system

Abstract

The system according to the embodiment aims to instantly determine unfavorable content even in a long contract. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives the text of a contract. The analysis unit analyzes the content of the contract received by the reception unit. The identification unit identifies disadvantageous clauses based on the content analyzed by the analysis unit. The provision unit warns the user of the disadvantageous content identified by the identification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to thoroughly read lengthy contracts and avoid overlooking any unfavorable content.

[0005] The system according to the embodiment aims to instantly determine unfavorable content even in a long contract. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives the text of the contract. The analysis unit analyzes the content of the contract received by the reception unit. The identification unit identifies disadvantageous clauses based on the content analyzed by the analysis unit. The provision unit warns the user of the disadvantageous content identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can instantly determine unfavorable content even in a long contract. [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) An AI system according to an embodiment of the present invention instantly determines whether a long contract contains unfavorable terms without requiring a thorough reading of the contract. The AI ​​system accepts and analyzes the contract text, identifies unfavorable clauses, and displays a warning to the user. For example, a user inputs the contract text by copying the entire contract and pasting it into the AI. The AI ​​system then analyzes the input contract content and identifies unfavorable clauses, such as contract termination clauses and damages clauses. The AI ​​system then displays a warning to the user about the identified unfavorable clauses. For example, the warning may read, "This contract contains a clause with strict termination conditions." This allows the user to instantly determine whether the contract contains unfavorable terms without having to read the contract thoroughly. The AI ​​system can also be applied to various contracts, such as business contracts and rental contracts. This allows the user to quickly understand the contents of the contract and avoid risks. The AI ​​system can instantly determine whether the contract contains unfavorable terms, saving the user the trouble of carefully reading the contract. For example, the AI ​​system can be applied to various contracts, such as business contracts and rental contracts. This allows users to quickly understand the contents of the contract and avoid risks.

[0029] The AI ​​system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives contract text. Examples of contract text include, but are not limited to, PDFs, Word documents, and scanned images. The reception unit inputs the contract text by, for example, having a user copy the entire contract text and paste it into the AI. The reception unit can also extract text from PDFs and images. The analysis unit analyzes the content of the contract received by the reception unit. The analysis can be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to, examples. The analysis unit, for example, analyzes each clause in the contract in detail to identify unfavorable or high-risk content. The identification unit identifies unfavorable clauses based on the content analyzed by the analysis unit. Examples of unfavorable clauses include, but are not limited to, contract termination clauses and damages clauses. The identification unit determines, for example, whether the contract contains clauses with strict contract termination clauses or damages clauses. The provision unit warns the user of the unfavorable content identified by the identification unit. The warning may be, for example, a pop-up notification or an email notification, but is not limited to these examples. The providing unit displays a warning such as, for example, "This contract contains a clause with strict termination conditions." In this way, the AI ​​system according to the embodiment analyzes the contents of the contract, identifies unfavorable clauses, and warns the user, allowing the user to instantly determine the unfavorable contents without having to carefully read the contract.

[0030] The AI ​​system further includes an extraction unit that extracts text from a PDF or an image. The extraction unit extracts text from a PDF or an image. For example, OCR technology or a text recognition algorithm may be used for the extraction, but is not limited to these examples. For example, the extraction unit reads a PDF contract and converts it into text data using OCR technology. The extraction unit can also extract text from scanned images. For example, the extracted text may be generated by analyzing the scanned image and performing character recognition. This allows the extraction of text from PDFs or images, making it possible to accept contracts regardless of their format.

[0031] Furthermore, the AI ​​system includes a reference unit that references past precedents and legal databases. The reference unit references past precedents and legal databases. For example, a specific legal database or collection of precedents is used for the reference, but is not limited to such examples. For example, when analyzing a contract, the reference unit references a database of past precedents to search for similar precedents. The reference unit can also reference a legal database to obtain related legal provisions. In this way, by referring to past precedents and legal databases, the accuracy of the analysis is improved.

[0032] Furthermore, the AI ​​system includes a notification unit that issues pop-up notifications and email notifications. The notification unit issues pop-up notifications and email notifications. Notifications include, but are not limited to, methods of displaying a warning to the user based on the analysis results of the contract. For example, if the contract contains an unfavorable clause, the notification unit displays a pop-up notification to warn the user. The notification unit can also issue email notifications and send the analysis results to the user. This allows for prompt warnings to be sent to the user by issuing pop-up notifications and email notifications.

[0033] The identification unit can identify unfavorable content such as contract termination conditions or liquidated damages clauses. The identification unit, for example, identifies contract termination conditions. For example, the identification unit analyzes the clauses of the contract and identifies clauses with strict contract termination conditions. The identification unit can also identify liquidated damages clauses. For example, the identification unit analyzes the clauses of the contract and identifies clauses with unfavorable amounts or conditions for liquidated damages. The identification unit can also identify other unfavorable content. For example, the identification unit analyzes the clauses of the contract and identifies content that poses a high risk to the user. In this way, by identifying unfavorable content such as contract termination conditions or liquidated damages clauses, it is possible to quickly grasp important risks for the user.

[0034] The reception unit can analyze the user's past contract submission history and select the optimal reception method. The reception unit, for example, automatically selects a reception method that the user has frequently used in the past. For example, the reception unit analyzes the user's past submission history and suggests the most efficient reception method. The reception unit can also select the optimal reception method for a specific time period based on the user's past submission history. For example, the reception unit analyzes the user's submission history in chronological order and identifies the optimal reception method for a specific time period. In this way, the optimal reception method for the user can be provided by analyzing the past submission history.

[0035] The reception unit can filter contracts based on the user's current project or area of ​​interest when receiving the contracts. For example, the reception unit preferentially receives contracts related to the user's current project. For example, the reception unit analyzes the user's project progress status and identifies related contracts. The reception unit can also filter and receive related contracts based on the user's area of ​​interest. For example, the reception unit analyzes the user's area of ​​interest and identifies related contracts. The reception unit can also select and receive the most appropriate contract depending on the user's project progress status. For example, the reception unit analyzes the user's project progress status chronologically and identifies the most appropriate contract. In this way, by filtering based on the user's current project or area of ​​interest, it is possible to preferentially receive highly relevant contracts.

[0036] When accepting a contract, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the contract using voice recognition technology. For example, the acceptance unit analyzes the user's voice and converts it into text data. Furthermore, if the user selects text input, the acceptance unit can also accept the contract using text analysis technology. For example, the acceptance unit analyzes the user's text data and identifies the content of the contract. Furthermore, if the user selects image input, the acceptance unit can also accept the contract using image recognition technology. For example, the acceptance unit analyzes the user's image data and converts it into text data. This allows the optimal acceptance means to be selected depending on the user's input method, thereby improving user convenience.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the contract. For example, the analysis unit performs a detailed analysis on a contract with high importance. For example, the analysis unit analyzes the contents of the contract and analyzes clauses with high importance in detail. The analysis unit can also perform a simplified analysis on a contract with low importance. For example, the analysis unit analyzes the contents of the contract and analyzes clauses with low importance in a simplified manner. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the contract. For example, the analysis unit evaluates the importance of the contract and adjusts the level of detail of the analysis. In this way, adjusting the level of detail of the analysis based on the importance of the contract enables efficient analysis.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the contract. For example, the analysis unit applies a business-specific analysis algorithm to a business contract. For example, the analysis unit analyzes the contents of a business contract and identifies business-specific risks. The analysis unit can also apply a rental-specific analysis algorithm to a rental contract. For example, the analysis unit analyzes the contents of a rental contract and identifies rental-specific risks. The analysis unit can also select and apply the optimal analysis algorithm depending on the category of the contract. For example, the analysis unit classifies the category of the contract and applies the optimal analysis algorithm. In this way, the accuracy of the analysis is improved by applying the optimal analysis algorithm depending on the category of the contract.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and identifies areas for improvement in the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit refers to the user's past analysis results and performs analysis based on similar analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. For example, the analysis unit analyzes the user's past analysis results in chronological order and identifies areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0040] The identification unit can improve the accuracy of identification by taking into account the interrelationships between contracts during identification. The identification unit, for example, analyzes the interrelationships between contracts and identifies related clauses. For example, the identification unit analyzes the contents of contracts and identifies related clauses. The identification unit can also improve the accuracy of identification by taking into account the interrelationships between contracts. For example, the identification unit evaluates the interrelationships between contracts and improves the accuracy of identification. The identification unit can also identify specific areas for improvement based on the interrelationships between contracts. For example, the identification unit analyzes the interrelationships between contracts in chronological order and identifies specific areas for improvement. In this way, the accuracy of identification is improved by taking into account the interrelationships between contracts.

