system

Abstract

The system according to this embodiment aims to improve operational accuracy by automating contract review, risk identification, and the provision of appropriate advice. [Solution] The system according to the embodiment comprises a verification unit, an analysis unit, and an advice unit. The verification unit verifies the format of the contract and identifies risks. The analysis unit analyzes the contract verified by the verification unit and identifies risks. The advice unit provides appropriate advice based on the risks identified by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in the confirmation of the contract format and the identification of risks, there have been problems such as delays in response due to the lack of expertise of the person in charge and the shortage of resources in the legal department.

[0005] The system according to the embodiment aims to automate the confirmation of contracts, the identification of risks, and the provision of appropriate advice, and improve the business accuracy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a verification unit, an analysis unit, and an advice unit. The verification unit verifies the format of the contract and identifies risks. The analysis unit analyzes the contract verified by the verification unit and identifies risks. The advice unit provides appropriate advice based on the risks identified by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate contract review, risk identification, and provision of appropriate advice, thereby improving the accuracy of operations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The legal assistant agent according to an embodiment of the present invention is a system that combines a generative AI with specialized legal tools. This legal assistant agent aims to improve the accuracy of operations by automating contract review, risk identification, and the provision of appropriate advice. Specifically, when sales or management departments enter into contracts using other companies' formats, the system reviews the contract format and identifies risks. Next, the generative AI analyzes the contract and identifies risks. Furthermore, the generative AI provides appropriate advice. This solves problems such as insufficient risk identification due to a lack of expertise on the part of the person in charge, and delays in response and communication due to a lack of resources on the part of the legal department. For example, it can analyze the clauses of the contract to prevent overlooking legal risks and clauses. It can also automatically acquire the latest legal revisions and regulations and reflect them in contract reviews. This prevents overlooking legal risks. Furthermore, by accumulating data on contracts and analyzing past cases and trends, the generative AI can provide more appropriate advice and countermeasures based on similar contract and trouble cases. This speeds up contract reviews and improves accuracy. It can reduce oversights and misjudgments of risks. Furthermore, automated notifications and feedback can be implemented to strengthen collaboration with the legal department. This streamlines interdepartmental communication, and unnecessary interactions are reduced as issues are escalated to the legal department only when necessary. This system is expected to significantly reduce manual review work and speed up contract signing. It also reduces the burden on sales and management departments, and allows the legal department to respond more efficiently. Even if the volume of contracts increases, the resources of the legal department can be utilized efficiently. For example, contract reviews will be faster and more accurate, reducing oversights and misjudgments of risks. In addition, automated notifications and feedback streamline interdepartmental communication, and unnecessary interactions are reduced as issues are escalated to the legal department only when necessary. As a result, legal assistant agents can automate contract review, risk identification, and the provision of appropriate advice, improving the accuracy of their work.

[0029] The legal assistant agent according to this embodiment comprises a verification unit, an analysis unit, and an advice unit. The verification unit verifies the format of the contract and identifies risks. The verification unit verifies the format based on, for example, which parts of the contract format to check and the verification procedure. The verification unit also identifies risks based on the type of risk and the procedure for identifying them. For example, the verification unit can check the clauses of the contract to prevent overlooking legal risks or clauses. The analysis unit uses a generation AI to analyze the contract verified by the verification unit and identify risks. For example, the analysis unit can use a generation AI to analyze the clauses of the contract to prevent overlooking legal risks or clauses. The analysis unit can also automatically acquire the latest legal revisions and regulations and reflect them in the contract review. For example, the generation AI can acquire the latest legal revisions and regulations and reflect them in the clauses of the contract. The advice unit uses a generation AI to provide appropriate advice based on the risks identified by the analysis unit. The advisory department, for example, uses a generation AI to accumulate data on contracts and analyze past cases and trends, enabling it to provide more appropriate advice and countermeasures based on similar contracts and dispute cases. For instance, the generation AI can provide advice on contract clauses based on past cases. This allows legal assistant agents to automate contract review, risk identification, and appropriate advice provision, thereby improving the accuracy of their work.

[0030] The verification department verifies the contract format and identifies risks. The verification department verifies the format based on factors such as which parts of the contract format to check and the verification procedure. Specifically, it checks each clause of the contract in detail to ensure it adheres to a standard format. For example, it verifies that basic elements such as the identification of the parties at the beginning of the contract, the purpose of the contract, the contract period, payment terms, and termination conditions are accurately stated. The verification department also identifies risks based on factors such as the type of risk and the procedure for identifying them. For example, it reviews the contract clauses to prevent legal risks and oversights. Specifically, it checks whether the clauses included in the contract comply with current laws and regulations, and whether there are any ambiguous expressions or areas for interpretation. Furthermore, the verification department verifies that the content of the contract accurately reflects the agreement between the parties and identifies areas that could potentially cause misunderstandings or disputes. Through this, the verification department can enhance the security and reliability of the contract through contract format verification and risk identification. Additionally, the verification department can record the results of the contract format verification and risk identification and store them in a database for future reference and improvement. This allows the verification unit to perform verification work more efficiently and effectively based on past verification results.

[0031] The analysis unit uses generative AI to analyze contracts reviewed by the verification unit and identify risks. For example, the generative AI can analyze the clauses of a contract, preventing legal risks and oversights of clauses. Specifically, the generative AI uses natural language processing technology to analyze the text of the contract and understand the meaning and intent of each clause. For example, the generative AI can automatically identify particularly important or high-risk clauses in a contract and evaluate the legal risks associated with these clauses. The generative AI can also automatically acquire the latest legal revisions and regulations and reflect them in the contract review. For example, the generative AI can acquire the latest legal revisions and regulations and reflect them in the clauses of the contract. This allows the analysis unit to always analyze contracts and identify risks based on the latest legal information. Furthermore, the analysis unit can accumulate past contracts and trouble cases in a database and analyze risk trends and patterns based on this data. For example, it can analyze the frequency and impact of risks in past contracts and predict future risks. In addition, the analysis unit can propose revisions and improvements to contracts based on the risk assessment results provided by the generative AI. This allows the analysis department to effectively manage the risks in contracts and improve the security and reliability of those contracts.

