system

Abstract

The system according to the embodiment aims to improve the efficiency of creating and judging legal documents. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a document based on the information analyzed by the analysis unit. The provision unit provides the document generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating and judging legal documents requires a great deal of time and effort.

[0005] The system according to the embodiment aims to improve the efficiency of creating and judging legal documents. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a document based on the information analyzed by the analysis unit. The provision unit provides the document generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of creating and judging legal documents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The document generation support system according to an embodiment of the present invention uses a generation AI to improve the work efficiency of lawyers and judicial scriveners. In this document generation support system, a user inputs necessary information, and the generation AI analyzes that information, generates an appropriate document, and provides it to the user. For example, in the case of contract drafting support, a user inputs the type of contract, party information, and contract content. This information is then input into the generation AI, which analyzes the input information based on laws and precedents and generates an appropriate document. The generated document is provided to the user in a downloadable format, allowing users to quickly and accurately obtain the necessary documents. This system significantly improves the work efficiency of lawyers and judicial scriveners, enabling companies to quickly and accurately evaluate business ventures and the pros and cons of drafting contracts. As a result, the document generation support system significantly improves the work efficiency of lawyers and judicial scriveners.

[0029] The document generation assistance system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, text information, image information, and audio information, for example. The reception unit receives, for example, text information entered by the user. The reception unit can also receive image information uploaded by the user. The reception unit can also receive audio information recorded by the user. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the text information using, for example, text analysis technology. The analysis unit can also analyze the image information using image analysis technology. The analysis unit can also analyze the audio information using audio analysis technology. The generation unit generates a document based on the information analyzed by the analysis unit. The generation unit generates, for example, a contract. The generation unit can also generate a legal notice. The generation unit can also generate an application form. The provision unit provides the document generated by the generation unit. The provision unit provides the generated document in a format that the user can download, for example. The providing unit can also send the generated document by email. Furthermore, the providing unit can also print and provide the generated document. This allows the document generation support system according to the embodiment to efficiently accept and analyze information from a user, generate a document, and provide it.

[0030] The document generation support system includes a contract generation unit that generates a contract. The contract generation unit generates, for example, a sales contract. A sales contract includes information about the seller and buyer, the object of the sale, the price, payment terms, etc. The contract generation unit can also generate an employment contract. An employment contract includes information about the employer and employee, employment terms, salary, working hours, etc. The contract generation unit can also generate a rental contract. A rental contract includes information about the lessor and lessee, the rental property, rent, contract period, etc. This allows the contract generation unit to efficiently generate contracts.

[0031] The document generation support system includes a legal notice generation unit that generates a legal notice. The legal notice generation unit generates, for example, a litigation notice. The litigation notice includes the parties to the litigation, the content of the litigation, the date and time of the litigation, and the location. The legal notice generation unit can also generate a warning letter. The warning letter includes the recipient of the warning, the content of the warning, the reason for the warning, and the deadline for the warning. The legal notice generation unit can also generate a cancellation notice. The cancellation notice includes the parties to the cancellation, the reason for the cancellation, the date and time of the cancellation, and the cancellation procedure. This allows the legal notice generation unit to efficiently generate legal notices.

[0032] The document generation support system includes an application generation unit that generates an application form. The application generation unit generates, for example, a permission application form. The permission application form includes information about the applicant, the content of the application, the reason for the application, the application procedure, etc. The application generation unit can also generate a registration application form. The registration application form includes the person to be registered, the content of the registration, the reason for the registration, the registration procedure, etc. The application generation unit can also generate a change application form. The change application form includes the person to be changed, the content of the change, the reason for the change, the change procedure, etc. This allows the application generation unit to efficiently generate application forms.

[0033] The document generation support system includes a rules generation unit that generates rules. The rules generation unit generates, for example, terms of use. The terms of use include user information, conditions of use, restrictions on use, and procedures for use. The rules generation unit can also generate a privacy policy. The privacy policy includes conditions for the collection, use, protection, and provision of personal information. The rules generation unit can also generate service terms. The service terms include the content of the service, conditions of use, restrictions on use, and procedures for use. This allows the rules generation unit to generate rules efficiently.