[0041] The identification unit can perform identification by taking into account attribute information of the contract submitter. The identification unit improves the accuracy of identification, for example, based on the attribute information of the contract submitter. For example, the identification unit analyzes the occupation or position of the contract submitter to improve the accuracy of identification. The identification unit can also adjust the identification criteria by taking into account the attribute information of the contract submitter. For example, the identification unit analyzes the contract submitter's past contract history and adjusts the identification criteria. The identification unit can also identify specific areas for improvement by referring to the attribute information of the contract submitter. For example, the identification unit analyzes the attribute information of the contract submitter in chronological order to identify specific areas for improvement. In this way, the accuracy of identification is improved by taking into account the attribute information of the contract submitter.

[0042] At the time of identification, the identification unit can assign a specific weight based on the frequency of contract submission. For example, the identification unit assigns a specific weight to contracts that are submitted frequently. For example, the identification unit analyzes the frequency of contract submission and assigns a weight. The identification unit can also assign a specific weight to contracts that are submitted infrequently. For example, the identification unit analyzes the frequency of contract submission and assigns a weight. The identification unit can also dynamically adjust the specific weight according to the frequency of contract submission. For example, the identification unit analyzes the frequency of contract submission over time and adjusts the weight. In this way, by assigning a specific weight based on the frequency of contract submission, the accuracy of identification is improved.

[0043] The providing unit can optimize the current warning by referring to past warning data when providing the warning. The providing unit optimizes the current warning, for example, based on past warning data. For example, the providing unit analyzes past warning data and identifies areas for improvement in the current warning. The providing unit can also improve the accuracy of the warning by referring to past warning data. For example, the providing unit refers to past warning data and issues a warning based on similar warning data. The providing unit can also analyze past warning data and identify areas for improvement in the warning. For example, the providing unit analyzes past warning data in chronological order and identifies areas for improvement in the warning. In this way, the accuracy of the current warning is improved by referring to past warning data.

[0044] The providing unit can apply different warning display methods to different categories of contracts when providing the warnings. For example, the providing unit applies a business-specific warning display method to a business contract. For example, the providing unit analyzes the contents of a business contract and warns of business-specific risks. The providing unit can also apply a rental-specific warning display method to a rental contract. For example, the providing unit analyzes the contents of a rental contract and warns of rental-specific risks. The providing unit can also select and apply the optimal warning display method according to the category of the contract. For example, the providing unit classifies the category of the contract and applies the optimal warning display method. This improves the accuracy of the warning by applying the optimal warning display method according to the category of the contract.

[0045] The providing unit can display a warning by taking into account the attribute information of the contract submitter when providing the data. The providing unit improves the accuracy of the warning, for example, based on the attribute information of the contract submitter. For example, the providing unit analyzes the occupation or position of the contract submitter to improve the accuracy of the warning. The providing unit can also adjust the criteria for the warning by taking into account the attribute information of the contract submitter. For example, the providing unit analyzes the contract submitter's past contract history to adjust the criteria for the warning. The providing unit can also identify areas for improvement in the warning by referring to the attribute information of the contract submitter. For example, the providing unit analyzes the attribute information of the contract submitter in chronological order to identify areas for improvement in the warning. In this way, the accuracy of the warning is improved by taking into account the attribute information of the contract submitter.

[0046] At the time of provision, the providing unit can analyze changes in warnings based on the time of submission of the contract. For example, the providing unit analyzes changes in warnings for a recently submitted contract. For example, the providing unit analyzes the time of submission of the contract and analyzes changes in warnings. The providing unit can also analyze changes in warnings for an older submitted contract. For example, the providing unit analyzes the time of submission of the contract and analyzes changes in warnings. The providing unit can also dynamically analyze changes in warnings according to the time of submission of the contract. For example, the providing unit analyzes the time of submission of the contract in chronological order and analyzes changes in warnings. In this way, the accuracy of warnings is improved by analyzing changes in warnings based on the time of submission of the contract.

[0047] The provision unit can display a warning by referring to market data related to the contract when providing the warning. For example, the provision unit improves the accuracy of the warning based on the market data related to the contract. For example, the provision unit adjusts the criteria for the warning by referring to the market data related to the contract. The provision unit can also analyze the market data related to the contract to identify areas for improvement in the warning. For example, the provision unit analyzes the market data related to the contract in chronological order to identify areas for improvement in the warning. In this way, the accuracy of the warning is improved by referring to the market data related to the contract.

[0048] The provision unit can display a warning taking into account the technical maturity of the contract when providing the contract. For example, the provision unit analyzes the technical maturity of the contract and improves the accuracy of the warning. For example, the provision unit evaluates the technical maturity of the contract and adjusts the criteria for the warning. The provision unit can also identify areas for improvement in the warning based on the technical maturity of the contract. For example, the provision unit analyzes the technical maturity of the contract over time and identifies areas for improvement in the warning. In this way, the accuracy of the warning is improved by taking into account the technical maturity of the contract.

[0049] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between contracts during extraction. The extraction unit, for example, analyzes the interrelationships between contracts and extracts related text. For example, the extraction unit analyzes the contents of the contracts and identifies related text. The extraction unit can also improve the accuracy of extraction by taking into account the interrelationships between contracts. For example, the extraction unit evaluates the interrelationships between contracts and improves the accuracy of extraction. The extraction unit can also identify areas for improvement in extraction based on the interrelationships between contracts. For example, the extraction unit analyzes the interrelationships between contracts in chronological order and identifies areas for improvement in extraction. In this way, the accuracy of extraction is improved by taking into account the interrelationships between contracts.

[0050] The extraction unit can perform extraction while taking into account the attribute information of the contract submitter. The extraction unit improves the accuracy of extraction based on, for example, the attribute information of the contract submitter. For example, the extraction unit analyzes the occupation or position of the contract submitter to improve the accuracy of extraction. The extraction unit can also adjust the extraction criteria by taking into account the attribute information of the contract submitter. For example, the extraction unit analyzes the past contract history of the contract submitter and adjusts the extraction criteria. The extraction unit can also identify areas for improvement in the extraction by referring to the attribute information of the contract submitter. For example, the extraction unit analyzes the attribute information of the contract submitter in chronological order to identify areas for improvement in the extraction. In this way, the accuracy of extraction is improved by taking into account the attribute information of the contract submitter.

[0051] The extraction unit can perform extraction while taking into account the geographical distribution of contracts. For example, the extraction unit analyzes the geographical distribution of contracts and extracts region-specific text. For example, the extraction unit analyzes the content of the contract and identifies region-specific text. The extraction unit can also improve the accuracy of extraction by taking into account the geographical distribution of contracts. For example, the extraction unit evaluates the geographical distribution of contracts and improves the accuracy of extraction. The extraction unit can also identify areas for improvement in extraction based on the geographical distribution of contracts. For example, the extraction unit analyzes the geographical distribution of contracts over time and identifies areas for improvement in extraction. In this way, the accuracy of extraction is improved by taking the geographical distribution of contracts into account.

