system

Abstract

The system according to this embodiment aims to streamline the creation and research of legal documents, legal consultations, and case management. [Solution] The system according to this embodiment comprises a document generation unit, an information retrieval unit, a consultation support unit, and a case management unit. The document generation unit generates legal documents. The information retrieval unit searches and analyzes information from a database of laws and precedents. The consultation support unit provides legal consultation support. The case management unit manages cases.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of time and effort to create legal documents, investigate laws and regulations and case precedents, provide legal consultations, and manage cases.

[0005] The system according to the embodiment aims to improve the efficiency of creating legal documents, conducting investigations, providing legal consultations, and managing cases.

Means for Solving the Problems

[0006] The system according to the embodiment includes a document generation unit, an information search unit, a consultation support unit, and a case management unit. The document generation unit generates legal documents. The information search unit searches for and analyzes information from databases of laws and regulations and case precedents. The consultation support unit supports legal consultations. The case management unit manages cases.

Effects of the Invention

[0007] The system according to this embodiment can streamline the creation and research of legal documents, legal consultations, and case management. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The generative AI agent according to an embodiment of the present invention is a system that automates the creation and research of legal documents, and can streamline legal consultations and case management. This generative AI agent analyzes and generates legal documents using natural language processing (NLP) and searches and analyzes information from databases of laws and precedents using machine learning. The generative AI agent provides document creation support through automatic template generation. This enables the provision of fast and accurate legal advice, reduces costs, and improves access to legal services. For example, when a lawyer is drafting a contract for a client, the generative AI agent selects an appropriate template based on the client's requirements and automatically generates the contract simply by inputting the necessary information. Also, when a law firm takes on a new case, the generative AI agent quickly searches for relevant laws and precedents and provides the necessary information. This generative AI agent is expected to see significant growth in the legal technology market, and as digitalization progresses, legal services are also required to have faster and more efficient solutions. With the advancement of AI technology, complex legal tasks that were once performed manually can now be automated, making now a good time to enter the market. The vision for this generative AI agent is to make legal services more accessible, enabling individuals and businesses to resolve legal issues more easily and strengthen fairness under the law. It aims to improve access to and the quality of legal assistance. This will allow the generative AI agent to automate the creation and research of legal documents and streamline legal consultations and case management.

[0029] The generative AI agent according to this embodiment comprises a document generation unit, an information retrieval unit, a consultation support unit, and a case management unit. The document generation unit generates legal documents. The document generation unit can generate legal documents such as contracts, litigation documents, and opinion letters. The document generation unit analyzes and generates legal documents using generative AI. For example, the document generation unit analyzes the content of legal documents using natural language processing technology and generates appropriate documents. The document generation unit can use natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. The information retrieval unit searches and analyzes information from a database of laws and precedents. For example, the information retrieval unit can search for and analyze necessary information from a database of laws and precedents. The information retrieval unit searches and analyzes information from a database of laws and precedents using machine learning. For example, the information retrieval unit can use machine learning algorithms such as K-means and support vector machines. The consultation support unit supports legal consultations. For example, the consultation support unit can support legal consultations based on legal advice provided by the generative AI agent. The Consultation Support Department provides legal consultation support based on legal advice provided by the Generating AI Agent. For example, the Consultation Support Department can determine the type of consultation and the method of support based on the legal advice provided by the Generating AI Agent. The Case Management Department manages cases. For example, the Case Management Department can manage cases based on legal advice provided by the Generating AI Agent. For example, the Case Management Department can determine the type of information to manage and the management method based on the legal advice provided by the Generating AI Agent. As a result, the Generating AI Agent according to this embodiment can automate the creation and research of legal documents, and streamline legal consultation and case management.

[0030] The document generation unit generates legal documents. For example, it can generate legal documents such as contracts, litigation documents, and legal opinions. The document generation unit uses generation AI to analyze and generate legal documents. Specifically, the document generation unit utilizes natural language processing technology to analyze the content of legal documents in detail and generate appropriate documents. For example, it uses morphological analysis to break down words within a document, understands sentence structure through grammatical analysis, and grasps the overall meaning of the document through semantic analysis. This allows the document generation unit to accurately grasp the context and intent of legal documents and generate documents that include the necessary information. Furthermore, the generation AI learns from past legal documents and precedents to improve the accuracy of document generation. For example, by learning from a large amount of past contracts and litigation documents, it can acquire document formats and expressions in specific legal fields and utilize this in generating new documents. This enables the document generation unit to create legal documents quickly and accurately, contributing to the efficiency of legal work.

[0031] The Information Retrieval Unit searches and analyzes information from databases of laws and precedents. Specifically, it uses machine learning to search and analyze information from these databases. For example, it uses K-means clustering to group related laws and precedents, and support vector machines to extract information relevant to specific legal issues. This allows the Information Retrieval Unit to quickly and accurately search and analyze necessary information from vast databases. Furthermore, the Information Retrieval Unit uses natural language processing to understand the meaning of search queries and provide more appropriate search results. For example, it analyzes the context of search queries and prioritizes the display of relevant laws and precedents. The Information Retrieval Unit also automatically links relevant legal information based on search results, enabling users to efficiently gather information. This contributes to improving the efficiency and accuracy of information gathering in legal work.

