system

Abstract

The system according to the embodiment aims to enable a user to efficiently organize legal issues and clarify points to be argued when consulting with a lawyer. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a calculation unit, a clarification unit, and a sorting unit. The reception unit receives input of legal issues from a user. The search unit searches for relevant laws and precedents based on the information received by the reception unit. The calculation unit calculates the winning rate in court based on the information obtained by the search unit. The clarification unit clarifies issues based on the results obtained by the calculation unit. The sorting unit organizes the points of argument that the user will make when consulting a lawyer based on the information obtained by the clarification unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult for users to efficiently organize legal issues and clarify the key points to argue when consulting with a lawyer.

[0005] The system according to the embodiment aims to enable a user to efficiently organize legal issues and clarify points to be argued when consulting with a lawyer. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, a calculation unit, a clarification unit, and a sorting unit. The reception unit receives input of legal issues from a user. The search unit searches for relevant laws and precedents based on the information received by the reception unit. The calculation unit calculates the winning rate in court based on the information obtained by the search unit. The clarification unit clarifies problems based on the results obtained by the calculation unit. The sorting unit organizes the points of argument that the user will make when consulting a lawyer based on the information obtained by the clarification unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently organize legal issues and clarify points to be argued when consulting with a lawyer. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A legal problem analysis system according to an embodiment of the present invention uses a generation AI to identify the success rate and problem areas for legal problems. In this legal problem analysis system, a user inputs the legal problem they wish to discuss, and the generation AI analyzes the input information and searches for relevant laws and precedents. The generation AI then calculates the success rate in court based on the extracted information and identifies the problem areas. Finally, the generation AI uses the obtained information to organize the points the user will argue when consulting a lawyer. This system deepens understanding of legal issues and facilitates the development of a strategy for court proceedings. It also shortens consultation times with lawyers and enables efficient problem resolution. For example, a user inputs the legal problem they wish to discuss. In this case, the user enters specific information, such as a breach of contract or labor issue. For example, the user might enter "problem related to breach of contract." This information is entered into the generation AI. The generation AI then analyzes the input information and searches for relevant laws and precedents. The generation AI references a vast legal database and precedent database to extract information relevant to the user's problem. For example, it searches for past precedents and related laws regarding breach of contract. The generation AI then calculates the success rate in court based on the extracted information. The generating AI calculates the success rate by referring to past precedents of similar cases. For example, it calculates the success rate based on past precedents for breach of contract. It also identifies problems and indicates to the user their weak points. Finally, the generating AI uses the information obtained to organize the points of argument the user will present when consulting with a lawyer. This allows users to consult with their lawyers efficiently and smoothly prepare for trial. For example, by consulting with a lawyer based on the points of argument organized by the generating AI, problems can be resolved efficiently. This system deepens understanding of legal issues and makes it easier to formulate strategies for trial. It also shortens consultation times with lawyers and enables efficient problem resolution. For example, based on the information provided by the generating AI, users can ask specific questions and make inquiries to their lawyers. This allows consultations with lawyers to proceed smoothly and problems to be resolved efficiently.This allows the legal problem analysis system to deepen the user's understanding of legal issues and make it easier to formulate strategies for court proceedings. It also reduces consultation time with lawyers and allows for efficient problem resolution.

[0029] A legal problem analysis system according to an embodiment includes a reception unit, a search unit, a calculation unit, a sifting unit, and a sorting unit. The reception unit receives input of a legal problem from a user. Examples of legal problems input by the user include, but are not limited to, breach of contract, labor issues, and family issues. The reception unit, for example, prompts the user to enter specific information when inputting a problem related to breach of contract. For example, the user can enter the content of the contract and specific circumstances of the breach. The search unit uses a generation AI to search for relevant laws and precedents based on the information received by the reception unit. The search unit, for example, references a vast legal database and a precedent database to extract information related to the user's problem. For example, it searches for past precedents related to breach of contract and related laws. The search unit can efficiently search for relevant laws and precedents using the generation AI. The calculation unit uses the generation AI to calculate the success rate in court based on the information obtained by the search unit. For example, the calculation unit references past precedents of similar cases to calculate the success rate. For example, the win rate is calculated based on past precedents related to breach of contract. The calculation unit can use the generation AI to accurately calculate the win rate based on past case data. The identification unit can use the generation AI to identify issues based on the results obtained by the calculation unit. The identification unit can, for example, identify issues in a trial based on extracted information. For example, it can identify legal risks, insufficient evidence, etc. The identification unit can efficiently identify issues using the generation AI. The organization unit can use the generation AI to organize points to be argued when the user consults with a lawyer based on the information obtained by the identification unit. For example, the organization unit can organize points to be argued when the user consults with a lawyer based on the obtained information. For example, it can organize legal grounds, methods of presenting evidence, etc. The organization unit can efficiently organize points to be argued using the generation AI. As a result, the legal problem analysis system according to the embodiment can deepen the user's understanding of legal issues and make it easier to formulate strategies for trial. It can also shorten the consultation time with a lawyer and achieve efficient problem resolution.

[0030] The search unit can refer to multiple legal databases and case law databases to extract information related to the user's problem. The search unit can refer to, for example, a national legal database, a specific case law collection, etc. For example, the search unit can refer to a national legal database to extract laws related to the user's problem. The search unit can also refer to a specific case law collection to extract case law related to the user's problem. For example, the search unit can search for past case law regarding breach of contract and extract related information. This allows the search unit to efficiently extract information related to the user's problem. Some or all of the above-mentioned processing in the search unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the search unit can input information related to the user's problem to a generation AI, which can then search for the relevant information.

[0031] The calculation unit can calculate the win rate by referring to past precedents of similar cases. The calculation unit, for example, calculates the win rate by referring to past precedents of similar cases. For example, the calculation unit calculates the win rate based on past precedents regarding breach of contract. The calculation unit can also calculate the win rate based on past precedents regarding labor issues. For example, the calculation unit can calculate the win rate based on past precedents regarding family issues. This allows the calculation unit to accurately calculate the win rate based on past precedents. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, a generation AI, for example. For example, the calculation unit can input past precedent data into the generation AI, which then calculates the win rate.

[0032] The clarification unit can identify problems in court based on the extracted information. The clarification unit, for example, identifies problems in court based on the extracted information. For example, the clarification unit identifies legal risks, insufficient evidence, etc. The clarification unit can also evaluate the credibility of evidence and the strength of legal grounds based on the extracted information. For example, the clarification unit can evaluate the credibility of evidence and identify legal risks. The clarification unit can also evaluate the strength of legal grounds and identify problems. This allows the clarification unit to efficiently identify problems in court. Some or all of the above-mentioned processing in the clarification unit may be performed using, or without, a generation AI, for example. For example, the clarification unit can input the extracted information into a generation AI, which can then identify problems.

[0033] The organizing unit can organize points to be argued when the user consults with a lawyer based on the obtained information. The organizing unit organizes points to be argued when the user consults with a lawyer based on, for example, the obtained information. For example, the organizing unit organizes legal grounds, methods of presenting evidence, etc. The organizing unit can also organize points that the user can use to ask specific questions or ask questions to the lawyer based on the obtained information. For example, the organizing unit can organize legal grounds and methods of presenting evidence. The organizing unit can also organize points that the user can use to ask specific questions or ask questions to the lawyer. This allows the organizing unit to efficiently consult with a lawyer. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the organizing unit can input the obtained information into a generation AI, which can organize the points to be argued.

