system

Abstract

The system according to the embodiment aims to automatically monitor the expiration date and important clauses of a contract and notify renewal or termination at the appropriate time. [Solution] A system according to an embodiment includes a monitoring unit, a notification unit, a comparison unit, and a generation unit. The monitoring unit monitors expiration dates or important clauses in a contract. The notification unit notifies the timing of contract renewal or termination based on the information monitored by the monitoring unit. The comparison unit compares new contract terms with existing contract terms based on the information notified by the notification unit. The generation unit generates a new contract proposal based on the changes identified by the comparison unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, contract deadlines and important clauses are monitored manually, which can lead to missed renewal or termination timing.

[0005] The system according to the embodiment aims to automatically monitor the expiration date and important clauses of a contract and notify renewal or termination at the appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a notification unit, a comparison unit, and a generation unit. The monitoring unit monitors expiration dates or important clauses in the contract. The notification unit notifies the timing of contract renewal or termination based on the information monitored by the monitoring unit. The comparison unit compares new contract terms with existing contract terms based on the information notified by the notification unit. The generation unit generates a new contract proposal based on the changes identified by the comparison unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically monitor the expiration date and important clauses of a contract and notify renewal or termination at the appropriate time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A contract management system according to an embodiment of the present invention uses generative AI to simplify the process from contract management to contract renewal. This contract management system automatically monitors contracts to prevent oversights in contract renewals or terminations. Furthermore, when renewal is necessary, it automatically compares the new contract terms with the existing contract terms and identifies changes. This provides information useful for contract renewal negotiations and automatically generates a new contract proposal. For example, the contract management system monitors contract expiration dates and important clauses and notifies parties of the timing of renewal or termination. This prevents oversights in contract renewal or termination. Next, when new contract terms are presented, the contract management system compares them with the existing contract terms and identifies changes. This provides information useful for contract renewal negotiations. Furthermore, the contract management system creates a new contract proposal based on the changes to support contract renewal negotiations. This streamlines the contract renewal process. This system simplifies the process from contract management to contract renewal, preventing oversights in contract renewal or termination. Furthermore, by providing information useful for contract renewal negotiations and automatically generating a new contract proposal, the contract renewal process is streamlined. This allows the contract management system to efficiently manage contracts from contract management to contract renewal.

[0029] A contract management system according to an embodiment includes a monitoring unit, a notification unit, a comparison unit, and a generation unit. The monitoring unit monitors the deadline or important clauses of a contract. For example, the monitoring unit analyzes contract data and extracts the deadline or important clauses. The monitoring unit can analyze the contents of the contract using text analysis technology, for example, to identify the deadline or important clauses. The monitoring unit can also analyze the contract data using a machine learning algorithm to extract important information. For example, the monitoring unit issues a notification when the contract deadline approaches. The notification unit notifies the user of the timing to renew or terminate the contract based on the information monitored by the monitoring unit. For example, the notification unit can notify the user by email or SMS when the contract deadline approaches. The notification unit can also notify the user of the timing to renew or terminate the contract using an in-app notification. The comparison unit compares new contract terms with existing contract terms based on the information notified by the notification unit. For example, when new contract terms are presented, the comparison unit compares them with the existing contract terms to identify changes. The comparison unit can compare contract terms and identify changes, for example, using text matching technology. The comparison unit can also perform numerical comparisons to identify changes to contract terms. The generation unit generates a new contract proposal based on the changes identified by the comparison unit. For example, the generation unit creates a new contract proposal based on the changes and supports contract renewal negotiations. The generation unit can create a new contract proposal, for example, using template-based generation technology. The generation unit can also automatically generate contract proposals using generation AI. This allows the contract management system according to the embodiment to efficiently perform tasks from contract management to contract renewal.

[0030] The monitoring unit can analyze contract data and extract deadlines or important clauses. For example, the monitoring unit can analyze the contents of the contract using text analysis technology and identify deadlines or important clauses. For example, the monitoring unit can analyze the text data of the contract using natural language processing technology and extract deadlines or important clauses. The monitoring unit can also analyze the contract data using a machine learning algorithm and extract important information. For example, the monitoring unit can classify the contract data using clustering technology and identify important clauses. This improves the accuracy of monitoring by automatically extracting important information from the contract. Some or all of the above-mentioned processing in the monitoring unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the text data of the contract into a generation AI, which can analyze the text data and extract deadlines or important clauses.

[0031] The notification unit can provide a notification when the contract expiration date is approaching. For example, the notification unit can provide a notification by email or SMS when the contract expiration date is approaching. For example, the notification unit can provide a notification by email when the contract expiration date is one week away. The notification unit can also provide a notification by SMS when the contract expiration date is one month away. Furthermore, the notification unit can provide a notification of the timing to renew or cancel the contract using an in-app notification. For example, the notification unit can display a pop-up notification within the app when the contract expiration date is approaching. This makes it possible to notify the user of the timing to renew or cancel the contract without missing it. Some or all of the above-mentioned processing in the notification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the contract expiration date information into the generation AI, which can analyze the expiration date information and provide a notification at the appropriate time.

[0032] The comparison unit can compare new contract terms and conditions with existing contract terms and conditions to identify changes when new contract terms and conditions are presented. For example, the comparison unit can compare new contract terms and conditions with existing contract terms and conditions to identify changes when new contract terms and conditions are presented. For example, the comparison unit can use text matching technology to compare contract terms and identify changes. For example, the comparison unit can compare the text of the new contract terms and existing contract terms and identify differences. The comparison unit can also perform numerical comparisons to identify changes to contract terms. For example, the comparison unit can compare numerical data between the new contract terms and existing contract terms and identify changes. This allows changes to the contract terms to be quickly identified. Some or all of the above-mentioned processing in the comparison unit can be performed using or without the generation AI. For example, the comparison unit can input data of the new contract terms and existing contract terms into the generation AI, which can analyze the data and identify changes.

[0033] The generation unit can create a new contract proposal based on the changes. For example, the generation unit creates a new contract proposal based on the changes to support contract renewal negotiations. For example, the generation unit can create a new contract proposal using a template-based generation technique. For example, the generation unit selects a template based on the changes and generates a new contract proposal. The generation unit can also automatically generate a contract proposal using a generation AI. For example, the generation unit inputs the changes to the generation AI, which then generates a new contract proposal based on the changes. This makes it possible to automatically generate a new contract proposal that is useful for contract renewal negotiations. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the changes to the generation AI, which then generates a new contract proposal based on the changes.

[0034] The monitoring unit can analyze the contract's past update history and select an appropriate monitoring method. For example, the monitoring unit prioritizes monitoring of contracts that have been frequently updated based on the past update history. For example, the monitoring unit can analyze the contract's past update history using data mining technology to identify contracts that have been frequently updated. The monitoring unit can also focus monitoring on a specific period based on the past update history. For example, the monitoring unit can analyze the contract's past update history using time series analysis technology and focus monitoring on a specific period. Furthermore, the monitoring unit can analyze the past update history and strengthen monitoring only when specific conditions are met. For example, the monitoring unit can analyze the contract's past update history using rule-based analysis technology and strengthen monitoring when specific conditions are met. This improves monitoring accuracy by selecting the optimal monitoring method based on the past update history. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the contract's past update history data into the generation AI, which can then analyze the data and select an appropriate monitoring method.

