System

Abstract

An object of a system according to an embodiment is to accurately determine the degree of difficulty of a case and the ratio of winning complaints and to efficiently perform matching between a client and lawyers.SOLUTION: A system includes a matter analysis part, a winning percentage calculation part, a matching part, and a document creation part. The case analysis unit analyzes information on a case provided by a client. The lawsuit winning ratio calculation part calculates a lawsuit winning ratio on the basis of the information analyzed by the matter analysis part. A matching part performs the matching of the client and the lawyer by inputting the rates of the gold and the success reward by the lawyer on the basis of the rate of the winning complaint calculated by the winning complaint rate calculation part. The document creation unit automatically creates a document to be submitted to the court including a complaint based on the information provided from the client.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to accurately assess the difficulty of a case and the success rate, and to efficiently match clients with lawyers.

[0005] The system according to the embodiment aims to accurately determine the difficulty of a case and the success rate, and to efficiently match clients with lawyers. [Means for solving the problem]

[0006] The system according to the embodiment comprises a case analysis unit, a win rate calculation unit, a matching unit, and a document preparation unit. The case analysis unit analyzes case information provided by the client. The win rate calculation unit calculates the win rate based on the information analyzed by the case analysis unit. The matching unit matches clients with lawyers by having the lawyer input the rate of retainer fees and success fees based on the win rate calculated by the win rate calculation unit. The document preparation unit automatically prepares documents to be submitted to the court, including a complaint, based on the information provided by the client. [Effects of the Invention]

[0007] The system according to the embodiment can accurately determine the difficulty of a case and the success rate, and efficiently match clients with lawyers. [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 matching system according to an embodiment of the present invention optimizes the matching of lawyers and clients, improving the work efficiency of lawyers. This matching system uses AI to determine the difficulty of a case and calculate the win rate. Based on the results, lawyers input the retainer fee and success fee rate to match clients with lawyers. In addition, AI automatically prepares documents to be submitted to the court, including the complaint, providing a mechanism that allows lawyers to work efficiently even on low-cost cases. This allows the matching system to optimize the matching of lawyers and clients, improving the work efficiency of lawyers.

[0029] A matching system according to an embodiment includes a case analysis unit, a win rate calculation unit, a matching unit, and a document preparation unit. The case analysis unit analyzes case information provided by a client. For example, the analysis is based on the content of the case, the presence or absence of evidence, past precedents for similar cases, etc. The win rate calculation unit calculates the win rate based on the information analyzed by the case analysis unit. For example, the possibility of winning is calculated based on past precedent data and the strength of evidence. The matching unit matches clients and lawyers by having the lawyer input the retainer fee and contingency fee rate based on the win rate calculated by the win rate calculation unit. For example, the matching unit recommends a lawyer that matches the client's budget and desired fee structure. The document preparation unit automatically prepares documents to be submitted to the court, including a complaint, based on information provided by the client. For example, the document preparation unit automatically generates the content of the complaint, the organization of evidence, legal arguments, etc. This allows the matching system according to an embodiment to optimize matching between lawyers and clients and improve the work efficiency of lawyers.

[0030] The Case Analysis Department can perform analysis by incorporating not only past case law data, but also the latest legal reform information and social trends. For example, the Case Analysis Department automatically collects the latest legal reform information and reflects it in determining the difficulty of a case. For example, it analyzes how new laws and regulations will affect a case. The Case Analysis Department also analyzes social trends and identifies factors that affect the difficulty of a case. For example, if a specific social issue is attracting attention, it adjusts the difficulty taking that impact into account. The Case Analysis Department also integrates past case law data with the latest legal reform information to make more accurate judgments of difficulty. For example, it adjusts the win rate based on past case law with the latest legal reform information. This makes it possible to perform analysis that takes the latest legal reform information and social trends into account.

[0031] The case analysis unit can evaluate the reliability of evidence provided by the client and reflect the evaluation results in the difficulty determination. The case analysis unit, for example, analyzes evidence data provided by the client and evaluates its reliability. For example, it calculates a reliability score based on the source and content of the evidence. The case analysis unit also adjusts the difficulty of the case based on the results of the evidence reliability evaluation. For example, if there is a lot of highly reliable evidence, it sets the difficulty level lower. The case analysis unit also develops an algorithm for evaluating the reliability of evidence and reflects the results in the calculation of the win rate. For example, it incorporates the reliability score of the evidence into the calculation of the win rate. This makes it possible to determine the difficulty level taking into account the reliability of the evidence.

[0032] The case analysis unit can also include audio data and video data provided by the client in its analysis targets. The case analysis unit, for example, analyzes audio data provided by the client and determines the difficulty of the case from the tone and content of the audio. For example, it reads the client's emotions and intentions from the audio data. The case analysis unit also analyzes video data and evaluates the difficulty of the case from the client's facial expressions and movements. For example, it determines the client's level of tension and confidence from the video data. The case analysis unit also integrates audio data and video data to make a more accurate judgment of the difficulty. For example, it makes a comprehensive evaluation based on information obtained from both audio and video. In this way, by including audio data and video data in its analysis targets, it becomes possible to make a more accurate judgment of the difficulty.