[0034] The reception unit can analyze the user's past legal consultation history and select the optimal input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. The reception unit can also preferentially suggest voice input if the user has used voice input in the past. For example, if the user has used voice input in the past, the reception unit can preferentially suggest voice input. The reception unit can also prompt the user to input during a specific time period if the user has previously consulted during that time period. For example, if the user has previously consulted during a specific time period, the reception unit can prompt the user to input during that time period. This allows the reception unit to provide the optimal input method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past legal consultation history into a generation AI, which can select the optimal input method.

[0035] The reception unit can filter input of legal issues based on the user's current living situation and areas of interest. For example, if the user is interested in contract issues in their current living situation, the reception unit can prioritize displaying input items related to contract issues. For example, if the user is interested in contract issues in their current living situation, the reception unit can prioritize displaying input items related to contract issues. The reception unit can also prioritize displaying input items related to labor issues in their current living situation. For example, if the user is interested in labor issues, the reception unit can prioritize displaying input items related to labor issues in their current living situation. The reception unit can also prioritize displaying input items related to family issues in their current living situation. For example, if the user is interested in family issues, the reception unit can prioritize displaying input items related to family issues in their current living situation. This allows the reception unit to provide input items according to the user's areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's current living situation and areas of interest into the generation AI, and the generation AI can perform filtering.

[0036] When inputting legal questions, the reception unit can prioritize inputting highly relevant questions taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting legal questions related to that area. For example, when the user is in a specific area, the reception unit prioritizes inputting legal questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize inputting legal questions related to the travel destination. For example, when the user is traveling, the reception unit can prioritize inputting legal questions related to the travel destination. Furthermore, when the user is planning to move, the reception unit can prioritize inputting legal questions related to the new place of residence. For example, when the user is planning to move, the reception unit can prioritize inputting legal questions related to the new place of residence. This allows the reception unit to input highly relevant legal questions based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI, and the generation AI can prioritize inputting highly relevant questions.

[0037] The reception unit can analyze the user's social media activity when inputting a legal issue and input related issues. For example, if the user mentions a contract issue on social media, the reception unit displays input items related to the contract issue. For example, if the user mentions a contract issue on social media, the reception unit displays input items related to the contract issue. Furthermore, if the user mentions a labor issue, the reception unit can display input items related to the labor issue. For example, if the user mentions a labor issue, the reception unit can display input items related to the labor issue. Furthermore, if the user mentions a family issue, the reception unit can display input items related to the family issue. For example, if the user mentions a family issue, the reception unit can display input items related to the family issue. This allows the reception unit to input related legal issues based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity to the generation AI, which then inputs related issues.

[0038] During a search, the search unit can adjust the level of detail of search results based on the importance of the laws and precedents. For example, the search unit can prioritize displaying laws and precedents with high importance and provide detailed information. For example, the search unit can prioritize displaying laws and precedents with high importance and provide detailed information. The search unit can also briefly display laws and precedents with low importance and provide detailed information as needed. For example, the search unit can briefly display laws and precedents with low importance and provide detailed information as needed. The search unit can also adjust the display order of search results based on importance. For example, the search unit can adjust the display order of search results based on importance. This allows the search unit to adjust the level of detail of the search results based on the importance of the laws and precedents. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input the importance of the laws and precedents into a generation AI, and the generation AI can adjust the level of detail of the search results.

[0039] The search unit can apply different search algorithms depending on the category of law or precedent during a search. For example, in a search related to contract law, the search unit applies an algorithm specialized for contract law. For example, in a search related to contract law, the search unit applies an algorithm specialized for contract law. The search unit can also apply an algorithm specialized for labor law in a search related to labor law. For example, in a search related to labor law, the search unit can apply an algorithm specialized for labor law. The search unit can also apply an algorithm specialized for family law in a search related to family law. For example, in a search related to family law, the search unit can apply an algorithm specialized for family law. This allows the search unit to apply an optimal search algorithm depending on the category of law or precedent. Some or all of the above-mentioned processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input a category of law or precedent into a generation AI, and the generation AI can apply a different search algorithm.

[0040] During a search, the search unit can determine the priority of search results based on the submission date of the laws and precedents. For example, the search unit can prioritize displaying the most recent laws and precedents. For example, the search unit can prioritize displaying the most recent laws and precedents. The search unit can also display older laws and precedents as needed. For example, the search unit can display older laws and precedents as needed. The search unit can also adjust the display order of search results based on the submission date. For example, the search unit can adjust the display order of search results based on the submission date. This allows the search unit to provide optimal search results based on the submission date of the laws and precedents. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input the submission date of the laws and precedents into the generation AI, and the generation AI can determine the priority of the search results.

[0041] During a search, the search unit can adjust the order of search results based on the relevance of the laws and precedents. For example, the search unit prioritizes displaying laws and precedents that are most relevant to the user's problem. For example, the search unit prioritizes displaying laws and precedents that are most relevant to the user's problem. The search unit can also concisely display laws and precedents that are less relevant. For example, the search unit can concisely display laws and precedents that are less relevant. The search unit can also adjust the display order of search results based on relevance. For example, the search unit can adjust the display order of search results based on relevance. This allows the search unit to provide optimal search results based on the relevance of the laws and precedents. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input the relevance of laws and precedents into a generation AI, and the generation AI can adjust the order of the search results.

[0042] When calculating the winning percentage, the calculation unit can optimize the calculation algorithm by referring to past case data. The calculation unit, for example, adjusts the winning percentage calculation algorithm based on similar past cases. For example, the calculation unit adjusts the winning percentage calculation algorithm based on similar past cases. The calculation unit can also analyze past case data to improve the accuracy of the winning percentage calculation. For example, the calculation unit can analyze past case data to improve the accuracy of the winning percentage calculation. The calculation unit can also optimize the winning percentage calculation algorithm based on past case data. For example, the calculation unit can optimize the winning percentage calculation algorithm based on past case data. This allows the calculation unit to improve the accuracy of the winning percentage calculation based on the past case data. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, a generation AI. For example, the calculation unit can input past case data into the generation AI, which then optimizes the calculation algorithm.

[0043] When calculating the winning rate, the calculation unit can apply different calculation methods for each legal problem category. For example, when calculating the winning rate related to contract law, the calculation unit applies a calculation method specialized for contract law. For example, when calculating the winning rate related to contract law, the calculation unit applies a calculation method specialized for contract law. Furthermore, when calculating the winning rate related to labor law, the calculation unit can apply a calculation method specialized for labor law. For example, when calculating the winning rate related to labor law, the calculation unit can apply a calculation method specialized for labor law. Furthermore, when calculating the winning rate related to family law, the calculation unit can apply a calculation method specialized for family law. For example, when calculating the winning rate related to family law, the calculation unit can apply a calculation method specialized for family law. This allows the calculation unit to perform an optimal winning rate calculation according to the legal problem category. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, a generation AI. For example, the calculation unit can input the legal problem category into the generation AI, and the generation AI can apply a different calculation method.

[0044] When calculating the winning percentage, the calculation unit can weight the calculation data based on the time when the legal questions were submitted. For example, the calculation unit sets a high weight for the most recent legal questions. For example, the calculation unit sets a high weight for the most recent legal questions. The calculation unit can also set a low weight for older legal questions. For example, the calculation unit can set a low weight for older legal questions. The calculation unit can also adjust the weighting of the calculation data based on the time of submission. For example, the calculation unit can adjust the weighting of the calculation data based on the time of submission. This allows the calculation unit to perform optimal weighting based on the time when the legal questions were submitted. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the calculation unit can input the time when the legal questions were submitted into the generation AI, and the generation AI can weight the calculation data.