[0032] The advisory department uses generative AI to provide appropriate advice based on the risks identified by the analysis department. For example, the advisory department can use generative AI to accumulate data on contracts and analyze past cases and trends to provide more suitable advice and countermeasures based on similar contracts and dispute cases. Specifically, the generative AI stores past contracts and dispute cases in a database and analyzes risk trends and patterns based on this data. For example, it analyzes the frequency and impact of risks in past contracts and predicts future risks. The generative AI can also provide advice on contract clauses. For example, the generative AI can provide advice on contract clauses based on past cases. This allows the advisory department to effectively manage contract risks and improve the safety and reliability of contracts. Furthermore, the advisory department can propose revisions and improvements to contracts based on the advice provided by the generative AI. For example, it can revise contract clauses based on the advice provided by the generative AI to reduce risks. The advisory department can also identify areas for improvement in contracts based on the advice provided by the generative AI and use this information to help create future contracts. This allows the advisory department to effectively manage contract risks and improve the security and reliability of contracts.

[0033] The update function can automatically acquire the latest legal amendments and regulations and reflect them in contract reviews. For example, the update function can automatically acquire the latest legal amendments and regulations and reflect them in the contract clauses. For example, the update function can set the frequency of legal amendments and regulations updates and acquire the latest information regularly. The update function can also set the method for acquiring legal amendments and regulations and automatically acquire the necessary information. This prevents overlooking legal risks by automatically acquiring the latest legal amendments and regulations and reflecting them in contract reviews. Some or all of the above processing in the update function may be performed using AI, for example, or not using AI. For example, the update function can input the sources of legal amendments and regulations into a generating AI, which can automatically acquire the latest information and reflect it in the contract clauses.

[0034] The Data Analysis Department can accumulate data related to contracts and analyze past cases and trends. For example, by accumulating contract data and analyzing past cases and trends, the Data Analysis Department can provide more appropriate advice and countermeasures based on similar contract and dispute cases. For example, the Data Analysis Department can collect data on past contracts and store it in a database. Furthermore, the Data Analysis Department can analyze past cases and trends based on the accumulated data. For example, the Data Analysis Department can analyze data on past contracts and identify similar contract and dispute cases. This allows for the provision of more appropriate advice and countermeasures based on similar contract and dispute cases by analyzing past cases and trends. Some or all of the above processing in the Data Analysis Department may be performed using AI, or not. For example, the Data Analysis Department can input data on past contracts into a generating AI, which can analyze the data and identify similar contract and dispute cases.

[0035] The notification department can provide automated notifications and feedback to strengthen collaboration with the legal department. For example, the notification department can automatically notify the legal department of the results of contract review and risk identification. Furthermore, the notification department can automatically receive feedback from the legal department and notify sales and management departments. For instance, the notification department can automatically send the results of contract review to the legal department and receive feedback from them. The notification department can also automatically notify sales and management departments of the content of this feedback. This facilitates smoother inter-departmental communication through automated notifications and feedback, reducing unnecessary interactions as issues are escalated to the legal department only when necessary. Some or all of the above processes in the notification department may be performed using AI, or not. For example, the notification department can input the results of contract review into a generating AI, which can then automatically notify the legal department and receive feedback.

[0036] The verification unit can improve verification accuracy by referring to data from similar past contracts when verifying the format of a contract. For example, the verification unit can refer to similar contracts that have been reviewed in the past to check the degree of formatting match. The verification unit can also refer to risk information from similar past contracts to prevent overlooking risks. Furthermore, the verification unit can refer to the revision history of similar past contracts to identify areas that need revision. As a result, by referring to data from similar past contracts, verification accuracy can be improved and risks can be prevented from being overlooked. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data from similar past contracts into a generating AI, which can then analyze the data to improve verification accuracy.

[0037] The verification unit can perform verification of the contract format while considering the attribute information of the contract submitter. For example, if the submitter is a new business partner, the verification unit will perform a detailed format check. Also, if the submitter is a business partner with a history of problems, the verification unit can focus on checking high-risk areas. Furthermore, if the submitter is a highly reliable business partner, the verification unit can apply the usual verification procedure. This allows for focusing on high-risk areas by considering the attribute information of the contract submitter. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, or not using AI. For example, the verification unit can input the submitter's attribute information into the generative AI, which can analyze the attribute information and determine the verification procedure.

[0038] The verification unit can perform verification of contract formats while considering the geographical distribution of the contracts. For example, if the contracts are submitted to different regions, the verification unit can perform verification while considering the legal regulations of those regions. Furthermore, if the contracts are submitted to multiple regions, the verification unit can perform verification while considering the legal regulations of each region. In addition, if the contracts are submitted to a specific region, the verification unit can perform verification while considering the unique risks of that region. In this way, by considering the geographical distribution of contracts, verification can be performed in accordance with the legal regulations of each region. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input geographical distribution data of contracts into a generating AI, which can analyze the data and determine the verification procedure.

[0039] The verification unit can improve the accuracy of its verification process by referring to relevant literature when checking the format of a contract. For example, the verification unit can refer to legal literature related to the clauses of the contract to verify the legality of the clauses. It can also refer to case law related to the clauses of the contract to prevent overlooking risks. Furthermore, the verification unit can refer to industry guidelines related to the clauses of the contract to verify their compliance. As a result, by referring to relevant literature for the contract, the accuracy of the verification process can be improved and risks can be prevented from being overlooked. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input data on relevant literature for the contract into a generating AI, which can then analyze the data to improve the accuracy of the verification process.

[0040] The analysis unit can improve the accuracy of its risk analysis by considering the interrelationships between contracts. For example, the analysis unit can analyze the interrelationships between contract clauses to prevent overlooking risks. It can also analyze related clauses in contracts to prevent risk duplication. Furthermore, the analysis unit can analyze the interrelationships between contracts to identify the scope of risk impact. In this way, considering the interrelationships between contracts can prevent overlooking risks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input contract interrelationship data into a generating AI, which can then analyze the data to improve the accuracy of the analysis.

[0041] The analysis unit can perform risk analysis while considering the attribute information of the contract submitter. For example, if the submitter is a new business partner, the analysis unit will perform a detailed risk analysis. Furthermore, if the submitter is a business partner with a history of problems, the analysis unit can focus its analysis on high-risk areas. In addition, if the submitter is a highly reliable business partner, the analysis unit can apply the normal analysis procedure. This allows for a focus on high-risk areas by considering the attribute information of the contract submitter. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submitter's attribute information into a generating AI, which can analyze the attribute information and determine the analysis procedure.

[0042] The analysis unit can perform risk analysis while considering the geographical distribution of contracts. For example, if the contracts are submitted to different regions, the analysis unit can perform the analysis while considering the legal regulations of those regions. Furthermore, if the contracts are submitted to multiple regions, the analysis unit can perform the analysis while considering the legal regulations of each region. In addition, if the contracts are submitted to a specific region, the analysis unit can perform the analysis while considering the risks specific to that region. In this way, by considering the geographical distribution of contracts, risk analysis can be performed in accordance with the legal regulations of each region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of contracts into a generating AI, which can analyze the data and determine the analysis procedure.

