System

Abstract

An object of a system according to an embodiment is to quickly create a contract and check the contents of the contract.SOLUTION: A system includes a contract creation part and a content confirmation part. The contract creation part automatically creates a contract on the basis of contract contents input from a user. The content confirmation unit confirms the content of the contract created by the contract creation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating contracts and verifying their contents takes time, which can lead to labor shortages.

[0005] The system according to the embodiment aims to quickly create a contract and check its contents. [Means for solving the problem]

[0006] The system according to the embodiment includes a contract creation unit and a content confirmation unit. The contract creation unit automatically creates a contract based on the contract content input by a user. The content confirmation unit confirms the content of the contract created by the contract creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly create a contract and check its contents. [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 contract creation system according to the embodiment of the present invention is a system in which a generation AI automatically creates a contract and checks its contents. This enables the contract creation system to quickly and accurately create a contract and check its contents.

[0029] A contract creation system according to an embodiment includes a contract creation unit and a content confirmation unit. The contract creation unit automatically creates a contract based on contract content entered by a user. For example, the contract creation unit analyzes the contract content entered by the user, selects an appropriate contract template, and generates a contract by embedding necessary information. The contract creation unit can also automatically select optimal clauses by referring to a database of similar past contracts. For example, when creating a sales contract, the most appropriate clauses are selected based on data from past sales contracts. The contract creation unit can also automatically reflect terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, medical terminology and medical regulations are automatically reflected. The content confirmation unit checks the content of the created contract. For example, the content confirmation unit analyzes each clause of the contract to check whether it is legally sound and whether all necessary information is included. The content confirmation unit can also automatically evaluate legal risks by referring to a database of past legal precedents. For example, the content confirmation unit checks whether the clauses in the contract are legally sound based on past legal precedents. The content confirmation unit can also automatically incorporate the opinions of experts. For example, the clauses of the contract are revised based on the opinions of legal experts. This allows the contract drafting system according to the embodiment to draft the contract and check its contents quickly and accurately.

[0030] The contract creation unit can analyze instructions from a user, select an appropriate contract template, and generate a contract by embedding the necessary information. For example, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating a sales contract, the contract creation unit selects the most appropriate clauses based on data from past sales contracts. Also, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating a lease contract, the contract creation unit selects the most appropriate clauses based on data from past lease contracts. Also, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating an employment contract, the contract creation unit selects the most appropriate clauses based on data from past employment contracts. This allows appropriate contracts to be generated based on user instructions.

[0031] The content verification unit analyzes each clause of the contract and checks whether it is legally sound and whether it contains all necessary information. For example, when the generation AI creates a contract, the content verification unit automatically reflects terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, it automatically reflects medical terminology and medical regulations. The content verification unit also automatically reflects terminology and regulations specific to the user's industry when the generation AI creates a contract. For example, when creating a contract for the construction industry, it automatically reflects construction terminology and construction regulations. The content verification unit also automatically reflects terminology and regulations specific to the user's industry when the generation AI creates a contract. For example, when creating a contract for the financial industry, it automatically reflects financial terminology and financial regulations. This allows the content of the contract to be legally verified and its accuracy to be guaranteed.

[0032] The contract creation unit can refer to a database of similar past contracts and automatically select the most appropriate clauses. For example, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating a sales contract, the most appropriate clauses are selected based on data from past sales contracts. Also, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating a lease contract, the most appropriate clauses are selected based on data from past lease contracts. Also, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating an employment contract, the most appropriate clauses are selected based on data from past employment contracts. This makes it possible to select the most appropriate clauses by referring to past data.

[0033] The contract creation unit can automatically reflect terminology and regulations specific to the user's industry. For example, when the generation AI creates a contract, the contract creation unit automatically reflects terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, medical terminology and medical regulations are automatically reflected. Furthermore, the contract creation unit automatically reflects terminology and regulations specific to the user's industry when the generation AI creates a contract. For example, when creating a contract for the construction industry, construction terminology and construction regulations are automatically reflected. Furthermore, the contract creation unit automatically reflects terminology and regulations specific to the user's industry when the generation AI creates a contract. For example, when creating a contract for the financial industry, financial terminology and financial regulations are automatically reflected. This makes it possible to create contracts that reflect industry-specific terminology and regulations.

[0034] The contract creation unit can automatically convert what a user dictates into text using voice input and reflect it in the contract. For example, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract content dictated by the user is converted into text in real time and reflected in the contract. Furthermore, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract terms dictated by the user are automatically converted into text and reflected in the contract. Furthermore, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract terms dictated by the user are automatically converted into text and reflected in the contract. In this way, contracts can be created using voice input.

[0035] The contract creation unit can simultaneously generate contracts in different languages, thereby achieving multilingual support. For example, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, thereby achieving multilingual support. For example, contracts in English and Japanese are generated simultaneously. Also, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, thereby achieving multilingual support. For example, contracts in French and German are generated simultaneously. Also, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, thereby achieving multilingual support. For example, contracts in Chinese and Spanish are generated simultaneously. This makes it possible to generate multilingual contracts.