[0033] The Case Analysis Department can refer to case law data from different jurisdictions and assess the difficulty of cases from an international perspective. For example, the Case Analysis Department collects case law data from different jurisdictions and reflects this in its assessment of the difficulty of a case. For example, it assesses the difficulty based on international precedents. The Case Analysis Department also develops algorithms to assess the difficulty of cases from an international perspective. For example, it performs assessments that take into account the laws and regulations of different jurisdictions. The Case Analysis Department also integrates case law data from different jurisdictions to make more accurate assessments of difficulty. For example, it compares and assesses international precedents with domestic precedents. This makes it possible to assess the difficulty of cases from an international perspective.

[0034] The matching department can propose the optimal ratio of retainer fee to success fee by taking into account the lawyer's past success rate and area of ​​expertise. For example, the matching department analyzes the lawyer's past success rate data and proposes the optimal ratio of retainer fee to success fee based on the results. For example, a higher fee is set for a lawyer with a high success rate. The matching department also considers the lawyer's area of ​​expertise and proposes the optimal ratio of retainer fee to success fee that is appropriate for that area. For example, a lawyer who is strong in a particular area of ​​expertise is set a fee that is appropriate for that area. The matching department also integrates data on past success rate and area of ​​expertise and proposes the optimal ratio of retainer fee to success fee. For example, a fee setting is made taking into account both the success rate and area of ​​expertise. This makes it possible to set the optimal fee by taking into account the lawyer's past success rate and area of ​​expertise.

[0035] The matching unit can perform a detailed analysis of the client's budget and desired conditions, and recommend a lawyer based on that. For example, the matching unit can perform a detailed analysis of the budget and desired conditions provided by the client, and recommend the most suitable lawyer based on the results. For example, it can select the lawyer who is most suitable within the budget. The matching unit also makes recommendations based on the client's desired conditions, taking into account the lawyer's specialty and experience. For example, it can recommend a lawyer who is strong in the field desired by the client. The matching unit can also analyze the client's budget and desired conditions in real time, and build a system that recommends lawyers based on the results. For example, it can automatically select the most suitable lawyer based on the budget and conditions. This makes it possible to recommend lawyers that take the client's budget and desired conditions into consideration.

[0036] The matching unit can analyze the ratings and reviews of lawyers and recommend the most suitable lawyer to the client. For example, the matching unit analyzes the ratings and reviews of lawyers and recommends the most suitable lawyer based on the results. For example, it may recommend lawyers with high ratings as a priority. The matching unit also analyzes the content of the ratings and reviews in detail to select a lawyer that meets the client's needs. For example, it may recommend lawyers with high ratings in a specific field. The matching unit also integrates rating and review data to build a system that recommends the most suitable lawyer to the client. For example, it may automatically select the most suitable lawyer based on rating scores. This makes it possible to recommend the most suitable lawyer taking into account the lawyer's ratings and reviews.

[0037] The matching unit can analyze video interview data provided by the client and evaluate the compatibility between the client and the lawyer. The matching unit, for example, analyzes the video interview data provided by the client and evaluates the compatibility between the client and the lawyer. For example, it determines compatibility based on facial expressions and language used during the interview. The matching unit also evaluates the communication style between the client and the lawyer based on the video interview data. For example, it analyzes the exchanges during the interview and scores the compatibility. The matching unit also analyzes the video interview data in real time and builds a system that evaluates compatibility with the lawyer based on the results. For example, it determines compatibility based on emotional data during the interview. This makes it possible to evaluate compatibility with a lawyer based on the video interview data.

[0038] The document creation department can learn from data on past successful complaints and generate optimal complaints based on that data. For example, the document creation department collects data on past successful complaints and has the generation AI learn from it. For example, it analyzes the content and structure of successful complaints and generates new complaints based on that. The document creation department also develops an algorithm to generate optimal complaints based on data on successful complaints. For example, it extracts success factors and reflects them in new complaints. The document creation department also builds a system that allows the generation AI to automatically generate optimal complaints based on past successful cases. For example, it learns patterns of successful complaints and applies them to new complaints. This makes it possible to generate optimal complaints based on past successful cases.

[0039] The document creation department can automatically organize not only legal documents but also evidentiary data provided by the client and reflect it in the complaint. For example, the document creation department will build a system that automatically organizes evidentiary data provided by the client and reflects it in the complaint. For example, it will organize it based on the type and importance of the evidence. The document creation department will also analyze the evidentiary data and adjust the content of the complaint based on the results. For example, it will evaluate the reliability and relevance of the evidence and reflect that in the complaint. The document creation department will also develop a system that automatically organizes evidentiary data provided by the client and integrates it with legal documents. For example, it will place the evidentiary data in the appropriate place in the complaint. This will make it possible to automatically organize the evidentiary data and reflect it in the complaint.