[0032] The Consultation Support Department provides legal consultation support. For example, the Consultation Support Department can provide legal consultation support based on legal advice provided by a generating AI agent. Specifically, the Consultation Support Department determines the type of consultation and the method of support based on the legal advice provided by the generating AI agent. For example, the generating AI agent analyzes the content of the user's consultation and provides appropriate legal advice. Based on this advice, the Consultation Support Department presents the user with a concrete action plan. Furthermore, based on the legal advice provided by the generating AI agent, the Consultation Support Department can provide customized support according to the content of the user's consultation. For example, it can provide support that meets the user's needs, such as detailed explanations of specific legal issues or provision of relevant laws and precedents. In addition, the Consultation Support Department collects feedback from users and continuously improves the accuracy and usefulness of the advice provided by the generating AI agent. In this way, the Consultation Support Department provides high-quality legal support to users and contributes to the efficiency and accuracy of legal consultations.

[0033] The Case Management Department manages cases. For example, the Case Management Department can manage cases based on legal advice provided by a generating AI agent. Specifically, the Case Management Department determines the types of information to manage and the management methods based on the legal advice provided by the generating AI agent. For example, the Case Management Department manages the progress and important deadlines of each case and executes necessary actions in a timely manner. The Case Management Department also determines the priority of cases and optimizes resource allocation based on the legal advice provided by the generating AI agent. Furthermore, the Case Management Department centrally manages documents and information related to cases based on the legal advice provided by the generating AI agent, and can quickly search and retrieve necessary information. As a result, the Case Management Department efficiently manages the progress of cases and contributes to the efficiency and accuracy of legal work. In addition, the Case Management Department can analyze past case data and identify areas for improvement in future case management. As a result, the Case Management Department achieves continuous business improvement and contributes to the improvement of the quality of legal work.

[0034] The document generation unit can analyze and generate legal documents using natural language processing. For example, the document generation unit can analyze the content of legal documents using morphological analysis. For example, the document generation unit can analyze the structure of legal documents using grammatical analysis. For example, the document generation unit can analyze the meaning of legal documents using semantic analysis. As a result, the analysis and generation of legal documents are made more efficient by using natural language processing. Some or all of the above-described processes in the document generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the document generation unit can input the content of a legal document into a generation AI, and the generation AI can analyze and generate the legal document.

[0035] The information retrieval unit can search and analyze information from a database of laws and precedents using machine learning. For example, the information retrieval unit can search and analyze information from a database of laws and precedents using K-means. For example, the information retrieval unit can search and analyze information from a database of laws and precedents using support vector machines. For example, the information retrieval unit can search and analyze information from a database of laws and precedents using random forests. This makes the search and analysis of laws and precedents more efficient by using machine learning. Some or all of the above processing in the information retrieval unit may be performed using AI, or it may be performed without AI. For example, the information retrieval unit can input information obtained from a database of laws and precedents into AI, and the AI ​​can search and analyze the information.

[0036] The document generation unit can provide document creation support through automatic template generation. For example, the document generation unit can automatically generate contract templates to support document creation. For example, the document generation unit can automatically generate litigation document templates to support document creation. For example, the document generation unit can automatically generate opinion letter templates to support document creation. This makes document creation more efficient through automatic template generation. Some or all of the above-described processes in the document generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the document generation unit can input the document template generation into a generation AI, and the generation AI can automatically generate the template.

[0037] The consultation support department can provide legal consultation support based on legal advice provided by a generating AI agent. For example, the consultation support department can provide legal consultation support based on legal advice provided by a generating AI agent. For example, the consultation support department can determine the type of consultation and the method of support based on legal advice provided by a generating AI agent. Some or all of the above processes in the consultation support department may be performed using AI or not. For example, the consultation support department can input legal advice provided by a generating AI agent into the AI, and the AI ​​can provide legal consultation support. This makes it possible to provide legal consultation support based on the advice of the generating AI agent.

[0038] The case management department can manage cases based on legal advice provided by a generating AI agent. For example, the case management department can manage cases based on legal advice provided by a generating AI agent. For example, the case management department can determine the types of information to manage and the management methods based on legal advice provided by a generating AI agent. Some or all of the above processes in the case management department may be performed using AI or not. For example, the case management department can input legal advice provided by a generating AI agent into the AI, and the AI ​​can manage the case. This makes it possible to manage cases based on the advice of the generating AI agent.

[0039] The document generation unit can propose the optimal document structure by referring to documents from similar past cases. For example, the document generation unit's generation AI can analyze documents from similar past cases and propose the most effective document structure. For example, the document generation unit's generation AI can optimize the document's section structure based on past success stories. For example, the document generation unit's generation AI can adjust the document structure to avoid past failures. This makes it possible to propose the optimal document structure by referring to documents from similar past cases. Some or all of the above processes in the document generation unit may be performed using the generation AI or not. For example, the document generation unit can input document data from similar past cases into the generation AI, and the generation AI can propose the optimal document structure.

[0040] The document generation unit can assess legal risks and propose revisions to the document according to those risks. For example, the document generation unit's generation AI can assess legal risks and propose revisions to high-risk expressions. For example, the document generation unit's generation AI can assess legal risks and propose additional information to mitigate those risks. For example, the document generation unit's generation AI can assess legal risks and propose alternative expressions to avoid those risks. This makes it possible to assess legal risks and propose revisions to the document according to those risks. Some or all of the above processes in the document generation unit may be performed using the generation AI or not. For example, the document generation unit can input the content of a document into the generation AI, which can then assess legal risks and propose revisions.

[0041] The document generation unit can automatically insert specialized terminology according to the user's field of expertise. For example, the document generation unit's generation AI can analyze the user's field of expertise and insert appropriate specialized terminology into the document. For example, the document generation unit's generation AI can add definitions of specialized terminology to the document based on the user's field of expertise. For example, the document generation unit's generation AI can adjust the frequency of use of specialized terminology according to the user's field of expertise. This enables the automatic insertion of specialized terminology according to the user's field of expertise. Some or all of the above processes in the document generation unit may be performed using the generation AI or not. For example, the document generation unit can input the user's field of expertise data into the generation AI, and the generation AI can automatically insert specialized terminology.