[0036] The contract creation unit can refer to the user's past contract history and propose optimal contract content. For example, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose optimal contract content. For example, it can propose the most appropriate clauses based on the past contract content. Also, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose optimal contract content. For example, it can propose the most appropriate conditions based on the past contract conditions. Also, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose optimal contract content. For example, it can propose the most appropriate clauses based on the past contract clauses. This makes it possible to propose optimal contract content based on the past contract history.

[0037] The contract creation unit can reflect legal amendment information in real time. For example, the contract creation unit reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, the contract content is automatically updated based on the latest legal amendment information. The contract creation unit also reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, if a new law comes into effect, the contract content is updated based on that information. The contract creation unit also reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, changes due to legal amendments are automatically reflected in the contract content. This makes it possible to create contracts that reflect the latest legal amendment information.

[0038] The contract creation unit can analyze images and drawings provided by the user and reflect them in the contract content. For example, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, in a construction contract, the contract creation unit analyzes building drawings and reflects them in the contract content. Furthermore, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, it analyzes product blueprints and reflects them in a manufacturing contract. Furthermore, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, it analyzes a land map and reflects it in a real estate contract. In this way, images and drawings can be analyzed and reflected in the contract content.

[0039] The content verification unit can automatically evaluate legal risks by referring to a database of past legal precedents. For example, when the generation AI checks the contents of a contract, the content verification unit automatically evaluates legal risks by referring to a database of past legal precedents. For example, it checks whether the clauses in the contract are legally problematic based on past legal precedents. In addition, when the generation AI checks the contents of a contract, the content verification unit automatically evaluates legal risks by referring to a database of past legal precedents. For example, it checks whether the clauses in the contract involve a risk of litigation based on past legal precedents. In addition, when the generation AI checks the contents of a contract, the content verification unit automatically evaluates legal risks by referring to a database of past legal precedents. For example, it checks whether the clauses in the contract include illegality based on past legal precedents. In this way, legal risks can be evaluated by referring to a database of past legal precedents.

[0040] The content verification unit can automatically incorporate expert opinions. For example, when the generation AI is verifying the content of a contract, the content verification unit automatically incorporates expert opinions. For example, it may revise the clauses of the contract based on the opinions of legal experts. The content verification unit also automatically incorporates expert opinions when the generation AI is verifying the content of a contract. For example, it may adjust the content of the contract based on the opinions of industry experts. The content verification unit also automatically incorporates expert opinions when the generation AI is verifying the content of a contract. For example, it may revise the technical clauses of the contract based on the opinions of technical experts. This makes it possible to verify the content of a contract by automatically incorporating expert opinions.

[0041] The content verification unit can evaluate international legal risks by referring to the laws of different jurisdictions. For example, when the generation AI reviews the content of a contract, the content verification unit refers to the laws of different jurisdictions to evaluate international legal risks. For example, it refers to the laws of the United States and Japan to check whether the contract terms are legal in both countries. The content verification unit also refers to the laws of different jurisdictions when the generation AI reviews the content of a contract to evaluate international legal risks. For example, it refers to the laws of the EU and China to check whether the contract terms are legal in both regions. The content verification unit also refers to the laws of different jurisdictions when the generation AI reviews the content of a contract to evaluate international legal risks. For example, it refers to the laws of Canada and Australia to check whether the contract terms are legal in both countries. This allows international legal risks to be evaluated by referring to the laws of different jurisdictions.

[0042] The content confirmation unit can visualize the contents of the contract to enable the user to intuitively understand it. For example, when the generation AI confirms the contents of the contract, the content confirmation unit visualizes the contents of the contract to enable the user to intuitively understand it. For example, the content confirmation unit displays the clauses of the contract in diagrams and graphs. The content confirmation unit also visualizes the contents of the contract when the generation AI confirms the contents of the contract to enable the user to intuitively understand it. For example, it creates a flowchart of the contract and visually displays each step. The content confirmation unit also visualizes the contents of the contract when the generation AI confirms the contents of the contract to enable the user to intuitively understand it. For example, it highlights important points of the contract with icons or color coding. In this way, the content of the contract is visualized to enable the user to intuitively understand it.

[0043] The content verification unit can use natural language processing technology to understand the context and detect errors. For example, when the generation AI checks for errors or deficiencies in a contract, the content verification unit uses natural language processing technology to understand the context and detect errors. For example, it analyzes the context of the contract and detects incorrect terminology or grammar. The content verification unit also uses natural language processing technology to understand the context and detect errors when the generation AI checks for errors or deficiencies in a contract. For example, it analyzes the context of the contract and detects contradictory clauses. The content verification unit also uses natural language processing technology to understand the context and detect errors when the generation AI checks for errors or deficiencies in a contract. For example, it analyzes the context of the contract and detects missing information. This makes it possible to understand the context and detect errors using natural language processing technology.

[0044] The content confirmation unit can refer to the user's past revision history to prevent similar errors. For example, when the generation AI checks for errors or deficiencies in a contract, the content confirmation unit can refer to the user's past revision history to prevent similar errors. For example, it can detect similar errors based on errors that have been corrected in the past. In addition, when the generation AI checks for errors or deficiencies in a contract, the content confirmation unit can refer to the user's past revision history to prevent similar errors. For example, it can detect similar errors based on clauses that have been corrected in the past. In addition, when the generation AI checks for errors or deficiencies in a contract, the content confirmation unit can refer to the user's past revision history to prevent similar errors. For example, it can detect similar errors based on grammatical errors that have been corrected in the past. In this way, it is possible to prevent similar errors by referring to the past revision history.