[0034] The document generation support system includes a legal document generation unit that generates legal documents. The legal document generation unit generates, for example, a complaint. The complaint includes the parties to the lawsuit, the content of the lawsuit, the reasons for the lawsuit, and the lawsuit procedures. The legal document generation unit can also generate a testimony. The testimony includes information about the witness, the content of the testimony, the reasons for the testimony, and the procedures for the testimony. The legal document generation unit can also generate documentary evidence. The documentary evidence includes the content of the evidence, the reasons for the evidence, and the procedures for the evidence. This allows the legal document generation unit to efficiently generate legal documents.

[0035] The reception unit can analyze the user's past information input history and select an appropriate reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of information that the user has input in the past and automatically generate an optimal input form. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past information input history.

[0036] The reception unit can filter information based on the user's current work situation and areas of interest when receiving the information. For example, the reception unit preferentially receives only information related to a project currently underway by the user. The reception unit can also filter and receive highly relevant information based on the user's areas of interest. Furthermore, the reception unit can select and receive only necessary information according to the user's work situation. In this way, by filtering information based on the user's current work situation and areas of interest, highly relevant information can be preferentially received.

[0037] When receiving information, the reception unit can preferentially receive highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially receives information related to that area. Furthermore, when the user is moving, the reception unit can also receive optimal information based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially receive information related to that location. In this way, by taking into account the user's geographical location information, highly relevant information can be preferentially received.

[0038] When receiving information, the reception unit can analyze the user's social media activity and receive related information. For example, the reception unit can preferentially receive related information based on information shared by the user on social media. The reception unit can also analyze the user's social media activity history and receive highly relevant information. Furthermore, the reception unit can also receive related information based on accounts the user follows on social media. In this way, by analyzing the user's social media activity, highly relevant information can be preferentially received.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a legal analysis algorithm to legal documents. The analysis unit can also apply a contract analysis algorithm to contracts. Furthermore, the analysis unit can also apply a rules analysis algorithm to rules. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of information submitted earlier. The analysis unit can also postpone information submitted later. Furthermore, the analysis unit can gradually adjust the priority of analysis depending on the time of submission. In this way, by determining the priority of analysis based on the time of submission of information, more appropriate analysis results can be provided.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can also adjust the order of analysis in stages according to the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided.

[0043] The generation unit can adjust the level of detail of the generated document based on the importance of the information when generating the document. For example, the generation unit generates a detailed document for information with a high level of importance. The generation unit can also generate a concise document for information with a low level of importance. Furthermore, the generation unit can gradually adjust the level of detail of the generated document depending on the importance of the information. This allows for the generation of a more appropriate document by adjusting the level of detail of the generated document based on the importance of the information.

[0044] The generation unit can apply different generation algorithms depending on the category of information when generating a document. For example, the generation unit applies a legal document generation algorithm to a legal document. The generation unit can also apply a contract generation algorithm to a contract. Furthermore, the generation unit can also apply a rules generation algorithm to rules. In this way, by applying different generation algorithms depending on the category of information, more appropriate documents can be generated.

[0045] When generating a document, the generation unit can determine the generation priority based on the time of submission of information. For example, the generation unit prioritizes documenting information that is submitted early. The generation unit can also postpone information that is submitted late. Furthermore, the generation unit can gradually adjust the generation priority according to the time of submission. In this way, by determining the generation priority based on the time of submission of information, more appropriate documents can be generated.

[0046] The generation unit can adjust the order of generation based on the relevance of information when generating a document. For example, the generation unit prioritizes documenting highly relevant information. The generation unit can also postpone less relevant information. Furthermore, the generation unit can gradually adjust the order of generation according to the relevance of information. In this way, by adjusting the order of generation based on the relevance of information, a more appropriate document can be generated.