[0040] The document creation department can refer to legal documents from different jurisdictions and create complaints from an international perspective. For example, the document creation department collects legal documents from different jurisdictions and trains the generation AI. For example, it creates complaints based on international precedents and laws. The document creation department also develops algorithms for creating complaints from an international perspective. For example, it generates complaints that take into account the laws and regulations of different jurisdictions. The document creation department also integrates legal documents from different jurisdictions and builds a system in which the generation AI automatically generates complaints from an international perspective. For example, it creates complaints by comparing international precedents with domestic precedents. This makes it possible to create complaints from an international perspective.

[0041] The document creation department can analyze audio and video data provided by the client and create a complaint based on that. For example, the document creation department analyzes audio data provided by the client and creates a complaint based on its content. For example, it extracts the client's claims and evidence from the audio data. The document creation department also analyzes video data and adjusts the content of the complaint based on the client's facial expressions and movements. For example, it reads the client's emotions and intentions from the video data. The document creation department also integrates audio and video data, and builds a system in which a generative AI automatically generates a complaint based on that. For example, a complaint can be created based on information obtained from both audio and video. This makes it possible to create a complaint based on audio and video data.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The case analysis unit can also include audio data and video data provided by the client in its analysis targets. The case analysis unit, for example, analyzes audio data provided by the client and determines the difficulty of the case from the tone and content of the audio. For example, it reads the client's emotions and intentions from the audio data. The case analysis unit also analyzes video data and evaluates the difficulty of the case from the client's facial expressions and movements. For example, it determines the client's level of tension and confidence from the video data. The case analysis unit also integrates audio data and video data to make a more accurate judgment of the difficulty. For example, it makes a comprehensive evaluation based on information obtained from both audio and video. In this way, by including audio data and video data in its analysis targets, it becomes possible to make a more accurate judgment of the difficulty.

[0044] The Case Analysis Department can perform analysis by incorporating not only past case law data, but also the latest legal reform information and social trends. For example, the Case Analysis Department automatically collects the latest legal reform information and reflects it in determining the difficulty of a case. For example, it analyzes how new laws and regulations will affect a case. The Case Analysis Department also analyzes social trends and identifies factors that affect the difficulty of a case. For example, if a specific social issue is attracting attention, it adjusts the difficulty taking that impact into account. The Case Analysis Department also integrates past case law data with the latest legal reform information to make more accurate judgments of difficulty. For example, it adjusts the win rate based on past case law with the latest legal reform information. This makes it possible to perform analysis that takes the latest legal reform information and social trends into account.

[0045] The case analysis unit can evaluate the reliability of evidence provided by the client and reflect the evaluation results in the difficulty determination. The case analysis unit, for example, analyzes evidence data provided by the client and evaluates its reliability. For example, it calculates a reliability score based on the source and content of the evidence. The case analysis unit also adjusts the difficulty of the case based on the results of the evidence reliability evaluation. For example, if there is a lot of highly reliable evidence, it sets the difficulty level lower. The case analysis unit also develops an algorithm for evaluating the reliability of evidence and reflects the results in the calculation of the win rate. For example, it incorporates the reliability score of the evidence into the calculation of the win rate. This makes it possible to determine the difficulty level taking into account the reliability of the evidence.

[0046] The Case Analysis Department can refer to case law data from different jurisdictions and assess the difficulty of cases from an international perspective. For example, the Case Analysis Department collects case law data from different jurisdictions and reflects this in its assessment of the difficulty of a case. For example, it assesses the difficulty based on international precedents. The Case Analysis Department also develops algorithms to assess the difficulty of cases from an international perspective. For example, it performs assessments that take into account the laws and regulations of different jurisdictions. The Case Analysis Department also integrates case law data from different jurisdictions to make more accurate assessments of difficulty. For example, it compares and assesses international precedents with domestic precedents. This makes it possible to assess the difficulty of cases from an international perspective.

[0047] The matching department can propose the optimal ratio of retainer fee to success fee by taking into account the lawyer's past success rate and area of ​​expertise. For example, the matching department analyzes the lawyer's past success rate data and proposes the optimal ratio of retainer fee to success fee based on the results. For example, a higher fee is set for a lawyer with a high success rate. The matching department also considers the lawyer's area of ​​expertise and proposes the optimal ratio of retainer fee to success fee that is appropriate for that area. For example, a lawyer who is strong in a particular area of ​​expertise is set a fee that is appropriate for that area. The matching department also integrates data on past success rate and area of ​​expertise and proposes the optimal ratio of retainer fee to success fee. For example, a fee setting is made taking into account both the success rate and area of ​​expertise. This makes it possible to set the optimal fee by taking into account the lawyer's past success rate and area of ​​expertise.