[0043] The analysis unit can improve the accuracy of its risk analysis by referring to relevant literature related to the contract. For example, the analysis unit can refer to legal literature related to the clauses of the contract to prevent overlooking risks. Furthermore, the analysis unit can refer to case law related to the clauses of the contract to prevent overlapping risks. In addition, the analysis unit can refer to industry guidelines related to the clauses of the contract to verify the suitability of the risks. This improves the accuracy of the analysis and prevents overlooking risks by referring to relevant literature related to the contract. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on relevant literature related to the contract into a generating AI, which then analyzes the data to improve the accuracy of the analysis.

[0044] The advisory unit can adjust the level of detail in its advice based on the severity of the risk when providing advice. For example, it can provide detailed advice for significant risks, moderately detailed advice for moderate risks, and concise advice for minor risks. By adjusting the level of detail in the advice according to the severity of the risk, more appropriate advice can be provided. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input risk severity data into a generating AI, which can analyze the data and adjust the level of detail in the advice.

[0045] The advisory unit can apply different advisory algorithms depending on the risk category when providing advice. For example, the advisory unit can provide advice from a legal perspective for legal risks, from a financial perspective for financial risks, and from an operational perspective for operational risks. This allows for the provision of optimal advice tailored to the risk category. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input risk category data into a generating AI, which can then analyze the data and apply an advisory algorithm.

[0046] The advisory unit can determine the priority of advice based on the timing of risk occurrence when providing advice. For example, the advisory unit can provide priority advice for risks that are likely to occur in the near future. It can also provide advice with a moderate priority for risks that are likely to occur in the medium term. Furthermore, it can provide advice with a normal priority for risks that are likely to occur in the long term. By determining the priority of advice according to the timing of risk occurrence, advice can be provided at a more appropriate time. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input risk occurrence timing data into a generating AI, which can analyze the data and determine the priority of advice.

[0047] The advisory unit can adjust the order of advice based on the relevance of the risks when providing advice. For example, the advisory unit may first provide advice related to significant risks. It may then provide advice related to moderate risks, and finally provide advice related to minor risks. By adjusting the order of advice according to the relevance of the risks, more appropriate advice can be provided. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input risk relevance data into a generating AI, which can analyze the data and adjust the order of advice.

[0048] The update unit can improve the accuracy of acquiring legal amendments and regulations by referring to past legal amendment data. For example, the update unit can refer to past legal amendment data to acquire relevant legal amendments and regulations. The update unit can also analyze past legal amendment data to identify legal amendments and regulations that should be acquired. Furthermore, based on past legal amendment data, the update unit can determine the priority of legal amendments and regulations to be acquired. As a result, by referring to past legal amendment data, the accuracy of acquisition can be improved and relevant legal amendments and regulations can be prevented from being overlooked. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past legal amendment data into a generating AI, which can then analyze the data and improve the accuracy of acquisition.

[0049] The update unit can acquire legal amendments and regulations while considering their geographical distribution. For example, the update unit can identify the legal amendments and regulations to be acquired by considering the areas covered by the legal amendments and regulations. Furthermore, the update unit can determine the priority of legal amendments and regulations to be acquired based on their geographical distribution. In addition, the update unit can identify the scope of legal amendments and regulations to be acquired while considering their geographical distribution. This allows for acquisition that corresponds to legal amendments and regulations for each region by considering their geographical distribution. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input geographical distribution data into a generating AI, which can then analyze the data and determine the acquisition procedure.

[0050] The data analysis unit can optimize its analysis algorithm by referring to past data during data analysis. For example, the data analysis unit can optimize the analysis algorithm by referring to past data. Furthermore, the data analysis unit can analyze past data and select the optimal analysis algorithm. In addition, the data analysis unit can improve the accuracy of the analysis algorithm based on past data. This allows for improved accuracy of the analysis algorithm and more appropriate data analysis by referring to past data. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input past data into a generating AI, which then analyzes the data and optimizes the analysis algorithm.

[0051] The data analysis unit can weight data based on the date of contract submission during data analysis. For example, the data analysis unit can set a higher weight for data submitted recently, and a lower weight for data submitted in the past. Furthermore, the data analysis unit can dynamically adjust the data weighting based on the contract submission date. This allows for more appropriate data analysis by weighting data based on the contract submission date. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input contract submission date data into a generating AI, which can then analyze the data and weight it.

[0052] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit can refer to past notification history and select the optimal notification method. The notification unit can also analyze past notification history and determine the optimal notification timing. Furthermore, the notification unit can optimize the notification content based on past notification history. As a result, by referring to past notification history, the optimal notification method is selected, and more appropriate notifications can be sent. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI, which can analyze the data and select the optimal notification method.

[0053] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit can send a push notification. If the user is using a tablet, the notification unit can send an email notification. Furthermore, if the user is using a desktop, the notification unit can send a pop-up notification. By considering the user's device information, the optimal notification method is selected, resulting in more appropriate notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, which can then analyze the data and select the optimal notification method.

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

[0055] The verification unit can perform a detailed format check of a contract, taking into account the attribute information of the contract submitter. For example, if the submitter is a new business partner, the verification unit will perform a detailed format check. Furthermore, if the submitter is a business partner with a history of problems, the verification unit can focus on checking high-risk areas. In addition, if the submitter is a highly reliable business partner, the verification unit can apply the usual verification procedures. This allows for a focus on checking high-risk areas by considering the attribute information of the contract submitter. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the submitter's attribute information into a generating AI, which can analyze the attribute information and determine the verification procedure.

[0056] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between contracts. For example, the analysis unit can analyze the interrelationships between clauses of a contract to prevent overlooking risks. It can also analyze related clauses of a contract to prevent overlapping risks. Furthermore, the analysis unit can analyze the interrelationships between contracts to identify the scope of risk impact. In this way, considering the interrelationships between contracts can prevent overlooking risks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input contract interrelationship data into a generating AI, which can then analyze the data to improve the accuracy of the analysis.

[0057] The advisory unit can adjust the level of detail of its advice based on the severity of the risk. For example, it can provide detailed advice for significant risks, moderately detailed advice for moderate risks, and concise advice for minor risks. By adjusting the level of detail of the advice according to the severity of the risk, more appropriate advice can be provided. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input risk severity data into a generating AI, which can analyze the data and adjust the level of detail of the advice.