[0052] The extraction unit can improve the accuracy of extraction by referring to related literature of the contract during extraction. The extraction unit, for example, improves the accuracy of extraction by referring to related literature of the contract. For example, the extraction unit analyzes the contents of the contract by referring to related academic papers and technical reports. The extraction unit can also identify areas for improvement in extraction based on the related literature of the contract. For example, the extraction unit analyzes related literature and improves the accuracy of extraction. The extraction unit can also improve the accuracy of extraction by referring to related literature of the contract. For example, the extraction unit identifies areas for improvement in extraction based on the related literature. In this way, the accuracy of extraction is improved by referring to related literature of the contract.

[0053] The reference unit can optimize the reference algorithm by referring to past reference data during reference. The reference unit, for example, optimizes the reference algorithm based on past reference data. For example, the reference unit analyzes past reference data and identifies areas for improvement in the reference algorithm. The reference unit can also improve the accuracy of the reference by referring to past reference data. For example, the reference unit refers to past reference data and performs reference based on similar reference data. The reference unit can also analyze past reference data and identify areas for improvement in the reference. For example, the reference unit analyzes past reference data in chronological order and identifies areas for improvement in the reference. As a result, the accuracy of the reference algorithm is improved by referring to past reference data.

[0054] The reference unit can update the reference data to reflect user feedback during reference. The reference unit, for example, updates the reference data based on user feedback. For example, the reference unit collects user evaluation comments and identifies areas for improvement in the reference data. The reference unit can also improve the accuracy of reference by reflecting user feedback. For example, the reference unit analyzes user feedback and identifies areas for improvement in the reference data. The reference unit can also update the reference data based on user feedback. For example, the reference unit analyzes user feedback in chronological order and identifies areas for improvement in the reference data. In this way, the accuracy of the reference data is improved by reflecting user feedback.

[0055] When referencing, the reference unit can weight the reference data based on the time of submission of the contract. For example, the reference unit preferentially references data of contracts that were submitted more recently. For example, the reference unit analyzes the time of submission of the contract and preferentially references newer data. The reference unit can also postpone data of contracts that were submitted earlier. For example, the reference unit analyzes the time of submission of the contract and postpones older data. The reference unit can also dynamically adjust the weighting of the reference data according to the time of submission of the contract. For example, the reference unit analyzes the time of submission of the contract in chronological order and adjusts the weighting. In this way, by weighting the reference data based on the time of submission of the contract, the latest data can be preferentially referenced.

[0056] During reference, the reference unit can integrate information from different data sources to enrich the reference data. For example, the reference unit integrates information from different data sources to enrich the reference data. For example, the reference unit integrates information from an internal database or an external API to enrich the reference data. The reference unit can also improve the accuracy of the reference data by referencing different data sources. For example, the reference unit analyzes different data sources to identify areas for improvement in the reference data. The reference unit can also enrich the reference data based on the different data sources. For example, the reference unit analyzes different data sources in chronological order to identify areas for improvement in the reference data. In this way, the accuracy of the reference data is improved by integrating information from different data sources.

[0057] When displaying a notification, the notification unit can select an optimal display method by referring to the user's past operation history. The notification unit selects an optimal notification display method based on, for example, the user's past operation history. For example, the notification unit analyzes the user's operation history and identifies an optimal display method. The notification unit can also improve the accuracy of notifications by referring to the user's past operation history. For example, the notification unit refers to the user's operation history and provides notifications based on similar operation histories. The notification unit can also analyze the user's past operation history and identify areas for improvement in notifications. For example, the notification unit analyzes the user's operation history in chronological order and identifies areas for improvement in notifications. In this way, an optimal notification display method can be provided by referring to the user's past operation history.

[0058] The notification unit can customize the display content according to the user's current task when displaying a notification. For example, the notification unit prioritizes displaying notifications related to the task the user is currently performing. For example, the notification unit analyzes the user's task progress and identifies relevant notifications. The notification unit can also customize the display content of notifications according to the user's current task. For example, the notification unit analyzes the user's task progress and identifies the most appropriate notification. The notification unit can also display the most appropriate notification according to the user's task progress. For example, the notification unit analyzes the user's task progress in chronological order and identifies the most appropriate notification. In this way, by customizing the display content according to the user's current task, it is possible to provide highly relevant notifications.

[0059] When displaying a notification, the notification unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the notification unit provides a notification display method that matches the screen size. For example, the notification unit analyzes the user's device information and identifies the optimal display method for the smartphone. Furthermore, if the user is using a tablet, the notification unit can also provide a notification display method that is optimized for a large screen. For example, the notification unit analyzes the user's device information and identifies the optimal display method for the tablet. Furthermore, if the user is using a smartwatch, the notification unit can also provide a simple and highly visible notification display method. For example, the notification unit analyzes the user's device information and identifies the optimal display method for the smartwatch. In this way, the optimal notification display method can be provided by taking into account the user's device information.

[0060] When displaying a notification, the notification unit can make the display content multilingual according to the user's language setting. The notification unit automatically sets the notification language based on, for example, the language setting of the user's device. For example, the notification unit analyzes the user's language setting and displays the notification in the most appropriate language. The notification unit can also provide a language switching function when the user uses multiple languages. For example, the notification unit analyzes the user's language setting and provides the language switching function. The notification unit can also provide notifications in a specific language when the user selects that language. For example, the notification unit analyzes the user's language setting and displays the notification in the selected language. This makes it possible to provide notifications that are easy for the user to understand by making the display content multilingual according to the user's language setting.

[0061] When displaying a notification, the notification unit can analyze the user's social media activity and provide related information. The notification unit, for example, provides information about places where the user has checked in on social media. For example, the notification unit analyzes the user's social media activity and identifies information related to the checked-in places. The notification unit can also analyze the content of the user's posts on social media and provide information about related tourist spots and stores. For example, the notification unit analyzes the content of the user's posts and identifies related information. The notification unit can also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the notification unit analyzes the activities of the user's friends and identifies related information. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The reception unit can analyze the user's past contract submission history and select the optimal reception method. For example, it automatically selects the reception method that the user has frequently used in the past. For example, the reception unit analyzes the user's past submission history and suggests the most efficient reception method. The reception unit can also select the optimal reception method for a specific time period based on the user's past submission history. For example, the reception unit analyzes the user's submission history in chronological order and identifies the optimal reception method for a specific time period. In this way, it is possible to provide the optimal reception method for the user by analyzing the past submission history.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the contract. For example, the analysis unit performs a detailed analysis on a contract with high importance. For example, the analysis unit analyzes the contents of the contract and analyzes clauses with high importance in detail. The analysis unit can also perform a simplified analysis on a contract with low importance. For example, the analysis unit analyzes the contents of the contract and analyzes clauses with low importance in a simplified manner. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the contract. For example, the analysis unit evaluates the importance of the contract and adjusts the level of detail of the analysis. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the contract.

[0065] The identification unit can improve the accuracy of identification by taking into account the interrelationships between contracts during identification. For example, the identification unit analyzes the interrelationships between contracts and identifies related clauses. For example, the identification unit analyzes the contents of contracts and identifies related clauses. The identification unit can also improve the accuracy of identification by taking into account the interrelationships between contracts. For example, the identification unit evaluates the interrelationships between contracts and improves the accuracy of identification. The identification unit can also identify specific areas for improvement based on the interrelationships between contracts. For example, the identification unit analyzes the interrelationships between contracts in chronological order and identifies specific areas for improvement. In this way, the accuracy of identification is improved by taking into account the interrelationships between contracts.

[0066] When providing the data, the providing unit can apply different warning display methods to different categories of contracts. For example, the providing unit applies a business-specific warning display method to a business contract. For example, the providing unit analyzes the contents of a business contract and warns of business-specific risks. The providing unit can also apply a rental-specific warning display method to a rental contract. For example, the providing unit analyzes the contents of a rental contract and warns of rental-specific risks. The providing unit can also select and apply the optimal warning display method according to the category of the contract. For example, the providing unit classifies the category of the contract and applies the optimal warning display method. This improves the accuracy of warnings by applying the optimal warning display method according to the category of the contract.