[0048] The matching unit can perform a detailed analysis of the client's budget and desired conditions, and recommend a lawyer based on that. For example, the matching unit can perform a detailed analysis of the budget and desired conditions provided by the client, and recommend the most suitable lawyer based on the results. For example, it can select the lawyer who is most suitable within the budget. The matching unit also makes recommendations based on the client's desired conditions, taking into account the lawyer's specialty and experience. For example, it can recommend a lawyer who is strong in the field desired by the client. The matching unit can also analyze the client's budget and desired conditions in real time, and build a system that recommends lawyers based on the results. For example, it can automatically select the most suitable lawyer based on the budget and conditions. This makes it possible to recommend lawyers that take the client's budget and desired conditions into consideration.

[0049] The matching unit can analyze video interview data provided by the client and evaluate the compatibility between the client and the lawyer. The matching unit, for example, analyzes the video interview data provided by the client and evaluates the compatibility between the client and the lawyer. For example, it determines compatibility based on facial expressions and language used during the interview. The matching unit also evaluates the communication style between the client and the lawyer based on the video interview data. For example, it analyzes the exchanges during the interview and scores the compatibility. The matching unit also analyzes the video interview data in real time and builds a system that evaluates compatibility with the lawyer based on the results. For example, it determines compatibility based on emotional data during the interview. This makes it possible to evaluate compatibility with a lawyer based on the video interview data.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The case analysis department analyzes the case information provided by the client, based on, for example, the content of the case, the presence or absence of evidence, and past precedents for similar cases. Step 2: The win rate calculation unit calculates the win rate based on the information analyzed by the case analysis unit. For example, it calculates the probability of winning based on past case data and the strength of evidence. Step 3: The matching section matches clients with lawyers by having the lawyer input the retainer fee and success fee percentage based on the win rate calculated by the win rate calculation section. For example, the matching section recommends a lawyer that matches the client's budget and desired fee structure. Step 4: The document preparation department automatically prepares documents to be submitted to the court, including the complaint, based on the information provided by the client. For example, it automatically generates the contents of the complaint, the compilation of evidence, and legal arguments.

[0052] (Example 2) A matching system according to an embodiment of the present invention optimizes the matching of lawyers and clients, improving the work efficiency of lawyers. This matching system uses AI to determine the difficulty of a case and calculate the win rate. Based on the results, lawyers input the retainer fee and success fee rate to match clients with lawyers. In addition, AI automatically prepares documents to be submitted to the court, including the complaint, providing a mechanism that allows lawyers to work efficiently even on low-cost cases. This allows the matching system to optimize the matching of lawyers and clients, improving the work efficiency of lawyers.

[0053] A matching system according to an embodiment includes a case analysis unit, a win rate calculation unit, a matching unit, and a document preparation unit. The case analysis unit analyzes case information provided by a client. For example, the analysis is based on the content of the case, the presence or absence of evidence, past precedents for similar cases, etc. The win rate calculation unit calculates the win rate based on the information analyzed by the case analysis unit. For example, the possibility of winning is calculated based on past precedent data and the strength of evidence. The matching unit matches clients and lawyers by having the lawyer input the retainer fee and contingency fee rate based on the win rate calculated by the win rate calculation unit. For example, the matching unit recommends a lawyer that matches the client's budget and desired fee structure. The document preparation unit automatically prepares documents to be submitted to the court, including a complaint, based on information provided by the client. For example, the document preparation unit automatically generates the content of the complaint, the organization of evidence, legal arguments, etc. This allows the matching system according to an embodiment to optimize matching between lawyers and clients and improve the work efficiency of lawyers.

[0054] The case analysis unit can analyze the client's emotional data and calculate the win rate taking emotional factors into consideration. The case analysis unit, for example, analyzes text data or voice data provided by the client to identify the intensity and type of emotion. For example, it quantifies the client's emotions, such as anxiety or expectation, and reflects this in the calculation of the win rate. The case analysis unit also adjusts the difficulty of the case based on the client's emotional data. For example, if the client is feeling strong anxiety, it sets the difficulty higher taking those emotions into consideration. The case analysis unit also analyzes the emotional data to evaluate the impact the client's emotional state has on the progress of the case and incorporates this into the calculation of the win rate. For example, if the client is feeling positive, it sets the possibility of win higher. This makes it possible to calculate the win rate taking the client's emotions into consideration.

[0055] The Case Analysis Department can perform analysis by incorporating not only past case law data, but also the latest legal reform information and social trends. For example, the Case Analysis Department automatically collects the latest legal reform information and reflects it in determining the difficulty of a case. For example, it analyzes how new laws and regulations will affect a case. The Case Analysis Department also analyzes social trends and identifies factors that affect the difficulty of a case. For example, if a specific social issue is attracting attention, it adjusts the difficulty taking that impact into account. The Case Analysis Department also integrates past case law data with the latest legal reform information to make more accurate judgments of difficulty. For example, it adjusts the win rate based on past case law with the latest legal reform information. This makes it possible to perform analysis that takes the latest legal reform information and social trends into account.