[0035] When monitoring a contract, the monitoring unit can adjust the level of monitoring detail based on the type and importance of the contract. For example, the monitoring unit performs detailed monitoring for important contracts and notifies the user of minor changes. For example, the monitoring unit can adjust the level of monitoring detail based on the type and importance of the contract, and perform detailed monitoring for important contracts. The monitoring unit can also perform basic monitoring for general contracts and notify the user of only major changes. For example, the monitoring unit can adjust the level of monitoring detail based on the type and importance of the contract, and perform basic monitoring for general contracts. Furthermore, the monitoring unit can adjust the level of monitoring detail based on the type of contract and provide appropriate information. For example, the monitoring unit can adjust the level of monitoring detail based on the type and importance of the contract and provide appropriate information. In this way, appropriate information can be provided by adjusting the level of monitoring detail based on the type and importance of the contract. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input data on the type and importance of the contract into the generation AI, which can analyze the data and adjust the level of monitoring detail.

[0036] When monitoring a contract, the monitoring unit can adjust the timing of monitoring based on the user's work schedule. The monitoring unit, for example, adjusts the timing of monitoring to match the user's work schedule. For example, the monitoring unit may refer to the user's calendar information and adjust the timing of monitoring to match the work schedule. The monitoring unit can also perform monitoring to avoid busy hours for the user. For example, the monitoring unit may refer to data from the user's task management system and perform monitoring to avoid busy hours. The monitoring unit can also set the optimal monitoring timing based on the user's work schedule. For example, the monitoring unit may analyze the user's work schedule and set the optimal monitoring timing. This enables efficient monitoring by adjusting the monitoring timing to match the user's work schedule. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the user's work schedule data into the generation AI, which can analyze the data and adjust the monitoring timing.

[0037] When monitoring a contract, the monitoring unit can prioritize monitoring related contracts by referring to the user's past contract history. The monitoring unit, for example, prioritizes monitoring related contracts based on the user's past contract history. For example, the monitoring unit can analyze the user's past contract data to identify related contracts. The monitoring unit can also prioritize monitoring contracts that the user has considered important in the past. For example, the monitoring unit can analyze the user's past contract history to identify contracts that the user has considered important. The monitoring unit can also analyze the user's past contract history to prioritize monitoring highly relevant contracts. For example, the monitoring unit can analyze the user's past contract history using data mining technology to identify highly relevant contracts. This prioritizes monitoring related contracts based on the user's past contract history, thereby preventing important contracts from being overlooked. Some or all of the above-described processing in the monitoring unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's past contract history data into a generation AI, which can analyze the data and prioritize monitoring related contracts.

[0038] The notification unit can adjust the level of detail of the notification based on the importance of the contract when sending a notification. For example, the notification unit provides detailed notifications for important contracts. For example, the notification unit adjusts the level of detail of the notification based on the importance of the contract, and provides detailed notifications for important contracts. The notification unit can also provide basic notifications for general contracts. For example, the notification unit adjusts the level of detail of the notification based on the importance of the contract, and provides basic notifications for general contracts. The notification unit can also adjust the level of detail of the notification based on the importance of the contract and provide appropriate information. In this way, appropriate information can be provided by adjusting the level of detail of the notification based on the importance of the contract. Some or all of the above-mentioned processing in the notification unit may be performed using or without the generation AI. For example, the notification unit can input contract importance data into the generation AI, which can analyze the data and adjust the level of detail of the notification.

[0039] The notification unit can apply different notification algorithms depending on the category of the contract when making a notification. The notification unit, for example, selects an appropriate notification algorithm depending on the category of the contract. For example, the notification unit selects an appropriate notification algorithm based on the category of the contract and issues a notification. The notification unit can also apply a detailed notification algorithm to important contracts. For example, the notification unit applies a detailed notification algorithm to important contracts based on the category of the contract. The notification unit can also apply a basic notification algorithm to general contracts. For example, the notification unit applies a basic notification algorithm to general contracts based on the category of the contract. This enables efficient notification by applying an appropriate notification algorithm depending on the category of the contract. Some or all of the above-mentioned processing in the notification unit may be performed using or without the generation AI. For example, the notification unit can input contract category data into the generation AI, which can analyze the data and select an appropriate notification algorithm.

[0040] At the time of notification, the notification unit can determine the priority of notifications based on the submission date of the contract. For example, the notification unit prioritizes notifications for contracts whose submission date is approaching. For example, the notification unit prioritizes notifications for contracts whose submission date is approaching based on the submission date of the contract. The notification unit can also provide basic notifications for contracts whose submission date is further away. For example, the notification unit provides basic notifications for contracts whose submission date is further away based on the submission date of the contract. Furthermore, the notification unit can adjust the priority of notifications according to the submission date. For example, the notification unit adjusts the priority of notifications according to the submission date of the contract. In this way, by determining the priority of notifications according to the submission date of the contract, important contracts can be given priority. Some or all of the above-mentioned processing in the notification unit may be performed using or without the generation AI. For example, the notification unit can input contract submission date data into the generation AI, and the generation AI can analyze the data and determine the priority of notifications.

[0041] The notification unit can adjust the order of notifications based on the relevance of the contracts when making notifications. For example, the notification unit prioritizes notifications of highly relevant contracts. For example, the notification unit prioritizes notifications of highly relevant contracts based on the relevance of the contracts. The notification unit can also provide basic notifications for less relevant contracts. For example, the notification unit provides basic notifications for less relevant contracts based on the relevance of the contracts. The notification unit can also adjust the order of notifications based on the relevance of the contracts. For example, the notification unit adjusts the order of notifications based on the relevance of the contracts. In this way, by adjusting the order of notifications based on the relevance of the contracts, important contracts can be given priority. Some or all of the above-mentioned processing in the notification unit may be performed using or without the generation AI. For example, the notification unit can input contract relevance data into the generation AI, which can analyze the data and adjust the order of notifications.

[0042] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between the contracts during the comparison. The comparison unit, for example, analyzes the interrelationships between the contracts and performs a highly accurate comparison. For example, the comparison unit analyzes the interrelationships between the contracts using data mining technology and performs a highly accurate comparison. The comparison unit can also perform an appropriate comparison by taking into account the relevance of the contracts. For example, the comparison unit performs a comparison based on the interrelationships between the contracts, taking into account the relevance. Furthermore, the comparison unit can perform a detailed comparison based on the interrelationships between the contracts. For example, the comparison unit analyzes the interrelationships between the contracts and performs a detailed comparison. This enables a highly accurate comparison by taking into account the interrelationships between the contracts. Some or all of the above-mentioned processing in the comparison unit may be performed using or without the generation AI. For example, the comparison unit can input data on the interrelationships between the contracts into the generation AI, which can analyze the data to improve the accuracy of the comparison.