[0045] When calculating the winning percentage, the calculation unit can improve the accuracy of the calculation by referring to market data related to the legal issue. For example, the calculation unit improves the accuracy of the winning percentage calculation based on the relevant market data. The calculation unit can also analyze market data and adjust the winning percentage calculation algorithm. For example, the calculation unit can analyze market data and adjust the winning percentage calculation algorithm. The calculation unit can also improve the accuracy of the winning percentage calculation by referring to the market data. For example, the calculation unit can improve the accuracy of the winning percentage calculation by referring to the market data. This allows the calculation unit to improve the accuracy of the winning percentage calculation based on the relevant market data. Some or all of the above-mentioned processing in the calculation unit can be performed using, or without, the generation AI. For example, the calculation unit can input relevant market data into the generation AI, which can improve the accuracy of the calculation.

[0046] The identification unit can optimize the identification algorithm by referring to past case data when identifying problems. The identification unit, for example, adjusts the problem identification algorithm based on similar past cases. For example, the identification unit adjusts the problem identification algorithm based on similar past cases. The identification unit can also analyze past case data to improve the accuracy of problem identification. For example, the identification unit can analyze past case data to improve the accuracy of problem identification. The identification unit can also optimize the problem identification algorithm based on past case data. For example, the identification unit can optimize the problem identification algorithm based on past case data. This allows the identification unit to improve the accuracy of problem identification based on past case data. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input past case data to the generation AI, which can optimize the identification algorithm.

[0047] When identifying issues, the identification unit can apply different identification methods for each category of legal issues. For example, when identifying issues related to contract law, the identification unit applies a method specialized for contract law. For example, when identifying issues related to contract law, the identification unit applies a method specialized for contract law. Furthermore, when identifying issues related to labor law, the identification unit can apply a method specialized for labor law. For example, when identifying issues related to labor law, the identification unit can apply a method specialized for labor law. Furthermore, when identifying issues related to family law, the identification unit can apply a method specialized for family law. For example, when identifying issues related to family law, the identification unit can apply a method specialized for family law. This allows the identification unit to apply an optimal identification method according to the category of legal issue. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input a category of legal issues into the generation AI, and the generation AI can apply different identification methods.

[0048] When identifying issues, the clarification unit can weight the identified data based on the time of submission of the legal questions. For example, the clarification unit sets a high weight for the most recent legal questions. For example, the clarification unit sets a high weight for the most recent legal questions. The clarification unit can also set a low weight for older legal questions. For example, the clarification unit can set a low weight for older legal questions. The clarification unit can also adjust the weight of the identified data based on the time of submission. For example, the clarification unit can adjust the weight of the identified data based on the time of submission. This allows the clarification unit to perform optimal weighting based on the time of submission of the legal questions. Some or all of the above-mentioned processing in the clarification unit may be performed using, or without, the generation AI. For example, the clarification unit can input the time of submission of the legal questions to the generation AI, and the generation AI can weight the identified data.

[0049] When identifying issues, the identification unit can improve the accuracy of the identification by referring to market data related to the legal issue. The identification unit, for example, improves the accuracy of identifying issues based on the related market data. For example, the identification unit improves the accuracy of identifying issues based on the related market data. The identification unit can also analyze the market data and adjust the problem identification algorithm. For example, the identification unit can analyze the market data and adjust the problem identification algorithm. The identification unit can also improve the accuracy of identifying issues by referring to the market data. For example, the identification unit can improve the accuracy of identifying issues by referring to the market data. In this way, the identification unit can improve the accuracy of identifying issues based on the related market data. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input related market data to the generation AI, which can improve the accuracy of the identification.

[0050] When organizing the argument points, the organizing unit can select an optimal organizing method by referring to past consultation history. The organizing unit, for example, suggests an optimal organizing method based on organizing methods used by the user in the past. For example, the organizing unit suggests an optimal organizing method based on organizing methods used by the user in the past. The organizing unit can also analyze past consultation history and optimize the organizing method of the argument points. For example, the organizing unit can analyze past consultation history and optimize the organizing method of the argument points. The organizing unit can also select a method of organizing the argument points based on the past consultation history. For example, the organizing unit can select a method of organizing the argument points based on the past consultation history. This allows the organizing unit to provide an optimal organizing method based on the past consultation history. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, a generation AI. For example, the organizing unit can input past consultation history into the generation AI, which can select an optimal organizing method.

[0051] When organizing the points of argument, the organizing unit can apply different organizing methods for each category of legal issue. For example, when organizing points of argument related to contract law, the organizing unit applies a method specialized for contract law. For example, when organizing points of argument related to contract law, the organizing unit applies a method specialized for contract law. Furthermore, when organizing points of argument related to labor law, the organizing unit can apply a method specialized for labor law. For example, when organizing points of argument related to labor law, the organizing unit can apply a method specialized for labor law. Furthermore, when organizing points of argument related to family law, the organizing unit can apply a method specialized for family law. For example, when organizing points of argument related to family law, the organizing unit can apply a method specialized for family law. This allows the organizing unit to apply an optimal organizing method according to the category of legal issue. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, a generation AI. For example, the organizing unit can input the category of legal issue into the generation AI, and the generation AI can apply different organizing methods.

[0052] When organizing the argument points, the organizing unit can weight the organized data based on the time of submission of the legal questions. For example, the organizing unit sets a high weight for the most recent legal questions. For example, the organizing unit sets a high weight for the most recent legal questions. The organizing unit can also set a low weight for older legal questions. For example, the organizing unit can set a low weight for older legal questions. The organizing unit can also adjust the weight of the organized data based on the time of submission. For example, the organizing unit can adjust the weight of the organized data based on the time of submission. This allows the organizing unit to perform optimal weighting based on the time of submission of the legal questions. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, a generation AI. For example, the organizing unit can input the time of submission of the legal questions into the generation AI, and the generation AI can weight the organized data.

[0053] When organizing the points to be claimed, the organizing unit can improve the accuracy of the organization by referring to market data related to the legal issue. The organizing unit, for example, improves the accuracy of organizing the points to be claimed based on the relevant market data. For example, the organizing unit improves the accuracy of organizing the points to be claimed based on the relevant market data. The organizing unit can also analyze market data and adjust the method of organizing the points to be claimed. For example, the organizing unit can analyze market data and adjust the method of organizing the points to be claimed. The organizing unit can also improve the accuracy of organizing the points to be claimed by referring to the market data. For example, the organizing unit can improve the accuracy of organizing the points to be claimed by referring to the market data. This allows the organizing unit to improve the accuracy of organizing the points to be claimed based on the relevant market data. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, a generation AI. For example, the organizing unit can input related market data into the generation AI, which can improve the accuracy of the organization.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can analyze the user's past legal consultation history and select the optimal input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the user has used voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has previously consulted during a specific time period, the reception unit can prompt the user to input during that time period. This allows the reception unit to provide the optimal input method based on the user's past history.

[0056] When calculating the winning rate, the calculation unit can apply different calculation methods to each legal issue category. For example, when calculating the winning rate for contract law, a calculation method specialized for contract law can be applied. Also, when calculating the winning rate for labor law, a calculation method specialized for labor law can be applied. Furthermore, when calculating the winning rate for family law, a calculation method specialized for family law can be applied. This allows the calculation unit to perform the optimal winning rate calculation according to the legal issue category.