[0056] The case analysis unit can evaluate the reliability of evidence provided by the client and reflect the evaluation results in the difficulty determination. The case analysis unit, for example, analyzes evidence data provided by the client and evaluates its reliability. For example, it calculates a reliability score based on the source and content of the evidence. The case analysis unit also adjusts the difficulty of the case based on the results of the evidence reliability evaluation. For example, if there is a lot of highly reliable evidence, it sets the difficulty level lower. The case analysis unit also develops an algorithm for evaluating the reliability of evidence and reflects the results in the calculation of the win rate. For example, it incorporates the reliability score of the evidence into the calculation of the win rate. This makes it possible to determine the difficulty level taking into account the reliability of the evidence.

[0057] The case analysis unit can also include audio data and video data provided by the client in its analysis targets. The case analysis unit, for example, analyzes audio data provided by the client and determines the difficulty of the case from the tone and content of the audio. For example, it reads the client's emotions and intentions from the audio data. The case analysis unit also analyzes video data and evaluates the difficulty of the case from the client's facial expressions and movements. For example, it determines the client's level of tension and confidence from the video data. The case analysis unit also integrates audio data and video data to make a more accurate judgment of the difficulty. For example, it makes a comprehensive evaluation based on information obtained from both audio and video. In this way, by including audio data and video data in its analysis targets, it becomes possible to make a more accurate judgment of the difficulty.

[0058] The Case Analysis Department can refer to case law data from different jurisdictions and assess the difficulty of cases from an international perspective. For example, the Case Analysis Department collects case law data from different jurisdictions and reflects this in its assessment of the difficulty of a case. For example, it assesses the difficulty based on international precedents. The Case Analysis Department also develops algorithms to assess the difficulty of cases from an international perspective. For example, it performs assessments that take into account the laws and regulations of different jurisdictions. The Case Analysis Department also integrates case law data from different jurisdictions to make more accurate assessments of difficulty. For example, it compares and assesses international precedents with domestic precedents. This makes it possible to assess the difficulty of cases from an international perspective.

[0059] The case analysis unit can use the emotion estimation function to adjust the difficulty of the case based on the client's emotions and provide feedback that puts the client at ease. The case analysis unit, for example, uses the emotion estimation function to analyze the client's emotions in real time and adjust the difficulty of the case based on the results. For example, if the client is feeling very anxious, the difficulty level is set low. The case analysis unit also builds a system that provides feedback that puts the client at ease based on the client's emotion data. For example, it displays positive messages or words of encouragement. The case analysis unit also provides feedback that puts the client at ease based on the emotion estimation data. For example, it provides appropriate advice according to the client's emotional state. This makes it possible to provide feedback that takes the client's emotions into consideration.

[0060] The matching department can propose the optimal ratio of retainer fee to success fee by taking into account the lawyer's past success rate and area of ​​expertise. For example, the matching department analyzes the lawyer's past success rate data and proposes the optimal ratio of retainer fee to success fee based on the results. For example, a higher fee is set for a lawyer with a high success rate. The matching department also considers the lawyer's area of ​​expertise and proposes the optimal ratio of retainer fee to success fee that is appropriate for that area. For example, a lawyer who is strong in a particular area of ​​expertise is set a fee that is appropriate for that area. The matching department also integrates data on past success rate and area of ​​expertise and proposes the optimal ratio of retainer fee to success fee. For example, a fee setting is made taking into account both the success rate and area of ​​expertise. This makes it possible to set the optimal fee by taking into account the lawyer's past success rate and area of ​​expertise.

[0061] The matching unit can perform a detailed analysis of the client's budget and desired conditions, and recommend a lawyer based on that. For example, the matching unit can perform a detailed analysis of the budget and desired conditions provided by the client, and recommend the most suitable lawyer based on the results. For example, it can select the lawyer who is most suitable within the budget. The matching unit also makes recommendations based on the client's desired conditions, taking into account the lawyer's specialty and experience. For example, it can recommend a lawyer who is strong in the field desired by the client. The matching unit can also analyze the client's budget and desired conditions in real time, and build a system that recommends lawyers based on the results. For example, it can automatically select the most suitable lawyer based on the budget and conditions. This makes it possible to recommend lawyers that take the client's budget and desired conditions into consideration.

[0062] The matching unit can analyze the emotional data of the client and achieve matching that provides a high level of emotional satisfaction. The matching unit, for example, analyzes the emotional data of the client and, based on the results, recommends a lawyer that provides a high level of emotional satisfaction. For example, it selects a lawyer that gives the client a sense of security. The matching unit also builds a system that performs matching that provides client satisfaction based on the emotional data. For example, it recommends the most suitable lawyer based on the emotional state of the client. The matching unit also analyzes the emotional data of the client in real time and, based on the results, achieves matching that provides a high level of emotional satisfaction. For example, it selects an appropriate lawyer based on the client's emotions. This makes it possible to achieve matching that provides a high level of satisfaction by taking the client's emotions into consideration.