[0043] The comparison unit can make the comparison while taking into account the attribute information of the contract submitter. The comparison unit, for example, makes an appropriate comparison based on the attribute information of the contract submitter. For example, the comparison unit analyzes the attribute information of the contract submitter and makes an appropriate comparison. The comparison unit can also make a highly accurate comparison by taking into account the submitter's past history. For example, the comparison unit analyzes the contract submitter's past history and makes a highly accurate comparison. The comparison unit can also analyze the submitter's attribute information and make a detailed comparison. For example, the comparison unit analyzes the attribute information of the contract submitter and makes a detailed comparison. This enables a highly accurate comparison by taking into account the attribute information of the contract submitter. Some or all of the above-mentioned processing in the comparison unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the comparison unit can input attribute information data of the contract submitter to the generation AI, which can analyze the data and make the comparison.

[0044] The comparison unit can take into account the geographical distribution of the contracts when making the comparison. The comparison unit, for example, makes an appropriate comparison based on the geographical distribution of the contracts. For example, the comparison unit analyzes the geographical distribution of the contracts and makes an appropriate comparison. The comparison unit can also make a highly accurate comparison by taking geographical factors into account. For example, the comparison unit makes a comparison based on the geographical distribution of the contracts and taking geographical factors into account. The comparison unit can also analyze the geographical distribution of the contracts and make a detailed comparison. For example, the comparison unit analyzes the geographical distribution of the contracts and makes a detailed comparison. This enables a highly accurate comparison by taking the geographical distribution of the contracts into account. Some or all of the above-mentioned processing in the comparison unit may be performed using or without the generation AI. For example, the comparison unit can input geographical distribution data of the contracts into the generation AI, which can analyze the data and make the comparison.

[0045] The comparison unit can improve the accuracy of the comparison by referring to the related literature of the contract during the comparison. The comparison unit, for example, performs a highly accurate comparison based on the related literature of the contract. For example, the comparison unit performs a highly accurate comparison by referring to the related literature of the contract. The comparison unit can also perform an appropriate comparison by referring to the related literature. For example, the comparison unit performs an appropriate comparison based on the related literature of the contract. Furthermore, the comparison unit can analyze the related literature of the contract and perform a detailed comparison. For example, the comparison unit analyzes the related literature of the contract and performs a detailed comparison. This enables a highly accurate comparison by referring to the related literature of the contract. Some or all of the above-mentioned processing in the comparison unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the comparison unit can input data on the related literature of the contract into the generation AI, which can analyze the data and perform the comparison.

[0046] The generation unit can improve the accuracy of generation by taking into account the interrelationships between contracts during generation. The generation unit, for example, analyzes the interrelationships between contracts and generates a highly accurate contract proposal. For example, the generation unit analyzes the interrelationships between contracts using data mining technology and generates a highly accurate contract proposal. The generation unit can also generate an appropriate contract proposal by taking into account the relevance of contracts. For example, the generation unit generates a contract proposal based on the interrelationships between contracts, taking into account the relevance. Furthermore, the generation unit can generate a detailed contract proposal based on the interrelationships between contracts. For example, the generation unit analyzes the interrelationships between contracts and generates a detailed contract proposal. In this way, a highly accurate contract proposal can be generated by taking into account the interrelationships between contracts. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input contract interrelation data into the generation AI, which can analyze the data and generate a contract proposal.

[0047] The generation unit can generate the contract while taking into account the attribute information of the contract submitter. The generation unit, for example, generates an appropriate contract proposal based on the attribute information of the contract submitter. For example, the generation unit analyzes the attribute information of the contract submitter and generates an appropriate contract proposal. The generation unit can also generate a highly accurate contract proposal by taking into account the submitter's past history. For example, the generation unit analyzes the contract submitter's past history and generates a highly accurate contract proposal. The generation unit can also analyze the submitter's attribute information and generate a detailed contract proposal. For example, the generation unit analyzes the attribute information of the contract submitter and generates a detailed contract proposal. In this way, a highly accurate contract proposal can be generated by taking into account the attribute information of the contract submitter. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input attribute information data of the contract submitter to the generation AI, which can analyze the data and generate a contract proposal.

[0048] The generation unit can generate the contracts taking into account the geographical distribution of the contracts. The generation unit, for example, generates an appropriate contract proposal based on the geographical distribution of the contracts. For example, the generation unit analyzes the geographical distribution of the contracts and generates an appropriate contract proposal. The generation unit can also generate a highly accurate contract proposal by taking geographical factors into account. For example, the generation unit generates a contract proposal based on the geographical distribution of the contracts and taking geographical factors into account. The generation unit can also analyze the geographical distribution of the contracts and generate a detailed contract proposal. For example, the generation unit analyzes the geographical distribution of the contracts and generates a detailed contract proposal. In this way, a highly accurate contract proposal can be generated by taking the geographical distribution of the contracts into account. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input geographical distribution data of the contracts into the generation AI, which can analyze the data and generate a contract proposal.

[0049] The generation unit can improve the accuracy of generation by referring to related literature of the contract during generation. The generation unit, for example, generates a highly accurate contract proposal based on related literature of the contract. For example, the generation unit generates a highly accurate contract proposal by referring to related literature of the contract. The generation unit can also generate an appropriate contract proposal by referring to related literature. For example, the generation unit generates an appropriate contract proposal based on related literature of the contract. Furthermore, the generation unit can analyze related literature of the contract and generate a detailed contract proposal. For example, the generation unit analyzes related literature of the contract and generates a detailed contract proposal. In this way, a highly accurate contract proposal can be generated by referring to related literature of the contract. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input related literature data of the contract into the generation AI, which can analyze the data and generate a contract proposal.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The contract management system can further include a risk assessment unit. The risk assessment unit can analyze the contents of a contract and assess potential risks. For example, the risk assessment unit can analyze the clauses of a contract and identify legal and financial risks. The risk assessment unit can also assess risks based on past contract history and market data. Furthermore, the risk assessment unit can provide the risk assessment results to the notification unit and prioritize notification of high-risk contracts. This allows the contract management system to assess the risks of contracts in advance and take appropriate measures against high-risk contracts.

[0052] The contract management system may further include a user feedback unit. The user feedback unit may collect user feedback regarding contract management and updates and use the collected feedback to improve the system. For example, the user feedback unit may conduct a survey regarding the contract update process to collect user opinions. The user feedback unit may also collect user complaints and requests regarding contract management and identify areas for improvement in the system. Furthermore, the user feedback unit may improve the system's functions based on the collected feedback and increase user satisfaction. This allows the contract management system to reflect user opinions and provide a system that is easier to use.

[0053] The contract management system may further include a contract performance monitoring unit. The contract performance monitoring unit may monitor the status of contract performance based on the terms of the contract. For example, the contract performance monitoring unit may analyze the terms of the contract and identify performance deadlines and performance conditions. The contract performance monitoring unit may also periodically check the status of contract performance and issue a notification if performance is delayed or if performance conditions are not met. Furthermore, the contract performance monitoring unit may generate reports on the performance status and visualize the contract performance status. This allows the contract management system to monitor the status of contract performance and prevent omissions in contract performance.

[0054] The contract management system may further include a contract version management unit. The version management unit can manage different versions of a contract and track change histories. For example, the version management unit automatically updates the version when a new version of a contract is created and records the change history. The version management unit can also compare past versions with the current version to identify changes. Furthermore, the version management unit allows users to access past versions and refer to them as needed. This allows the contract management system to efficiently manage contract versions and track change histories.