[0047] When providing a document, the providing unit can select an appropriate delivery method by referring to the user's past usage history. For example, the providing unit preferentially suggests delivery methods (email, download, etc.) that the user has frequently used in the past. The providing unit can also automatically select the optimal delivery format from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and suggest the optimal delivery method for a specific time period. In this way, the optimal delivery method can be selected by referring to the user's past usage history.

[0048] The providing unit can customize the means of providing the document based on the user's current work situation when providing the document. For example, if the user is in a meeting, the providing unit can provide the document after the meeting ends. Furthermore, if the user is out, the providing unit can provide the document in a format optimized for a mobile device. Furthermore, if the user is doing desk work, the providing unit can provide the document in a format optimized for a desktop. In this way, by customizing the means of providing the document based on the user's current work situation, the document can be provided in a more appropriate format.

[0049] When providing documents, the providing unit can select an appropriate delivery method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide documents related to that area with priority. Furthermore, if the user is on the move, the providing unit can also select the optimal delivery method based on the user's current location. Furthermore, if the user is in a specific location, the providing unit can also provide documents related to that location with priority. In this way, the optimal delivery method can be selected by taking into account the user's geographical location information.

[0050] When providing documents, the providing unit can analyze the user's social media activity and suggest a means of providing the documents. For example, the providing unit can provide relevant documents preferentially based on information shared by the user on social media. The providing unit can also analyze the user's social media activity history to provide highly relevant documents. Furthermore, the providing unit can provide relevant documents based on accounts the user follows on social media. In this way, by analyzing the user's social media activity, highly relevant documents can be provided preferentially.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When accepting information input by a user, the acceptance unit can refer to the user's past input history and suggest the optimal input method. For example, it can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The acceptance unit can also analyze patterns of information entered by the user in the past and automatically generate the optimal input form. Furthermore, the acceptance unit can also suggest the optimal acceptance method for a specific time period based on the user's past input history. In this way, the optimal acceptance method can be selected by analyzing the user's past information input history.

[0053] The legal notice generation unit can select different notification methods depending on the content of the legal notice to be generated. For example, in the case of a lawsuit notice, a formal notification method such as email or mail can be selected. In addition, in the case of a warning letter, a quick notification can be sent via email or a messaging app. Furthermore, in the case of a termination notice, a notification by mail or in person can be selected. This allows for more appropriate legal notices to be provided by selecting the optimal notification method depending on the content of the legal notice.

[0054] The terms generation unit can apply different generation algorithms depending on the content of the terms to be generated. For example, a terms of use generation algorithm can be applied to terms of use. Also, a privacy policy generation algorithm can be applied to privacy policies. Furthermore, a service terms generation algorithm can be applied to service terms. In this way, by applying different generation algorithms depending on the content of the terms, more appropriate terms can be generated.

[0055] The reception unit can preferentially receive highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, information related to that area is preferentially received. Also, when the user is moving, optimal information can be received based on the user's current location. Furthermore, when the user is in a specific location, information related to that location can be preferentially received. In this way, highly relevant information can be preferentially received by taking into account the user's geographical location information.

[0056] When generating a document, the generation unit can determine the generation priority based on the time when the information was submitted. For example, information submitted earlier can be given priority in documenting. Information submitted later can also be postponed. Furthermore, the generation priority can be adjusted in stages depending on the time of submission. In this way, by determining the generation priority based on the time when the information was submitted, more appropriate documents can be generated.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives information from the user. The information from the user includes text information, image information, and audio information. For example, the reception unit receives text information entered by the user, image information uploaded by the user, and audio information recorded by the user. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the text information using text analysis technology, the image information using image analysis technology, and the audio information using audio analysis technology. Step 3: The generator generates documents based on the information analyzed by the analyzer, such as contracts, legal notices, and applications. Step 4: The providing unit provides the document generated by the generating unit. The providing unit provides the generated document in a format that can be downloaded by the user, sent by email, or printed.