[0067] During reference, the reference unit can integrate information from different data sources to enrich the reference data. For example, the reference unit integrates information from different data sources to enrich the reference data. For example, the reference unit integrates information from an internal database or an external API to enrich the reference data. The reference unit can also improve the accuracy of the reference data by referencing different data sources. For example, the reference unit analyzes different data sources to identify areas for improvement in the reference data. The reference unit can also enrich the reference data based on the different data sources. For example, the reference unit analyzes different data sources in chronological order to identify areas for improvement in the reference data. In this way, the accuracy of the reference data is improved by integrating information from different data sources.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit accepts the contract text. The contract text can be in a PDF, Word document, scanned image, etc. The user inputs the contract text by copying the entire contract text and pasting it into the AI. It can also extract text from PDFs and images. Step 2: The analysis unit analyzes the contents of the contract received by the reception unit. The analysis is performed using natural language processing technology and machine learning algorithms. The analysis unit analyzes each clause of the contract in detail to identify any unfavorable or high-risk content. Step 3: The identification unit identifies unfavorable clauses based on the content analyzed by the analysis unit. Unfavorable clauses include contract termination clauses and damages clauses. The identification unit determines whether the contract contains clauses with strict contract termination clauses or damages clauses. Step 4: The provider warns the user of the unfavorable content identified by the identifyr. The warning is given by a pop-up notification, email notification, or other method. The provider displays a warning such as "This contract contains strict terms for termination."

[0070] (Example 2) An AI system according to an embodiment of the present invention instantly determines whether a long contract contains unfavorable terms without requiring a thorough reading of the contract. The AI ​​system accepts and analyzes the contract text, identifies unfavorable clauses, and displays a warning to the user. For example, a user inputs the contract text by copying the entire contract and pasting it into the AI. The AI ​​system then analyzes the input contract content and identifies unfavorable clauses, such as contract termination clauses and damages clauses. The AI ​​system then displays a warning to the user about the identified unfavorable clauses. For example, the warning may read, "This contract contains a clause with strict termination conditions." This allows the user to instantly determine whether the contract contains unfavorable terms without having to read the contract thoroughly. The AI ​​system can also be applied to various contracts, such as business contracts and rental contracts. This allows the user to quickly understand the contents of the contract and avoid risks. The AI ​​system can instantly determine whether the contract contains unfavorable terms, saving the user the trouble of carefully reading the contract. For example, the AI ​​system can be applied to various contracts, such as business contracts and rental contracts. This allows users to quickly understand the contents of the contract and avoid risks.

[0071] The AI ​​system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives contract text. Examples of contract text include, but are not limited to, PDFs, Word documents, and scanned images. The reception unit inputs the contract text by, for example, having a user copy the entire contract text and paste it into the AI. The reception unit can also extract text from PDFs and images. The analysis unit analyzes the content of the contract received by the reception unit. The analysis can be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to, examples. The analysis unit, for example, analyzes each clause in the contract in detail to identify unfavorable or high-risk content. The identification unit identifies unfavorable clauses based on the content analyzed by the analysis unit. Examples of unfavorable clauses include, but are not limited to, contract termination clauses and damages clauses. The identification unit determines, for example, whether the contract contains clauses with strict contract termination clauses or damages clauses. The provision unit warns the user of the unfavorable content identified by the identification unit. The warning may be, for example, a pop-up notification or an email notification, but is not limited to these examples. The providing unit displays a warning such as, for example, "This contract contains a clause with strict termination conditions." In this way, the AI ​​system according to the embodiment analyzes the contents of the contract, identifies unfavorable clauses, and warns the user, allowing the user to instantly determine the unfavorable contents without having to carefully read the contract.

[0072] The AI ​​system further includes an extraction unit that extracts text from a PDF or an image. The extraction unit extracts text from a PDF or an image. For example, OCR technology or a text recognition algorithm may be used for the extraction, but is not limited to these examples. For example, the extraction unit reads a PDF contract and converts it into text data using OCR technology. The extraction unit can also extract text from scanned images. For example, the extracted text may be generated by analyzing the scanned image and performing character recognition. This allows the extraction of text from PDFs or images, making it possible to accept contracts regardless of their format.

[0073] Furthermore, the AI ​​system includes a reference unit that references past precedents and legal databases. The reference unit references past precedents and legal databases. For example, a specific legal database or collection of precedents is used for the reference, but is not limited to such examples. For example, when analyzing a contract, the reference unit references a database of past precedents to search for similar precedents. The reference unit can also reference a legal database to obtain related legal provisions. In this way, by referring to past precedents and legal databases, the accuracy of the analysis is improved.

[0074] Furthermore, the AI ​​system includes a notification unit that issues pop-up notifications and email notifications. The notification unit issues pop-up notifications and email notifications. Notifications include, but are not limited to, methods of displaying a warning to the user based on the analysis results of the contract. For example, if the contract contains an unfavorable clause, the notification unit displays a pop-up notification to warn the user. The notification unit can also issue email notifications and send the analysis results to the user. This allows for prompt warnings to be sent to the user by issuing pop-up notifications and email notifications.

[0075] The identification unit can identify unfavorable content such as contract termination conditions or liquidated damages clauses. The identification unit, for example, identifies contract termination conditions. For example, the identification unit analyzes the clauses of the contract and identifies clauses with strict contract termination conditions. The identification unit can also identify liquidated damages clauses. For example, the identification unit analyzes the clauses of the contract and identifies clauses with unfavorable amounts or conditions for liquidated damages. The identification unit can also identify other unfavorable content. For example, the identification unit analyzes the clauses of the contract and identifies content that poses a high risk to the user. In this way, by identifying unfavorable content such as contract termination conditions or liquidated damages clauses, it is possible to quickly grasp important risks for the user.

[0076] The reception unit can estimate the user's emotions and adjust the timing of contract acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can accept the contract at a time when the user is able to relax. For example, the reception unit can analyze the user's facial expression to determine whether the user is feeling stressed. Furthermore, if the user is in a hurry, the reception unit can immediately accept the contract and quickly start analysis. For example, the reception unit can analyze the user's voice to determine whether the user is in a hurry. Furthermore, if the user is relaxed, the reception unit can accept the contract using an interface that includes detailed explanations. For example, the reception unit can analyze the user's biometric data to determine whether the user is relaxed. This allows the timing of acceptance to be adjusted according to the user's emotions, thereby reducing the user's stress and allowing the contract to be accepted at the optimal time. Emotion estimation is realized using an emotion estimation function, such as 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.

[0077] The reception unit can analyze the user's past contract submission history and select the optimal reception method. The reception unit, for example, automatically selects a reception method that the user has frequently used in the past. For example, the reception unit analyzes the user's past submission history and suggests the most efficient reception method. The reception unit can also select the optimal reception method for a specific time period based on the user's past submission history. For example, the reception unit analyzes the user's submission history in chronological order and identifies the optimal reception method for a specific time period. In this way, the optimal reception method for the user can be provided by analyzing the past submission history.

[0078] The reception unit can filter contracts based on the user's current project or area of ​​interest when receiving the contracts. For example, the reception unit preferentially receives contracts related to the user's current project. For example, the reception unit analyzes the user's project progress status and identifies related contracts. The reception unit can also filter and receive related contracts based on the user's area of ​​interest. For example, the reception unit analyzes the user's area of ​​interest and identifies related contracts. The reception unit can also select and receive the most appropriate contract depending on the user's project progress status. For example, the reception unit analyzes the user's project progress status chronologically and identifies the most appropriate contract. In this way, by filtering based on the user's current project or area of ​​interest, it is possible to preferentially receive highly relevant contracts.