[0055] The contract management system may further include a contract template management unit. The template management unit manages contract templates, allowing users to easily create new contracts. For example, the template management unit may provide different types of contract templates and allow users to select an appropriate template. The template management unit may also provide a template customization function, allowing users to edit templates to suit specific conditions. Furthermore, the template management unit may manage the update history of templates and always provide the latest templates. In this way, the contract management system streamlines the contract creation process and allows users to easily create new contracts.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The monitoring unit monitors the deadlines or important clauses of the contract. For example, the monitoring unit analyzes the contract data and extracts deadlines and important clauses. Text analysis technology and machine learning algorithms can be used to analyze the content of the contract and identify important information. Step 2: The notification unit notifies the user when to renew or terminate the contract based on the information monitored by the monitoring unit. For example, the notification unit can notify the user by email, SMS, or in-app notification when the contract expiration date is approaching. Step 3: The comparison unit compares the new contract terms with the existing contract terms based on the information notified by the notification unit, for example, by using text matching techniques or numerical comparisons to compare the contract terms and identify changes. Step 4: The generator generates a new contract proposal based on the changes identified by the comparator, for example, using template-based generation techniques or generative AI to create a new contract proposal and assist in contract renewal negotiations.

[0058] (Example 2) A contract management system according to an embodiment of the present invention uses generative AI to simplify the process from contract management to contract renewal. This contract management system automatically monitors contracts to prevent oversights in contract renewals or terminations. Furthermore, when renewal is necessary, it automatically compares the new contract terms with the existing contract terms and identifies changes. This provides information useful for contract renewal negotiations and automatically generates a new contract proposal. For example, the contract management system monitors contract expiration dates and important clauses and notifies parties of the timing of renewal or termination. This prevents oversights in contract renewal or termination. Next, when new contract terms are presented, the contract management system compares them with the existing contract terms and identifies changes. This provides information useful for contract renewal negotiations. Furthermore, the contract management system creates a new contract proposal based on the changes to support contract renewal negotiations. This streamlines the contract renewal process. This system simplifies the process from contract management to contract renewal, preventing oversights in contract renewal or termination. Furthermore, by providing information useful for contract renewal negotiations and automatically generating a new contract proposal, the contract renewal process is streamlined. This allows the contract management system to efficiently manage contracts from contract management to contract renewal.

[0059] A contract management system according to an embodiment includes a monitoring unit, a notification unit, a comparison unit, and a generation unit. The monitoring unit monitors the deadline or important clauses of a contract. For example, the monitoring unit analyzes contract data and extracts the deadline or important clauses. The monitoring unit can analyze the contents of the contract using text analysis technology, for example, to identify the deadline or important clauses. The monitoring unit can also analyze the contract data using a machine learning algorithm to extract important information. For example, the monitoring unit issues a notification when the contract deadline approaches. The notification unit notifies the user of the timing to renew or terminate the contract based on the information monitored by the monitoring unit. For example, the notification unit can notify the user by email or SMS when the contract deadline approaches. The notification unit can also notify the user of the timing to renew or terminate the contract using an in-app notification. The comparison unit compares new contract terms with existing contract terms based on the information notified by the notification unit. For example, when new contract terms are presented, the comparison unit compares them with the existing contract terms to identify changes. The comparison unit can compare contract terms and identify changes, for example, using text matching technology. The comparison unit can also perform numerical comparisons to identify changes to contract terms. The generation unit generates a new contract proposal based on the changes identified by the comparison unit. For example, the generation unit creates a new contract proposal based on the changes and supports contract renewal negotiations. The generation unit can create a new contract proposal, for example, using template-based generation technology. The generation unit can also automatically generate contract proposals using generation AI. This allows the contract management system according to the embodiment to efficiently perform tasks from contract management to contract renewal.

[0060] The monitoring unit can analyze contract data and extract deadlines or important clauses. For example, the monitoring unit can analyze the contents of the contract using text analysis technology and identify deadlines or important clauses. For example, the monitoring unit can analyze the text data of the contract using natural language processing technology and extract deadlines or important clauses. The monitoring unit can also analyze the contract data using a machine learning algorithm and extract important information. For example, the monitoring unit can classify the contract data using clustering technology and identify important clauses. This improves the accuracy of monitoring by automatically extracting important information from the contract. Some or all of the above-mentioned processing in the monitoring unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the text data of the contract into a generation AI, which can analyze the text data and extract deadlines or important clauses.

[0061] The notification unit can provide a notification when the contract expiration date is approaching. For example, the notification unit can provide a notification by email or SMS when the contract expiration date is approaching. For example, the notification unit can provide a notification by email when the contract expiration date is one week away. The notification unit can also provide a notification by SMS when the contract expiration date is one month away. Furthermore, the notification unit can provide a notification of the timing to renew or cancel the contract using an in-app notification. For example, the notification unit can display a pop-up notification within the app when the contract expiration date is approaching. This makes it possible to notify the user of the timing to renew or cancel the contract without missing it. Some or all of the above-mentioned processing in the notification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the contract expiration date information into the generation AI, which can analyze the expiration date information and provide a notification at the appropriate time.

[0062] The comparison unit can compare new contract terms and conditions with existing contract terms and conditions to identify changes when new contract terms and conditions are presented. For example, the comparison unit can compare new contract terms and conditions with existing contract terms and conditions to identify changes when new contract terms and conditions are presented. For example, the comparison unit can use text matching technology to compare contract terms and identify changes. For example, the comparison unit can compare the text of the new contract terms and existing contract terms and identify differences. The comparison unit can also perform numerical comparisons to identify changes to contract terms. For example, the comparison unit can compare numerical data between the new contract terms and existing contract terms and identify changes. This allows changes to the contract terms to be quickly identified. Some or all of the above-mentioned processing in the comparison unit can be performed using or without the generation AI. For example, the comparison unit can input data of the new contract terms and existing contract terms into the generation AI, which can analyze the data and identify changes.

[0063] The generation unit can create a new contract proposal based on the changes. For example, the generation unit creates a new contract proposal based on the changes to support contract renewal negotiations. For example, the generation unit can create a new contract proposal using a template-based generation technique. For example, the generation unit selects a template based on the changes and generates a new contract proposal. The generation unit can also automatically generate a contract proposal using a generation AI. For example, the generation unit inputs the changes to the generation AI, which then generates a new contract proposal based on the changes. This makes it possible to automatically generate a new contract proposal that is useful for contract renewal negotiations. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the changes to the generation AI, which then generates a new contract proposal based on the changes.

[0064] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit reduces the monitoring frequency and only notifies the user at important times. For example, the monitoring unit can estimate the user's emotions using facial expression recognition technology and reduce the monitoring frequency when the user is feeling stressed. The monitoring unit can also increase the monitoring frequency and provide more detailed information when the user is relaxed. For example, the monitoring unit can estimate the user's emotions using voice analysis technology and increase the monitoring frequency when the user is relaxed. Furthermore, the monitoring unit can increase the monitoring frequency and provide real-time information when the user is in a hurry. For example, the monitoring unit can estimate the user's emotions using biometric data (heart rate and electrodermal activity) and increase the monitoring frequency when the user is in a hurry. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit may input user emotion data into the generation AI, which may then analyze the emotion data and adjust the monitoring frequency.