[0059] (Example 2) The document generation support system according to an embodiment of the present invention uses a generation AI to improve the work efficiency of lawyers and judicial scriveners. In this document generation support system, a user inputs necessary information, and the generation AI analyzes that information, generates an appropriate document, and provides it to the user. For example, in the case of contract drafting support, a user inputs the type of contract, party information, and contract content. This information is then input into the generation AI, which analyzes the input information based on laws and precedents and generates an appropriate document. The generated document is provided to the user in a downloadable format, allowing users to quickly and accurately obtain the necessary documents. This system significantly improves the work efficiency of lawyers and judicial scriveners, enabling companies to quickly and accurately evaluate business ventures and the pros and cons of drafting contracts. As a result, the document generation support system significantly improves the work efficiency of lawyers and judicial scriveners.

[0060] The document generation assistance system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, text information, image information, and audio information, for example. The reception unit receives, for example, text information entered by the user. The reception unit can also receive image information uploaded by the user. The reception unit can also receive audio information recorded by the user. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the text information using, for example, text analysis technology. The analysis unit can also analyze the image information using image analysis technology. The analysis unit can also analyze the audio information using audio analysis technology. The generation unit generates a document based on the information analyzed by the analysis unit. The generation unit generates, for example, a contract. The generation unit can also generate a legal notice. The generation unit can also generate an application form. The provision unit provides the document generated by the generation unit. The provision unit provides the generated document in a format that the user can download, for example. The providing unit can also send the generated document by email. Furthermore, the providing unit can also print and provide the generated document. This allows the document generation support system according to the embodiment to efficiently accept and analyze information from a user, generate a document, and provide it.

[0061] The document generation support system includes a contract generation unit that generates a contract. The contract generation unit generates, for example, a sales contract. A sales contract includes information about the seller and buyer, the object of the sale, the price, payment terms, etc. The contract generation unit can also generate an employment contract. An employment contract includes information about the employer and employee, employment terms, salary, working hours, etc. The contract generation unit can also generate a rental contract. A rental contract includes information about the lessor and lessee, the rental property, rent, contract period, etc. This allows the contract generation unit to efficiently generate contracts.

[0062] The document generation support system includes a legal notice generation unit that generates a legal notice. The legal notice generation unit generates, for example, a litigation notice. The litigation notice includes the parties to the litigation, the content of the litigation, the date and time of the litigation, and the location. The legal notice generation unit can also generate a warning letter. The warning letter includes the recipient of the warning, the content of the warning, the reason for the warning, and the deadline for the warning. The legal notice generation unit can also generate a cancellation notice. The cancellation notice includes the parties to the cancellation, the reason for the cancellation, the date and time of the cancellation, and the cancellation procedure. This allows the legal notice generation unit to efficiently generate legal notices.

[0063] The document generation support system includes an application generation unit that generates an application form. The application generation unit generates, for example, a permission application form. The permission application form includes information about the applicant, the content of the application, the reason for the application, the application procedure, etc. The application generation unit can also generate a registration application form. The registration application form includes the person to be registered, the content of the registration, the reason for the registration, the registration procedure, etc. The application generation unit can also generate a change application form. The change application form includes the person to be changed, the content of the change, the reason for the change, the change procedure, etc. This allows the application generation unit to efficiently generate application forms.

[0064] The document generation support system includes a rules generation unit that generates rules. The rules generation unit generates, for example, terms of use. The terms of use include user information, conditions of use, restrictions on use, and procedures for use. The rules generation unit can also generate a privacy policy. The privacy policy includes conditions for the collection, use, protection, and provision of personal information. The rules generation unit can also generate service terms. The service terms include the content of the service, conditions of use, restrictions on use, and procedures for use. This allows the rules generation unit to generate rules efficiently.

[0065] The document generation support system includes a legal document generation unit that generates legal documents. The legal document generation unit generates, for example, a complaint. The complaint includes the parties to the lawsuit, the content of the lawsuit, the reasons for the lawsuit, and the lawsuit procedures. The legal document generation unit can also generate a testimony. The testimony includes information about the witness, the content of the testimony, the reasons for the testimony, and the procedures for the testimony. The legal document generation unit can also generate documentary evidence. The documentary evidence includes the content of the evidence, the reasons for the evidence, and the procedures for the evidence. This allows the legal document generation unit to efficiently generate legal documents.

