling anxious, it can display detailed assessment results to provide reassurance. If the user is relaxed, it can display standard assessment results. Furthermore, if the user is in a hurry, it can display concise assessment results to provide information quickly. In this way, by adjusting how legal risk assessment results are displayed according to the user's emotions, it becomes possible to provide information that is appropriate for the user.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk receives the user's legal consultation request. Legal consultation requests include, but are not limited to, contract review, legal advice, and litigation support. Users can enter questions such as, "Is this contract legally sound?" Step 2: The evaluation department assesses and analyzes legal risks based on the information entered by the reception department. The evaluation department assesses and analyzes legal risks based on past cases, the latest legal amendments, company-specific rules, and industry-specific regulations. For example, it refers to similar past cases and takes into account the latest legal amendments to determine whether there are any legal risks in the content of the contract. Step 3: The Evolution Department continuously evolves through dialogue with legal personnel based on the evaluation results obtained by the Evaluation Department. The Evolution Department learns new rules and regulations through dialogue with legal personnel and can reflect them in future legal consultations. Step 4: The warning section proactively warns of future legal risks based on the information evolved by the evolution section. The warning section predicts future legal changes and industry trends and issues warnings if the contract contains future legal risks.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, evaluation unit, evolution unit, warning unit, learning unit, and prediction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs legal consultation matters. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it evaluates and analyzes legal risks based on the input information. The evolution unit is implemented by the specific processing unit 290 of the data processing unit 12, where it continuously evolves through dialogue with legal personnel. The warning unit is implemented by the specific processing unit 290 of the data processing unit 12, where it warns of future legal risks in advance. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, where it learns new rules and regulations through dialogue with legal personnel. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12, where it predicts future legal revisions and industry trends. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0139] 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.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0141] Each of the multiple elements described above, including the reception unit, evaluation unit, evolution unit, warning unit, learning unit, and prediction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs legal consultation matters. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it evaluates and analyzes legal risks based on the input information. The evolution unit is implemented by the specific processing unit 290 of the data processing unit 12, where it continuously evolves through dialogue with legal personnel. The warning unit is implemented by the specific processing unit 290 of the data processing unit 12, where it warns of future legal risks in advance. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, where it learns new rules and regulations through dialogue with legal personnel. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12, where it predicts future legal revisions and industry trends. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0157] Each of the multiple elements described above, including the reception unit, evaluation unit, evolution unit, warning unit, learning unit, and prediction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs legal consultation matters. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it evaluates and analyzes legal risks based on the input information. The evolution unit is implemented by the specific processing unit 290 of the data processing unit 12, where it continuously evolves through dialogue with legal personnel. The warning unit is implemented by the specific processing unit 290 of the data processing unit 12, where it warns of future legal risks in advance. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, where it learns new rules and regulations through dialogue with legal personnel. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12, where it predicts future legal revisions and industry trends. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0172] 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.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0174] Each of the multiple elements described above, including the reception unit, evaluation unit, evolution unit, warning unit, learning unit, and prediction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs legal consultation matters. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it evaluates and analyzes legal risks based on the input information. The evolution unit is implemented by the specific processing unit 290 of the data processing unit 12, where it continuously evolves through dialogue with legal personnel. The warning unit is implemented by the specific processing unit 290 of the data processing unit 12, where it warns of future legal risks in advance. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, where it learns new rules and regulations through dialogue with legal personnel. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12, where it predicts future legal revisions and industry trends. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) The reception area where legal consultation matters are entered, An evaluation unit that evaluates and analyzes legal risks based on the information entered by the aforementioned reception unit, Based on the evaluation results obtained by the aforementioned evaluation unit, the evolution unit continuously evolves through dialogue with legal personnel, The system includes a warning unit that provides advance warnings of future legal risks based on information evolved by the aforementioned evolution unit. A system characterized by the following features. (Note 2) We will further enhance our learning department by providing opportunities to learn about new rules and regulations through dialogue with legal professionals. The system described in Appendix 1, characterized by the features described herein. (Note 3) We will further enhance our forecasting department to predict future legal changes and industry trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, We evaluate and analyze legal risks based on past cases, the latest legal revisions, company-specific rules, and industry-specific regulations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned warning unit is Predicting future legal changes and industry trends, and providing advance warnings about legal risks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for legal consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past legal consultation history and propose the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter legal consultation details, the system filters the input based on the user's industry and company characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the legal consultation items entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter legal consultation details, the system prioritizes retrieving highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter legal consultation details, the system analyzes their social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, We estimate user sentiment and adjust the legal risk assessment method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, When assessing legal risks, adjust the level of detail based on the success rate of similar past cases. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, When assessing legal risks, different assessment algorithms are applied depending on the characteristics of the company and the industry. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, The system estimates user sentiment and adjusts how legal risk assessment results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, When assessing legal risks, the priority of the assessment is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, When assessing legal risks, referencing relevant legal literature improves the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned evolutionary section is It estimates user emotions and adjusts the evolution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned evolutionary section is During the evolution process, the optimal evolution method is selected by analyzing the history of conversations with legal personnel. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned evolutionary section is During evolution, the means of evolution are customized based on the characteristics of the company and the industry. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned evolutionary section is It estimates user emotions and determines evolutionary priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned evolutionary section is During evolution, the optimal evolution method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned evolutionary section is During evolution, we analyze users' social media activity and propose methods for evolution. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is It estimates the user's emotions and adjusts how warnings are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned warning unit is When a warning is issued, the level of detail of the warning is adjusted by referring to past warning history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned warning unit is When a warning is issued, different warning algorithms are applied depending on the characteristics of the company and industry. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned warning unit is The system estimates the user's emotions and prioritizes warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned warning unit is When issuing a warning, adjust the severity of the warning based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned warning unit is When issuing a warning, we will refer to relevant legal literature to improve the accuracy of the warning. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned learning unit, During the learning process, learning data is weighted based on the timing of submission of legal consultation matters. The system described in Appendix 2, characterized by the features described herein. (Note 34) The prediction unit, It estimates the user's emotions and adjusts how predictions are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The prediction unit, When making predictions, the prediction algorithm is optimized by referring to past prediction data. The system described in Appendix 3, characterized by the features described herein. (Note 36) The prediction unit, It estimates the user's emotions and determines the priority of predictions based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The prediction unit, When making predictions, the prediction data is weighted based on the timing of submission of legal consultation matters. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0194] 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. The reception area where legal consultation matters are entered, An evaluation unit that evaluates and analyzes legal risks based on the information entered by the aforementioned reception unit, Based on the evaluation results obtained by the aforementioned evaluation unit, the evolution unit continuously evolves through dialogue with legal personnel, The system includes a warning unit that provides advance warnings of future legal risks based on information evolved by the aforementioned evolution unit. A system characterized by the following features.

2. We will further enhance our learning department by providing opportunities to learn about new rules and regulations through dialogue with legal professionals. The system according to feature 1.

3. We will further enhance our forecasting department to predict future legal changes and industry trends. The system according to feature 1.

4. The evaluation unit described above, We evaluate and analyze legal risks based on past cases, the latest legal revisions, company-specific rules, and industry-specific regulations. The system according to feature 1.

5. The aforementioned warning unit is Predicting future legal changes and industry trends, and providing advance warnings about legal risks. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for legal consultations based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past legal consultation history and propose the optimal input format. The system according to feature 1.

8. The aforementioned reception unit is When users enter legal consultation details, the system filters the input based on the user's industry and company characteristics. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the legal consultation items entered based on those estimated emotions. The system according to feature 1.

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