[0065] The monitoring unit can analyze the contract's past update history and select an appropriate monitoring method. For example, the monitoring unit prioritizes monitoring of contracts that have been frequently updated based on the past update history. For example, the monitoring unit can analyze the contract's past update history using data mining technology to identify contracts that have been frequently updated. The monitoring unit can also focus monitoring on a specific period based on the past update history. For example, the monitoring unit can analyze the contract's past update history using time series analysis technology and focus monitoring on a specific period. Furthermore, the monitoring unit can analyze the past update history and strengthen monitoring only when specific conditions are met. For example, the monitoring unit can analyze the contract's past update history using rule-based analysis technology and strengthen monitoring when specific conditions are met. This improves monitoring accuracy by selecting the optimal monitoring method based on the past update history. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the contract's past update history data into the generation AI, which can then analyze the data and select an appropriate monitoring method.

[0066] When monitoring a contract, the monitoring unit can adjust the level of monitoring detail based on the type and importance of the contract. For example, the monitoring unit performs detailed monitoring for important contracts and notifies the user of minor changes. For example, the monitoring unit can adjust the level of monitoring detail based on the type and importance of the contract, and perform detailed monitoring for important contracts. The monitoring unit can also perform basic monitoring for general contracts and notify the user of only major changes. For example, the monitoring unit can adjust the level of monitoring detail based on the type and importance of the contract, and perform basic monitoring for general contracts. Furthermore, the monitoring unit can adjust the level of monitoring detail based on the type of contract and provide appropriate information. For example, the monitoring unit can adjust the level of monitoring detail based on the type and importance of the contract and provide appropriate information. In this way, appropriate information can be provided by adjusting the level of monitoring detail based on the type and importance of the contract. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input data on the type and importance of the contract into the generation AI, which can analyze the data and adjust the level of monitoring detail.

[0067] The monitoring unit can estimate the user's emotions and determine the priority of contracts to monitor based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit prioritizes monitoring important contracts. For example, the monitoring unit can estimate the user's emotions using facial expression recognition technology and prioritize monitoring important contracts when the user is feeling stressed. The monitoring unit can also monitor all contracts equally when the user is relaxed. For example, the monitoring unit can estimate the user's emotions using voice analysis technology and monitor all contracts equally when the user is relaxed. Furthermore, the monitoring unit can prioritize monitoring contracts with upcoming deadlines when the user is in a hurry. For example, the monitoring unit can estimate the user's emotions using biometric data (heart rate or electrodermal activity) and prioritize monitoring contracts with upcoming deadlines when the user is in a hurry. This allows the priority of contracts to be monitored to be determined according to the user's emotions, thereby enabling important contracts to be monitored preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input user emotion data into the generation AI, and the generation AI may analyze the emotion data and determine the priority of the contracts to monitor.

[0068] When monitoring a contract, the monitoring unit can adjust the timing of monitoring based on the user's work schedule. The monitoring unit, for example, adjusts the timing of monitoring to match the user's work schedule. For example, the monitoring unit may refer to the user's calendar information and adjust the timing of monitoring to match the work schedule. The monitoring unit can also perform monitoring to avoid busy hours for the user. For example, the monitoring unit may refer to data from the user's task management system and perform monitoring to avoid busy hours. The monitoring unit can also set the optimal monitoring timing based on the user's work schedule. For example, the monitoring unit may analyze the user's work schedule and set the optimal monitoring timing. This enables efficient monitoring by adjusting the monitoring timing to match the user's work schedule. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the user's work schedule data into the generation AI, which can analyze the data and adjust the monitoring timing.

[0069] When monitoring a contract, the monitoring unit can prioritize monitoring related contracts by referring to the user's past contract history. The monitoring unit, for example, prioritizes monitoring related contracts based on the user's past contract history. For example, the monitoring unit can analyze the user's past contract data to identify related contracts. The monitoring unit can also prioritize monitoring contracts that the user has considered important in the past. For example, the monitoring unit can analyze the user's past contract history to identify contracts that the user has considered important. The monitoring unit can also analyze the user's past contract history to prioritize monitoring highly relevant contracts. For example, the monitoring unit can analyze the user's past contract history using data mining technology to identify highly relevant contracts. This prioritizes monitoring related contracts based on the user's past contract history, thereby preventing important contracts from being overlooked. Some or all of the above-described processing in the monitoring unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's past contract history data into a generation AI, which can analyze the data and prioritize monitoring related contracts.

[0070] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user emotions. For example, the notification unit provides a concise notification when the user is stressed. For example, the notification unit estimates the user's emotions using facial expression recognition technology and provides a concise notification when the user is stressed. The notification unit can also provide a detailed notification when the user is relaxed. For example, the notification unit estimates the user's emotions using voice analysis technology and provides a detailed notification when the user is relaxed. Furthermore, the notification unit can also provide a prompt notification when the user is in a hurry. For example, the notification unit estimates the user's emotions using biometric data (heart rate or electrodermal activity) and provides a prompt notification when the user is in a hurry. This allows for more appropriate notification by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using or without the generation AI. For example, the notification unit may input user emotion data into the generation AI, which may analyze the emotion data and adjust the notification method.

[0071] The notification unit can adjust the level of detail of the notification based on the importance of the contract when sending a notification. For example, the notification unit provides detailed notifications for important contracts. For example, the notification unit adjusts the level of detail of the notification based on the importance of the contract, and provides detailed notifications for important contracts. The notification unit can also provide basic notifications for general contracts. For example, the notification unit adjusts the level of detail of the notification based on the importance of the contract, and provides basic notifications for general contracts. The notification unit can also adjust the level of detail of the notification based on the importance of the contract and provide appropriate information. In this way, appropriate information can be provided by adjusting the level of detail of the notification based on the importance of the contract. Some or all of the above-mentioned processing in the notification unit may be performed using or without the generation AI. For example, the notification unit can input contract importance data into the generation AI, which can analyze the data and adjust the level of detail of the notification.

[0072] The notification unit can apply different notification algorithms depending on the category of the contract when making a notification. The notification unit, for example, selects an appropriate notification algorithm depending on the category of the contract. For example, the notification unit selects an appropriate notification algorithm based on the category of the contract and issues a notification. The notification unit can also apply a detailed notification algorithm to important contracts. For example, the notification unit applies a detailed notification algorithm to important contracts based on the category of the contract. The notification unit can also apply a basic notification algorithm to general contracts. For example, the notification unit applies a basic notification algorithm to general contracts based on the category of the contract. This enables efficient notification by applying an appropriate notification algorithm depending on the category of the contract. Some or all of the above-mentioned processing in the notification unit may be performed using or without the generation AI. For example, the notification unit can input contract category data into the generation AI, which can analyze the data and select an appropriate notification algorithm.