[0066] The reception unit can estimate the user's emotions and adjust the timing of receiving information based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving information to provide the user with time to relax. Furthermore, if the user is relaxed, the reception unit can immediately receive information and start processing quickly. Furthermore, if the user is in a hurry, the reception unit can advance the timing of receiving information to quickly receive information. In this way, by adjusting the timing of receiving information according to the user's emotions, information can be received at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0067] The reception unit can analyze the user's past information input history and select an appropriate reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of information that the user has input in the past and automatically generate an optimal input form. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past information input history.

[0068] The reception unit can filter information based on the user's current work situation and areas of interest when receiving the information. For example, the reception unit preferentially receives only information related to a project currently underway by the user. The reception unit can also filter and receive highly relevant information based on the user's areas of interest. Furthermore, the reception unit can select and receive only necessary information according to the user's work situation. In this way, by filtering information based on the user's current work situation and areas of interest, highly relevant information can be preferentially received.

[0069] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated emotions. For example, if the user is feeling stressed, the reception unit postpones information of low importance. Furthermore, if the user is relaxed, the reception unit can also prioritize receiving information of high importance. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving information of high urgency. In this way, by determining the priority of information according to the user's emotions, more appropriate information can be received preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0070] When receiving information, the reception unit can preferentially receive highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially receives information related to that area. Furthermore, when the user is moving, the reception unit can also receive optimal information based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially receive information related to that location. In this way, by taking into account the user's geographical location information, highly relevant information can be preferentially received.

[0071] When receiving information, the reception unit can analyze the user's social media activity and receive related information. For example, the reception unit can preferentially receive related information based on information shared by the user on social media. The reception unit can also analyze the user's social media activity history and receive highly relevant information. Furthermore, the reception unit can also receive related information based on accounts the user follows on social media. In this way, by analyzing the user's social media activity, highly relevant information can be preferentially received.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This allows for adjusting the way the analysis is presented according to the user's emotions, making it possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a legal analysis algorithm to legal documents. The analysis unit can also apply a contract analysis algorithm to contracts. Furthermore, the analysis unit can also apply a rules analysis algorithm to rules. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of information submitted earlier. The analysis unit can also postpone information submitted later. Furthermore, the analysis unit can gradually adjust the priority of analysis depending on the time of submission. In this way, by determining the priority of analysis based on the time of submission of information, more appropriate analysis results can be provided.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can also adjust the order of analysis in stages according to the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided.

[0078] The generation unit can estimate the user's emotions and adjust the expression method of the generated document based on the estimated emotions. For example, if the user is nervous, the generation unit generates a simple, highly readable document. The generation unit can also generate a detailed document if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate a concise document that focuses on the main points. This allows for the generation of a more appropriate document by adjusting the expression method of the document according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] The generation unit can adjust the level of detail of the generated document based on the importance of the information when generating the document. For example, the generation unit generates a detailed document for information with a high level of importance. The generation unit can also generate a concise document for information with a low level of importance. Furthermore, the generation unit can gradually adjust the level of detail of the generated document depending on the importance of the information. This allows for the generation of a more appropriate document by adjusting the level of detail of the generated document based on the importance of the information.

[0080] The generation unit can apply different generation algorithms depending on the category of information when generating a document. For example, the generation unit applies a legal document generation algorithm to a legal document. The generation unit can also apply a contract generation algorithm to a contract. Furthermore, the generation unit can also apply a rules generation algorithm to rules. In this way, by applying different generation algorithms depending on the category of information, more appropriate documents can be generated.

[0081] The generation unit can estimate the user's emotions and adjust the length of the generated document based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point document. If the user is relaxed, the generation unit can also generate a longer document with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate a document with visually stimulating effects. This allows for adjusting the length of the document according to the user's emotions, thereby generating a more appropriate document. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] When generating a document, the generation unit can determine the generation priority based on the time of submission of information. For example, the generation unit prioritizes documenting information that is submitted early. The generation unit can also postpone information that is submitted late. Furthermore, the generation unit can gradually adjust the generation priority according to the time of submission. In this way, by determining the generation priority based on the time of submission of information, more appropriate documents can be generated.