[0057] When organizing the points of contention, the organizing unit can select the optimal organizing method by referring to the past consultation history. For example, the organizing unit can propose the optimal organizing method based on the organizing method used by the user in the past. The organizing unit can also analyze the past consultation history and optimize the organizing method of the points of contention. Furthermore, the organizing unit can select the organizing method of the points of contention based on the past consultation history. This allows the organizing unit to provide the optimal organizing method based on the past consultation history.

[0058] When searching, the search unit can apply different search algorithms depending on the category of law or precedent. For example, in a search related to contract law, an algorithm specialized for contract law can be applied. In addition, in a search related to labor law, an algorithm specialized for labor law can be applied. Furthermore, in a search related to family law, an algorithm specialized for family law can be applied. This allows the search unit to apply the optimal search algorithm depending on the category of law or precedent.

[0059] When identifying issues, the identification unit can apply different identification methods to each category of legal issue. For example, when identifying issues related to contract law, a method specialized for contract law can be applied. When identifying issues related to labor law, a method specialized for labor law can be applied. Furthermore, when identifying issues related to family law, a method specialized for family law can be applied. This allows the identification unit to apply the optimal identification method according to the category of legal issue.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives input of a legal issue from a user. Examples of legal issues input by the user include, but are not limited to, breach of contract, labor issues, and family issues. For example, when the user inputs an issue related to a breach of contract, the reception unit prompts the user to input specific information. For example, the user can input the content of the contract, specific circumstances of the breach, etc. Step 2: The search unit uses the generation AI to search for relevant laws and precedents based on the information received by the reception unit. For example, the search unit references a vast database of laws and precedents to extract information relevant to the user's problem. For example, it searches for past precedents and related laws regarding breach of contract. Step 3: The calculation unit uses the generation AI to calculate the success rate in court based on the information obtained by the search unit. For example, the calculation unit calculates the success rate by referring to past precedents of similar cases. For example, the calculation unit calculates the success rate based on past precedents regarding breach of contract. Step 4: The Identification Unit uses the generation AI to identify problems based on the results obtained by the Calculation Unit. For example, the Identification Unit can identify potential problems in court based on the extracted information. For example, it can identify legal risks, insufficient evidence, etc. Step 5: The organizing unit uses the generation AI to organize the points to be argued when the user consults with a lawyer based on the information obtained by the extraction unit. The organizing unit organizes the points to be argued when the user consults with a lawyer based on the information obtained, for example. For example, it can organize legal grounds, methods of presenting evidence, etc.

[0062] (Example 2) A legal problem analysis system according to an embodiment of the present invention uses a generation AI to identify the success rate and problem areas for legal problems. In this legal problem analysis system, a user inputs the legal problem they wish to discuss, and the generation AI analyzes the input information and searches for relevant laws and precedents. The generation AI then calculates the success rate in court based on the extracted information and identifies the problem areas. Finally, the generation AI uses the obtained information to organize the points the user will argue when consulting a lawyer. This system deepens understanding of legal issues and facilitates the development of a strategy for court proceedings. It also shortens consultation times with lawyers and enables efficient problem resolution. For example, a user inputs the legal problem they wish to discuss. In this case, the user enters specific information, such as a breach of contract or labor issue. For example, the user might enter "problem related to breach of contract." This information is entered into the generation AI. The generation AI then analyzes the input information and searches for relevant laws and precedents. The generation AI references a vast legal database and precedent database to extract information relevant to the user's problem. For example, it searches for past precedents and related laws regarding breach of contract. The generation AI then calculates the success rate in court based on the extracted information. The generating AI calculates the success rate by referring to past precedents of similar cases. For example, it calculates the success rate based on past precedents for breach of contract. It also identifies problems and indicates to the user their weak points. Finally, the generating AI uses the information obtained to organize the points of argument the user will present when consulting with a lawyer. This allows users to consult with their lawyers efficiently and smoothly prepare for trial. For example, by consulting with a lawyer based on the points of argument organized by the generating AI, problems can be resolved efficiently. This system deepens understanding of legal issues and makes it easier to formulate strategies for trial. It also shortens consultation times with lawyers and enables efficient problem resolution. For example, based on the information provided by the generating AI, users can ask specific questions and make inquiries to their lawyers. This allows consultations with lawyers to proceed smoothly and problems to be resolved efficiently.This allows the legal problem analysis system to deepen the user's understanding of legal issues and make it easier to formulate strategies for court proceedings. It also reduces consultation time with lawyers and allows for efficient problem resolution.

[0063] A legal problem analysis system according to an embodiment includes a reception unit, a search unit, a calculation unit, a sifting unit, and a sorting unit. The reception unit receives input of a legal problem from a user. Examples of legal problems input by the user include, but are not limited to, breach of contract, labor issues, and family issues. The reception unit, for example, prompts the user to enter specific information when inputting a problem related to breach of contract. For example, the user can enter the content of the contract and specific circumstances of the breach. The search unit uses a generation AI to search for relevant laws and precedents based on the information received by the reception unit. The search unit, for example, references a vast legal database and a precedent database to extract information related to the user's problem. For example, it searches for past precedents related to breach of contract and related laws. The search unit can efficiently search for relevant laws and precedents using the generation AI. The calculation unit uses the generation AI to calculate the success rate in court based on the information obtained by the search unit. For example, the calculation unit references past precedents of similar cases to calculate the success rate. For example, the win rate is calculated based on past precedents related to breach of contract. The calculation unit can use the generation AI to accurately calculate the win rate based on past case data. The identification unit can use the generation AI to identify issues based on the results obtained by the calculation unit. The identification unit can, for example, identify issues in a trial based on extracted information. For example, it can identify legal risks, insufficient evidence, etc. The identification unit can efficiently identify issues using the generation AI. The organization unit can use the generation AI to organize points to be argued when the user consults with a lawyer based on the information obtained by the identification unit. For example, the organization unit can organize points to be argued when the user consults with a lawyer based on the obtained information. For example, it can organize legal grounds, methods of presenting evidence, etc. The organization unit can efficiently organize points to be argued using the generation AI. As a result, the legal problem analysis system according to the embodiment can deepen the user's understanding of legal issues and make it easier to formulate strategies for trial. It can also shorten the consultation time with a lawyer and achieve efficient problem resolution.

[0064] The search unit can refer to multiple legal databases and case law databases to extract information related to the user's problem. The search unit can refer to, for example, a national legal database, a specific case law collection, etc. For example, the search unit can refer to a national legal database to extract laws related to the user's problem. The search unit can also refer to a specific case law collection to extract case law related to the user's problem. For example, the search unit can search for past case law regarding breach of contract and extract related information. This allows the search unit to efficiently extract information related to the user's problem. Some or all of the above-mentioned processing in the search unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the search unit can input information related to the user's problem to a generation AI, which can then search for the relevant information.

[0065] The calculation unit can calculate the win rate by referring to past precedents of similar cases. The calculation unit, for example, calculates the win rate by referring to past precedents of similar cases. For example, the calculation unit calculates the win rate based on past precedents regarding breach of contract. The calculation unit can also calculate the win rate based on past precedents regarding labor issues. For example, the calculation unit can calculate the win rate based on past precedents regarding family issues. This allows the calculation unit to accurately calculate the win rate based on past precedents. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, a generation AI, for example. For example, the calculation unit can input past precedent data into the generation AI, which then calculates the win rate.