[0042] The document generation unit can maintain document consistency by referring to the user's past document creation history. For example, the document generation unit's generation AI can analyze the user's past document creation history and maintain a consistent document style. For example, the document generation unit's generation AI can unify document terminology and expressions based on the user's past document creation history. For example, the document generation unit's generation AI can refer to the user's past document creation history and unify document formatting. This makes it possible to maintain document consistency by referring to the user's past document creation history. Some or all of the above processes in the document generation unit may be performed using the generation AI or not. For example, the document generation unit can input the user's past document creation history data into the generation AI, which can then maintain document consistency.

[0043] The information retrieval unit can optimize the search algorithm by referring to past search history. For example, the information retrieval unit can use AI to analyze the user's past search history and optimize the search algorithm. For example, the information retrieval unit can use AI to prioritize displaying highly relevant information based on the user's past search history. For example, the information retrieval unit can use AI to refer to the user's past search history and improve the accuracy of search results. This makes it possible to optimize the search algorithm by referring to past search history. Some or all of the above processes in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input the user's past search history data into AI, and the AI ​​can optimize the search algorithm.

[0044] The information retrieval unit can reflect updates to laws and precedents in real time. For example, the information retrieval unit can use AI to acquire updates to laws and precedents in real time and reflect them in the search results. For example, the information retrieval unit can use AI to provide the latest information based on updates to laws and precedents. For example, the information retrieval unit can use AI to reflect updates to laws and precedents in real time and improve the accuracy of search results. This allows the system to provide the latest information by reflecting updates to laws and precedents in real time. Some or all of the above-described processes in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input updates to laws and precedents into AI, which can then reflect them in real time.

[0045] The information retrieval unit can prioritize displaying highly relevant information by considering the user's geographical information. For example, the information retrieval unit can use AI to analyze the user's geographical information and prioritize displaying highly relevant information. For example, the information retrieval unit can use AI to provide region-specific information based on the user's geographical information. For example, the information retrieval unit can use AI to improve the accuracy of search results by considering the user's geographical information. This makes it possible to prioritize the display of highly relevant information by considering the user's geographical information. Some or all of the above processing in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input the user's geographical information data into AI, and the AI ​​can prioritize displaying highly relevant information.

[0046] The information retrieval unit can provide customized search results tailored to the user's area of ​​expertise. For example, the information retrieval unit can use AI to analyze the user's area of ​​expertise and provide customized search results. For example, the information retrieval unit can use AI to prioritize displaying highly relevant information based on the user's area of ​​expertise. For example, the information retrieval unit can use AI to improve the accuracy of search results by considering the user's area of ​​expertise. This makes it possible to provide customized search results tailored to the user's area of ​​expertise. Some or all of the above-described processes in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input user area of ​​expertise data into AI, and the AI ​​can provide customized search results.

[0047] The consultation support department can provide optimal advice by referring to past consultation history. For example, the consultation support department can use AI to analyze past consultation history and provide optimal advice. For example, the consultation support department can use AI to provide effective advice based on past success stories. For example, the consultation support department can use AI to adjust the content of advice to avoid past failures. This makes it possible to provide optimal advice by referring to past consultation history. Some or all of the above processes in the consultation support department may be performed using AI or not. For example, the consultation support department can input past consultation history data into AI, and the AI ​​can provide optimal advice.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The generation AI agent can automatically insert specialized terminology according to the user's field of expertise. For example, the document generation unit can use the generation AI to analyze the user's field of expertise and insert appropriate terminology into the document. The generation AI can also add definitions of terminology to the document based on the user's field of expertise. Furthermore, the generation AI can adjust the frequency of use of terminology according to the user's field of expertise. This enables the automatic insertion of specialized terminology tailored to the user's field of expertise.

[0050] The generation AI agent can maintain document consistency by referring to the user's past document creation history. For example, the document generation unit can use the generation AI to analyze the user's past document creation history and maintain a consistent document style. The generation AI can also standardize document terminology and expressions based on the user's past document creation history. Furthermore, the generation AI can standardize document formatting by referring to the user's past document creation history. This makes it possible to maintain document consistency by referring to the user's past document creation history.

[0051] The generating AI agent can optimize the search algorithm by referring to past search history. For example, the information retrieval unit can use AI to analyze the user's past search history and optimize the search algorithm. Furthermore, the AI ​​can prioritize displaying highly relevant information based on the user's past search history. In addition, the AI ​​can improve the accuracy of search results by referring to the user's past search history. This enables the optimization of the search algorithm by referencing past search history.

[0052] The generating AI agent can reflect updates to laws and precedents in real time. For example, the information retrieval unit can use AI to acquire updates to laws and precedents in real time and reflect them in the search results. Furthermore, the AI ​​can provide the latest information based on these updates. In addition, the AI ​​can improve the accuracy of search results by reflecting updates to laws and precedents in real time. This allows for the provision of the latest information by reflecting updates to laws and precedents in real time.