[0045] The content verification unit can detect errors from a cross-industry perspective by referring to contracts from different industries. For example, when the generation AI checks for errors or deficiencies in a contract, the content verification unit can refer to contracts from different industries and detect errors from a cross-industry perspective. For example, it can refer to contracts from the IT industry and the medical industry and detect errors. Furthermore, when the generation AI checks for errors or deficiencies in a contract, the content verification unit can refer to contracts from different industries and detect errors from a cross-industry perspective. For example, it can refer to contracts from the construction industry and the financial industry and detect errors. Furthermore, when the generation AI checks for errors or deficiencies in a contract, the content verification unit can refer to contracts from different industries and detect errors from a cross-industry perspective. For example, it can refer to contracts from the education industry and the entertainment industry and detect errors. This makes it possible to detect errors by referring to contracts from different industries.

[0046] The content confirmation unit can read out the contents of the contract aloud so that the user can confirm them audibly. For example, when the generation AI checks for errors or deficiencies in the contract, the content confirmation unit reads out the contents of the contract aloud so that the user can confirm them audibly. For example, each clause of the contract is read out loud so that the user can confirm them audibly. Also, when the generation AI checks for errors or deficiencies in the contract, the content confirmation unit reads out the contents of the contract aloud so that the user can confirm them audibly. For example, important points of the contract are highlighted aloud so that the user can confirm them audibly. Also, when the generation AI checks for errors or deficiencies in the contract, the content confirmation unit reads out the contents of the contract aloud so that the user can confirm them audibly. For example, corrections to the contract are read out loud so that the user can confirm them audibly. In this way, the contents of the contract are read out loud so that the user can confirm them audibly.

[0047] The contract creation unit can manage versions of contracts and record revision history. For example, when the generation AI creates a contract, the contract creation unit manages versions of the contract and records revision history. For example, it saves each version of the contract and records what revisions have been made. In addition, when the generation AI creates a contract, the contract creation unit manages versions of the contract and records revision history. For example, it saves each version of the contract and records what revisions have been made. In addition, when the generation AI creates a contract, the contract creation unit manages versions of the contract and records revision history. For example, it saves each version of the contract and records what revisions have been made. This enables contract version management and revision history recording.

[0048] The contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign. For example, when the generation AI creates a contract, the contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign. For example, it can identify parts that require a signature in a contract and prompt the user to sign. Also, when the generation AI creates a contract, the contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign. For example, it can identify parts that require a signature in a contract and prompt the user to sign. Also, when the generation AI creates a contract, the contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign. For example, it can identify parts that require a signature in a contract and prompt the user to sign. In this way, the contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The contract creation unit can refer to the user's past contract history and propose the optimal contract content. For example, it can propose the most appropriate clauses based on past contract content. In addition, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose the optimal contract content. For example, it can propose the most appropriate conditions based on past contract conditions. In addition, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose the optimal contract content. For example, it can propose the most appropriate clauses based on past contract terms. This makes it possible to propose the optimal contract content based on past contract history.

[0051] The contract creation unit can simultaneously generate contracts in different languages, achieving multilingual support. For example, it can simultaneously generate contracts in English and Japanese. In addition, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, achieving multilingual support. For example, it can simultaneously generate contracts in French and German. In addition, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, achieving multilingual support. For example, it can simultaneously generate contracts in Chinese and Spanish. This allows multilingual contracts to be generated.

[0052] The contract creation unit can automatically convert what a user dictates into text using voice input and reflect it in the contract. For example, the contract contents dictated by the user are converted into text in real time and reflected in the contract. In addition, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract terms dictated by the user are automatically converted into text and reflected in the contract. In addition, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract terms dictated by the user are automatically converted into text and reflected in the contract. In this way, contracts can be created using voice input.

[0053] The contract creation unit can automatically reflect terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, medical terminology and medical regulations are automatically reflected. In addition, when the generation AI creates a contract, the contract creation unit automatically reflects terminology and regulations specific to the user's industry. For example, when creating a contract for the construction industry, construction terminology and construction regulations are automatically reflected. In addition, when the generation AI creates a contract, the contract creation unit automatically reflects terminology and regulations specific to the user's industry. For example, when creating a contract for the financial industry, financial terminology and financial regulations are automatically reflected. This makes it possible to create contracts that reflect industry-specific terminology and regulations.

[0054] The contract creation unit can reflect legal amendment information in real time. For example, it automatically updates the contract content based on the latest legal amendment information. The contract creation unit also reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, if a new law comes into effect, the contract content is updated based on that information. The contract creation unit also reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, it automatically reflects changes due to legal amendments in the contract content. This makes it possible to create contracts that reflect the latest legal amendment information.

[0055] The contract creation unit can analyze images and drawings provided by the user and reflect them in the contract content. For example, in a construction contract, it analyzes building drawings and reflects them in the contract content. In addition, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, it analyzes product blueprints and reflects them in a manufacturing contract. In addition, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, it analyzes a land map and reflects it in a real estate contract. This makes it possible to analyze images and drawings and reflect them in the contract content.