[0063] The matching unit can analyze the ratings and reviews of lawyers and recommend the most suitable lawyer to the client. For example, the matching unit analyzes the ratings and reviews of lawyers and recommends the most suitable lawyer based on the results. For example, it may recommend lawyers with high ratings as a priority. The matching unit also analyzes the content of the ratings and reviews in detail to select a lawyer that meets the client's needs. For example, it may recommend lawyers with high ratings in a specific field. The matching unit also integrates rating and review data to build a system that recommends the most suitable lawyer to the client. For example, it may automatically select the most suitable lawyer based on rating scores. This makes it possible to recommend the most suitable lawyer taking into account the lawyer's ratings and reviews.

[0064] The matching unit can analyze video interview data provided by the client and evaluate the compatibility between the client and the lawyer. The matching unit, for example, analyzes the video interview data provided by the client and evaluates the compatibility between the client and the lawyer. For example, it determines compatibility based on facial expressions and language used during the interview. The matching unit also evaluates the communication style between the client and the lawyer based on the video interview data. For example, it analyzes the exchanges during the interview and scores the compatibility. The matching unit also analyzes the video interview data in real time and builds a system that evaluates compatibility with the lawyer based on the results. For example, it determines compatibility based on emotional data during the interview. This makes it possible to evaluate compatibility with a lawyer based on the video interview data.

[0065] The matching unit can use the emotion estimation function to recommend a lawyer based on the client's emotions, allowing the client to make a choice with confidence. The matching unit, for example, uses the emotion estimation function to analyze the client's emotions in real time and recommend a lawyer based on the results. For example, it selects a lawyer that the client can feel comfortable with. The matching unit also builds a system that recommends lawyers that the client can choose with confidence based on the client's emotion data. For example, it recommends the most suitable lawyer based on the client's emotional state. The matching unit also analyzes the emotion estimation data in real time and recommends a lawyer that the client can choose with confidence based on the results. For example, it selects an appropriate lawyer based on the client's emotions. This makes it possible to recommend a lawyer that the client can choose with confidence, taking into account the client's emotions.

[0066] The document creation unit can analyze the client's emotional data and create an emotionally appealing complaint. The document creation unit, for example, analyzes the client's emotional data and creates an emotionally appealing complaint based on the results. For example, it uses expressions that reflect the client's emotions. The document creation unit also builds a system that adjusts the content of the complaint based on the emotional data. For example, it selects appropriate expressions according to the client's emotional state. The document creation unit also analyzes the client's emotional data in real time and creates an emotionally appealing complaint based on the results. For example, it uses appropriate expressions according to the client's emotions. This makes it possible to create an appealing complaint that reflects the client's emotions.

[0067] The document creation department can learn from data on past successful complaints and generate optimal complaints based on that data. For example, the document creation department collects data on past successful complaints and has the generation AI learn from it. For example, it analyzes the content and structure of successful complaints and generates new complaints based on that. The document creation department also develops an algorithm to generate optimal complaints based on data on successful complaints. For example, it extracts success factors and reflects them in new complaints. The document creation department also builds a system that allows the generation AI to automatically generate optimal complaints based on past successful cases. For example, it learns patterns of successful complaints and applies them to new complaints. This makes it possible to generate optimal complaints based on past successful cases.

[0068] The document creation department can automatically organize not only legal documents but also evidentiary data provided by the client and reflect it in the complaint. For example, the document creation department will build a system that automatically organizes evidentiary data provided by the client and reflects it in the complaint. For example, it will organize it based on the type and importance of the evidence. The document creation department will also analyze the evidentiary data and adjust the content of the complaint based on the results. For example, it will evaluate the reliability and relevance of the evidence and reflect that in the complaint. The document creation department will also develop a system that automatically organizes evidentiary data provided by the client and integrates it with legal documents. For example, it will place the evidentiary data in the appropriate place in the complaint. This will make it possible to automatically organize the evidentiary data and reflect it in the complaint.

[0069] The document creation department can refer to legal documents from different jurisdictions and create complaints from an international perspective. For example, the document creation department collects legal documents from different jurisdictions and trains the generation AI. For example, it creates complaints based on international precedents and laws. The document creation department also develops algorithms for creating complaints from an international perspective. For example, it generates complaints that take into account the laws and regulations of different jurisdictions. The document creation department also integrates legal documents from different jurisdictions and builds a system in which the generation AI automatically generates complaints from an international perspective. For example, it creates complaints by comparing international precedents with domestic precedents. This makes it possible to create complaints from an international perspective.

[0070] The document creation department can analyze audio and video data provided by the client and create a complaint based on that. For example, the document creation department analyzes audio data provided by the client and creates a complaint based on its content. For example, it extracts the client's claims and evidence from the audio data. The document creation department also analyzes video data and adjusts the content of the complaint based on the client's facial expressions and movements. For example, it reads the client's emotions and intentions from the video data. The document creation department also integrates audio and video data, and builds a system in which a generative AI automatically generates a complaint based on that. For example, a complaint can be created based on information obtained from both audio and video. This makes it possible to create a complaint based on audio and video data.