[0058] The update unit can improve the accuracy of acquiring legal amendments and regulations by referring to past legal amendment data. For example, the update unit can refer to past legal amendment data to acquire relevant legal amendments and regulations. The update unit can also analyze past legal amendment data to identify legal amendments and regulations that should be acquired. Furthermore, based on past legal amendment data, the update unit can determine the priority of legal amendments and regulations to be acquired. As a result, by referring to past legal amendment data, the accuracy of acquisition can be improved and relevant legal amendments and regulations can be prevented from being overlooked. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past legal amendment data into a generating AI, which can then analyze the data and improve the accuracy of acquisition.

[0059] The data analysis unit can weight data based on the date of contract submission during data analysis. For example, the data analysis unit can set a higher weight for data submitted recently, and a lower weight for data submitted in the past. Furthermore, the data analysis unit can dynamically adjust the data weighting based on the contract submission date. This allows for more appropriate data analysis by weighting data based on the contract submission date. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input contract submission date data into a generating AI, which can then analyze the data and weight it.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The verification team checks the contract format and identifies risks. The verification team checks the contract format based on which parts to check and the verification procedure. They also identify risks based on the types of risks and the procedure for identifying them. For example, they can check the clauses of the contract to prevent legal risks or overlooking clauses. Step 2: The analysis unit uses generation AI to analyze the contracts reviewed by the verification unit and identify risks. The analysis unit can prevent legal risks and oversights by having the generation AI analyze the contract clauses. It can also automatically acquire the latest legal revisions and regulations and reflect them in the contract review. Step 3: The advice department uses a generation AI to provide appropriate advice based on the risks identified by the analysis department. The advice department can provide more suitable advice and countermeasures based on similar contracts and trouble cases by having the generation AI accumulate data on contracts and analyze past cases and trends.

[0062] (Example of form 2) The legal assistant agent according to an embodiment of the present invention is a system that combines a generative AI with specialized legal tools. This legal assistant agent aims to improve the accuracy of operations by automating contract review, risk identification, and the provision of appropriate advice. Specifically, when sales or management departments enter into contracts using other companies' formats, the system reviews the contract format and identifies risks. Next, the generative AI analyzes the contract and identifies risks. Furthermore, the generative AI provides appropriate advice. This solves problems such as insufficient risk identification due to a lack of expertise on the part of the person in charge, and delays in response and communication due to a lack of resources on the part of the legal department. For example, it can analyze the clauses of the contract to prevent overlooking legal risks and clauses. It can also automatically acquire the latest legal revisions and regulations and reflect them in contract reviews. This prevents overlooking legal risks. Furthermore, by accumulating data on contracts and analyzing past cases and trends, the generative AI can provide more appropriate advice and countermeasures based on similar contract and trouble cases. This speeds up contract reviews and improves accuracy. It can reduce oversights and misjudgments of risks. Furthermore, automated notifications and feedback can be implemented to strengthen collaboration with the legal department. This streamlines interdepartmental communication, and unnecessary interactions are reduced as issues are escalated to the legal department only when necessary. This system is expected to significantly reduce manual review work and speed up contract signing. It also reduces the burden on sales and management departments, and allows the legal department to respond more efficiently. Even if the volume of contracts increases, the resources of the legal department can be utilized efficiently. For example, contract reviews will be faster and more accurate, reducing oversights and misjudgments of risks. In addition, automated notifications and feedback streamline interdepartmental communication, and unnecessary interactions are reduced as issues are escalated to the legal department only when necessary. As a result, legal assistant agents can automate contract review, risk identification, and the provision of appropriate advice, improving the accuracy of their work.

[0063] The legal assistant agent according to this embodiment comprises a verification unit, an analysis unit, and an advice unit. The verification unit verifies the format of the contract and identifies risks. The verification unit verifies the format based on, for example, which parts of the contract format to check and the verification procedure. The verification unit also identifies risks based on the type of risk and the procedure for identifying them. For example, the verification unit can check the clauses of the contract to prevent overlooking legal risks or clauses. The analysis unit uses a generation AI to analyze the contract verified by the verification unit and identify risks. For example, the analysis unit can use a generation AI to analyze the clauses of the contract to prevent overlooking legal risks or clauses. The analysis unit can also automatically acquire the latest legal revisions and regulations and reflect them in the contract review. For example, the generation AI can acquire the latest legal revisions and regulations and reflect them in the clauses of the contract. The advice unit uses a generation AI to provide appropriate advice based on the risks identified by the analysis unit. The advisory department, for example, uses a generation AI to accumulate data on contracts and analyze past cases and trends, enabling it to provide more appropriate advice and countermeasures based on similar contracts and dispute cases. For instance, the generation AI can provide advice on contract clauses based on past cases. This allows legal assistant agents to automate contract review, risk identification, and appropriate advice provision, thereby improving the accuracy of their work.

[0064] The verification department verifies the contract format and identifies risks. The verification department verifies the format based on factors such as which parts of the contract format to check and the verification procedure. Specifically, it checks each clause of the contract in detail to ensure it adheres to a standard format. For example, it verifies that basic elements such as the identification of the parties at the beginning of the contract, the purpose of the contract, the contract period, payment terms, and termination conditions are accurately stated. The verification department also identifies risks based on factors such as the type of risk and the procedure for identifying them. For example, it reviews the contract clauses to prevent legal risks and oversights. Specifically, it checks whether the clauses included in the contract comply with current laws and regulations, and whether there are any ambiguous expressions or areas for interpretation. Furthermore, the verification department verifies that the content of the contract accurately reflects the agreement between the parties and identifies areas that could potentially cause misunderstandings or disputes. Through this, the verification department can enhance the security and reliability of the contract through contract format verification and risk identification. Additionally, the verification department can record the results of the contract format verification and risk identification and store them in a database for future reference and improvement. This allows the verification unit to perform verification work more efficiently and effectively based on past verification results.

[0065] The analysis unit uses generative AI to analyze contracts reviewed by the verification unit and identify risks. For example, the generative AI can analyze the clauses of a contract, preventing legal risks and oversights of clauses. Specifically, the generative AI uses natural language processing technology to analyze the text of the contract and understand the meaning and intent of each clause. For example, the generative AI can automatically identify particularly important or high-risk clauses in a contract and evaluate the legal risks associated with these clauses. The generative AI can also automatically acquire the latest legal revisions and regulations and reflect them in the contract review. For example, the generative AI can acquire the latest legal revisions and regulations and reflect them in the clauses of the contract. This allows the analysis unit to always analyze contracts and identify risks based on the latest legal information. Furthermore, the analysis unit can accumulate past contracts and trouble cases in a database and analyze risk trends and patterns based on this data. For example, it can analyze the frequency and impact of risks in past contracts and predict future risks. In addition, the analysis unit can propose revisions and improvements to contracts based on the risk assessment results provided by the generative AI. This allows the analysis department to effectively manage the risks in contracts and improve the security and reliability of those contracts.