[0056] The content verification unit can refer to the laws of different jurisdictions to assess international legal risks. For example, it can refer to the laws of the United States and Japan to verify whether the contract terms are legal in both countries. The content verification unit also refers to the laws of different jurisdictions when the generation AI reviews the content of a contract to assess international legal risks. For example, it can refer to the laws of the EU and China to verify whether the contract terms are legal in both countries. The content verification unit also refers to the laws of different jurisdictions when the generation AI reviews the content of a contract to assess international legal risks. For example, it can refer to the laws of Canada and Australia to verify whether the contract terms are legal in both countries. This allows international legal risks to be assessed by referring to the laws of different jurisdictions.

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

[0058] Step 1: The contract creation unit automatically creates a contract based on the contract details entered by the user. Specifically, the contract creation unit analyzes the contract details entered by the user, selects an appropriate contract template, and generates a contract by embedding the necessary information. It can also reference a database of similar past contracts to automatically select the most appropriate clauses. For example, when creating a sales contract, it selects the most appropriate clauses based on data from past sales contracts. Furthermore, the contract creation unit can automatically reflect terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, it automatically reflects medical terminology and medical regulations. Step 2: The content verification unit checks the content of the created contract. Specifically, the content verification unit analyzes each clause of the contract to check whether it is legally sound and whether it contains all the necessary information. It can also refer to a database of past legal precedents to automatically assess legal risks. For example, it checks whether the clauses in the contract are legally sound based on past legal precedents. Furthermore, the content verification unit can automatically incorporate the opinions of experts. For example, it can revise the clauses in the contract based on the opinions of legal experts.

[0059] (Example 2) The contract creation system according to the embodiment of the present invention is a system in which a generation AI automatically creates a contract and checks its contents. This enables the contract creation system to quickly and accurately create a contract and check its contents.

[0060] A contract creation system according to an embodiment includes a contract creation unit and a content confirmation unit. The contract creation unit automatically creates a contract based on contract content entered by a user. For example, the contract creation unit analyzes the contract content entered by the user, selects an appropriate contract template, and generates a contract by embedding necessary information. The contract creation unit can also automatically select optimal clauses by referring to a database of similar past contracts. For example, when creating a sales contract, the most appropriate clauses are selected based on data from past sales contracts. The contract creation unit can also automatically reflect terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, medical terminology and medical regulations are automatically reflected. The content confirmation unit checks the content of the created contract. For example, the content confirmation unit analyzes each clause of the contract to check whether it is legally sound and whether all necessary information is included. The content confirmation unit can also automatically evaluate legal risks by referring to a database of past legal precedents. For example, the content confirmation unit checks whether the clauses in the contract are legally sound based on past legal precedents. The content confirmation unit can also automatically incorporate the opinions of experts. For example, the clauses of the contract are revised based on the opinions of legal experts. This allows the contract drafting system according to the embodiment to draft the contract and check its contents quickly and accurately.

[0061] The contract creation unit can analyze instructions from a user, select an appropriate contract template, and generate a contract by embedding the necessary information. For example, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating a sales contract, the contract creation unit selects the most appropriate clauses based on data from past sales contracts. Also, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating a lease contract, the contract creation unit selects the most appropriate clauses based on data from past lease contracts. Also, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating an employment contract, the contract creation unit selects the most appropriate clauses based on data from past employment contracts. This allows appropriate contracts to be generated based on user instructions.

[0062] The content verification unit analyzes each clause of the contract and checks whether it is legally sound and whether it contains all necessary information. For example, when the generation AI creates a contract, the content verification unit automatically reflects terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, it automatically reflects medical terminology and medical regulations. The content verification unit also automatically reflects terminology and regulations specific to the user's industry when the generation AI creates a contract. For example, when creating a contract for the construction industry, it automatically reflects construction terminology and construction regulations. The content verification unit also automatically reflects terminology and regulations specific to the user's industry when the generation AI creates a contract. For example, when creating a contract for the financial industry, it automatically reflects financial terminology and financial regulations. This allows the content of the contract to be legally verified and its accuracy to be guaranteed.

[0063] The contract creation unit can refer to a database of similar past contracts and automatically select the most appropriate clauses. For example, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating a sales contract, the most appropriate clauses are selected based on data from past sales contracts. Also, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating a lease contract, the most appropriate clauses are selected based on data from past lease contracts. Also, when the generation AI creates a contract, the contract creation unit refers to a database of similar past contracts and automatically selects the most appropriate clauses. For example, when creating an employment contract, the most appropriate clauses are selected based on data from past employment contracts. This makes it possible to select the most appropriate clauses by referring to past data.

[0064] The contract creation unit can automatically reflect terminology and regulations specific to the user's industry. For example, when the generation AI creates a contract, the contract creation unit automatically reflects terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, medical terminology and medical regulations are automatically reflected. Furthermore, the contract creation unit automatically reflects terminology and regulations specific to the user's industry when the generation AI creates a contract. For example, when creating a contract for the construction industry, construction terminology and construction regulations are automatically reflected. Furthermore, the contract creation unit automatically reflects terminology and regulations specific to the user's industry when the generation AI creates a contract. For example, when creating a contract for the financial industry, financial terminology and financial regulations are automatically reflected. This makes it possible to create contracts that reflect industry-specific terminology and regulations.