[0053] The generating AI agent can prioritize displaying highly relevant information by considering the user's geographical information. For example, the information retrieval unit can use AI to analyze the user's geographical information and prioritize displaying highly relevant information. Furthermore, the AI ​​can provide region-specific information based on the user's geographical information. In addition, the AI ​​can improve the accuracy of search results by considering the user's geographical information. This enables the priority display of highly relevant information by considering the user's geographical information.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The document generation unit generates legal documents. The document generation unit can generate legal documents such as contracts, litigation documents, and opinions. The document generation unit analyzes and generates legal documents using generation AI. For example, the document generation unit uses natural language processing technology to analyze the content of legal documents and generate appropriate documents. The document generation unit can use natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. Step 2: The information retrieval unit searches and analyzes information from databases of laws and precedents. The information retrieval unit can, for example, search for and analyze necessary information from databases of laws and precedents. The information retrieval unit uses machine learning to search and analyze information from databases of laws and precedents. For example, the information retrieval unit can use machine learning algorithms such as K-means and support vector machines. Step 3: The consultation support department provides legal consultation support. The consultation support department can provide legal consultation support based on legal advice provided by a generating AI agent, for example. The consultation support department can provide legal consultation support based on legal advice provided by a generating AI agent. For example, the consultation support department can determine the type of consultation and the method of support based on legal advice provided by a generating AI agent. Step 4: The Case Management Department manages the cases. The Case Management Department can manage cases based on legal advice provided by the Generating AI Agent, for example. The Case Management Department manages cases based on legal advice provided by the Generating AI Agent. For example, the Case Management Department can determine the types of information to manage and the management methods based on legal advice provided by the Generating AI Agent.

[0056] (Example of form 2) The generative AI agent according to an embodiment of the present invention is a system that automates the creation and research of legal documents, and can streamline legal consultations and case management. This generative AI agent analyzes and generates legal documents using natural language processing (NLP) and searches and analyzes information from databases of laws and precedents using machine learning. The generative AI agent provides document creation support through automatic template generation. This enables the provision of fast and accurate legal advice, reduces costs, and improves access to legal services. For example, when a lawyer is drafting a contract for a client, the generative AI agent selects an appropriate template based on the client's requirements and automatically generates the contract simply by inputting the necessary information. Also, when a law firm takes on a new case, the generative AI agent quickly searches for relevant laws and precedents and provides the necessary information. This generative AI agent is expected to see significant growth in the legal technology market, and as digitalization progresses, legal services are also required to have faster and more efficient solutions. With the advancement of AI technology, complex legal tasks that were once performed manually can now be automated, making now a good time to enter the market. The vision for this generative AI agent is to make legal services more accessible, enabling individuals and businesses to resolve legal issues more easily and strengthen fairness under the law. It aims to improve access to and the quality of legal assistance. This will allow the generative AI agent to automate the creation and research of legal documents and streamline legal consultations and case management.

[0057] The generative AI agent according to this embodiment comprises a document generation unit, an information retrieval unit, a consultation support unit, and a case management unit. The document generation unit generates legal documents. The document generation unit can generate legal documents such as contracts, litigation documents, and opinion letters. The document generation unit analyzes and generates legal documents using generative AI. For example, the document generation unit analyzes the content of legal documents using natural language processing technology and generates appropriate documents. The document generation unit can use natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. The information retrieval unit searches and analyzes information from a database of laws and precedents. For example, the information retrieval unit can search for and analyze necessary information from a database of laws and precedents. The information retrieval unit searches and analyzes information from a database of laws and precedents using machine learning. For example, the information retrieval unit can use machine learning algorithms such as K-means and support vector machines. The consultation support unit supports legal consultations. For example, the consultation support unit can support legal consultations based on legal advice provided by the generative AI agent. The Consultation Support Department provides legal consultation support based on legal advice provided by the Generating AI Agent. For example, the Consultation Support Department can determine the type of consultation and the method of support based on the legal advice provided by the Generating AI Agent. The Case Management Department manages cases. For example, the Case Management Department can manage cases based on legal advice provided by the Generating AI Agent. For example, the Case Management Department can determine the type of information to manage and the management method based on the legal advice provided by the Generating AI Agent. As a result, the Generating AI Agent according to this embodiment can automate the creation and research of legal documents, and streamline legal consultation and case management.

[0058] The document generation unit generates legal documents. For example, it can generate legal documents such as contracts, litigation documents, and legal opinions. The document generation unit uses generation AI to analyze and generate legal documents. Specifically, the document generation unit utilizes natural language processing technology to analyze the content of legal documents in detail and generate appropriate documents. For example, it uses morphological analysis to break down words within a document, understands sentence structure through grammatical analysis, and grasps the overall meaning of the document through semantic analysis. This allows the document generation unit to accurately grasp the context and intent of legal documents and generate documents that include the necessary information. Furthermore, the generation AI learns from past legal documents and precedents to improve the accuracy of document generation. For example, by learning from a large amount of past contracts and litigation documents, it can acquire document formats and expressions in specific legal fields and utilize this in generating new documents. This enables the document generation unit to create legal documents quickly and accurately, contributing to the efficiency of legal work.

[0059] The Information Retrieval Unit searches and analyzes information from databases of laws and precedents. Specifically, it uses machine learning to search and analyze information from these databases. For example, it uses K-means clustering to group related laws and precedents, and support vector machines to extract information relevant to specific legal issues. This allows the Information Retrieval Unit to quickly and accurately search and analyze necessary information from vast databases. Furthermore, the Information Retrieval Unit uses natural language processing to understand the meaning of search queries and provide more appropriate search results. For example, it analyzes the context of search queries and prioritizes the display of relevant laws and precedents. The Information Retrieval Unit also automatically links relevant legal information based on search results, enabling users to efficiently gather information. This contributes to improving the efficiency and accuracy of information gathering in legal work.