[0066] The clarification unit can identify problems in court based on the extracted information. The clarification unit, for example, identifies problems in court based on the extracted information. For example, the clarification unit identifies legal risks, insufficient evidence, etc. The clarification unit can also evaluate the credibility of evidence and the strength of legal grounds based on the extracted information. For example, the clarification unit can evaluate the credibility of evidence and identify legal risks. The clarification unit can also evaluate the strength of legal grounds and identify problems. This allows the clarification unit to efficiently identify problems in court. Some or all of the above-mentioned processing in the clarification unit may be performed using, or without, a generation AI, for example. For example, the clarification unit can input the extracted information into a generation AI, which can then identify problems.

[0067] The organizing unit can organize points to be argued when the user consults with a lawyer based on the obtained information. The organizing unit organizes points to be argued when the user consults with a lawyer based on, for example, the obtained information. For example, the organizing unit organizes legal grounds, methods of presenting evidence, etc. The organizing unit can also organize points that the user can use to ask specific questions or ask questions to the lawyer based on the obtained information. For example, the organizing unit can organize legal grounds and methods of presenting evidence. The organizing unit can also organize points that the user can use to ask specific questions or ask questions to the lawyer. This allows the organizing unit to efficiently consult with a lawyer. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the organizing unit can input the obtained information into a generation AI, which can organize the points to be argued.

[0068] The reception unit can estimate the user's emotions and adjust the timing of inputting the legal questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input the legal questions during a time when the user can relax. For example, the reception unit prompts the user to input the legal questions during a time when the user can relax. The reception unit can also prompt the user to input the legal questions immediately when the user is concentrating. For example, if the user is concentrating, the reception unit can prompt the user to input the legal questions immediately. The reception unit can also prompt the user to input the legal questions after a break when the user is tired. For example, if the user is tired, the reception unit can prompt the user to input the legal questions after a break. This allows the reception unit to input the legal questions at the optimal timing depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotions into the generation AI, which can then infer the emotions and adjust the timing of inputting legal questions.

[0069] The reception unit can analyze the user's past legal consultation history and select the optimal input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. The reception unit can also preferentially suggest voice input if the user has used voice input in the past. For example, if the user has used voice input in the past, the reception unit can preferentially suggest voice input. The reception unit can also prompt the user to input during a specific time period if the user has previously consulted during that time period. For example, if the user has previously consulted during a specific time period, the reception unit can prompt the user to input during that time period. This allows the reception unit to provide the optimal input method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past legal consultation history into a generation AI, which can select the optimal input method.

[0070] The reception unit can filter input of legal issues based on the user's current living situation and areas of interest. For example, if the user is interested in contract issues in their current living situation, the reception unit can prioritize displaying input items related to contract issues. For example, if the user is interested in contract issues in their current living situation, the reception unit can prioritize displaying input items related to contract issues. The reception unit can also prioritize displaying input items related to labor issues in their current living situation. For example, if the user is interested in labor issues, the reception unit can prioritize displaying input items related to labor issues in their current living situation. The reception unit can also prioritize displaying input items related to family issues in their current living situation. For example, if the user is interested in family issues, the reception unit can prioritize displaying input items related to family issues in their current living situation. This allows the reception unit to provide input items according to the user's areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's current living situation and areas of interest into the generation AI, and the generation AI can perform filtering.

[0071] The reception unit can estimate the user's emotions and determine the priority of legal questions to be input based on the estimated user emotions. For example, when the user is feeling anxious, the reception unit prioritizes input of legal questions with high urgency. For example, when the user is feeling anxious, the reception unit prioritizes input of legal questions with high urgency. Furthermore, when the user is relaxed, the reception unit can input detailed legal questions. For example, when the user is relaxed, the reception unit can input detailed legal questions. Furthermore, when the user is in a hurry, the reception unit can prioritize input of concise legal questions. For example, when the user is in a hurry, the reception unit can prioritize input of concise legal questions. In this way, the reception unit can determine the priority of legal questions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotions into the generation AI, which can then infer the emotions and determine the priority of legal issues.

[0072] When inputting legal questions, the reception unit can prioritize inputting highly relevant questions taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting legal questions related to that area. For example, when the user is in a specific area, the reception unit prioritizes inputting legal questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize inputting legal questions related to the travel destination. For example, when the user is traveling, the reception unit can prioritize inputting legal questions related to the travel destination. Furthermore, when the user is planning to move, the reception unit can prioritize inputting legal questions related to the new place of residence. For example, when the user is planning to move, the reception unit can prioritize inputting legal questions related to the new place of residence. This allows the reception unit to input highly relevant legal questions based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI, and the generation AI can prioritize inputting highly relevant questions.

[0073] The reception unit can analyze the user's social media activity when inputting a legal issue and input related issues. For example, if the user mentions a contract issue on social media, the reception unit displays input items related to the contract issue. For example, if the user mentions a contract issue on social media, the reception unit displays input items related to the contract issue. Furthermore, if the user mentions a labor issue, the reception unit can display input items related to the labor issue. For example, if the user mentions a labor issue, the reception unit can display input items related to the labor issue. Furthermore, if the user mentions a family issue, the reception unit can display input items related to the family issue. For example, if the user mentions a family issue, the reception unit can display input items related to the family issue. This allows the reception unit to input related legal issues based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity to the generation AI, which then inputs related issues.

[0074] The search unit can estimate the user's emotions and adjust the display method of search results based on the estimated user's emotions. For example, if the user is nervous, the search unit displays search results that are simple and highly visible. For example, if the user is nervous, the search unit displays search results that are simple and highly visible. Furthermore, if the user is relaxed, the search unit can display search results that include detailed information. For example, if the user is relaxed, the search unit can display search results that include detailed information. Furthermore, if the user is in a hurry, the search unit can display search results that focus on the main points. For example, if the user is in a hurry, the search unit can display search results that focus on the main points. This allows the search unit to display optimal search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input the user's emotions into the generation AI, which can then infer the emotions and adjust how search results are displayed.

[0075] During a search, the search unit can adjust the level of detail of search results based on the importance of the laws and precedents. For example, the search unit can prioritize displaying laws and precedents with high importance and provide detailed information. For example, the search unit can prioritize displaying laws and precedents with high importance and provide detailed information. The search unit can also briefly display laws and precedents with low importance and provide detailed information as needed. For example, the search unit can briefly display laws and precedents with low importance and provide detailed information as needed. The search unit can also adjust the display order of search results based on importance. For example, the search unit can adjust the display order of search results based on importance. This allows the search unit to adjust the level of detail of the search results based on the importance of the laws and precedents. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input the importance of the laws and precedents into a generation AI, and the generation AI can adjust the level of detail of the search results.

[0076] The search unit can apply different search algorithms depending on the category of law or precedent during a search. For example, in a search related to contract law, the search unit applies an algorithm specialized for contract law. For example, in a search related to contract law, the search unit applies an algorithm specialized for contract law. The search unit can also apply an algorithm specialized for labor law in a search related to labor law. For example, in a search related to labor law, the search unit can apply an algorithm specialized for labor law. The search unit can also apply an algorithm specialized for family law in a search related to family law. For example, in a search related to family law, the search unit can apply an algorithm specialized for family law. This allows the search unit to apply an optimal search algorithm depending on the category of law or precedent. Some or all of the above-mentioned processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input a category of law or precedent into a generation AI, and the generation AI can apply a different search algorithm.