[0071] The document creation unit can use the emotion estimation function to adjust the content of the complaint based on the client's emotions and create a complaint that puts the client at ease. The document creation unit, for example, uses the emotion estimation function to analyze the client's emotions in real time and adjust the content of the complaint based on the results. For example, if the client is feeling very anxious, it uses expressions that give a sense of security. The document creation unit also builds a system that creates a complaint that puts the client at ease based on the client's emotion data. For example, it incorporates positive messages and words of encouragement into the complaint. The document creation unit also analyzes the emotion estimation data in real time and creates a complaint that puts the client at ease based on the results. For example, it uses appropriate expressions according to the client's emotions. This makes it possible to create a complaint that takes the client's emotions into consideration and puts the client at ease.

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

[0073] The case analysis unit can also include audio data and video data provided by the client in its analysis targets. The case analysis unit, for example, analyzes audio data provided by the client and determines the difficulty of the case from the tone and content of the audio. For example, it reads the client's emotions and intentions from the audio data. The case analysis unit also analyzes video data and evaluates the difficulty of the case from the client's facial expressions and movements. For example, it determines the client's level of tension and confidence from the video data. The case analysis unit also integrates audio data and video data to make a more accurate judgment of the difficulty. For example, it makes a comprehensive evaluation based on information obtained from both audio and video. In this way, by including audio data and video data in its analysis targets, it becomes possible to make a more accurate judgment of the difficulty.

[0074] The case analysis unit can analyze the client's emotional data and calculate the win rate taking emotional factors into consideration. The case analysis unit, for example, analyzes text data or voice data provided by the client to identify the intensity and type of emotion. For example, it quantifies the client's emotions, such as anxiety or expectation, and reflects this in the calculation of the win rate. The case analysis unit also adjusts the difficulty of the case based on the client's emotional data. For example, if the client is feeling strong anxiety, it sets the difficulty higher taking those emotions into consideration. The case analysis unit also analyzes the emotional data to evaluate the impact the client's emotional state has on the progress of the case and incorporates this into the calculation of the win rate. For example, if the client is feeling positive, it sets the possibility of win higher. This makes it possible to calculate the win rate taking the client's emotions into consideration.

[0075] The Case Analysis Department can perform analysis by incorporating not only past case law data, but also the latest legal reform information and social trends. For example, the Case Analysis Department automatically collects the latest legal reform information and reflects it in determining the difficulty of a case. For example, it analyzes how new laws and regulations will affect a case. The Case Analysis Department also analyzes social trends and identifies factors that affect the difficulty of a case. For example, if a specific social issue is attracting attention, it adjusts the difficulty taking that impact into account. The Case Analysis Department also integrates past case law data with the latest legal reform information to make more accurate judgments of difficulty. For example, it adjusts the win rate based on past case law with the latest legal reform information. This makes it possible to perform analysis that takes the latest legal reform information and social trends into account.

[0076] The case analysis unit can evaluate the reliability of evidence provided by the client and reflect the evaluation results in the difficulty determination. The case analysis unit, for example, analyzes evidence data provided by the client and evaluates its reliability. For example, it calculates a reliability score based on the source and content of the evidence. The case analysis unit also adjusts the difficulty of the case based on the results of the evidence reliability evaluation. For example, if there is a lot of highly reliable evidence, it sets the difficulty level lower. The case analysis unit also develops an algorithm for evaluating the reliability of evidence and reflects the results in the calculation of the win rate. For example, it incorporates the reliability score of the evidence into the calculation of the win rate. This makes it possible to determine the difficulty level taking into account the reliability of the evidence.

[0077] The Case Analysis Department can refer to case law data from different jurisdictions and assess the difficulty of cases from an international perspective. For example, the Case Analysis Department collects case law data from different jurisdictions and reflects this in its assessment of the difficulty of a case. For example, it assesses the difficulty based on international precedents. The Case Analysis Department also develops algorithms to assess the difficulty of cases from an international perspective. For example, it performs assessments that take into account the laws and regulations of different jurisdictions. The Case Analysis Department also integrates case law data from different jurisdictions to make more accurate assessments of difficulty. For example, it compares and assesses international precedents with domestic precedents. This makes it possible to assess the difficulty of cases from an international perspective.

[0078] The matching department can propose the optimal ratio of retainer fee to success fee by taking into account the lawyer's past success rate and area of ​​expertise. For example, the matching department analyzes the lawyer's past success rate data and proposes the optimal ratio of retainer fee to success fee based on the results. For example, a higher fee is set for a lawyer with a high success rate. The matching department also considers the lawyer's area of ​​expertise and proposes the optimal ratio of retainer fee to success fee that is appropriate for that area. For example, a lawyer who is strong in a particular area of ​​expertise is set a fee that is appropriate for that area. The matching department also integrates data on past success rate and area of ​​expertise and proposes the optimal ratio of retainer fee to success fee. For example, a fee setting is made taking into account both the success rate and area of ​​expertise. This makes it possible to set the optimal fee by taking into account the lawyer's past success rate and area of ​​expertise.