[0079] When accepting a contract, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the contract using voice recognition technology. For example, the acceptance unit analyzes the user's voice and converts it into text data. Furthermore, if the user selects text input, the acceptance unit can also accept the contract using text analysis technology. For example, the acceptance unit analyzes the user's text data and identifies the content of the contract. Furthermore, if the user selects image input, the acceptance unit can also accept the contract using image recognition technology. For example, the acceptance unit analyzes the user's image data and converts it into text data. This allows the optimal acceptance means to be selected depending on the user's input method, thereby improving user convenience.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, the analysis unit analyzes the user's facial expression to determine whether the user is nervous. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit analyzes the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a more concise analysis result. For example, the analysis unit analyzes the user's biometric data to determine whether the user is in a hurry. This allows the analysis result to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the contract. For example, the analysis unit performs a detailed analysis on a contract with high importance. For example, the analysis unit analyzes the contents of the contract and analyzes clauses with high importance in detail. The analysis unit can also perform a simplified analysis on a contract with low importance. For example, the analysis unit analyzes the contents of the contract and analyzes clauses with low importance in a simplified manner. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the contract. For example, the analysis unit evaluates the importance of the contract and adjusts the level of detail of the analysis. In this way, adjusting the level of detail of the analysis based on the importance of the contract enables efficient analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the contract. For example, the analysis unit applies a business-specific analysis algorithm to a business contract. For example, the analysis unit analyzes the contents of a business contract and identifies business-specific risks. The analysis unit can also apply a rental-specific analysis algorithm to a rental contract. For example, the analysis unit analyzes the contents of a rental contract and identifies rental-specific risks. The analysis unit can also select and apply the optimal analysis algorithm depending on the category of the contract. For example, the analysis unit classifies the category of the contract and applies the optimal analysis algorithm. In this way, the accuracy of the analysis is improved by applying the optimal analysis algorithm depending on the category of the contract.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and identifies areas for improvement in the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit refers to the user's past analysis results and performs analysis based on similar analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. For example, the analysis unit analyzes the user's past analysis results in chronological order and identifies areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0084] The identification unit can estimate the user's emotions and adjust the specific criteria based on the estimated user emotions. For example, if the user is nervous, the identification unit can identify unfavorable content using strict criteria. For example, the identification unit can analyze the user's facial expressions to determine whether the user is nervous. Also, if the user is relaxed, the identification unit can identify unfavorable content using normal criteria. For example, the identification unit can analyze the user's voice to determine whether the user is relaxed. Also, if the user is in a hurry, the identification unit can apply criteria that can quickly identify the user. For example, the identification unit can analyze the user's biometric data to determine whether the user is in a hurry. This allows for more appropriate identification of unfavorable content by adjusting the specific criteria according to the user's emotions. The 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.

[0085] The identification unit can improve the accuracy of identification by taking into account the interrelationships between contracts during identification. The identification unit, for example, analyzes the interrelationships between contracts and identifies related clauses. For example, the identification unit analyzes the contents of contracts and identifies related clauses. The identification unit can also improve the accuracy of identification by taking into account the interrelationships between contracts. For example, the identification unit evaluates the interrelationships between contracts and improves the accuracy of identification. The identification unit can also identify specific areas for improvement based on the interrelationships between contracts. For example, the identification unit analyzes the interrelationships between contracts in chronological order and identifies specific areas for improvement. In this way, the accuracy of identification is improved by taking into account the interrelationships between contracts.

[0086] The identification unit can perform identification by taking into account attribute information of the contract submitter. The identification unit improves the accuracy of identification, for example, based on the attribute information of the contract submitter. For example, the identification unit analyzes the occupation or position of the contract submitter to improve the accuracy of identification. The identification unit can also adjust the identification criteria by taking into account the attribute information of the contract submitter. For example, the identification unit analyzes the contract submitter's past contract history and adjusts the identification criteria. The identification unit can also identify specific areas for improvement by referring to the attribute information of the contract submitter. For example, the identification unit analyzes the attribute information of the contract submitter in chronological order to identify specific areas for improvement. In this way, the accuracy of identification is improved by taking into account the attribute information of the contract submitter.

[0087] At the time of identification, the identification unit can assign a specific weight based on the frequency of contract submission. For example, the identification unit assigns a specific weight to contracts that are submitted frequently. For example, the identification unit analyzes the frequency of contract submission and assigns a weight. The identification unit can also assign a specific weight to contracts that are submitted infrequently. For example, the identification unit analyzes the frequency of contract submission and assigns a weight. The identification unit can also dynamically adjust the specific weight according to the frequency of contract submission. For example, the identification unit analyzes the frequency of contract submission over time and adjusts the weight. In this way, by assigning a specific weight based on the frequency of contract submission, the accuracy of identification is improved.

[0088] The providing unit can estimate the user's emotions and adjust the warning display method based on the estimated user emotions. For example, if the user is nervous, the providing unit displays a simple, highly visible warning. For example, the providing unit analyzes the user's facial expressions to determine whether the user is nervous. The providing unit can also display a detailed warning if the user is relaxed. For example, the providing unit analyzes the user's voice to determine whether the user is relaxed. The providing unit can also display a warning that focuses on the main points if the user is in a hurry. For example, the providing unit analyzes the user's biometric data to determine whether the user is in a hurry. This allows the warning display method to be adjusted according to the user's emotions, thereby providing a warning that is easy for the user to understand. 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.

[0089] The providing unit can optimize the current warning by referring to past warning data when providing the warning. The providing unit optimizes the current warning, for example, based on past warning data. For example, the providing unit analyzes past warning data and identifies areas for improvement in the current warning. The providing unit can also improve the accuracy of the warning by referring to past warning data. For example, the providing unit refers to past warning data and issues a warning based on similar warning data. The providing unit can also analyze past warning data and identify areas for improvement in the warning. For example, the providing unit analyzes past warning data in chronological order and identifies areas for improvement in the warning. In this way, the accuracy of the current warning is improved by referring to past warning data.

[0090] The providing unit can apply different warning display methods to different categories of contracts when providing the warnings. For example, the providing unit applies a business-specific warning display method to a business contract. For example, the providing unit analyzes the contents of a business contract and warns of business-specific risks. The providing unit can also apply a rental-specific warning display method to a rental contract. For example, the providing unit analyzes the contents of a rental contract and warns of rental-specific risks. The providing unit can also select and apply the optimal warning display method according to the category of the contract. For example, the providing unit classifies the category of the contract and applies the optimal warning display method. This improves the accuracy of the warning by applying the optimal warning display method according to the category of the contract.

[0091] The providing unit can display a warning by taking into account the attribute information of the contract submitter when providing the data. The providing unit improves the accuracy of the warning, for example, based on the attribute information of the contract submitter. For example, the providing unit analyzes the occupation or position of the contract submitter to improve the accuracy of the warning. The providing unit can also adjust the criteria for the warning by taking into account the attribute information of the contract submitter. For example, the providing unit analyzes the contract submitter's past contract history to adjust the criteria for the warning. The providing unit can also identify areas for improvement in the warning by referring to the attribute information of the contract submitter. For example, the providing unit analyzes the attribute information of the contract submitter in chronological order to identify areas for improvement in the warning. In this way, the accuracy of the warning is improved by taking into account the attribute information of the contract submitter.

[0092] The providing unit can estimate the user's emotions and adjust the importance of warnings based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize displaying warnings with a higher importance. For example, the providing unit can analyze the user's facial expressions to determine whether the user is nervous. The providing unit can also display detailed warnings if the user is relaxed. For example, the providing unit can analyze the user's voice to determine whether the user is relaxed. The providing unit can also prioritize displaying warnings that focus on the main points if the user is in a hurry. For example, the providing unit can analyze the user's biometric data to determine whether the user is in a hurry. This allows the importance of warnings to be adjusted according to the user's emotions, thereby prioritizing the display of important warnings. 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.