[0077] The search unit can estimate the user's emotions and adjust the length of search results based on the estimated user emotions. For example, if the user is in a hurry, the search unit displays search results that are short and to the point. For example, if the user is in a hurry, the search unit displays search results that are short and to the point. Furthermore, if the user is relaxed, the search unit can display longer search results with detailed explanations. For example, if the user is relaxed, the search unit can display longer search results with detailed explanations. Furthermore, if the user is excited, the search unit can display search results with visually stimulating effects. For example, if the user is excited, the search unit can display search results with visually stimulating effects. This allows the search unit to provide search results of an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input the user's emotions into the generation AI, which can then infer the emotions and adjust the length of the search results.

[0078] During a search, the search unit can determine the priority of search results based on the submission date of the laws and precedents. For example, the search unit can prioritize displaying the most recent laws and precedents. For example, the search unit can prioritize displaying the most recent laws and precedents. The search unit can also display older laws and precedents as needed. For example, the search unit can display older laws and precedents as needed. The search unit can also adjust the display order of search results based on the submission date. For example, the search unit can adjust the display order of search results based on the submission date. This allows the search unit to provide optimal search results based on the submission date of the laws and precedents. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input the submission date of the laws and precedents into the generation AI, and the generation AI can determine the priority of the search results.

[0079] During a search, the search unit can adjust the order of search results based on the relevance of the laws and precedents. For example, the search unit prioritizes displaying laws and precedents that are most relevant to the user's problem. For example, the search unit prioritizes displaying laws and precedents that are most relevant to the user's problem. The search unit can also concisely display laws and precedents that are less relevant. For example, the search unit can concisely display laws and precedents that are less relevant. The search unit can also adjust the display order of search results based on relevance. For example, the search unit can adjust the display order of search results based on relevance. This allows the search unit to provide optimal search results based on the relevance of the laws and precedents. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input the relevance of laws and precedents into a generation AI, and the generation AI can adjust the order of the search results.

[0080] The calculation unit can estimate the user's emotions and adjust the winning percentage calculation method based on the estimated user's emotions. For example, if the user is feeling anxious, the calculation unit performs a conservative winning percentage calculation. For example, if the user is feeling anxious, the calculation unit performs a conservative winning percentage calculation. The calculation unit can also perform an aggressive winning percentage calculation if the user is feeling confident. For example, if the user is feeling confident, the calculation unit can perform an aggressive winning percentage calculation. The calculation unit can also perform a standard winning percentage calculation if the user is feeling neutral. For example, if the user is feeling neutral, the calculation unit can perform a standard winning percentage calculation. This allows the calculation unit to perform an optimal winning percentage calculation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the calculation unit can input the user's emotions into the generation AI, which can then infer the emotions and adjust the method of calculating the win rate.

[0081] When calculating the winning percentage, the calculation unit can optimize the calculation algorithm by referring to past case data. The calculation unit, for example, adjusts the winning percentage calculation algorithm based on similar past cases. For example, the calculation unit adjusts the winning percentage calculation algorithm based on similar past cases. The calculation unit can also analyze past case data to improve the accuracy of the winning percentage calculation. For example, the calculation unit can analyze past case data to improve the accuracy of the winning percentage calculation. The calculation unit can also optimize the winning percentage calculation algorithm based on past case data. For example, the calculation unit can optimize the winning percentage calculation algorithm based on past case data. This allows the calculation unit to improve the accuracy of the winning percentage calculation based on the past case data. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, a generation AI. For example, the calculation unit can input past case data into the generation AI, which then optimizes the calculation algorithm.

[0082] When calculating the winning rate, the calculation unit can apply different calculation methods for each legal problem category. For example, when calculating the winning rate related to contract law, the calculation unit applies a calculation method specialized for contract law. For example, when calculating the winning rate related to contract law, the calculation unit applies a calculation method specialized for contract law. Furthermore, when calculating the winning rate related to labor law, the calculation unit can apply a calculation method specialized for labor law. For example, when calculating the winning rate related to labor law, the calculation unit can apply a calculation method specialized for labor law. Furthermore, when calculating the winning rate related to family law, the calculation unit can apply a calculation method specialized for family law. For example, when calculating the winning rate related to family law, the calculation unit can apply a calculation method specialized for family law. This allows the calculation unit to perform an optimal winning rate calculation according to the legal problem category. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, a generation AI. For example, the calculation unit can input the legal problem category into the generation AI, and the generation AI can apply a different calculation method.

[0083] The calculation unit can estimate the user's emotions and determine the priority of win rate calculations based on the estimated user emotions. For example, if the user is feeling anxious, the calculation unit prioritizes the calculation of win rates for legal issues with high urgency. For example, if the user is feeling anxious, the calculation unit prioritizes the calculation of win rates for legal issues with high urgency. The calculation unit can also perform detailed win rate calculations if the user is relaxed. For example, the calculation unit can perform detailed win rate calculations if the user is relaxed. The calculation unit can also prioritize concise win rate calculations if the user is in a hurry. For example, the calculation unit can prioritize concise win rate calculations if the user is in a hurry. This allows the calculation unit to calculate win rates in an optimal order depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the calculation unit can input the user's emotions into the generation AI, which can then estimate the emotions and determine priorities for calculating the win rate.

[0084] When calculating the winning percentage, the calculation unit can weight the calculation data based on the time when the legal questions were submitted. For example, the calculation unit sets a high weight for the most recent legal questions. For example, the calculation unit sets a high weight for the most recent legal questions. The calculation unit can also set a low weight for older legal questions. For example, the calculation unit can set a low weight for older legal questions. The calculation unit can also adjust the weighting of the calculation data based on the time of submission. For example, the calculation unit can adjust the weighting of the calculation data based on the time of submission. This allows the calculation unit to perform optimal weighting based on the time when the legal questions were submitted. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the calculation unit can input the time when the legal questions were submitted into the generation AI, and the generation AI can weight the calculation data.

[0085] When calculating the winning percentage, the calculation unit can improve the accuracy of the calculation by referring to market data related to the legal issue. For example, the calculation unit improves the accuracy of the winning percentage calculation based on the relevant market data. The calculation unit can also analyze market data and adjust the winning percentage calculation algorithm. For example, the calculation unit can analyze market data and adjust the winning percentage calculation algorithm. The calculation unit can also improve the accuracy of the winning percentage calculation by referring to the market data. For example, the calculation unit can improve the accuracy of the winning percentage calculation by referring to the market data. This allows the calculation unit to improve the accuracy of the winning percentage calculation based on the relevant market data. Some or all of the above-mentioned processing in the calculation unit can be performed using, or without, the generation AI. For example, the calculation unit can input relevant market data into the generation AI, which can improve the accuracy of the calculation.

[0086] The clarification unit can estimate the user's emotions and adjust the display method of the problem based on the estimated user's emotions. For example, when the user is nervous, the clarification unit provides a simple, highly visible display method. For example, when the user is nervous, the clarification unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the clarification unit can provide a display method including detailed information. For example, when the user is relaxed, the clarification unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the clarification unit can provide a display method that emphasizes the main points. For example, when the user is in a hurry, the clarification unit can provide a display method that emphasizes the main points. This allows the clarification unit to present the problem in an optimal display method depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the clarification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the user's emotions into the generation AI, which can then infer the emotions and adjust how the problems are displayed.