[0065] The contract creation unit can use the emotion estimation function to analyze the emotional state of the user and suggest contract wording that will reduce stress. The contract creation unit, for example, uses the emotion estimation function to analyze the emotional state of the user and suggest contract wording that will reduce stress. For example, if the user is nervous, it suggests wording that will relax the user. The contract creation unit also uses the emotion estimation function to analyze the emotional state of the user and suggest contract wording that will reduce stress. For example, if the user is feeling anxious, it suggests wording that will reassure the user. The contract creation unit also uses the emotion estimation function to analyze the emotional state of the user and suggest contract wording that will reduce stress. For example, if the user is feeling angry, it suggests wording that will calm the user. In this way, wording that matches the emotional state of the user can be suggested, reducing stress.

[0066] The contract creation unit can automatically convert what a user dictates into text using voice input and reflect it in the contract. For example, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract content dictated by the user is converted into text in real time and reflected in the contract. Furthermore, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract terms dictated by the user are automatically converted into text and reflected in the contract. Furthermore, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract terms dictated by the user are automatically converted into text and reflected in the contract. In this way, contracts can be created using voice input.

[0067] The contract creation unit can simultaneously generate contracts in different languages, thereby achieving multilingual support. For example, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, thereby achieving multilingual support. For example, contracts in English and Japanese are generated simultaneously. Also, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, thereby achieving multilingual support. For example, contracts in French and German are generated simultaneously. Also, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, thereby achieving multilingual support. For example, contracts in Chinese and Spanish are generated simultaneously. This makes it possible to generate multilingual contracts.

[0068] The contract creation unit can use the emotion estimation function to analyze the emotional reaction to the contract content entered by the user in real time and make suggestions that elicit positive emotions. The contract creation unit, for example, uses the emotion estimation function to analyze the emotional reaction to the contract content entered by the user in real time and make suggestions that elicit positive emotions. For example, if the user is feeling anxious, it makes suggestions that will reassure the user. The contract creation unit also uses the emotion estimation function to analyze the emotional reaction to the contract content entered by the user in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling nervous, it makes suggestions that will help the user relax. The contract creation unit also uses the emotion estimation function to analyze the emotional reaction to the contract content entered by the user in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling angry, it makes suggestions that will help the user calm down. In this way, positive suggestions can be made based on the user's emotional reaction, giving the user a sense of security.

[0069] The contract creation unit can refer to the user's past contract history and propose optimal contract content. For example, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose optimal contract content. For example, it can propose the most appropriate clauses based on the past contract content. Also, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose optimal contract content. For example, it can propose the most appropriate conditions based on the past contract conditions. Also, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose optimal contract content. For example, it can propose the most appropriate clauses based on the past contract clauses. This makes it possible to propose optimal contract content based on the past contract history.

[0070] The contract creation unit can reflect legal amendment information in real time. For example, the contract creation unit reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, the contract content is automatically updated based on the latest legal amendment information. The contract creation unit also reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, if a new law comes into effect, the contract content is updated based on that information. The contract creation unit also reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, changes due to legal amendments are automatically reflected in the contract content. This makes it possible to create contracts that reflect the latest legal amendment information.

[0071] The contract creation unit can analyze images and drawings provided by the user and reflect them in the contract content. For example, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, in a construction contract, the contract creation unit analyzes building drawings and reflects them in the contract content. Furthermore, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, it analyzes product blueprints and reflects them in a manufacturing contract. Furthermore, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, it analyzes a land map and reflects it in a real estate contract. In this way, images and drawings can be analyzed and reflected in the contract content.

[0072] The content verification unit can automatically evaluate legal risks by referring to a database of past legal precedents. For example, when the generation AI checks the contents of a contract, the content verification unit automatically evaluates legal risks by referring to a database of past legal precedents. For example, it checks whether the clauses in the contract are legally problematic based on past legal precedents. In addition, when the generation AI checks the contents of a contract, the content verification unit automatically evaluates legal risks by referring to a database of past legal precedents. For example, it checks whether the clauses in the contract involve a risk of litigation based on past legal precedents. In addition, when the generation AI checks the contents of a contract, the content verification unit automatically evaluates legal risks by referring to a database of past legal precedents. For example, it checks whether the clauses in the contract include illegality based on past legal precedents. In this way, legal risks can be evaluated by referring to a database of past legal precedents.

[0073] The content verification unit can automatically incorporate expert opinions. For example, when the generation AI is verifying the content of a contract, the content verification unit automatically incorporates expert opinions. For example, it may revise the clauses of the contract based on the opinions of legal experts. The content verification unit also automatically incorporates expert opinions when the generation AI is verifying the content of a contract. For example, it may adjust the content of the contract based on the opinions of industry experts. The content verification unit also automatically incorporates expert opinions when the generation AI is verifying the content of a contract. For example, it may revise the technical clauses of the contract based on the opinions of technical experts. This makes it possible to verify the content of a contract by automatically incorporating expert opinions.