[0066] The advisory department uses generative AI to provide appropriate advice based on the risks identified by the analysis department. For example, the advisory department can use generative AI to accumulate data on contracts and analyze past cases and trends to provide more suitable advice and countermeasures based on similar contracts and dispute cases. Specifically, the generative AI stores past contracts and dispute cases in a database and analyzes risk trends and patterns based on this data. For example, it analyzes the frequency and impact of risks in past contracts and predicts future risks. The generative AI can also provide advice on contract clauses. For example, the generative AI can provide advice on contract clauses based on past cases. This allows the advisory department to effectively manage contract risks and improve the safety and reliability of contracts. Furthermore, the advisory department can propose revisions and improvements to contracts based on the advice provided by the generative AI. For example, it can revise contract clauses based on the advice provided by the generative AI to reduce risks. The advisory department can also identify areas for improvement in contracts based on the advice provided by the generative AI and use this information to help create future contracts. This allows the advisory department to effectively manage contract risks and improve the security and reliability of contracts.

[0067] The update function can automatically acquire the latest legal amendments and regulations and reflect them in contract reviews. For example, the update function can automatically acquire the latest legal amendments and regulations and reflect them in the contract clauses. For example, the update function can set the frequency of legal amendments and regulations updates and acquire the latest information regularly. The update function can also set the method for acquiring legal amendments and regulations and automatically acquire the necessary information. This prevents overlooking legal risks by automatically acquiring the latest legal amendments and regulations and reflecting them in contract reviews. Some or all of the above processing in the update function may be performed using AI, for example, or not using AI. For example, the update function can input the sources of legal amendments and regulations into a generating AI, which can automatically acquire the latest information and reflect it in the contract clauses.

[0068] The Data Analysis Department can accumulate data related to contracts and analyze past cases and trends. For example, by accumulating contract data and analyzing past cases and trends, the Data Analysis Department can provide more appropriate advice and countermeasures based on similar contract and dispute cases. For example, the Data Analysis Department can collect data on past contracts and store it in a database. Furthermore, the Data Analysis Department can analyze past cases and trends based on the accumulated data. For example, the Data Analysis Department can analyze data on past contracts and identify similar contract and dispute cases. This allows for the provision of more appropriate advice and countermeasures based on similar contract and dispute cases by analyzing past cases and trends. Some or all of the above processing in the Data Analysis Department may be performed using AI, or not. For example, the Data Analysis Department can input data on past contracts into a generating AI, which can analyze the data and identify similar contract and dispute cases.

[0069] The notification department can provide automated notifications and feedback to strengthen collaboration with the legal department. For example, the notification department can automatically notify the legal department of the results of contract review and risk identification. Furthermore, the notification department can automatically receive feedback from the legal department and notify sales and management departments. For instance, the notification department can automatically send the results of contract review to the legal department and receive feedback from them. The notification department can also automatically notify sales and management departments of the content of this feedback. This facilitates smoother inter-departmental communication through automated notifications and feedback, reducing unnecessary interactions as issues are escalated to the legal department only when necessary. Some or all of the above processes in the notification department may be performed using AI, or not. For example, the notification department can input the results of contract review into a generating AI, which can then automatically notify the legal department and receive feedback.

[0070] The verification unit can estimate the user's emotions and determine the priority of contract format verification based on the estimated emotions. For example, if the user is stressed, the verification unit will prioritize the verification of important contract formats. If the user is relaxed, the verification unit can perform contract format verification with normal priority. Furthermore, if the user is in a hurry, the verification unit can prioritize the verification of urgent contract formats. This allows for more appropriate verification by adjusting the priority of contract format verification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not using AI. For example, the verification unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of contract format verification.

[0071] The verification unit can improve verification accuracy by referring to data from similar past contracts when verifying the format of a contract. For example, the verification unit can refer to similar contracts that have been reviewed in the past to check the degree of formatting match. The verification unit can also refer to risk information from similar past contracts to prevent overlooking risks. Furthermore, the verification unit can refer to the revision history of similar past contracts to identify areas that need revision. As a result, by referring to data from similar past contracts, verification accuracy can be improved and risks can be prevented from being overlooked. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data from similar past contracts into a generating AI, which can then analyze the data to improve verification accuracy.

[0072] The verification unit can perform verification of the contract format while considering the attribute information of the contract submitter. For example, if the submitter is a new business partner, the verification unit will perform a detailed format check. Also, if the submitter is a business partner with a history of problems, the verification unit can focus on checking high-risk areas. Furthermore, if the submitter is a highly reliable business partner, the verification unit can apply the usual verification procedure. This allows for focusing on high-risk areas by considering the attribute information of the contract submitter. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, or not using AI. For example, the verification unit can input the submitter's attribute information into the generative AI, which can analyze the attribute information and determine the verification procedure.

[0073] The verification unit can estimate the user's emotions and determine the priority of risk identification based on the estimated emotions. For example, if the user is stressed, the verification unit will prioritize identifying serious risks. If the user is relaxed, the verification unit can identify risks with normal priorities. Furthermore, if the user is in a hurry, the verification unit can prioritize identifying high-urgency risks. By adjusting the priority of risk identification according to the user's emotions, more appropriate risk identification can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, or not using AI. For example, the verification unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of risk identification.

[0074] The verification unit can perform verification of contract formats while considering the geographical distribution of the contracts. For example, if the contracts are submitted to different regions, the verification unit can perform verification while considering the legal regulations of those regions. Furthermore, if the contracts are submitted to multiple regions, the verification unit can perform verification while considering the legal regulations of each region. In addition, if the contracts are submitted to a specific region, the verification unit can perform verification while considering the unique risks of that region. In this way, by considering the geographical distribution of contracts, verification can be performed in accordance with the legal regulations of each region. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input geographical distribution data of contracts into a generating AI, which can analyze the data and determine the verification procedure.

[0075] The verification unit can improve the accuracy of its verification process by referring to relevant literature when checking the format of a contract. For example, the verification unit can refer to legal literature related to the clauses of the contract to verify the legality of the clauses. It can also refer to case law related to the clauses of the contract to prevent overlooking risks. Furthermore, the verification unit can refer to industry guidelines related to the clauses of the contract to verify their compliance. As a result, by referring to relevant literature for the contract, the accuracy of the verification process can be improved and risks can be prevented from being overlooked. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input data on relevant literature for the contract into a generating AI, which can then analyze the data to improve the accuracy of the verification process.