[0087] The identification unit can optimize the identification algorithm by referring to past case data when identifying problems. The identification unit, for example, adjusts the problem identification algorithm based on similar past cases. For example, the identification unit adjusts the problem identification algorithm based on similar past cases. The identification unit can also analyze past case data to improve the accuracy of problem identification. For example, the identification unit can analyze past case data to improve the accuracy of problem identification. The identification unit can also optimize the problem identification algorithm based on past case data. For example, the identification unit can optimize the problem identification algorithm based on past case data. This allows the identification unit to improve the accuracy of problem identification based on past case data. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input past case data to the generation AI, which can optimize the identification algorithm.

[0088] When identifying issues, the identification unit can apply different identification methods for each category of legal issues. For example, when identifying issues related to contract law, the identification unit applies a method specialized for contract law. For example, when identifying issues related to contract law, the identification unit applies a method specialized for contract law. Furthermore, when identifying issues related to labor law, the identification unit can apply a method specialized for labor law. For example, when identifying issues related to labor law, the identification unit can apply a method specialized for labor law. Furthermore, when identifying issues related to family law, the identification unit can apply a method specialized for family law. For example, when identifying issues related to family law, the identification unit can apply a method specialized for family law. This allows the identification unit to apply an optimal identification method according to the category of legal issue. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input a category of legal issues into the generation AI, and the generation AI can apply different identification methods.

[0089] The clarification unit can estimate the user's emotions and determine the priority of problems based on the estimated user's emotions. For example, if the user is feeling anxious, the clarification unit can prioritize displaying problems with high urgency. For example, if the user is feeling anxious, the clarification unit can prioritize displaying problems with high urgency. The clarification unit can also display detailed problems when the user is relaxed. For example, if the user is relaxed, the clarification unit can display detailed problems. The clarification unit can also prioritize displaying concise problems when the user is in a hurry. For example, if the user is in a hurry, the clarification unit can prioritize displaying concise problems. This allows the clarification unit to display problems in an optimal order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the clarification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the user's emotions into the generation AI, which can then infer the emotions and determine the priority of the problems.

[0090] When identifying issues, the clarification unit can weight the identified data based on the time of submission of the legal questions. For example, the clarification unit sets a high weight for the most recent legal questions. For example, the clarification unit sets a high weight for the most recent legal questions. The clarification unit can also set a low weight for older legal questions. For example, the clarification unit can set a low weight for older legal questions. The clarification unit can also adjust the weight of the identified data based on the time of submission. For example, the clarification unit can adjust the weight of the identified data based on the time of submission. This allows the clarification unit to perform optimal weighting based on the time of submission of the legal questions. Some or all of the above-mentioned processing in the clarification unit may be performed using, or without, the generation AI. For example, the clarification unit can input the time of submission of the legal questions to the generation AI, and the generation AI can weight the identified data.

[0091] When identifying issues, the identification unit can improve the accuracy of the identification by referring to market data related to the legal issue. The identification unit, for example, improves the accuracy of identifying issues based on the related market data. For example, the identification unit improves the accuracy of identifying issues based on the related market data. The identification unit can also analyze the market data and adjust the problem identification algorithm. For example, the identification unit can analyze the market data and adjust the problem identification algorithm. The identification unit can also improve the accuracy of identifying issues by referring to the market data. For example, the identification unit can improve the accuracy of identifying issues by referring to the market data. In this way, the identification unit can improve the accuracy of identifying issues based on the related market data. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input related market data to the generation AI, which can improve the accuracy of the identification.

[0092] The organizing unit can estimate the user's emotions and adjust the display method of the assertion points based on the estimated user's emotions. For example, when the user is nervous, the organizing unit provides a simple, highly visible display method. For example, when the user is nervous, the organizing unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the organizing unit can provide a display method including detailed information. For example, when the user is relaxed, the organizing unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the organizing unit can provide a display method that emphasizes the main points. For example, when the user is in a hurry, the organizing unit can provide a display method that emphasizes the main points. This allows the organizing unit to provide the assertion points in an optimal display method depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the organizing unit can input the user's emotions into the generation AI, which can then infer the emotions and adjust the way the points are displayed.

[0093] When organizing the argument points, the organizing unit can select an optimal organizing method by referring to past consultation history. The organizing unit, for example, suggests an optimal organizing method based on organizing methods used by the user in the past. For example, the organizing unit suggests an optimal organizing method based on organizing methods used by the user in the past. The organizing unit can also analyze past consultation history and optimize the organizing method of the argument points. For example, the organizing unit can analyze past consultation history and optimize the organizing method of the argument points. The organizing unit can also select a method of organizing the argument points based on the past consultation history. For example, the organizing unit can select a method of organizing the argument points based on the past consultation history. This allows the organizing unit to provide an optimal organizing method based on the past consultation history. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, a generation AI. For example, the organizing unit can input past consultation history into the generation AI, which can select an optimal organizing method.

[0094] When organizing the points of argument, the organizing unit can apply different organizing methods for each category of legal issue. For example, when organizing points of argument related to contract law, the organizing unit applies a method specialized for contract law. For example, when organizing points of argument related to contract law, the organizing unit applies a method specialized for contract law. Furthermore, when organizing points of argument related to labor law, the organizing unit can apply a method specialized for labor law. For example, when organizing points of argument related to labor law, the organizing unit can apply a method specialized for labor law. Furthermore, when organizing points of argument related to family law, the organizing unit can apply a method specialized for family law. For example, when organizing points of argument related to family law, the organizing unit can apply a method specialized for family law. This allows the organizing unit to apply an optimal organizing method according to the category of legal issue. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, a generation AI. For example, the organizing unit can input the category of legal issue into the generation AI, and the generation AI can apply different organizing methods.

[0095] The organizing unit can estimate the user's emotions and determine the priority of the assertion points based on the estimated user's emotions. For example, when the user is feeling anxious, the organizing unit prioritizes displaying assertion points with a high level of urgency. For example, when the user is feeling anxious, the organizing unit prioritizes displaying assertion points with a high level of urgency. The organizing unit can also display detailed assertion points when the user is relaxed. For example, when the user is relaxed, the organizing unit can display detailed assertion points. The organizing unit can also prioritize displaying concise assertion points when the user is in a hurry. For example, when the user is in a hurry, the organizing unit can prioritize displaying concise assertion points. This allows the organizing unit to display the assertion points in an optimal order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the organizing unit can input the user's emotions into the generation AI, which can then estimate the emotions and determine the priority of the points being argued.

[0096] When organizing the argument points, the organizing unit can weight the organized data based on the time of submission of the legal questions. For example, the organizing unit sets a high weight for the most recent legal questions. For example, the organizing unit sets a high weight for the most recent legal questions. The organizing unit can also set a low weight for older legal questions. For example, the organizing unit can set a low weight for older legal questions. The organizing unit can also adjust the weight of the organized data based on the time of submission. For example, the organizing unit can adjust the weight of the organized data based on the time of submission. This allows the organizing unit to perform optimal weighting based on the time of submission of the legal questions. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, a generation AI. For example, the organizing unit can input the time of submission of the legal questions into the generation AI, and the generation AI can weight the organized data.