[0083] The generation unit can adjust the order of generation based on the relevance of information when generating a document. For example, the generation unit prioritizes documenting highly relevant information. The generation unit can also postpone less relevant information. Furthermore, the generation unit can gradually adjust the order of generation according to the relevance of information. In this way, by adjusting the order of generation based on the relevance of information, a more appropriate document can be generated.

[0084] The providing unit can estimate the user's emotions and adjust the method of providing the document based on the estimated emotions. For example, if the user is nervous, the providing unit can provide the document in a simple, highly visible format. Furthermore, if the user is relaxed, the providing unit can also provide the document in a detailed format. Furthermore, if the user is in a hurry, the providing unit can also provide the document in a concise format that focuses on the main points. In this way, by adjusting the method of providing the document according to the user's emotions, the document can be provided in a more appropriate format. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] When providing a document, the providing unit can select an appropriate delivery method by referring to the user's past usage history. For example, the providing unit preferentially suggests delivery methods (email, download, etc.) that the user has frequently used in the past. The providing unit can also automatically select the optimal delivery format from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and suggest the optimal delivery method for a specific time period. In this way, the optimal delivery method can be selected by referring to the user's past usage history.

[0086] The providing unit can customize the means of providing the document based on the user's current work situation when providing the document. For example, if the user is in a meeting, the providing unit can provide the document after the meeting ends. Furthermore, if the user is out, the providing unit can provide the document in a format optimized for a mobile device. Furthermore, if the user is doing desk work, the providing unit can provide the document in a format optimized for a desktop. In this way, by customizing the means of providing the document based on the user's current work situation, the document can be provided in a more appropriate format.

[0087] The providing unit can estimate the user's emotions and determine the priority of document provision based on the estimated emotions. For example, if the user is feeling stressed, the providing unit postpones documents of low importance. Furthermore, if the user is relaxed, the providing unit can also provide documents of high importance with priority. Furthermore, if the user is in a hurry, the providing unit can provide documents of high urgency with top priority. In this way, by determining the priority of document provision according to the user's emotions, more appropriate documents can be provided with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] When providing documents, the providing unit can select an appropriate delivery method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide documents related to that area with priority. Furthermore, if the user is on the move, the providing unit can also select the optimal delivery method based on the user's current location. Furthermore, if the user is in a specific location, the providing unit can also provide documents related to that location with priority. In this way, the optimal delivery method can be selected by taking into account the user's geographical location information.

[0089] When providing documents, the providing unit can analyze the user's social media activity and suggest a means of providing the documents. For example, the providing unit can provide relevant documents preferentially based on information shared by the user on social media. The providing unit can also analyze the user's social media activity history to provide highly relevant documents. Furthermore, the providing unit can provide relevant documents based on accounts the user follows on social media. In this way, by analyzing the user's social media activity, highly relevant documents can be provided preferentially. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text information, image information, and audio information from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a document based on the analyzed information. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated document to a user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a document based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated document to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives voice information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a document based on the analyzed information. The provision unit is realized, for example, by the display 343 of the headset type terminal 314 and provides the generated document to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a document based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated document to the user.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When accepting information input by a user, the acceptance unit can refer to the user's past input history and suggest the optimal input method. For example, it can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The acceptance unit can also analyze patterns of information entered by the user in the past and automatically generate the optimal input form. Furthermore, the acceptance unit can also suggest the optimal acceptance method for a specific time period based on the user's past input history. In this way, the optimal acceptance method can be selected by analyzing the user's past information input history.

[0092] The contract generation unit can estimate the user's emotions and adjust the way the contract is expressed to be generated based on the estimated emotions. For example, if the user is nervous, a simple, highly readable contract can be generated. If the user is relaxed, a detailed contract can be generated. Furthermore, if the user is in a hurry, a concise contract that focuses on the main points can be generated. In this way, by adjusting the way the contract is expressed depending on the user's emotions, more appropriate contracts can be generated.