[0073] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit only notifies at important times. For example, the notification unit estimates the user's emotions using facial expression recognition technology and only notifies at important times when the user is feeling stressed. The notification unit can also notify frequently when the user is relaxed. For example, the notification unit estimates the user's emotions using voice analysis technology and notifies frequently when the user is relaxed. Furthermore, the notification unit can also notify quickly when the user is in a hurry. For example, the notification unit estimates the user's emotions using biometric data (heart rate or electrodermal activity) and notifies quickly when the user is in a hurry. This allows for more appropriate timing of notifications by adjusting the timing of notifications according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using or without the generation AI. For example, the notification unit may input user emotion data into the generation AI, which may then analyze the emotion data and adjust the timing of notification.

[0074] At the time of notification, the notification unit can determine the priority of notifications based on the submission date of the contract. For example, the notification unit prioritizes notifications for contracts whose submission date is approaching. For example, the notification unit prioritizes notifications for contracts whose submission date is approaching based on the submission date of the contract. The notification unit can also provide basic notifications for contracts whose submission date is further away. For example, the notification unit provides basic notifications for contracts whose submission date is further away based on the submission date of the contract. Furthermore, the notification unit can adjust the priority of notifications according to the submission date. For example, the notification unit adjusts the priority of notifications according to the submission date of the contract. In this way, by determining the priority of notifications according to the submission date of the contract, important contracts can be given priority. Some or all of the above-mentioned processing in the notification unit may be performed using or without the generation AI. For example, the notification unit can input contract submission date data into the generation AI, and the generation AI can analyze the data and determine the priority of notifications.

[0075] The notification unit can adjust the order of notifications based on the relevance of the contracts when making notifications. For example, the notification unit prioritizes notifications of highly relevant contracts. For example, the notification unit prioritizes notifications of highly relevant contracts based on the relevance of the contracts. The notification unit can also provide basic notifications for less relevant contracts. For example, the notification unit provides basic notifications for less relevant contracts based on the relevance of the contracts. The notification unit can also adjust the order of notifications based on the relevance of the contracts. For example, the notification unit adjusts the order of notifications based on the relevance of the contracts. In this way, by adjusting the order of notifications based on the relevance of the contracts, important contracts can be given priority. Some or all of the above-mentioned processing in the notification unit may be performed using or without the generation AI. For example, the notification unit can input contract relevance data into the generation AI, which can analyze the data and adjust the order of notifications.

[0076] The comparison unit can estimate the user's emotion and adjust the comparison criteria based on the estimated user's emotion. For example, the comparison unit uses a simple comparison criterion when the user is stressed. For example, the comparison unit estimates the user's emotion using facial expression recognition technology and uses a simple comparison criterion when the user is stressed. The comparison unit can also use a detailed comparison criterion when the user is relaxed. For example, the comparison unit estimates the user's emotion using voice analysis technology and uses a detailed comparison criterion when the user is relaxed. The comparison unit can also use a quick comparison criterion when the user is in a hurry. For example, the comparison unit estimates the user's emotion using biometric data (heart rate or electrodermal activity) and uses a quick comparison criterion when the user is in a hurry. This allows for more appropriate comparison by adjusting the comparison criterion according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using or without the generation AI. For example, the comparison unit may input user emotion data into the generation AI, which may then analyze the emotion data and adjust the comparison criteria.

[0077] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between the contracts during the comparison. The comparison unit, for example, analyzes the interrelationships between the contracts and performs a highly accurate comparison. For example, the comparison unit analyzes the interrelationships between the contracts using data mining technology and performs a highly accurate comparison. The comparison unit can also perform an appropriate comparison by taking into account the relevance of the contracts. For example, the comparison unit performs a comparison based on the interrelationships between the contracts, taking into account the relevance. Furthermore, the comparison unit can perform a detailed comparison based on the interrelationships between the contracts. For example, the comparison unit analyzes the interrelationships between the contracts and performs a detailed comparison. This enables a highly accurate comparison by taking into account the interrelationships between the contracts. Some or all of the above-mentioned processing in the comparison unit may be performed using or without the generation AI. For example, the comparison unit can input data on the interrelationships between the contracts into the generation AI, which can analyze the data to improve the accuracy of the comparison.

[0078] The comparison unit can make the comparison while taking into account the attribute information of the contract submitter. The comparison unit, for example, makes an appropriate comparison based on the attribute information of the contract submitter. For example, the comparison unit analyzes the attribute information of the contract submitter and makes an appropriate comparison. The comparison unit can also make a highly accurate comparison by taking into account the submitter's past history. For example, the comparison unit analyzes the contract submitter's past history and makes a highly accurate comparison. The comparison unit can also analyze the submitter's attribute information and make a detailed comparison. For example, the comparison unit analyzes the attribute information of the contract submitter and makes a detailed comparison. This enables a highly accurate comparison by taking into account the attribute information of the contract submitter. Some or all of the above-mentioned processing in the comparison unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the comparison unit can input attribute information data of the contract submitter to the generation AI, which can analyze the data and make the comparison.

[0079] The comparison unit can estimate the user's emotion and adjust the display order of the comparison results based on the estimated user emotion. For example, if the user is feeling stressed, the comparison unit prioritizes displaying important results. For example, the comparison unit estimates the user's emotion using facial expression recognition technology and prioritizes displaying important results when the user is feeling stressed. The comparison unit can also display detailed results when the user is relaxed. For example, the comparison unit estimates the user's emotion using voice analysis technology and displays detailed results when the user is relaxed. Furthermore, the comparison unit can quickly display results when the user is in a hurry. For example, the comparison unit estimates the user's emotion using biometric data (heart rate or electrodermal activity) and displays results quickly when the user is in a hurry. This allows the order in which the comparison results are displayed to be adjusted according to the user's emotion, thereby prioritizing display of important results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using or without the generation AI. For example, the comparison unit may input the user's emotion data into the generation AI, and the generation AI may analyze the emotion data and adjust the order in which the comparison results are displayed.

[0080] The comparison unit can take into account the geographical distribution of the contracts when making the comparison. The comparison unit, for example, makes an appropriate comparison based on the geographical distribution of the contracts. For example, the comparison unit analyzes the geographical distribution of the contracts and makes an appropriate comparison. The comparison unit can also make a highly accurate comparison by taking geographical factors into account. For example, the comparison unit makes a comparison based on the geographical distribution of the contracts and taking geographical factors into account. The comparison unit can also analyze the geographical distribution of the contracts and make a detailed comparison. For example, the comparison unit analyzes the geographical distribution of the contracts and makes a detailed comparison. This enables a highly accurate comparison by taking the geographical distribution of the contracts into account. Some or all of the above-mentioned processing in the comparison unit may be performed using or without the generation AI. For example, the comparison unit can input geographical distribution data of the contracts into the generation AI, which can analyze the data and make the comparison.

[0081] The comparison unit can improve the accuracy of the comparison by referring to the related literature of the contract during the comparison. The comparison unit, for example, performs a highly accurate comparison based on the related literature of the contract. For example, the comparison unit performs a highly accurate comparison by referring to the related literature of the contract. The comparison unit can also perform an appropriate comparison by referring to the related literature. For example, the comparison unit performs an appropriate comparison based on the related literature of the contract. Furthermore, the comparison unit can analyze the related literature of the contract and perform a detailed comparison. For example, the comparison unit analyzes the related literature of the contract and performs a detailed comparison. This enables a highly accurate comparison by referring to the related literature of the contract. Some or all of the above-mentioned processing in the comparison unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the comparison unit can input data on the related literature of the contract into the generation AI, which can analyze the data and perform the comparison.