[0074] The content confirmation unit can use the emotion estimation function to analyze the user's emotion regarding the content of the contract and make revision suggestions that will give the user a sense of security. The content confirmation unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the content of the contract and make revision suggestions that will give the user a sense of security. For example, if the user is feeling anxious, the content confirmation unit makes revision suggestions that will give the user a sense of security. Furthermore, the content confirmation unit uses the emotion estimation function to analyze the user's emotion regarding the content of the contract and make revision suggestions that will give the user a sense of security. For example, if the user is feeling nervous, the content confirmation unit makes revision suggestions that will make the user relaxed. Furthermore, the content confirmation unit uses the emotion estimation function to analyze the user's emotion regarding the content of the contract and make revision suggestions that will give the user a sense of security. For example, if the user is feeling angry, the content confirmation unit makes revision suggestions that will calm the user down. In this way, the user's emotion can be analyzed and revision suggestions that will give the user a sense of security.

[0075] The content verification unit can evaluate international legal risks by referring to the laws of different jurisdictions. For example, when the generation AI reviews the content of a contract, the content verification unit refers to the laws of different jurisdictions to evaluate international legal risks. For example, it refers to the laws of the United States and Japan to check whether the contract terms are legal in both countries. The content verification unit also refers to the laws of different jurisdictions when the generation AI reviews the content of a contract to evaluate international legal risks. For example, it refers to the laws of the EU and China to check whether the contract terms are legal in both regions. The content verification unit also refers to the laws of different jurisdictions when the generation AI reviews the content of a contract to evaluate international legal risks. For example, it refers to the laws of Canada and Australia to check whether the contract terms are legal in both countries. This allows international legal risks to be evaluated by referring to the laws of different jurisdictions.

[0076] The content confirmation unit can visualize the contents of the contract to enable the user to intuitively understand it. For example, when the generation AI confirms the contents of the contract, the content confirmation unit visualizes the contents of the contract to enable the user to intuitively understand it. For example, the content confirmation unit displays the clauses of the contract in diagrams and graphs. The content confirmation unit also visualizes the contents of the contract when the generation AI confirms the contents of the contract to enable the user to intuitively understand it. For example, it creates a flowchart of the contract and visually displays each step. The content confirmation unit also visualizes the contents of the contract when the generation AI confirms the contents of the contract to enable the user to intuitively understand it. For example, it highlights important points of the contract with icons or color coding. In this way, the content of the contract is visualized to enable the user to intuitively understand it.

[0077] The content confirmation unit can use the emotion estimation function to analyze the user's emotional response to the content of the contract in real time and make suggestions to elicit positive emotions. The content confirmation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the content of the contract in real time and make suggestions to elicit positive emotions. For example, if the user is feeling anxious, it makes suggestions to reassure the user. The content confirmation unit also uses the emotion estimation function to analyze the user's emotional response to the content of the contract in real time and make suggestions to elicit positive emotions. For example, if the user is feeling nervous, it makes suggestions to relax the user. The content confirmation unit also uses the emotion estimation function to analyze the user's emotional response to the content of the contract in real time and make suggestions to elicit positive emotions. For example, if the user is feeling angry, it makes suggestions to calm the user down. In this way, positive suggestions can be made based on the user's emotional response, providing a sense of security.

[0078] The content verification unit can use natural language processing technology to understand the context and detect errors. For example, when the generation AI checks for errors or deficiencies in a contract, the content verification unit uses natural language processing technology to understand the context and detect errors. For example, it analyzes the context of the contract and detects incorrect terminology or grammar. The content verification unit also uses natural language processing technology to understand the context and detect errors when the generation AI checks for errors or deficiencies in a contract. For example, it analyzes the context of the contract and detects contradictory clauses. The content verification unit also uses natural language processing technology to understand the context and detect errors when the generation AI checks for errors or deficiencies in a contract. For example, it analyzes the context of the contract and detects missing information. This makes it possible to understand the context and detect errors using natural language processing technology.

[0079] The content confirmation unit can refer to the user's past revision history to prevent similar errors. For example, when the generation AI checks for errors or deficiencies in a contract, the content confirmation unit can refer to the user's past revision history to prevent similar errors. For example, it can detect similar errors based on errors that have been corrected in the past. In addition, when the generation AI checks for errors or deficiencies in a contract, the content confirmation unit can refer to the user's past revision history to prevent similar errors. For example, it can detect similar errors based on clauses that have been corrected in the past. In addition, when the generation AI checks for errors or deficiencies in a contract, the content confirmation unit can refer to the user's past revision history to prevent similar errors. For example, it can detect similar errors based on grammatical errors that have been corrected in the past. In this way, it is possible to prevent similar errors by referring to the past revision history.

[0080] The content confirmation unit can use the emotion estimation function to analyze the user's emotions regarding errors or defects in the contract and make revision suggestions to reduce stress. The content confirmation unit, for example, uses the emotion estimation function to analyze the user's emotions regarding errors or defects in the contract and make revision suggestions to reduce stress. For example, if the user is feeling anxious, it makes revision suggestions to reassure the user. The content confirmation unit also uses the emotion estimation function to analyze the user's emotions regarding errors or defects in the contract and make revision suggestions to reduce stress. For example, if the user is feeling nervous, it makes revision suggestions to relax the user. The content confirmation unit also uses the emotion estimation function to analyze the user's emotions regarding errors or defects in the contract and make revision suggestions to reduce stress. For example, if the user is feeling angry, it makes revision suggestions to calm the user. In this way, it is possible to analyze the user's emotions and make revision suggestions to reduce stress.