[0060] The Consultation Support Department provides legal consultation support. For example, the Consultation Support Department can provide legal consultation support based on legal advice provided by a generating AI agent. Specifically, the Consultation Support Department determines the type of consultation and the method of support based on the legal advice provided by the generating AI agent. For example, the generating AI agent analyzes the content of the user's consultation and provides appropriate legal advice. Based on this advice, the Consultation Support Department presents the user with a concrete action plan. Furthermore, based on the legal advice provided by the generating AI agent, the Consultation Support Department can provide customized support according to the content of the user's consultation. For example, it can provide support that meets the user's needs, such as detailed explanations of specific legal issues or provision of relevant laws and precedents. In addition, the Consultation Support Department collects feedback from users and continuously improves the accuracy and usefulness of the advice provided by the generating AI agent. In this way, the Consultation Support Department provides high-quality legal support to users and contributes to the efficiency and accuracy of legal consultations.

[0061] The Case Management Department manages cases. For example, the Case Management Department can manage cases based on legal advice provided by a generating AI agent. Specifically, the Case Management Department determines the types of information to manage and the management methods based on the legal advice provided by the generating AI agent. For example, the Case Management Department manages the progress and important deadlines of each case and executes necessary actions in a timely manner. The Case Management Department also determines the priority of cases and optimizes resource allocation based on the legal advice provided by the generating AI agent. Furthermore, the Case Management Department centrally manages documents and information related to cases based on the legal advice provided by the generating AI agent, and can quickly search and retrieve necessary information. As a result, the Case Management Department efficiently manages the progress of cases and contributes to the efficiency and accuracy of legal work. In addition, the Case Management Department can analyze past case data and identify areas for improvement in future case management. As a result, the Case Management Department achieves continuous business improvement and contributes to the improvement of the quality of legal work.

[0062] The document generation unit can analyze and generate legal documents using natural language processing. For example, the document generation unit can analyze the content of legal documents using morphological analysis. For example, the document generation unit can analyze the structure of legal documents using grammatical analysis. For example, the document generation unit can analyze the meaning of legal documents using semantic analysis. As a result, the analysis and generation of legal documents are made more efficient by using natural language processing. Some or all of the above-described processes in the document generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the document generation unit can input the content of a legal document into a generation AI, and the generation AI can analyze and generate the legal document.

[0063] The information retrieval unit can search and analyze information from a database of laws and precedents using machine learning. For example, the information retrieval unit can search and analyze information from a database of laws and precedents using K-means. For example, the information retrieval unit can search and analyze information from a database of laws and precedents using support vector machines. For example, the information retrieval unit can search and analyze information from a database of laws and precedents using random forests. This makes the search and analysis of laws and precedents more efficient by using machine learning. Some or all of the above processing in the information retrieval unit may be performed using AI, or it may be performed without AI. For example, the information retrieval unit can input information obtained from a database of laws and precedents into AI, and the AI ​​can search and analyze the information.

[0064] The document generation unit can provide document creation support through automatic template generation. For example, the document generation unit can automatically generate contract templates to support document creation. For example, the document generation unit can automatically generate litigation document templates to support document creation. For example, the document generation unit can automatically generate opinion letter templates to support document creation. This makes document creation more efficient through automatic template generation. Some or all of the above-described processes in the document generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the document generation unit can input the document template generation into a generation AI, and the generation AI can automatically generate the template.

[0065] The consultation support department can provide legal consultation support based on legal advice provided by a generating AI agent. For example, the consultation support department can provide legal consultation support based on legal advice provided by a generating AI agent. For example, the consultation support department can determine the type of consultation and the method of support based on legal advice provided by a generating AI agent. Some or all of the above processes in the consultation support department may be performed using AI or not. For example, the consultation support department can input legal advice provided by a generating AI agent into the AI, and the AI ​​can provide legal consultation support. This makes it possible to provide legal consultation support based on the advice of the generating AI agent.

[0066] The case management department can manage cases based on legal advice provided by a generating AI agent. For example, the case management department can manage cases based on legal advice provided by a generating AI agent. For example, the case management department can determine the types of information to manage and the management methods based on legal advice provided by a generating AI agent. Some or all of the above processes in the case management department may be performed using AI or not. For example, the case management department can input legal advice provided by a generating AI agent into the AI, and the AI ​​can manage the case. This makes it possible to manage cases based on the advice of the generating AI agent.

[0067] The document generation unit can estimate the user's emotions and adjust the tone and style of the document based on the estimated emotions. For example, if the user is stressed, the generating AI can soften the tone of the document and use reassuring language. If the user is in a hurry, the generating AI can make the document style concise and easy to understand. If the user is relaxed, the generating AI can make the tone of the document friendly and use approachable language. This allows for adjustment of the document's tone and style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the document generation unit may be performed using or without a generating AI. For example, the document generation unit can input user emotion data into a generating AI, which can then adjust the tone and style of the document.

[0068] The document generation unit can propose the optimal document structure by referring to documents from similar past cases. For example, the document generation unit's generation AI can analyze documents from similar past cases and propose the most effective document structure. For example, the document generation unit's generation AI can optimize the document's section structure based on past success stories. For example, the document generation unit's generation AI can adjust the document structure to avoid past failures. This makes it possible to propose the optimal document structure by referring to documents from similar past cases. Some or all of the above processes in the document generation unit may be performed using the generation AI or not. For example, the document generation unit can input document data from similar past cases into the generation AI, and the generation AI can propose the optimal document structure.