[0093] At the time of provision, the providing unit can analyze changes in warnings based on the time of submission of the contract. For example, the providing unit analyzes changes in warnings for a recently submitted contract. For example, the providing unit analyzes the time of submission of the contract and analyzes changes in warnings. The providing unit can also analyze changes in warnings for an older submitted contract. For example, the providing unit analyzes the time of submission of the contract and analyzes changes in warnings. The providing unit can also dynamically analyze changes in warnings according to the time of submission of the contract. For example, the providing unit analyzes the time of submission of the contract in chronological order and analyzes changes in warnings. In this way, the accuracy of warnings is improved by analyzing changes in warnings based on the time of submission of the contract.

[0094] The provision unit can display a warning by referring to market data related to the contract when providing the warning. For example, the provision unit improves the accuracy of the warning based on the market data related to the contract. For example, the provision unit adjusts the criteria for the warning by referring to the market data related to the contract. The provision unit can also analyze the market data related to the contract to identify areas for improvement in the warning. For example, the provision unit analyzes the market data related to the contract in chronological order to identify areas for improvement in the warning. In this way, the accuracy of the warning is improved by referring to the market data related to the contract.

[0095] The provision unit can display a warning taking into account the technical maturity of the contract when providing the contract. For example, the provision unit analyzes the technical maturity of the contract and improves the accuracy of the warning. For example, the provision unit evaluates the technical maturity of the contract and adjusts the criteria for the warning. The provision unit can also identify areas for improvement in the warning based on the technical maturity of the contract. For example, the provision unit analyzes the technical maturity of the contract over time and identifies areas for improvement in the warning. In this way, the accuracy of the warning is improved by taking into account the technical maturity of the contract.

[0096] The extraction unit can estimate the user's emotion and determine the priority of text to be extracted based on the estimated user emotion. For example, if the user is nervous, the extraction unit prioritizes extracting important text. For example, the extraction unit can analyze the user's facial expression to determine whether the user is nervous. Furthermore, if the user is relaxed, the extraction unit can also extract detailed text. For example, the extraction unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the extraction unit can prioritize extracting text that highlights the main points. For example, the extraction unit can analyze the user's biometric data to determine whether the user is in a hurry. Thus, by prioritizing the text to be extracted according to the user's emotion, important text can be preferentially extracted. Emotion estimation is realized 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.

[0097] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between contracts during extraction. The extraction unit, for example, analyzes the interrelationships between contracts and extracts related text. For example, the extraction unit analyzes the contents of the contracts and identifies related text. The extraction unit can also improve the accuracy of extraction by taking into account the interrelationships between contracts. For example, the extraction unit evaluates the interrelationships between contracts and improves the accuracy of extraction. The extraction unit can also identify areas for improvement in extraction based on the interrelationships between contracts. For example, the extraction unit analyzes the interrelationships between contracts in chronological order and identifies areas for improvement in extraction. In this way, the accuracy of extraction is improved by taking into account the interrelationships between contracts.

[0098] The extraction unit can perform extraction while taking into account the attribute information of the contract submitter. The extraction unit improves the accuracy of extraction based on, for example, the attribute information of the contract submitter. For example, the extraction unit analyzes the occupation or position of the contract submitter to improve the accuracy of extraction. The extraction unit can also adjust the extraction criteria by taking into account the attribute information of the contract submitter. For example, the extraction unit analyzes the past contract history of the contract submitter and adjusts the extraction criteria. The extraction unit can also identify areas for improvement in the extraction by referring to the attribute information of the contract submitter. For example, the extraction unit analyzes the attribute information of the contract submitter in chronological order to identify areas for improvement in the extraction. In this way, the accuracy of extraction is improved by taking into account the attribute information of the contract submitter.

[0099] The extraction unit can estimate the user's emotion and adjust the display method of the extracted text based on the estimated user emotion. For example, if the user is nervous, the extraction unit provides a simple, highly visible display method. For example, the extraction unit can analyze the user's facial expression to determine whether the user is nervous. Furthermore, if the user is relaxed, the extraction unit can display detailed text. For example, the extraction unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the extraction unit can display text that focuses on the main points. For example, the extraction unit can analyze the user's biometric data to determine whether the user is in a hurry. This allows the display method of the extracted text to be adjusted according to the user's emotion, thereby enabling a display that is easy for the user to understand. 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.

[0100] The extraction unit can perform extraction while taking into account the geographical distribution of contracts. For example, the extraction unit analyzes the geographical distribution of contracts and extracts region-specific text. For example, the extraction unit analyzes the content of the contract and identifies region-specific text. The extraction unit can also improve the accuracy of extraction by taking into account the geographical distribution of contracts. For example, the extraction unit evaluates the geographical distribution of contracts and improves the accuracy of extraction. The extraction unit can also identify areas for improvement in extraction based on the geographical distribution of contracts. For example, the extraction unit analyzes the geographical distribution of contracts over time and identifies areas for improvement in extraction. In this way, the accuracy of extraction is improved by taking the geographical distribution of contracts into account.

[0101] The extraction unit can improve the accuracy of extraction by referring to related literature of the contract during extraction. The extraction unit, for example, improves the accuracy of extraction by referring to related literature of the contract. For example, the extraction unit analyzes the contents of the contract by referring to related academic papers and technical reports. The extraction unit can also identify areas for improvement in extraction based on the related literature of the contract. For example, the extraction unit analyzes related literature and improves the accuracy of extraction. The extraction unit can also improve the accuracy of extraction by referring to related literature of the contract. For example, the extraction unit identifies areas for improvement in extraction based on the related literature. In this way, the accuracy of extraction is improved by referring to related literature of the contract.

[0102] The reference unit can estimate the user's emotions and select data to reference based on the estimated user emotions. For example, if the user is nervous, the reference unit prioritizes reference to important data. For example, the reference unit can analyze the user's facial expressions to determine whether the user is nervous. Furthermore, if the user is relaxed, the reference unit can also reference detailed data. For example, the reference unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the reference unit can prioritize reference to data that highlights the key points. For example, the reference unit can analyze the user's biometric data to determine whether the user is in a hurry. Thus, by selecting data to reference based on the user's emotions, important data can be prioritized for reference. The emotion estimation is realized 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.

[0103] The reference unit can optimize the reference algorithm by referring to past reference data during reference. The reference unit, for example, optimizes the reference algorithm based on past reference data. For example, the reference unit analyzes past reference data and identifies areas for improvement in the reference algorithm. The reference unit can also improve the accuracy of the reference by referring to past reference data. For example, the reference unit refers to past reference data and performs reference based on similar reference data. The reference unit can also analyze past reference data and identify areas for improvement in the reference. For example, the reference unit analyzes past reference data in chronological order and identifies areas for improvement in the reference. As a result, the accuracy of the reference algorithm is improved by referring to past reference data.

[0104] The reference unit can update the reference data to reflect user feedback during reference. The reference unit, for example, updates the reference data based on user feedback. For example, the reference unit collects user evaluation comments and identifies areas for improvement in the reference data. The reference unit can also improve the accuracy of reference by reflecting user feedback. For example, the reference unit analyzes user feedback and identifies areas for improvement in the reference data. The reference unit can also update the reference data based on user feedback. For example, the reference unit analyzes user feedback in chronological order and identifies areas for improvement in the reference data. In this way, the accuracy of the reference data is improved by reflecting user feedback.

[0105] The reference unit can estimate the user's emotions and adjust the frequency of reference based on the estimated user emotions. For example, if the user is nervous, the reference unit updates the reference data frequently. For example, the reference unit analyzes the user's facial expressions to determine whether the user is nervous. Furthermore, if the user is relaxed, the reference unit can update the reference data at an appropriate frequency. For example, the reference unit analyzes the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the reference unit can quickly update the reference data. For example, the reference unit analyzes the user's biometric data to determine whether the user is in a hurry. Thus, by adjusting the frequency of reference according to the user's emotions, data can be referenced at an appropriate frequency. 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.