[0081] The content verification unit can detect errors from a cross-industry perspective by referring to contracts from different industries. For example, when the generation AI checks for errors or deficiencies in a contract, the content verification unit can refer to contracts from different industries and detect errors from a cross-industry perspective. For example, it can refer to contracts from the IT industry and the medical industry and detect errors. Furthermore, when the generation AI checks for errors or deficiencies in a contract, the content verification unit can refer to contracts from different industries and detect errors from a cross-industry perspective. For example, it can refer to contracts from the construction industry and the financial industry and detect errors. Furthermore, when the generation AI checks for errors or deficiencies in a contract, the content verification unit can refer to contracts from different industries and detect errors from a cross-industry perspective. For example, it can refer to contracts from the education industry and the entertainment industry and detect errors. This makes it possible to detect errors by referring to contracts from different industries.

[0082] The content confirmation unit can read out the contents of the contract aloud so that the user can confirm them audibly. For example, when the generation AI checks for errors or deficiencies in the contract, the content confirmation unit reads out the contents of the contract aloud so that the user can confirm them audibly. For example, each clause of the contract is read out loud so that the user can confirm them audibly. Also, when the generation AI checks for errors or deficiencies in the contract, the content confirmation unit reads out the contents of the contract aloud so that the user can confirm them audibly. For example, important points of the contract are highlighted aloud so that the user can confirm them audibly. Also, when the generation AI checks for errors or deficiencies in the contract, the content confirmation unit reads out the contents of the contract aloud so that the user can confirm them audibly. For example, corrections to the contract are read out loud so that the user can confirm them audibly. In this way, the contents of the contract are read out loud so that the user can confirm them audibly.

[0083] The content confirmation unit can use the emotion estimation function to analyze the user's emotional reaction to errors or defects in the contract in real time and make suggestions that will elicit positive emotions. The content confirmation unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to errors or defects in the contract in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling anxious, it makes suggestions that will reassure the user. The content confirmation unit also uses the emotion estimation function to analyze the user's emotional reaction to errors or defects in the contract in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling nervous, it makes suggestions that will help the user relax. The content confirmation unit also uses the emotion estimation function to analyze the user's emotional reaction to errors or defects in the contract in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling angry, it makes suggestions that will help the user calm down. In this way, positive suggestions can be made based on the user's emotional reaction, giving the user a sense of security.

[0084] The contract creation unit can manage versions of contracts and record revision history. For example, when the generation AI creates a contract, the contract creation unit manages versions of the contract and records revision history. For example, it saves each version of the contract and records what revisions have been made. In addition, when the generation AI creates a contract, the contract creation unit manages versions of the contract and records revision history. For example, it saves each version of the contract and records what revisions have been made. In addition, when the generation AI creates a contract, the contract creation unit manages versions of the contract and records revision history. For example, it saves each version of the contract and records what revisions have been made. This enables contract version management and revision history recording.

[0085] The contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign. For example, when the generation AI creates a contract, the contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign. For example, it can identify parts that require a signature in a contract and prompt the user to sign. Also, when the generation AI creates a contract, the contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign. For example, it can identify parts that require a signature in a contract and prompt the user to sign. Also, when the generation AI creates a contract, the contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign. For example, it can identify parts that require a signature in a contract and prompt the user to sign. In this way, the contract creation unit can assist in the contract signing process, identify parts that require a signature, and prompt the user to sign.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The contract creation unit can refer to the user's past contract history and propose the optimal contract content. For example, it can propose the most appropriate clauses based on past contract content. In addition, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose the optimal contract content. For example, it can propose the most appropriate conditions based on past contract conditions. In addition, when the generation AI analyzes the contract content, the contract creation unit can refer to the user's past contract history and propose the optimal contract content. For example, it can propose the most appropriate clauses based on past contract terms. This makes it possible to propose the optimal contract content based on past contract history.

[0088] The contract creation unit can simultaneously generate contracts in different languages, achieving multilingual support. For example, it can simultaneously generate contracts in English and Japanese. In addition, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, achieving multilingual support. For example, it can simultaneously generate contracts in French and German. In addition, when the generation AI creates a contract, the contract creation unit simultaneously generates contracts in different languages, achieving multilingual support. For example, it can simultaneously generate contracts in Chinese and Spanish. This allows multilingual contracts to be generated.

[0089] The contract creation unit can use the emotion estimation function to analyze the emotional state of the user and suggest contract wording that will reduce stress. For example, if the user is nervous, it suggests wording that will relax the user. The contract creation unit can also use the emotion estimation function to analyze the emotional state of the user and suggest contract wording that will reduce stress. For example, if the user is feeling anxious, it suggests wording that will reassure the user. The contract creation unit can also use the emotion estimation function to analyze the emotional state of the user and suggest contract wording that will reduce stress. For example, if the user is feeling angry, it suggests wording that will calm the user. In this way, it is possible to suggest wording that matches the emotional state of the user and reduce stress.

[0090] The contract creation unit can automatically convert what a user dictates into text using voice input and reflect it in the contract. For example, the contract contents dictated by the user are converted into text in real time and reflected in the contract. In addition, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract terms dictated by the user are automatically converted into text and reflected in the contract. In addition, when the generation AI creates a contract, the contract creation unit automatically converts what a user dictates into text using voice input and reflects it in the contract. For example, the contract terms dictated by the user are automatically converted into text and reflected in the contract. In this way, contracts can be created using voice input.