[0069] The document generation unit can assess legal risks and propose revisions to the document according to those risks. For example, the document generation unit's generation AI can assess legal risks and propose revisions to high-risk expressions. For example, the document generation unit's generation AI can assess legal risks and propose additional information to mitigate those risks. For example, the document generation unit's generation AI can assess legal risks and propose alternative expressions to avoid those risks. This makes it possible to assess legal risks and propose revisions to the document according to those risks. Some or all of the above processes in the document generation unit may be performed using the generation AI or not. For example, the document generation unit can input the content of a document into the generation AI, which can then assess legal risks and propose revisions.

[0070] The document generation unit can estimate the user's emotions and highlight important parts of the document based on the estimated emotions. For example, if the user is stressed, the generating AI can highlight important parts of the document to aid understanding. For example, if the user is in a hurry, the generating AI can highlight important parts of the document to allow for quick information acquisition. For example, if the user is relaxed, the generating AI can highlight important parts of the document to draw attention. This enables the highlighting of important parts of the document in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the document generation unit may be performed using or without a generating AI. For example, the document generation unit can input user emotion data into a generating AI, which can then highlight important parts of the document.

[0071] The document generation unit can automatically insert specialized terminology according to the user's field of expertise. For example, the document generation unit's generation AI can analyze the user's field of expertise and insert appropriate specialized terminology into the document. For example, the document generation unit's generation AI can add definitions of specialized terminology to the document based on the user's field of expertise. For example, the document generation unit's generation AI can adjust the frequency of use of specialized terminology according to the user's field of expertise. This enables the automatic insertion of specialized terminology according to the user's field of expertise. Some or all of the above processes in the document generation unit may be performed using the generation AI or not. For example, the document generation unit can input the user's field of expertise data into the generation AI, and the generation AI can automatically insert specialized terminology.

[0072] The document generation unit can maintain document consistency by referring to the user's past document creation history. For example, the document generation unit's generation AI can analyze the user's past document creation history and maintain a consistent document style. For example, the document generation unit's generation AI can unify document terminology and expressions based on the user's past document creation history. For example, the document generation unit's generation AI can refer to the user's past document creation history and unify document formatting. This makes it possible to maintain document consistency by referring to the user's past document creation history. Some or all of the above processes in the document generation unit may be performed using the generation AI or not. For example, the document generation unit can input the user's past document creation history data into the generation AI, which can then maintain document consistency.

[0073] The information retrieval unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is stressed, the AI ​​in the information retrieval unit can adjust the display order of search results to prioritize important information. For example, if the user is in a hurry, the AI ​​in the information retrieval unit can adjust the display order of search results to allow for quick information retrieval. For example, if the user is relaxed, the AI ​​in the information retrieval unit can adjust the display order of search results to provide more detailed information. This makes it possible to adjust the display order of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input user emotion data into the AI, and the AI ​​can adjust the display order of search results.

[0074] The information retrieval unit can optimize the search algorithm by referring to past search history. For example, the information retrieval unit can use AI to analyze the user's past search history and optimize the search algorithm. For example, the information retrieval unit can use AI to prioritize displaying highly relevant information based on the user's past search history. For example, the information retrieval unit can use AI to refer to the user's past search history and improve the accuracy of search results. This makes it possible to optimize the search algorithm by referring to past search history. Some or all of the above processes in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input the user's past search history data into AI, and the AI ​​can optimize the search algorithm.

[0075] The information retrieval unit can reflect updates to laws and precedents in real time. For example, the information retrieval unit can use AI to acquire updates to laws and precedents in real time and reflect them in the search results. For example, the information retrieval unit can use AI to provide the latest information based on updates to laws and precedents. For example, the information retrieval unit can use AI to reflect updates to laws and precedents in real time and improve the accuracy of search results. This allows the system to provide the latest information by reflecting updates to laws and precedents in real time. Some or all of the above-described processes in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input updates to laws and precedents into AI, which can then reflect them in real time.

[0076] The information retrieval unit can estimate the user's emotions and filter search results based on the estimated emotions. For example, if the user is stressed, the AI ​​in the information retrieval unit can filter search results and prioritize displaying important information. For example, if the user is in a hurry, the AI ​​in the information retrieval unit can filter search results to allow for quick information retrieval. For example, if the user is relaxed, the AI ​​in the information retrieval unit can filter search results and provide more detailed information. This enables filtering of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs. Some or all of the above-described processes in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input user emotion data into an AI, which can then filter the search results.

[0077] The information retrieval unit can prioritize displaying highly relevant information by considering the user's geographical information. For example, the information retrieval unit can use AI to analyze the user's geographical information and prioritize displaying highly relevant information. For example, the information retrieval unit can use AI to provide region-specific information based on the user's geographical information. For example, the information retrieval unit can use AI to improve the accuracy of search results by considering the user's geographical information. This makes it possible to prioritize the display of highly relevant information by considering the user's geographical information. Some or all of the above processing in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input the user's geographical information data into AI, and the AI ​​can prioritize displaying highly relevant information.

[0078] The information retrieval unit can provide customized search results tailored to the user's area of ​​expertise. For example, the information retrieval unit can use AI to analyze the user's area of ​​expertise and provide customized search results. For example, the information retrieval unit can use AI to prioritize displaying highly relevant information based on the user's area of ​​expertise. For example, the information retrieval unit can use AI to improve the accuracy of search results by considering the user's area of ​​expertise. This makes it possible to provide customized search results tailored to the user's area of ​​expertise. Some or all of the above-described processes in the information retrieval unit may be performed using AI or not. For example, the information retrieval unit can input user area of ​​expertise data into AI, and the AI ​​can provide customized search results.