[0076] The analysis unit can estimate the user's emotions and adjust the risk analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can set stricter risk analysis criteria. If the user is relaxed, the analysis unit can perform risk analysis using normal criteria. Furthermore, if the user is in a hurry, the analysis unit can focus on high-urgency risks. By adjusting the risk analysis criteria according to the user's emotions, a more appropriate risk analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the risk analysis criteria.

[0077] The analysis unit can improve the accuracy of its risk analysis by considering the interrelationships between contracts. For example, the analysis unit can analyze the interrelationships between contract clauses to prevent overlooking risks. It can also analyze related clauses in contracts to prevent risk duplication. Furthermore, the analysis unit can analyze the interrelationships between contracts to identify the scope of risk impact. In this way, considering the interrelationships between contracts can prevent overlooking risks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input contract interrelationship data into a generating AI, which can then analyze the data to improve the accuracy of the analysis.

[0078] The analysis unit can perform risk analysis while considering the attribute information of the contract submitter. For example, if the submitter is a new business partner, the analysis unit will perform a detailed risk analysis. Furthermore, if the submitter is a business partner with a history of problems, the analysis unit can focus its analysis on high-risk areas. In addition, if the submitter is a highly reliable business partner, the analysis unit can apply the normal analysis procedure. This allows for a focus on high-risk areas by considering the attribute information of the contract submitter. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submitter's attribute information into a generating AI, which can analyze the attribute information and determine the analysis procedure.

[0079] The analysis unit can estimate the user's emotions and adjust the order in which the risk analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit can display significant risks first. If the user is relaxed, the analysis unit can display risks in the normal order. Furthermore, if the user is in a hurry, the analysis unit can display high-urgency risks first. This allows for more appropriate risk management by adjusting the order in which the risk analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the order in which the risk analysis results are displayed.

[0080] The analysis unit can perform risk analysis while considering the geographical distribution of contracts. For example, if the contracts are submitted to different regions, the analysis unit can perform the analysis while considering the legal regulations of those regions. Furthermore, if the contracts are submitted to multiple regions, the analysis unit can perform the analysis while considering the legal regulations of each region. In addition, if the contracts are submitted to a specific region, the analysis unit can perform the analysis while considering the risks specific to that region. In this way, by considering the geographical distribution of contracts, risk analysis can be performed in accordance with the legal regulations of each region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of contracts into a generating AI, which can analyze the data and determine the analysis procedure.

[0081] The analysis unit can improve the accuracy of its risk analysis by referring to relevant literature related to the contract. For example, the analysis unit can refer to legal literature related to the clauses of the contract to prevent overlooking risks. Furthermore, the analysis unit can refer to case law related to the clauses of the contract to prevent overlapping risks. In addition, the analysis unit can refer to industry guidelines related to the clauses of the contract to verify the suitability of the risks. This improves the accuracy of the analysis and prevents overlooking risks by referring to relevant literature related to the contract. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on relevant literature related to the contract into a generating AI, which then analyzes the data to improve the accuracy of the analysis.

[0082] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide concise and clear advice. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is in a hurry, it can provide quick and to-the-point advice. By adjusting the way advice is expressed according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the way advice is expressed.

[0083] The advisory unit can adjust the level of detail in its advice based on the severity of the risk when providing advice. For example, it can provide detailed advice for significant risks, moderately detailed advice for moderate risks, and concise advice for minor risks. By adjusting the level of detail in the advice according to the severity of the risk, more appropriate advice can be provided. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input risk severity data into a generating AI, which can analyze the data and adjust the level of detail in the advice.

[0084] The advisory unit can apply different advisory algorithms depending on the risk category when providing advice. For example, the advisory unit can provide advice from a legal perspective for legal risks, from a financial perspective for financial risks, and from an operational perspective for operational risks. This allows for the provision of optimal advice tailored to the risk category. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input risk category data into a generating AI, which can then analyze the data and apply an advisory algorithm.

[0085] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide short, concise advice. If the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can provide quick and concise advice. By adjusting the length of the advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the length of the advice.

[0086] The advisory unit can determine the priority of advice based on the timing of risk occurrence when providing advice. For example, the advisory unit can provide priority advice for risks that are likely to occur in the near future. It can also provide advice with a moderate priority for risks that are likely to occur in the medium term. Furthermore, it can provide advice with a normal priority for risks that are likely to occur in the long term. By determining the priority of advice according to the timing of risk occurrence, advice can be provided at a more appropriate time. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input risk occurrence timing data into a generating AI, which can analyze the data and determine the priority of advice.

[0087] The advisory unit can adjust the order of advice based on the relevance of the risks when providing advice. For example, the advisory unit may first provide advice related to significant risks. It may then provide advice related to moderate risks, and finally provide advice related to minor risks. By adjusting the order of advice according to the relevance of the risks, more appropriate advice can be provided. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input risk relevance data into a generating AI, which can analyze the data and adjust the order of advice.

[0088] The update unit can estimate the user's emotions and adjust the frequency of acquiring legal changes and regulations based on the estimated emotions. For example, if the user is stressed, the update unit will increase the frequency of acquiring legal changes and regulations. If the user is relaxed, the update unit can acquire legal changes and regulations at a normal frequency. Furthermore, if the user is in a hurry, the update unit can prioritize acquiring urgent legal changes and regulations. This allows for more appropriate acquisition of legal changes and regulations by adjusting the frequency of acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, or not using AI. For example, the update unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the frequency of acquiring legal changes and regulations.

[0089] The update unit can improve the accuracy of acquiring legal amendments and regulations by referring to past legal amendment data. For example, the update unit can refer to past legal amendment data to acquire relevant legal amendments and regulations. The update unit can also analyze past legal amendment data to identify legal amendments and regulations that should be acquired. Furthermore, based on past legal amendment data, the update unit can determine the priority of legal amendments and regulations to be acquired. As a result, by referring to past legal amendment data, the accuracy of acquisition can be improved and relevant legal amendments and regulations can be prevented from being overlooked. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past legal amendment data into a generating AI, which can then analyze the data and improve the accuracy of acquisition.

[0090] The update unit can estimate the user's emotions and determine the priority for acquiring legal amendments and regulations based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize acquiring important legal amendments and regulations. If the user is relaxed, the update unit can acquire legal amendments and regulations with normal priority. Furthermore, if the user is in a hurry, the update unit can prioritize acquiring urgent legal amendments and regulations. This allows for more appropriate acquisition of legal amendments and regulations by determining the priority for acquiring them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority for acquiring legal amendments and regulations.