[0082] The generation unit can estimate the user's emotions and determine the priority of contract proposals to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit prioritizes generating important contract proposals. For example, the generation unit estimates the user's emotions using facial expression recognition technology and prioritizes generating important contract proposals when the user is feeling stressed. The generation unit can also generate all contract proposals equally when the user is relaxed. For example, the generation unit estimates the user's emotions using voice analysis technology and generates all contract proposals equally when the user is relaxed. Furthermore, the generation unit can quickly generate contract proposals when the user is in a hurry. For example, the generation unit estimates the user's emotions using biometric data (heart rate or electrodermal activity) and quickly generates contract proposals when the user is in a hurry. This allows important contract proposals to be generated preferentially by prioritizing contract proposals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI, and the generation AI may analyze the emotion data and determine the priority of contract proposals.

[0083] The generation unit can improve the accuracy of generation by taking into account the interrelationships between contracts during generation. The generation unit, for example, analyzes the interrelationships between contracts and generates a highly accurate contract proposal. For example, the generation unit analyzes the interrelationships between contracts using data mining technology and generates a highly accurate contract proposal. The generation unit can also generate an appropriate contract proposal by taking into account the relevance of contracts. For example, the generation unit generates a contract proposal based on the interrelationships between contracts, taking into account the relevance. Furthermore, the generation unit can generate a detailed contract proposal based on the interrelationships between contracts. For example, the generation unit analyzes the interrelationships between contracts and generates a detailed contract proposal. In this way, a highly accurate contract proposal can be generated by taking into account the interrelationships between contracts. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input contract interrelation data into the generation AI, which can analyze the data and generate a contract proposal.

[0084] The generation unit can generate the contract while taking into account the attribute information of the contract submitter. The generation unit, for example, generates an appropriate contract proposal based on the attribute information of the contract submitter. For example, the generation unit analyzes the attribute information of the contract submitter and generates an appropriate contract proposal. The generation unit can also generate a highly accurate contract proposal by taking into account the submitter's past history. For example, the generation unit analyzes the contract submitter's past history and generates a highly accurate contract proposal. The generation unit can also analyze the submitter's attribute information and generate a detailed contract proposal. For example, the generation unit analyzes the attribute information of the contract submitter and generates a detailed contract proposal. In this way, a highly accurate contract proposal can be generated by taking into account the attribute information of the contract submitter. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input attribute information data of the contract submitter to the generation AI, which can analyze the data and generate a contract proposal.

[0085] The generation unit can estimate the user's emotions and adjust the display method of the contract proposal generated based on the estimated user emotions. For example, the generation unit provides a concise display method when the user is stressed. For example, the generation unit estimates the user's emotions using facial expression recognition technology and provides a concise display method when the user is stressed. The generation unit can also provide a detailed display method when the user is relaxed. For example, the generation unit estimates the user's emotions using voice analysis technology and provides a detailed display method when the user is relaxed. Furthermore, the generation unit can also provide a quick display method when the user is in a hurry. For example, the generation unit estimates the user's emotions using biometric data (heart rate or electrodermal activity) and provides a quick display method when the user is in a hurry. This allows for a more appropriate display by adjusting the display method of the contract proposal according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit may input user emotion data into the generation AI, and the generation AI may analyze the emotion data and adjust the display method of the contract proposal.

[0086] The generation unit can generate the contracts taking into account the geographical distribution of the contracts. The generation unit, for example, generates an appropriate contract proposal based on the geographical distribution of the contracts. For example, the generation unit analyzes the geographical distribution of the contracts and generates an appropriate contract proposal. The generation unit can also generate a highly accurate contract proposal by taking geographical factors into account. For example, the generation unit generates a contract proposal based on the geographical distribution of the contracts and taking geographical factors into account. The generation unit can also analyze the geographical distribution of the contracts and generate a detailed contract proposal. For example, the generation unit analyzes the geographical distribution of the contracts and generates a detailed contract proposal. In this way, a highly accurate contract proposal can be generated by taking the geographical distribution of the contracts into account. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input geographical distribution data of the contracts into the generation AI, which can analyze the data and generate a contract proposal.

[0087] The generation unit can improve the accuracy of generation by referring to related literature of the contract during generation. The generation unit, for example, generates a highly accurate contract proposal based on related literature of the contract. For example, the generation unit generates a highly accurate contract proposal by referring to related literature of the contract. The generation unit can also generate an appropriate contract proposal by referring to related literature. For example, the generation unit generates an appropriate contract proposal based on related literature of the contract. Furthermore, the generation unit can analyze related literature of the contract and generate a detailed contract proposal. For example, the generation unit analyzes related literature of the contract and generates a detailed contract proposal. In this way, a highly accurate contract proposal can be generated by referring to related literature of the contract. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input related literature data of the contract into the generation AI, which can analyze the data and generate a contract proposal. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, notification unit, comparison unit, and generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart device 14 and monitors the expiration date and important clauses of the contract. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies the timing of contract renewal or termination. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares new contract terms with existing contract terms. The generation unit is realized, for example, by the control unit 46A of the smart device 14 and generates a new contract proposal. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, notification unit, comparison unit, and generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart glasses 214 and monitors the expiration date and important clauses of the contract. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies the timing of contract renewal or termination. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares new contract terms with existing contract terms. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates a new contract proposal. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, notification unit, comparison unit, and generation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the headset type terminal 314 and monitors the expiration date and important clauses of the contract. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies the timing of contract renewal or termination. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares new contract terms with existing contract terms. The generation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and generates a new contract proposal. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, notification unit, comparison unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the robot 414 and monitors the expiration date and important clauses of the contract. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies the timing of contract renewal or termination. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares new contract terms with existing contract terms. The generation unit is realized, for example, by the control unit 46A of the robot 414 and generates a new contract proposal.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The contract management system can further include a risk assessment unit. The risk assessment unit can analyze the contents of a contract and assess potential risks. For example, the risk assessment unit can analyze the clauses of a contract and identify legal and financial risks. The risk assessment unit can also assess risks based on past contract history and market data. Furthermore, the risk assessment unit can provide the risk assessment results to the notification unit and prioritize notification of high-risk contracts. This allows the contract management system to assess the risks of contracts in advance and take appropriate measures against high-risk contracts.

[0090] The contract management system may further include a user feedback unit. The user feedback unit may collect user feedback regarding contract management and updates and use the collected feedback to improve the system. For example, the user feedback unit may conduct a survey regarding the contract update process to collect user opinions. The user feedback unit may also collect user complaints and requests regarding contract management and identify areas for improvement in the system. Furthermore, the user feedback unit may improve the system's functions based on the collected feedback and increase user satisfaction. This allows the contract management system to reflect user opinions and provide a system that is easier to use.