[0097] When organizing the points to be claimed, the organizing unit can improve the accuracy of the organization by referring to market data related to the legal issue. The organizing unit, for example, improves the accuracy of organizing the points to be claimed based on the relevant market data. For example, the organizing unit improves the accuracy of organizing the points to be claimed based on the relevant market data. The organizing unit can also analyze market data and adjust the method of organizing the points to be claimed. For example, the organizing unit can analyze market data and adjust the method of organizing the points to be claimed. The organizing unit can also improve the accuracy of organizing the points to be claimed by referring to the market data. For example, the organizing unit can improve the accuracy of organizing the points to be claimed by referring to the market data. This allows the organizing unit to improve the accuracy of organizing the points to be claimed based on the relevant market data. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, a generation AI. For example, the organizing unit can input related market data into the generation AI, which can improve the accuracy of the organization. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, search unit, calculation unit, clarification unit, and organization unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input of legal issues from a user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for relevant laws and precedents using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the winning rate in court. The clarification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and clarifies problems. The organization unit is realized, for example, by the control unit 46A of the smart device 14 and organizes the points of argument that the user will present when consulting a lawyer. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, search unit, calculation unit, clarification unit, and organization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input of legal issues from a user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for relevant laws and precedents using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the winning rate in court. The clarification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and clarifies problems. The organization unit is realized, for example, by the control unit 46A of the smart glasses 214 and organizes points of argument that the user will present when consulting a lawyer. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, search unit, calculation unit, clarification unit, and organization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input of a legal problem from a user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for related laws and precedents using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the winning rate in court. The clarification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and clarifies problems. The organization unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and organizes the points of argument that the user will present when consulting a lawyer. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, search unit, calculation unit, clarification unit, and organization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of a legal problem from a user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for related laws and precedents using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the winning rate in court. The clarification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and clarifies problems. The organization unit is realized, for example, by the control unit 46A of the robot 414 and organizes the points of argument that the user will present when consulting a lawyer.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can analyze the user's past legal consultation history and select the optimal input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the user has used voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has previously consulted during a specific time period, the reception unit can prompt the user to input during that time period. This allows the reception unit to provide the optimal input method based on the user's past history.

[0100] The search unit can estimate the user's emotions and adjust the display method of search results based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible search results can be displayed. If the user is relaxed, search results including detailed information can be displayed. Furthermore, if the user is in a hurry, search results that focus on the main points can be displayed. In this way, the search unit can display optimal search results according to the user's emotions.

[0101] When calculating the winning rate, the calculation unit can apply different calculation methods to each legal issue category. For example, when calculating the winning rate for contract law, a calculation method specialized for contract law can be applied. Also, when calculating the winning rate for labor law, a calculation method specialized for labor law can be applied. Furthermore, when calculating the winning rate for family law, a calculation method specialized for family law can be applied. This allows the calculation unit to perform the optimal winning rate calculation according to the legal issue category.

[0102] The clarifying unit can estimate the user's emotions and adjust the display method of the problems based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the clarifying unit can present problems in the optimal display method according to the user's emotions.

[0103] When organizing the points of contention, the organizing unit can select the optimal organizing method by referring to the past consultation history. For example, the organizing unit can propose the optimal organizing method based on the organizing method used by the user in the past. The organizing unit can also analyze the past consultation history and optimize the organizing method of the points of contention. Furthermore, the organizing unit can select the organizing method of the points of contention based on the past consultation history. This allows the organizing unit to provide the optimal organizing method based on the past consultation history.

[0104] The reception unit can estimate the user's emotions and adjust the timing for inputting legal questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input legal questions during a time when the user can relax. Also, if the user is concentrating, the reception unit can prompt the user to input legal questions immediately. Furthermore, if the user is tired, the reception unit can prompt the user to input legal questions after a break. This allows the reception unit to input legal questions at the optimal timing according to the user's emotions.

[0105] When searching, the search unit can apply different search algorithms depending on the category of law or precedent. For example, in a search related to contract law, an algorithm specialized for contract law can be applied. In addition, in a search related to labor law, an algorithm specialized for labor law can be applied. Furthermore, in a search related to family law, an algorithm specialized for family law can be applied. This allows the search unit to apply the optimal search algorithm depending on the category of law or precedent.

[0106] The calculation unit can estimate the user's emotions and adjust the winning percentage calculation method based on the estimated user's emotions. For example, if the user is feeling anxious, a conservative winning percentage calculation can be performed. Alternatively, if the user is feeling confident, an aggressive winning percentage calculation can be performed. Furthermore, if the user is feeling neutral, a standard winning percentage calculation can be performed. This allows the calculation unit to perform an optimal winning percentage calculation according to the user's emotions.

[0107] When identifying issues, the identification unit can apply different identification methods to each category of legal issue. For example, when identifying issues related to contract law, a method specialized for contract law can be applied. When identifying issues related to labor law, a method specialized for labor law can be applied. Furthermore, when identifying issues related to family law, a method specialized for family law can be applied. This allows the identification unit to apply the optimal identification method according to the category of legal issue.

[0108] The organizing unit can estimate the user's emotions and determine the priority of the assertion points based on the estimated user's emotions. For example, if the user is feeling anxious, it can prioritize displaying assertion points with high urgency. Also, if the user is relaxed, it can display detailed assertion points. Furthermore, if the user is in a hurry, it can prioritize displaying concise assertion points. In this way, the organizing unit can display assertion points in the optimal order according to the user's emotions.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit receives input of a legal issue from a user. Examples of legal issues input by the user include, but are not limited to, breach of contract, labor issues, and family issues. For example, when the user inputs an issue related to a breach of contract, the reception unit prompts the user to input specific information. For example, the user can input the content of the contract, specific circumstances of the breach, etc. Step 2: The search unit uses the generation AI to search for relevant laws and precedents based on the information received by the reception unit. For example, the search unit references a vast database of laws and precedents to extract information relevant to the user's problem. For example, it searches for past precedents and related laws regarding breach of contract. Step 3: The calculation unit uses the generation AI to calculate the success rate in court based on the information obtained by the search unit. For example, the calculation unit calculates the success rate by referring to past precedents of similar cases. For example, the calculation unit calculates the success rate based on past precedents regarding breach of contract. Step 4: The Identification Unit uses the generation AI to identify problems based on the results obtained by the Calculation Unit. For example, the Identification Unit can identify potential problems in court based on the extracted information. For example, it can identify legal risks, insufficient evidence, etc. Step 5: The organizing unit uses the generation AI to organize the points to be argued when the user consults with a lawyer based on the information obtained by the extraction unit. The organizing unit organizes the points to be argued when the user consults with a lawyer based on the information obtained, for example. For example, it can organize legal grounds, methods of presenting evidence, etc.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of legal issues from a user; a search unit that searches for relevant laws and precedents based on the information received by the reception unit; a calculation unit that calculates the winning rate in a lawsuit based on the information obtained by the search unit; a clarification unit that clarifies problems based on the results obtained by the calculation unit; and a sorting unit that sorts out points to be argued when the user consults with a lawyer based on the information obtained by the sorting unit. A system characterized by:

2. The search unit Consult multiple legal and case law databases to extract information relevant to your problem 2. The system of claim 1.

3. The calculation unit Calculate the success rate by looking at past precedents of similar cases 2. The system of claim 1.

4. The washing section includes: Identify issues in court based on the extracted information 2. The system of claim 1.

5. The organizing unit Based on the information obtained, organize the points that users should argue when consulting a lawyer.

2. The system of claim 1.

6. The reception unit Estimating user emotions and adjusting the timing of legal question input based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past legal consultation history and select the optimal input method 2. The system of claim 1.

8. The reception unit Filter legal questions as they are entered based on the user's current life situation and areas of interest 2. The system of claim 1.

Images