[0091] The update unit can acquire legal amendments and regulations while considering their geographical distribution. For example, the update unit can identify the legal amendments and regulations to be acquired by considering the areas covered by the legal amendments and regulations. Furthermore, the update unit can determine the priority of legal amendments and regulations to be acquired based on their geographical distribution. In addition, the update unit can identify the scope of legal amendments and regulations to be acquired while considering their geographical distribution. This allows for acquisition that corresponds to legal amendments and regulations for each region by considering their geographical distribution. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input geographical distribution data into a generating AI, which can then analyze the data and determine the acquisition procedure.

[0092] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the data analysis unit can perform a detailed data analysis. If the user is relaxed, the data analysis unit can perform a normal data analysis. Furthermore, if the user is in a hurry, the data analysis unit can perform a rapid data analysis. This allows for more appropriate data analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, or not using AI. For example, the data analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the data analysis method.

[0093] The data analysis unit can optimize its analysis algorithm by referring to past data during data analysis. For example, the data analysis unit can optimize the analysis algorithm by referring to past data. Furthermore, the data analysis unit can analyze past data and select the optimal analysis algorithm. In addition, the data analysis unit can improve the accuracy of the analysis algorithm based on past data. This allows for improved accuracy of the analysis algorithm and more appropriate data analysis by referring to past data. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input past data into a generating AI, which then analyzes the data and optimizes the analysis algorithm.

[0094] The data analysis unit can estimate the user's emotions and adjust the frequency of data analysis based on the estimated emotions. For example, if the user is stressed, the data analysis unit will increase the frequency of data analysis. If the user is relaxed, the data analysis unit can perform data analysis at a normal frequency. Furthermore, if the user is in a hurry, the data analysis unit can prioritize data analysis of high urgency. By adjusting the frequency of data analysis according to the user's emotions, more appropriate data analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, or not using AI. For example, the data analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the frequency of data analysis.

[0095] The data analysis unit can weight data based on the date of contract submission during data analysis. For example, the data analysis unit can set a higher weight for data submitted recently, and a lower weight for data submitted in the past. Furthermore, the data analysis unit can dynamically adjust the data weighting based on the contract submission date. This allows for more appropriate data analysis by weighting data based on the contract submission date. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input contract submission date data into a generating AI, which can then analyze the data and weight it.

[0096] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a concise and clear notification. If the user is relaxed, the notification unit can provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a quick and to-the-point notification. This allows for more appropriate notifications by adjusting the content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the content of the notification.

[0097] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit can refer to past notification history and select the optimal notification method. The notification unit can also analyze past notification history and determine the optimal notification timing. Furthermore, the notification unit can optimize the notification content based on past notification history. As a result, by referring to past notification history, the optimal notification method is selected, and more appropriate notifications can be sent. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI, which can analyze the data and select the optimal notification method.

[0098] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. If the user is relaxed, the notification unit can deliver notifications with normal priority. Furthermore, if the user is in a hurry, the notification unit can prioritize urgent notifications. This allows for more appropriate notifications by determining notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotions and determine notification priorities.

[0099] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit can send a push notification. If the user is using a tablet, the notification unit can send an email notification. Furthermore, if the user is using a desktop, the notification unit can send a pop-up notification. By considering the user's device information, the optimal notification method is selected, resulting in more appropriate notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, which can then analyze the data and select the optimal notification method.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The verification unit can perform a detailed format check of a contract, taking into account the attribute information of the contract submitter. For example, if the submitter is a new business partner, the verification unit will perform a detailed format check. Furthermore, if the submitter is a business partner with a history of problems, the verification unit can focus on checking high-risk areas. In addition, if the submitter is a highly reliable business partner, the verification unit can apply the usual verification procedures. This allows for a focus on checking high-risk areas by considering the attribute information of the contract submitter. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the submitter's attribute information into a generating AI, which can analyze the attribute information and determine the verification procedure.

[0102] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between contracts. For example, the analysis unit can analyze the interrelationships between clauses of a contract to prevent overlooking risks. It can also analyze related clauses of a contract to prevent overlapping risks. Furthermore, the analysis unit can analyze the interrelationships between contracts to identify the scope of risk impact. In this way, considering the interrelationships between contracts can prevent overlooking risks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input contract interrelationship data into a generating AI, which can then analyze the data to improve the accuracy of the analysis.

[0103] The advisory unit can adjust the level of detail of its advice based on the severity of the risk. For example, it can provide detailed advice for significant risks, moderately detailed advice for moderate risks, and concise advice for minor risks. By adjusting the level of detail of the advice according to the severity of the risk, more appropriate advice can be provided. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input risk severity data into a generating AI, which can analyze the data and adjust the level of detail of the advice.

[0104] The update unit can improve the accuracy of acquiring legal amendments and regulations by referring to past legal amendment data. For example, the update unit can refer to past legal amendment data to acquire relevant legal amendments and regulations. The update unit can also analyze past legal amendment data to identify legal amendments and regulations that should be acquired. Furthermore, based on past legal amendment data, the update unit can determine the priority of legal amendments and regulations to be acquired. As a result, by referring to past legal amendment data, the accuracy of acquisition can be improved and relevant legal amendments and regulations can be prevented from being overlooked. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past legal amendment data into a generating AI, which can then analyze the data and improve the accuracy of acquisition.

[0105] The data analysis unit can weight data based on the date of contract submission during data analysis. For example, the data analysis unit can set a higher weight for data submitted recently, and a lower weight for data submitted in the past. Furthermore, the data analysis unit can dynamically adjust the data weighting based on the contract submission date. This allows for more appropriate data analysis by weighting data based on the contract submission date. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input contract submission date data into a generating AI, which can then analyze the data and weight it.

[0106] The verification unit can estimate the user's emotions and determine the priority of contract format verification based on the estimated emotions. For example, if the user is stressed, the verification unit will prioritize the verification of important contract formats. If the user is relaxed, the verification unit can perform contract format verification with normal priority. Furthermore, if the user is in a hurry, the verification unit can prioritize the verification of urgent contract formats. This allows for more appropriate verification by adjusting the priority of contract format verification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not using AI. For example, the verification unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of contract format verification.

[0107] The analysis unit can estimate the user's emotions and adjust the risk analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can set stricter risk analysis criteria. If the user is relaxed, the analysis unit can perform risk analysis using normal criteria. Furthermore, if the user is in a hurry, the analysis unit can focus on high-urgency risks. By adjusting the risk analysis criteria according to the user's emotions, a more appropriate risk analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the risk analysis criteria.