[0093] The legal notice generation unit can select different notification methods depending on the content of the legal notice to be generated. For example, in the case of a lawsuit notice, a formal notification method such as email or mail can be selected. In addition, in the case of a warning letter, a quick notification can be sent via email or a messaging app. Furthermore, in the case of a termination notice, a notification by mail or in person can be selected. This allows for more appropriate legal notices to be provided by selecting the optimal notification method depending on the content of the legal notice.

[0094] The application form generation unit can estimate the user's emotions and adjust the length of the application form to be generated based on the estimated emotions. For example, if the user is in a hurry, a short application form that is to the point can be generated. If the user is relaxed, a longer application form that includes detailed explanations can be generated. Furthermore, if the user is excited, an application form with visually stimulating effects can be generated. In this way, by adjusting the length of the application form according to the user's emotions, a more appropriate application form can be generated.

[0095] The terms generation unit can apply different generation algorithms depending on the content of the terms to be generated. For example, a terms of use generation algorithm can be applied to terms of use. Also, a privacy policy generation algorithm can be applied to privacy policies. Furthermore, a service terms generation algorithm can be applied to service terms. In this way, by applying different generation algorithms depending on the content of the terms, more appropriate terms can be generated.

[0096] The legal document generation unit can estimate the user's emotions and adjust the expression method of the generated legal document based on the estimated emotions. For example, if the user is nervous, a simple and highly visible legal document can be generated. If the user is relaxed, a detailed legal document can be generated. Furthermore, if the user is in a hurry, a concise legal document that focuses on the main points can be generated. In this way, by adjusting the expression method of the legal document according to the user's emotions, more appropriate legal documents can be generated.

[0097] The reception unit can preferentially receive highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, information related to that area is preferentially received. Also, when the user is moving, optimal information can be received based on the user's current location. Furthermore, when the user is in a specific location, information related to that location can be preferentially received. In this way, highly relevant information can be preferentially received by taking into account the user's geographical location information.

[0098] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, if the user is in a hurry, it can also provide concise analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide more appropriate analysis results.

[0099] When generating a document, the generation unit can determine the generation priority based on the time when the information was submitted. For example, information submitted earlier can be given priority in documenting. Information submitted later can also be postponed. Furthermore, the generation priority can be adjusted in stages depending on the time of submission. In this way, by determining the generation priority based on the time when the information was submitted, more appropriate documents can be generated.

[0100] The providing unit can estimate the user's emotions and adjust the method of providing the document based on the estimated emotions. For example, if the user is nervous, the document can be provided in a simple, highly visible format. If the user is relaxed, the document can be provided in a detailed format. Furthermore, if the user is in a hurry, the document can be provided in a concise format that focuses on the main points. In this way, by adjusting the method of providing the document according to the user's emotions, the document can be provided in a more appropriate format.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives information from the user. The information from the user includes text information, image information, and audio information. For example, the reception unit receives text information entered by the user, image information uploaded by the user, and audio information recorded by the user. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the text information using text analysis technology, the image information using image analysis technology, and the audio information using audio analysis technology. Step 3: The generator generates documents based on the information analyzed by the analyzer, such as contracts, legal notices, and applications. Step 4: The providing unit provides the document generated by the generating unit. The providing unit provides the generated document in a format that can be downloaded by the user, sent by email, or printed.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a document based on the information analyzed by the analysis unit; a providing unit that provides the document generated by the generating unit; Equipped with A system characterized by:

2. A contract generation unit is provided to generate a contract. The system of claim 1 .

3. A legal notice generation unit is provided to generate legal notices. The system of claim 1 .

4. An application form generation unit is provided to generate an application form. The system of claim 1 .

5. Equipped with a rule generation unit that generates rules The system of claim 1 .

6. A legal document generation unit is provided to generate legal documents. The system of claim 1 .

7. The reception unit Estimates the user's emotions and adjusts the timing of receiving information based on the estimated emotions. The system of claim 1 .

8. The reception unit Analyze the user's past information input history and select the appropriate reception method The system of claim 1 .

Images