[0079] The consultation support department can estimate the user's emotions and prioritize consultation topics based on the estimated emotions. For example, if the user is stressed, the AI ​​can adjust the priority of consultation topics and address important issues first. For example, if the user is in a hurry, the AI ​​can adjust the priority of consultation topics and respond quickly. For example, if the user is relaxed, the AI ​​can adjust the priority of consultation topics and provide more detailed support. This makes it possible to determine the priority of consultation topics according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation support department may be performed using AI or not. For example, the consultation support department can input user emotion data into AI, and the AI ​​can determine the priority of consultation topics.

[0080] The consultation support department can provide optimal advice by referring to past consultation history. For example, the consultation support department can use AI to analyze past consultation history and provide optimal advice. For example, the consultation support department can use AI to provide effective advice based on past success stories. For example, the consultation support department can use AI to adjust the content of advice to avoid past failures. This makes it possible to provide optimal advice by referring to past consultation history. Some or all of the above processes in the consultation support department may be performed using AI or not. For example, the consultation support department can input past consultation history data into AI, and the AI ​​can provide optimal advice.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The AI-generating agent can estimate the user's emotions and adjust the generation of legal documents based on those emotions. For example, if the user is stressed, the document generator can soften the tone of the document and use reassuring language. If the user is in a hurry, the document generator can make the style concise and easy to understand. Furthermore, if the user is relaxed, the document generator can make the tone friendly and use approachable language. This allows for adjustments to the tone and style of documents according to the user's emotions.

[0083] The generation AI agent can automatically insert specialized terminology according to the user's field of expertise. For example, the document generation unit can use the generation AI to analyze the user's field of expertise and insert appropriate terminology into the document. The generation AI can also add definitions of terminology to the document based on the user's field of expertise. Furthermore, the generation AI can adjust the frequency of use of terminology according to the user's field of expertise. This enables the automatic insertion of specialized terminology tailored to the user's field of expertise.

[0084] The generation AI agent can maintain document consistency by referring to the user's past document creation history. For example, the document generation unit can use the generation AI to analyze the user's past document creation history and maintain a consistent document style. The generation AI can also standardize document terminology and expressions based on the user's past document creation history. Furthermore, the generation AI can standardize document formatting by referring to the user's past document creation history. This makes it possible to maintain document consistency by referring to the user's past document creation history.

[0085] The AI-generating agent can estimate the user's emotions and highlight important sections of a document based on those emotions. For example, if the user is stressed, the document generator can highlight important sections to aid understanding. Similarly, if the user is in a hurry, the generator can highlight important sections to allow for quick information acquisition. Furthermore, if the user is relaxed, the generator can highlight important sections to draw their attention. This enables the highlighting of important sections of a document in accordance with the user's emotions.

[0086] The generating AI agent can estimate the user's emotions and adjust the display order of search results based on those emotions. For example, if the user is stressed, the information retrieval unit can adjust the display order of search results to prioritize important information. If the user is in a hurry, the information retrieval unit can adjust the display order of search results to allow for quick information retrieval. Furthermore, if the user is relaxed, the information retrieval unit can adjust the display order of search results to provide more detailed information. This makes it possible to adjust the display order of search results according to the user's emotions.

[0087] The generating AI agent can optimize the search algorithm by referring to past search history. For example, the information retrieval unit can use AI to analyze the user's past search history and optimize the search algorithm. Furthermore, the AI ​​can prioritize displaying highly relevant information based on the user's past search history. In addition, the AI ​​can improve the accuracy of search results by referring to the user's past search history. This enables the optimization of the search algorithm by referencing past search history.

[0088] The generating AI agent can reflect updates to laws and precedents in real time. For example, the information retrieval unit can use AI to acquire updates to laws and precedents in real time and reflect them in the search results. Furthermore, the AI ​​can provide the latest information based on these updates. In addition, the AI ​​can improve the accuracy of search results by reflecting updates to laws and precedents in real time. This allows for the provision of the latest information by reflecting updates to laws and precedents in real time.

[0089] The generating AI agent can estimate the user's emotions and filter search results based on those emotions. For example, if the user is stressed, the information retrieval unit can filter the search results to prioritize important information. If the user is in a hurry, the information retrieval unit can filter the search results to allow for quick information retrieval. Furthermore, if the user is relaxed, the information retrieval unit can filter the search results to provide more detailed information. This enables the filtering of search results according to the user's emotions.

[0090] The generating AI agent can prioritize displaying highly relevant information by considering the user's geographical information. For example, the information retrieval unit can use AI to analyze the user's geographical information and prioritize displaying highly relevant information. Furthermore, the AI ​​can provide region-specific information based on the user's geographical information. In addition, the AI ​​can improve the accuracy of search results by considering the user's geographical information. This enables the priority display of highly relevant information by considering the user's geographical information.