[0091] The contract creation unit uses the emotion estimation function to analyze the emotional response to the contract content entered by the user in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling anxious, the contract creation unit makes suggestions that will reassure the user. The contract creation unit also uses the emotion estimation function to analyze the emotional response to the contract content entered by the user in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling nervous, the contract creation unit makes suggestions that will relax the user. The contract creation unit also uses the emotion estimation function to analyze the emotional response to the contract content entered by the user in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling angry, the contract creation unit makes suggestions that will calm the user. In this way, positive suggestions can be made based on the user's emotional response, giving the user a sense of security.

[0092] The contract creation unit can automatically reflect terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, medical terminology and medical regulations are automatically reflected. In addition, when the generation AI creates a contract, the contract creation unit automatically reflects terminology and regulations specific to the user's industry. For example, when creating a contract for the construction industry, construction terminology and construction regulations are automatically reflected. In addition, when the generation AI creates a contract, the contract creation unit automatically reflects terminology and regulations specific to the user's industry. For example, when creating a contract for the financial industry, financial terminology and financial regulations are automatically reflected. This makes it possible to create contracts that reflect industry-specific terminology and regulations.

[0093] The contract creation unit can reflect legal amendment information in real time. For example, it automatically updates the contract content based on the latest legal amendment information. The contract creation unit also reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, if a new law comes into effect, the contract content is updated based on that information. The contract creation unit also reflects legal amendment information in real time when the generation AI analyzes the contract content. For example, it automatically reflects changes due to legal amendments in the contract content. This makes it possible to create contracts that reflect the latest legal amendment information.

[0094] The contract creation unit can analyze images and drawings provided by the user and reflect them in the contract content. For example, in a construction contract, it analyzes building drawings and reflects them in the contract content. In addition, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, it analyzes product blueprints and reflects them in a manufacturing contract. In addition, when the generation AI analyzes the contract content, the contract creation unit analyzes images and drawings provided by the user and reflects them in the contract content. For example, it analyzes a land map and reflects it in a real estate contract. This makes it possible to analyze images and drawings and reflect them in the contract content.

[0095] The content confirmation unit uses the emotion estimation function to analyze the user's emotions regarding the content of the contract and make revision suggestions that will give the user a sense of security. For example, if the user is feeling anxious, the content confirmation unit makes revision suggestions that will give the user a sense of security. Furthermore, the content confirmation unit uses the emotion estimation function to analyze the user's emotions regarding the content of the contract and make revision suggestions that will give the user a sense of security. For example, if the user is feeling nervous, the content confirmation unit makes revision suggestions that will make the user relaxed. Furthermore, the content confirmation unit uses the emotion estimation function to analyze the user's emotions regarding the content of the contract and make revision suggestions that will give the user a sense of security. For example, if the user is feeling angry, the content confirmation unit makes revision suggestions that will make the user calm down. In this way, the user's emotions can be analyzed and revision suggestions that will give the user a sense of security.

[0096] The content verification unit can refer to the laws of different jurisdictions to assess international legal risks. For example, it can refer to the laws of the United States and Japan to verify whether the contract terms are legal in both countries. The content verification unit also refers to the laws of different jurisdictions when the generation AI reviews the content of a contract to assess international legal risks. For example, it can refer to the laws of the EU and China to verify whether the contract terms are legal in both countries. The content verification unit also refers to the laws of different jurisdictions when the generation AI reviews the content of a contract to assess international legal risks. For example, it can refer to the laws of Canada and Australia to verify whether the contract terms are legal in both countries. This allows international legal risks to be assessed by referring to the laws of different jurisdictions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The contract creation unit automatically creates a contract based on the contract details entered by the user. Specifically, the contract creation unit analyzes the contract details entered by the user, selects an appropriate contract template, and generates a contract by embedding the necessary information. It can also reference a database of similar past contracts to automatically select the most appropriate clauses. For example, when creating a sales contract, it selects the most appropriate clauses based on data from past sales contracts. Furthermore, the contract creation unit can automatically reflect terminology and regulations specific to the user's industry. For example, when creating a contract for the medical industry, it automatically reflects medical terminology and medical regulations. Step 2: The content verification unit checks the content of the created contract. Specifically, the content verification unit analyzes each clause of the contract to check whether it is legally sound and whether it contains all the necessary information. It can also refer to a database of past legal precedents to automatically assess legal risks. For example, it checks whether the clauses in the contract are legally sound based on past legal precedents. Furthermore, the content verification unit can automatically incorporate the opinions of experts. For example, it can revise the clauses in the contract based on the opinions of legal experts.

[0099] 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.

[0100] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 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 contract creation unit that automatically creates a contract based on the contract contents input by a user; a content confirmation unit that confirms the content of the contract created by the contract creation unit. A system characterized by:

2. The contract creation unit Analyzing the instructions from the user, selecting an appropriate contract template, and generating the contract by embedding necessary information.

2. The system of claim 1.

3. The content confirmation unit Analyze each clause in the contract to ensure it is legally sound and contains all the necessary information 2. The system of claim 1.

4. The contract creation unit Refer to a database of similar past contracts and automatically select the most appropriate clauses 2. The system of claim 1.

5. The contract creation unit Automatically reflects the user's industry-specific terminology and regulations 2. The system of claim 1.

Images