[0106] When referencing, the reference unit can weight the reference data based on the time of submission of the contract. For example, the reference unit preferentially references data of contracts that were submitted more recently. For example, the reference unit analyzes the time of submission of the contract and preferentially references newer data. The reference unit can also postpone data of contracts that were submitted earlier. For example, the reference unit analyzes the time of submission of the contract and postpones older data. The reference unit can also dynamically adjust the weighting of the reference data according to the time of submission of the contract. For example, the reference unit analyzes the time of submission of the contract in chronological order and adjusts the weighting. In this way, by weighting the reference data based on the time of submission of the contract, the latest data can be preferentially referenced.

[0107] During reference, the reference unit can integrate information from different data sources to enrich the reference data. For example, the reference unit integrates information from different data sources to enrich the reference data. For example, the reference unit integrates information from an internal database or an external API to enrich the reference data. The reference unit can also improve the accuracy of the reference data by referencing different data sources. For example, the reference unit analyzes different data sources to identify areas for improvement in the reference data. The reference unit can also enrich the reference data based on the different data sources. For example, the reference unit analyzes different data sources in chronological order to identify areas for improvement in the reference data. In this way, the accuracy of the reference data is improved by integrating information from different data sources.

[0108] The notification unit can estimate the user's emotions and adjust the notification display method based on the estimated user emotions. For example, if the user is nervous, the notification unit displays a simple, highly visible notification. For example, the notification unit analyzes the user's facial expression to determine whether the user is nervous. The notification unit can also display a detailed notification if the user is relaxed. For example, the notification unit analyzes the user's voice to determine whether the user is relaxed. The notification unit can also display a notification that focuses on the main points if the user is in a hurry. For example, the notification unit analyzes the user's biometric data to determine whether the user is in a hurry. This allows the notification display method to be adjusted according to the user's emotions, thereby providing notifications that are easy for the user to understand. 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.

[0109] When displaying a notification, the notification unit can select an optimal display method by referring to the user's past operation history. The notification unit selects an optimal notification display method based on, for example, the user's past operation history. For example, the notification unit analyzes the user's operation history and identifies an optimal display method. The notification unit can also improve the accuracy of notifications by referring to the user's past operation history. For example, the notification unit refers to the user's operation history and provides notifications based on similar operation histories. The notification unit can also analyze the user's past operation history and identify areas for improvement in notifications. For example, the notification unit analyzes the user's operation history in chronological order and identifies areas for improvement in notifications. In this way, an optimal notification display method can be provided by referring to the user's past operation history.

[0110] The notification unit can customize the display content according to the user's current task when displaying a notification. For example, the notification unit prioritizes displaying notifications related to the task the user is currently performing. For example, the notification unit analyzes the user's task progress and identifies relevant notifications. The notification unit can also customize the display content of notifications according to the user's current task. For example, the notification unit analyzes the user's task progress and identifies the most appropriate notification. The notification unit can also display the most appropriate notification according to the user's task progress. For example, the notification unit analyzes the user's task progress in chronological order and identifies the most appropriate notification. In this way, by customizing the display content according to the user's current task, it is possible to provide highly relevant notifications.

[0111] The notification unit can estimate the user's emotions and adjust the notification operation procedure based on the estimated user's emotions. For example, if the user is nervous, the notification unit provides simple operation procedures. For example, the notification unit can analyze the user's facial expression to determine whether the user is nervous. The notification unit can also provide detailed operation procedures if the user is relaxed. For example, the notification unit can analyze the user's voice to determine whether the user is relaxed. The notification unit can also provide quick operation procedures if the user is in a hurry. For example, the notification unit can analyze the user's biometric data to determine whether the user is in a hurry. This allows the notification operation procedure to be adjusted according to the user's emotions, thereby providing an easy-to-use operation procedure for the user. 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.

[0112] When displaying a notification, the notification unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the notification unit provides a notification display method that matches the screen size. For example, the notification unit analyzes the user's device information and identifies the optimal display method for the smartphone. Furthermore, if the user is using a tablet, the notification unit can also provide a notification display method that is optimized for a large screen. For example, the notification unit analyzes the user's device information and identifies the optimal display method for the tablet. Furthermore, if the user is using a smartwatch, the notification unit can also provide a simple and highly visible notification display method. For example, the notification unit analyzes the user's device information and identifies the optimal display method for the smartwatch. In this way, the optimal notification display method can be provided by taking into account the user's device information.

[0113] When displaying a notification, the notification unit can make the display content multilingual according to the user's language setting. The notification unit automatically sets the notification language based on, for example, the language setting of the user's device. For example, the notification unit analyzes the user's language setting and displays the notification in the most appropriate language. The notification unit can also provide a language switching function when the user uses multiple languages. For example, the notification unit analyzes the user's language setting and provides the language switching function. The notification unit can also provide notifications in a specific language when the user selects that language. For example, the notification unit analyzes the user's language setting and displays the notification in the selected language. This makes it possible to provide notifications that are easy for the user to understand by making the display content multilingual according to the user's language setting.

[0114] When displaying a notification, the notification unit can analyze the user's social media activity and provide related information. The notification unit, for example, provides information about places where the user has checked in on social media. For example, the notification unit analyzes the user's social media activity and identifies information related to the checked-in places. The notification unit can also analyze the content of the user's posts on social media and provide information about related tourist spots and stores. For example, the notification unit analyzes the content of the user's posts and identifies related information. The notification unit can also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the notification unit analyzes the activities of the user's friends and identifies related information. In this way, highly relevant information can be provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, provision unit, extraction unit, reference unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs the text of the contract by copying the entire text of the contract and pasting it into the AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the content of the contract using natural language processing technology or a machine learning algorithm. The identification unit is realized by the specific processing unit 290 of the data processing device 12, and identifies unfavorable clauses based on the analyzed content. The provision unit is realized by the control unit 46A of the smart device 14, and displays a warning to the user about the identified unfavorable content. The extraction unit is realized by the control unit 46A of the smart device 14, and extracts text from PDFs and images. The reference unit is realized by the specific processing unit 290 of the data processing device 12, and references past court cases and legal databases. The notification unit is realized by the control unit 46A of the smart device 14, and performs pop-up notifications and email notifications. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, provision unit, extraction unit, reference unit, and notification unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs the text of the contract by copying the entire text of the contract and pasting it into the AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the content of the contract using natural language processing technology or a machine learning algorithm. The identification unit is realized by the specific processing unit 290 of the data processing device 12, and identifies unfavorable clauses based on the analyzed content. The provision unit is realized by the control unit 46A of the smart glasses 214, and displays a warning to the user about the identified unfavorable content. The extraction unit is realized by the control unit 46A of the smart glasses 214, and extracts text from PDFs and images. The reference unit is realized by the specific processing unit 290 of the data processing device 12, and references past court cases and legal databases. The notification unit is realized by the control unit 46A of the smart glasses 214, and performs pop-up notifications and email notifications. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, provision unit, extraction unit, reference unit, and notification unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and the user inputs the text of the contract by copying the entire text of the contract and pasting it into the AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the content of the contract using natural language processing technology or a machine learning algorithm. The identification unit is realized by the specific processing unit 290 of the data processing device 12, and identifies unfavorable clauses based on the analyzed content. The provision unit is realized by the control unit 46A of the headset-type terminal 314, and displays a warning to the user about the identified unfavorable content. The extraction unit is realized by the control unit 46A of the headset-type terminal 314, and extracts text from PDFs and images. The reference unit is realized by the specific processing unit 290 of the data processing device 12, and references past court cases and legal databases. The notification unit is realized by the control unit 46A of the headset type terminal 314, and performs pop-up notifications and email notifications. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, provision unit, extraction unit, reference unit, and notification unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs the text of the contract by copying the entire text of the contract and pasting it into the AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the content of the contract using natural language processing technology or a machine learning algorithm. The identification unit is realized by the specific processing unit 290 of the data processing device 12, and identifies unfavorable clauses based on the analyzed content. The provision unit is realized by the control unit 46A of the robot 414, and displays a warning to the user about the identified unfavorable content. The extraction unit is realized by the control unit 46A of the robot 414, and extracts text from PDFs and images. The reference unit is realized by the specific processing unit 290 of the data processing device 12, and references past court cases and legal databases. The notification unit is realized by the control unit 46A of the robot 414, and performs pop-up notifications and email notifications.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The reception unit can analyze the user's past contract submission history and select the optimal reception method. For example, it automatically selects the reception method that the user has frequently used in the past. For example, the reception unit analyzes the user's past submission history and suggests the most efficient reception method. The reception unit can also select the optimal reception method for a specific time period based on the user's past submission history. For example, the reception unit analyzes the user's submission history in chronological order and identifies the optimal reception method for a specific time period. In this way, it is possible to provide the optimal reception method for the user by analyzing the past submission history.