[0108] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide concise and clear advice. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is in a hurry, it can provide quick and to-the-point advice. By adjusting the way advice is expressed according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the way advice is expressed.

[0109] The update unit can estimate the user's emotions and adjust the frequency of acquiring legal changes and regulations based on the estimated emotions. For example, if the user is stressed, the update unit will increase the frequency of acquiring legal changes and regulations. If the user is relaxed, the update unit can acquire legal changes and regulations at a normal frequency. Furthermore, if the user is in a hurry, the update unit can prioritize acquiring urgent legal changes and regulations. This allows for more appropriate acquisition of legal changes and regulations by adjusting the frequency of acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, or not using AI. For example, the update unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the frequency of acquiring legal changes and regulations.

[0110] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a concise and clear notification. If the user is relaxed, the notification unit can provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a quick and to-the-point notification. This allows for more appropriate notifications by adjusting the content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the content of the notification.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The verification team checks the contract format and identifies risks. The verification team checks the contract format based on which parts to check and the verification procedure. They also identify risks based on the types of risks and the procedure for identifying them. For example, they can check the clauses of the contract to prevent legal risks or overlooking clauses. Step 2: The analysis unit uses generation AI to analyze the contracts reviewed by the verification unit and identify risks. The analysis unit can prevent legal risks and oversights by having the generation AI analyze the contract clauses. It can also automatically acquire the latest legal revisions and regulations and reflect them in the contract review. Step 3: The advice department uses a generation AI to provide appropriate advice based on the risks identified by the analysis department. The advice department can provide more suitable advice and countermeasures based on similar contracts and trouble cases by having the generation AI accumulate data on contracts and analyze past cases and trends.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the verification unit, analysis unit, advice unit, update unit, data analysis unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the smart device 14 and verifies the format of the contract and identifies risks. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contract using generating AI to identify risks. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate advice using generating AI. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically acquires the latest legal revisions and regulations and reflects them in the contract review. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores data related to the contract and analyzes past cases and trends. The notification unit is implemented by the control unit 46A of the smart device 14 and provides automatic notifications and feedback to strengthen cooperation with the legal department. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the verification unit, analysis unit, advice unit, update unit, data analysis unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the smart glasses 214 and verifies the format of the contract and identifies risks. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the contract using generating AI to identify risks. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides appropriate advice using generating AI. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically acquires the latest legal revisions and regulations and reflects them in the contract review. The data analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and stores data related to the contract and analyzes past cases and trends. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides automatic notifications and feedback to strengthen cooperation with the legal department. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the verification unit, analysis unit, advice unit, update unit, data analysis unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the headset terminal 314 and verifies the format of the contract and identifies risks. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contract using generating AI to identify risks. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate advice using generating AI. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically acquires the latest legal revisions and regulations and reflects them in the contract review. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores data related to the contract and analyzes past cases and trends. The notification unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and provides automatic notifications and feedback to strengthen cooperation with the legal department. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the verification unit, analysis unit, advice unit, update unit, data analysis unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the robot 414 and verifies the format of the contract and identifies risks. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the contract using generating AI to identify risks. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides appropriate advice using generating AI. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically acquires the latest legal revisions and regulations and reflects them in the contract review. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and stores data related to the contract and analyzes past cases and trends. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and provides automatic notifications and feedback to strengthen cooperation with the legal department. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The verification department checks the contract format and identifies risks, The analysis unit analyzes the contract confirmed by the aforementioned verification unit and identifies risks, The system includes an advice unit that provides appropriate advice based on the risks identified by the analysis unit. A system characterized by the following features. (Note 2) It includes an update function that automatically acquires the latest legal revisions and regulations and reflects them in contract reviews. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has a data analysis department that stores data related to contracts and analyzes past cases and trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) To strengthen collaboration with the legal department, a notification system is provided to automatically send notifications and provide feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned verification unit is The system estimates the user's emotions and prioritizes the review of contract formats based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is When reviewing contract formats, we improve accuracy by referencing data from similar past contracts. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is When reviewing the contract format, the attribute information of the person submitting the contract should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned verification unit is We estimate user emotions and prioritize risk identification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned verification unit is When reviewing the contract format, the geographical distribution of the contracts should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned verification unit is When reviewing contract formats, refer to relevant documents related to the contract to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate user sentiment and adjust risk analysis criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When conducting risk analysis, consider the interrelationships between contracts to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When conducting risk analysis, the attribute information of the person submitting the contract will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the order in which the risk analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When conducting risk analysis, the geographical distribution of contracts should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When conducting risk analysis, referencing relevant literature in the contract improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the risk. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, we apply different advisory algorithms depending on the risk category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, we prioritize the advice based on when the risk occurred. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing advice, we adjust the order of advice based on the relevance of the risks. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is We estimate user sentiment and adjust the frequency of obtaining legal amendments and regulations based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is When acquiring information on legal revisions and regulations, we improve the accuracy of the acquisition by referring to past legal revision data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is We estimate user sentiment and determine the priority of legal amendments and regulatory acquisitions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is When acquiring legal amendments or regulations, consider their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned data analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned data analysis unit, When analyzing data, we optimize the analysis algorithm by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned data analysis unit, It estimates the user's emotions and adjusts the frequency of data analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned data analysis unit, During data analysis, the data is weighted based on the date the contract was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the system will refer to past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The verification department checks the contract format and identifies risks, The analysis unit analyzes the contract confirmed by the aforementioned verification unit and identifies risks, The system includes an advice unit that provides appropriate advice based on the risks identified by the analysis unit. A system characterized by the following features.

2. It includes an update function that automatically acquires the latest legal revisions and regulations and reflects them in contract reviews. The system according to feature 1.

3. The company has a data analysis department that stores data related to contracts and analyzes past cases and trends. The system according to feature 1.

4. To strengthen collaboration with the legal department, a notification system is provided to automatically send notifications and provide feedback. The system according to feature 1.

5. The aforementioned verification unit is The system estimates the user's emotions and prioritizes the review of contract formats based on those estimated emotions. The system according to feature 1.

6. The aforementioned verification unit is When reviewing contract formats, we improve accuracy by referencing data from similar past contracts. The system according to feature 1.

7. The aforementioned verification unit is When reviewing the contract format, the attribute information of the person submitting the contract should be taken into consideration. The system according to feature 1.

8. The aforementioned verification unit is We estimate user emotions and prioritize risk identification based on those estimated emotions. The system according to feature 1.

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