[0091] The contract management system may further include a contract performance monitoring unit. The contract performance monitoring unit may monitor the status of contract performance based on the terms of the contract. For example, the contract performance monitoring unit may analyze the terms of the contract and identify performance deadlines and performance conditions. The contract performance monitoring unit may also periodically check the status of contract performance and issue a notification if performance is delayed or if performance conditions are not met. Furthermore, the contract performance monitoring unit may generate reports on the performance status and visualize the contract performance status. This allows the contract management system to monitor the status of contract performance and prevent omissions in contract performance.

[0092] The contract management system may further include a contract version management unit. The version management unit can manage different versions of a contract and track change histories. For example, the version management unit automatically updates the version when a new version of a contract is created and records the change history. The version management unit can also compare past versions with the current version to identify changes. Furthermore, the version management unit allows users to access past versions and refer to them as needed. This allows the contract management system to efficiently manage contract versions and track change histories.

[0093] The contract management system may further include a contract template management unit. The template management unit manages contract templates, allowing users to easily create new contracts. For example, the template management unit may provide different types of contract templates and allow users to select an appropriate template. The template management unit may also provide a template customization function, allowing users to edit templates to suit specific conditions. Furthermore, the template management unit may manage the update history of templates and always provide the latest templates. In this way, the contract management system streamlines the contract creation process and allows users to easily create new contracts.

[0094] The contract management system may further include a review support unit that estimates the user's emotions and supports the review of the contract based on the estimated emotions. If the user is feeling stressed, the review support unit provides a review that briefly summarizes the important points. For example, the review support unit estimates the user's emotions using facial expression recognition technology, and provides a concise review if the user is feeling stressed. It may also provide a detailed review if the user is relaxed. For example, the review support unit estimates the user's emotions using voice analysis technology, and provides a detailed review if the user is relaxed. It may also provide a quick review if the user is in a hurry. For example, the review support unit estimates the user's emotions using biometric data (heart rate and electrodermal activity), and provides a quick review if the user is in a hurry. This enables more appropriate reviews by supporting the review of the contract based on the user's emotions.

[0095] The contract management system may further include an importance evaluation unit that estimates the user's emotions and evaluates the importance of contracts based on the estimated emotions. The importance evaluation unit prioritizes evaluation of important contracts when the user is feeling stressed. For example, the importance evaluation unit estimates the user's emotions using facial expression recognition technology, and prioritizes evaluation of important contracts when the user is feeling stressed. It is also possible to equally evaluate all contracts when the user is relaxed. For example, the importance evaluation unit estimates the user's emotions using voice analysis technology, and prioritizes evaluation of all contracts when the user is relaxed. It is also possible to quickly evaluate the importance of contracts when the user is in a hurry. For example, the importance evaluation unit estimates the user's emotions using biometric data (heart rate or electrodermal activity), and quickly evaluates the importance of contracts when the user is in a hurry. This allows the importance of contracts to be evaluated according to the user's emotions, thereby prioritizing evaluation of important contracts.

[0096] The contract management system may further include a reminder adjustment unit that estimates the user's emotions and adjusts contract reminders based on the estimated emotions. If the user is feeling stressed, the reminder adjustment unit reduces the frequency of reminders and only notifies the user at important times. For example, the reminder adjustment unit estimates the user's emotions using facial expression recognition technology and reduces the frequency of reminders when the user is feeling stressed. It may also increase the frequency of reminders and provide more detailed information when the user is relaxed. For example, the reminder adjustment unit estimates the user's emotions using voice analysis technology and increases the frequency of reminders when the user is relaxed. Furthermore, if the user is in a hurry, it may increase the frequency of reminders and provide information in real time. For example, the reminder adjustment unit estimates the user's emotions using biometric data (heart rate and electrodermal activity) and increases the frequency of reminders when the user is in a hurry. This allows for more appropriate notifications by adjusting reminders according to the user's emotions.

[0097] The contract management system may further include a search filtering unit that estimates the user's emotions and filters search results for contracts based on the estimated emotions. The search filtering unit prioritizes displaying important contracts when the user is feeling stressed. For example, the search filtering unit estimates the user's emotions using facial expression recognition technology, and prioritizes displaying important contracts when the user is feeling stressed. It is also possible to display all contracts equally when the user is relaxed. For example, the search filtering unit estimates the user's emotions using voice analysis technology, and prioritizes displaying all contracts when the user is relaxed. Furthermore, it is also possible to quickly display search results when the user is in a hurry. For example, the search filtering unit estimates the user's emotions using biometric data (heart rate and electrodermal activity), and quickly display search results when the user is in a hurry. This allows important contracts to be prioritized by filtering search results according to the user's emotions.

[0098] The contract management system may further include an alert adjustment unit that estimates the user's emotions and adjusts contract alerts based on the estimated emotions. The alert adjustment unit reduces the frequency of alerts when the user is feeling stressed and only notifies at important times. For example, the alert adjustment unit estimates the user's emotions using facial expression recognition technology and reduces the frequency of alerts when the user is feeling stressed. It may also increase the frequency of alerts and provide more detailed information when the user is relaxed. For example, the alert adjustment unit estimates the user's emotions using voice analysis technology and increases the frequency of alerts when the user is relaxed. It may also increase the frequency of alerts and provide information in real time when the user is in a hurry. For example, the alert adjustment unit estimates the user's emotions using biometric data (heart rate and electrodermal activity) and increases the frequency of alerts when the user is in a hurry. This allows for more appropriate notifications by adjusting alerts according to the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The monitoring unit monitors the deadlines or important clauses of the contract. For example, the monitoring unit analyzes the contract data and extracts deadlines and important clauses. Text analysis technology and machine learning algorithms can be used to analyze the content of the contract and identify important information. Step 2: The notification unit notifies the user when to renew or terminate the contract based on the information monitored by the monitoring unit. For example, the notification unit can notify the user by email, SMS, or in-app notification when the contract expiration date is approaching. Step 3: The comparison unit compares the new contract terms with the existing contract terms based on the information notified by the notification unit, for example, by using text matching techniques or numerical comparisons to compare the contract terms and identify changes. Step 4: The generator generates a new contract proposal based on the changes identified by the comparator, for example, using template-based generation techniques or generative AI to create a new contract proposal and assist in contract renewal negotiations.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as voice data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring department that monitors deadlines or important clauses in the contract; a notification unit that notifies the timing of contract renewal or termination based on the information monitored by the monitoring unit; a comparison unit that compares new contract terms with existing contract terms based on the information notified by the notification unit; a generating unit that generates a new contract proposal based on the changes identified by the comparing unit. A system characterized by:

2. The monitoring unit Parse contract data and extract deadlines or important clauses The system of claim 1 .

3. The notification unit Notify you when a contract is about to expire The system of claim 1 .

4. The comparison unit When new contract terms are presented, compare them with existing contract terms and identify any changes The system of claim 1 .

5. The generation unit Create a new contract proposal based on the changes The system of claim 1 .

6. The monitoring unit Estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. The system of claim 1 .

7. The monitoring unit Analyze past contract update history and select appropriate monitoring methods The system of claim 1 .

8. The monitoring unit When monitoring contracts, adjust the level of monitoring detail based on the type and importance of the contract The system of claim 1 .

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