[0091] The generating AI agent can estimate the user's emotions and prioritize consultation topics based on those emotions. For example, if the user is stressed, the consultation support department can adjust the priority of consultation topics and address important issues first. Also, if the user is in a hurry, the consultation support department can adjust the priority of consultation topics and respond quickly. Furthermore, if the user is relaxed, the consultation support department can adjust the priority of consultation topics and provide more detailed support. This makes it possible to determine the priority of consultation topics in accordance with the user's emotions.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The document generation unit generates legal documents. The document generation unit can generate legal documents such as contracts, litigation documents, and opinions. The document generation unit analyzes and generates legal documents using generation AI. For example, the document generation unit uses natural language processing technology to analyze the content of legal documents and generate appropriate documents. The document generation unit can use natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. Step 2: The information retrieval unit searches and analyzes information from databases of laws and precedents. The information retrieval unit can, for example, search for and analyze necessary information from databases of laws and precedents. The information retrieval unit uses machine learning to search and analyze information from databases of laws and precedents. For example, the information retrieval unit can use machine learning algorithms such as K-means and support vector machines. Step 3: The consultation support department provides legal consultation support. The consultation support department can provide legal consultation support based on legal advice provided by a generating AI agent, for example. The consultation support department can provide legal consultation support based on legal advice provided by a generating AI agent. For example, the consultation support department can determine the type of consultation and the method of support based on legal advice provided by a generating AI agent. Step 4: The Case Management Department manages the cases. The Case Management Department can manage cases based on legal advice provided by the Generating AI Agent, for example. The Case Management Department manages cases based on legal advice provided by the Generating AI Agent. For example, the Case Management Department can determine the types of information to manage and the management methods based on legal advice provided by the Generating AI Agent.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] Each of the multiple elements described above, including the document generation unit, information retrieval unit, consultation support unit, and case management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the document generation unit is implemented by the control unit 46A of the smart device 14 and generates legal documents such as contracts and litigation documents. The information retrieval unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches and analyzes information from a database of laws and precedents. The consultation support unit is implemented by the control unit 46A of the smart device 14 and supports legal consultations based on legal advice provided by the generating AI agent. The case management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages cases. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the document generation unit, information retrieval unit, consultation support unit, and case management unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the document generation unit is implemented by the control unit 46A of the smart glasses 214 and generates legal documents such as contracts and litigation documents. The information retrieval unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches and analyzes information from a database of laws and precedents. The consultation support unit is implemented by the control unit 46A of the smart glasses 214 and supports legal consultations based on legal advice provided by the generated AI agent. The case management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages cases. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the document generation unit, information retrieval unit, consultation support unit, and case management unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the document generation unit is implemented by the control unit 46A of the headset terminal 314 and generates legal documents such as contracts and litigation documents. The information retrieval unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches and analyzes information from a database of laws and precedents. The consultation support unit is implemented by the control unit 46A of the headset terminal 314 and supports legal consultations based on legal advice provided by the generated AI agent. The case management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages cases. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the document generation unit, information retrieval unit, consultation support unit, and case management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the document generation unit is implemented by the control unit 46A of the robot 414 and generates legal documents such as contracts and litigation documents. The information retrieval unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches and analyzes information from a database of laws and precedents. The consultation support unit is implemented by, for example, the control unit 46A of the robot 414 and supports legal consultations based on legal advice provided by the generating AI agent. The case management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages cases. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0147] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] 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.

[0157] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] (Note 1) A document generation unit that generates legal documents, The information retrieval unit searches and analyzes information from databases of laws and precedents, The consultation support department provides legal advice, It includes a project management department that handles project management. A system characterized by the following features. (Note 2) The document generation unit, Analyze and generate legal documents using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned information retrieval unit, Using machine learning to search and analyze information from databases of laws and precedents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The document generation unit, Provides document creation support through automatic template generation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned consultation and support department, Legal consultations are supported based on legal advice provided by a generated AI agent. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned project management department, Manage cases based on legal advice provided by a generated AI agent. The system described in Appendix 1, characterized by the features described herein. (Note 7) The document generation unit, It estimates the user's emotions and adjusts the tone and style of the document based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The document generation unit, We will propose the optimal document structure by referring to documents from similar past cases. The system described in Appendix 1, characterized by the features described herein. (Note 9) The document generation unit, We assess legal risks and propose document revisions tailored to those risks. The system described in Appendix 1, characterized by the features described herein. (Note 10) The document generation unit, It estimates the user's sentiment and highlights important parts of the document based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The document generation unit, Automatically inserts specialized terminology according to the user's area of ​​expertise. The system described in Appendix 1, characterized by the features described herein. (Note 12) The document generation unit, Maintain document consistency by referencing the user's past document creation history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned information retrieval unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned information retrieval unit, Optimize the search algorithm by referring to past search history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned information retrieval unit, It reflects updates to laws and precedents in real time. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned information retrieval unit, It estimates the user's emotions and filters search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned information retrieval unit, The system prioritizes displaying highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned information retrieval unit, Provides customized search results tailored to the user's area of ​​expertise. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned consultation and support department, The system estimates the user's emotions and prioritizes the consultation topics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned consultation and support department, We provide the best advice by referring to past consultation history. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A document generation unit that generates legal documents, The information retrieval unit searches and analyzes information from databases of laws and precedents, The consultation support department provides legal advice, It includes a project management department that handles project management. A system characterized by the following features.

2. The document generation unit, Analyze and generate legal documents using natural language processing. The system according to feature 1.

3. The aforementioned information retrieval unit, Using machine learning to search and analyze information from databases of laws and precedents. The system according to feature 1.

4. The document generation unit, Provides document creation support through automatic template generation. The system according to feature 1.

5. The aforementioned consultation and support department, Legal consultations are supported based on legal advice provided by a generated AI agent. The system according to feature 1.

6. The aforementioned project management department, Manage cases based on legal advice provided by a generated AI agent. The system according to feature 1.

7. The document generation unit, It estimates the user's emotions and adjusts the tone and style of the document based on those estimated emotions. The system according to feature 1.

8. The document generation unit, We will propose the optimal document structure by referring to documents from similar past cases. The system according to feature 1.

9. The document generation unit, We assess legal risks and propose document revisions tailored to those risks. The system according to feature 1.

10. The document generation unit, It estimates the user's sentiment and highlights important parts of the document based on that estimated sentiment. The system according to feature 1.

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