[0117] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the contract. For example, the analysis unit performs a detailed analysis on a contract with high importance. For example, the analysis unit analyzes the contents of the contract and analyzes clauses with high importance in detail. The analysis unit can also perform a simplified analysis on a contract with low importance. For example, the analysis unit analyzes the contents of the contract and analyzes clauses with low importance in a simplified manner. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the contract. For example, the analysis unit evaluates the importance of the contract and adjusts the level of detail of the analysis. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the contract.

[0118] The identification unit can improve the accuracy of identification by taking into account the interrelationships between contracts during identification. For example, the identification unit analyzes the interrelationships between contracts and identifies related clauses. For example, the identification unit analyzes the contents of contracts and identifies related clauses. The identification unit can also improve the accuracy of identification by taking into account the interrelationships between contracts. For example, the identification unit evaluates the interrelationships between contracts and improves the accuracy of identification. The identification unit can also identify specific areas for improvement based on the interrelationships between contracts. For example, the identification unit analyzes the interrelationships between contracts in chronological order and identifies specific areas for improvement. In this way, the accuracy of identification is improved by taking into account the interrelationships between contracts.

[0119] When providing the data, the providing unit can apply different warning display methods to different categories of contracts. For example, the providing unit applies a business-specific warning display method to a business contract. For example, the providing unit analyzes the contents of a business contract and warns of business-specific risks. The providing unit can also apply a rental-specific warning display method to a rental contract. For example, the providing unit analyzes the contents of a rental contract and warns of rental-specific risks. The providing unit can also select and apply the optimal warning display method according to the category of the contract. For example, the providing unit classifies the category of the contract and applies the optimal warning display method. This improves the accuracy of warnings by applying the optimal warning display method according to the category of the contract.

[0120] During reference, the reference unit can integrate information from different data sources to enrich the reference data. For example, the reference unit integrates information from different data sources to enrich the reference data. For example, the reference unit integrates information from an internal database or an external API to enrich the reference data. The reference unit can also improve the accuracy of the reference data by referencing different data sources. For example, the reference unit analyzes different data sources to identify areas for improvement in the reference data. The reference unit can also enrich the reference data based on the different data sources. For example, the reference unit analyzes different data sources in chronological order to identify areas for improvement in the reference data. In this way, the accuracy of the reference data is improved by integrating information from different data sources.

[0121] The reception unit can estimate the user's emotions and adjust the timing of contract acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can accept the contract during a time when the user is able to relax. For example, the reception unit can analyze the user's facial expression to determine whether the user is feeling stressed. Furthermore, if the user is in a hurry, the reception unit can immediately accept the contract and quickly start analysis. For example, the reception unit can analyze the user's voice to determine whether the user is in a hurry. Furthermore, if the user is relaxed, the reception unit can accept the contract using an interface that includes detailed explanations. For example, the reception unit can analyze the user's biometric data to determine whether the user is relaxed. This allows the timing of acceptance to be adjusted according to the user's emotions, reducing the user's stress and allowing the contract to be accepted at the optimal time. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0122] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible analysis result is provided. For example, the analysis unit can analyze the user's facial expression to determine whether the user is nervous. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a more concise analysis result. For example, the analysis unit can analyze the user's biometric data to determine whether the user is in a hurry. This allows the analysis result to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The identification unit can estimate the user's emotions and adjust the specific criteria based on the estimated user emotions. For example, if the user is nervous, the identification unit can identify unfavorable content using strict criteria. For example, the identification unit can analyze the user's facial expressions to determine whether the user is nervous. Furthermore, if the user is relaxed, the identification unit can also identify unfavorable content using normal criteria. For example, the identification unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the identification unit can apply criteria that can quickly identify the user. For example, the identification unit can analyze the user's biometric data to determine whether the user is in a hurry. This allows the specific criteria to be adjusted according to the user's emotions, thereby enabling more appropriate identification of unfavorable content. The 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.

[0124] The providing unit can estimate the user's emotions and adjust the warning display method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible warning is displayed. For example, the providing unit can analyze the user's facial expressions to determine whether the user is nervous. Furthermore, if the user is relaxed, the providing unit can also display a detailed warning. For example, the providing unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the providing unit can display a warning that focuses on the main points. For example, the providing unit can analyze the user's biometric data to determine whether the user is in a hurry. This allows the warning display method to be adjusted according to the user's emotions, thereby providing a warning that is easy for the user to understand. 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.

[0125] The reference unit can estimate the user's emotions and select data to reference based on the estimated user emotions. For example, if the user is nervous, it prioritizes reference to important data. For example, the reference unit can analyze the user's facial expressions to determine whether the user is nervous. Furthermore, if the user is relaxed, the reference unit can also reference detailed data. For example, the reference unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the reference unit can prioritize reference to data that highlights the key points. For example, the reference unit can analyze the user's biometric data to determine whether the user is in a hurry. Thus, by selecting data to reference based on the user's emotions, it is possible to prioritize reference to important data. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The reception unit accepts the contract text. The contract text can be in a PDF, Word document, scanned image, etc. The user inputs the contract text by copying the entire contract text and pasting it into the AI. It can also extract text from PDFs and images. Step 2: The analysis unit analyzes the contents of the contract received by the reception unit. The analysis is performed using natural language processing technology and machine learning algorithms. The analysis unit analyzes each clause of the contract in detail to identify any unfavorable or high-risk content. Step 3: The identification unit identifies unfavorable clauses based on the content analyzed by the analysis unit. Unfavorable clauses include contract termination clauses and damages clauses. The identification unit determines whether the contract contains clauses with strict contract termination clauses or damages clauses. Step 4: The provider warns the user of the unfavorable content identified by the identifyr. The warning is given by a pop-up notification, email notification, or other method. The provider displays a warning such as "This contract contains strict terms for termination."

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The 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.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives the contract text; an analysis unit that analyzes the content of the contract accepted by the acceptance unit; an identification unit that identifies an unfavorable clause based on the content analyzed by the analysis unit; a providing unit that warns a user of the adverse content identified by the identifying unit. A system characterized by:

2. The reception unit Includes an extractor to extract text from PDFs or images 2. The system of claim 1.

3. The analysis unit Includes a reference section that refers to past cases and legal databases 2. The system of claim 1.

4. The providing unit Equipped with a notification section that provides popup notifications and email notifications 2. The system of claim 1.

5. The identification unit Identify any unfavorable terms, such as termination clauses or liquidated damages clauses 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of contract acceptance based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past contract submission history and select the optimal reception method 2. The system of claim 1.

8. The reception unit Filter contract acceptance based on the user's current projects and interests 2. The system of claim 1.

9. The reception unit When accepting a contract, select the most appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

Images