[0079] The matching unit can analyze the emotional data of the client and achieve matching that provides a high level of emotional satisfaction. The matching unit, for example, analyzes the emotional data of the client and, based on the results, recommends a lawyer that provides a high level of emotional satisfaction. For example, it selects a lawyer that gives the client a sense of security. The matching unit also builds a system that performs matching that provides client satisfaction based on the emotional data. For example, it recommends the most suitable lawyer based on the emotional state of the client. The matching unit also analyzes the emotional data of the client in real time and, based on the results, achieves matching that provides a high level of emotional satisfaction. For example, it selects an appropriate lawyer based on the client's emotions. This makes it possible to achieve matching that provides a high level of satisfaction by taking the client's emotions into consideration.

[0080] The matching unit can perform a detailed analysis of the client's budget and desired conditions, and recommend a lawyer based on that. For example, the matching unit can perform a detailed analysis of the budget and desired conditions provided by the client, and recommend the most suitable lawyer based on the results. For example, it can select the lawyer who is most suitable within the budget. The matching unit also makes recommendations based on the client's desired conditions, taking into account the lawyer's specialty and experience. For example, it can recommend a lawyer who is strong in the field desired by the client. The matching unit can also analyze the client's budget and desired conditions in real time, and build a system that recommends lawyers based on the results. For example, it can automatically select the most suitable lawyer based on the budget and conditions. This makes it possible to recommend lawyers that take the client's budget and desired conditions into consideration.

[0081] The matching unit can analyze video interview data provided by the client and evaluate the compatibility between the client and the lawyer. The matching unit, for example, analyzes the video interview data provided by the client and evaluates the compatibility between the client and the lawyer. For example, it determines compatibility based on facial expressions and language used during the interview. The matching unit also evaluates the communication style between the client and the lawyer based on the video interview data. For example, it analyzes the exchanges during the interview and scores the compatibility. The matching unit also analyzes the video interview data in real time and builds a system that evaluates compatibility with the lawyer based on the results. For example, it determines compatibility based on emotional data during the interview. This makes it possible to evaluate compatibility with a lawyer based on the video interview data.

[0082] The document creation unit can use the emotion estimation function to adjust the content of the complaint based on the client's emotions and create a complaint that puts the client at ease. The document creation unit, for example, uses the emotion estimation function to analyze the client's emotions in real time and adjust the content of the complaint based on the results. For example, if the client is feeling very anxious, it uses expressions that give a sense of security. The document creation unit also builds a system that creates a complaint that puts the client at ease based on the client's emotion data. For example, it incorporates positive messages and words of encouragement into the complaint. The document creation unit also analyzes the emotion estimation data in real time and creates a complaint that puts the client at ease based on the results. For example, it uses appropriate expressions according to the client's emotions. This makes it possible to create a complaint that takes the client's emotions into consideration and puts the client at ease.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The case analysis department analyzes the case information provided by the client, based on, for example, the content of the case, the presence or absence of evidence, and past precedents for similar cases. Step 2: The win rate calculation unit calculates the win rate based on the information analyzed by the case analysis unit. For example, it calculates the probability of winning based on past case data and the strength of evidence. Step 3: The matching section matches clients with lawyers by having the lawyer input the retainer fee and success fee percentage based on the win rate calculated by the win rate calculation section. For example, the matching section recommends a lawyer that matches the client's budget and desired fee structure. Step 4: The document preparation department automatically prepares documents to be submitted to the court, including the complaint, based on the information provided by the client. For example, it automatically generates the contents of the complaint, the compilation of evidence, and legal arguments.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] 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. [Explanation of symbols]

[0152] 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 case analysis unit that analyzes case information provided by a client; a winning ratio calculation unit that calculates a winning ratio based on the information analyzed by the case analysis unit; a matching unit that matches clients with lawyers by having the lawyer input the percentage of the initial fee and success fee based on the percentage of the win calculated by the win percentage calculation unit; and a document creation unit that automatically creates documents to be submitted to the court, including a complaint, based on the information provided by the client. A system characterized by:

2. The case analysis unit Analyze the client's emotional data and calculate the winning rate taking emotional factors into account.

2. The system of claim 1.

3. The case analysis unit Analysis incorporates not only past case law data but also the latest legal reform information and social trends.

2. The system of claim 1.

4. The case analysis unit Evaluate the reliability of the evidence provided by the client and reflect the results of that evaluation in determining the difficulty of the case 2. The system of claim 1.

5. The case analysis unit The analysis will also include audio and video data provided by the client.

2. The system of claim 1.

6. The case analysis unit Referencing case law data from different jurisdictions to assess difficulty from an international perspective 2. The system of claim 1.

7. The case analysis unit Adjust the difficulty of the case based on the client's feelings and provide feedback that puts the client at ease 2. The system of claim 1.

8. The matching unit Considering the lawyer's past success rate and area of ​​expertise, we will propose the optimal ratio of retainer fee and success fee.

2. The system of claim 1.

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