System

Abstract

An object of a system according to an embodiment is to make it easy for a user to understand the contents of a covenant or a contract at the time of making a contract.SOLUTION: A system includes a reception part, a summarization part, a comparison part, an analysis part, and a provision part. The reception part inputs a covenant and a rule to be contracted by the user. The summarization part analyzes the clauses and the rules inputted by the reception part and summarizes concrete points. The comparer compares the information summarized by the summarizer with standard clauses. The analysis unit analyzes a specific legal risk and economic risk based on the result obtained by the comparison unit. The providing unit provides the user with an easy-to-understand report on the basis of the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for users to fully understand the terms and conditions when signing a contract, which can lead to risks being overlooked.

[0005] The system according to the embodiment aims to make it easier for users to understand the contents of the terms and conditions and regulations when signing a contract. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a summarization unit, a comparison unit, an analysis unit, and a provision unit. The reception unit inputs the terms and conditions and regulations that a user intends to enter into a contract. The summarization unit analyzes the terms and conditions and regulations input by the reception unit and summarizes specific points. The comparison unit compares the information summarized by the summarization unit with standard terms and conditions. The analysis unit analyzes specific legal and economic risks based on the results obtained by the comparison unit. The provision unit provides an easy-to-understand report to the user based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it easier for users to understand the contents of the terms and conditions and regulations when signing a 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) The generation AI service according to an embodiment of the present invention is a system that efficiently analyzes, summarizes, compares, and analyzes the terms and conditions of a contract that a user is about to enter into, and provides an easy-to-understand report. The generation AI service inputs the terms and conditions of a contract that a user is about to enter into. The generation AI analyzes the content and summarizes key points. For example, it concisely summarizes the contract's main conditions and restrictions, fee structure, and cancellation conditions. Next, the generation AI service compares the input terms and conditions with standard terms and conditions. For example, it compares them with general service contracts to identify any differences in specific terms or additional restrictions. Furthermore, the generation AI service analyzes legal and economic risks. For example, it evaluates potential legal risks and economic burdens contained in the contract. The generation AI service also evaluates the validity of the contract based on the applicant's prior information. For example, it considers information such as the user's age, occupation, and income to determine whether the contract is appropriate. Finally, the generation AI service presents specific problems. For example, it points out any unclear points or unfavorable terms in the contract. This allows the generation AI service to easily understand complex terms and conditions and identify risks in advance. This allows the generative AI service to efficiently analyze, summarize, compare, and analyze the terms and conditions and regulations that users are about to enter into, and provide easy-to-understand reports.For example, by quickly and accurately analyzing the terms and conditions and regulations that users are about to enter into, and providing important information, it becomes possible to prevent problems at the time of signing and to conclude a safe and appropriate contract.

[0029] The generation AI service according to the embodiment includes a reception unit, a summarization unit, a comparison unit, an analysis unit, and a provision unit. The reception unit inputs the terms and conditions a user intends to enter into. The terms and conditions a user intends to enter into include, but are not limited to, service contracts, sales contracts, and license agreements. The reception unit inputs the terms and conditions a user intends to enter into in text format. The reception unit can also input the terms and conditions a user intends to enter into in image format and convert them into text data using OCR technology. The summarization unit uses a generation AI to analyze the terms and conditions input by the reception unit and summarize key points. The summarization is based on, for example, but is not limited to, the main conditions and constraints of the contract, the fee structure, and cancellation conditions. For example, the generation AI uses text generation to concisely summarize the terms and conditions. The summarization unit can also use the generation AI to extract and summarize key points of the contract. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The comparison unit compares the information summarized by the summarization unit with standard terms and conditions. The comparison is performed, for example, but not limited to, with a standard service contract template. For example, the comparison unit identifies the uniqueness and risks of the entered terms and conditions and regulations based on a standard service contract template. The analysis unit uses the generation AI to analyze legal and economic risks based on the results obtained by the comparison unit. The analysis is performed, for example, but not limited to, with reference to past court cases and legal documents. For example, the generation AI evaluates potential legal risks contained in the contract based on past court case data. The analysis unit can also evaluate the fee structure and payment terms contained in the contract. For example, the generation AI evaluates economic risks based on the contract's fee structure and payment terms. The provision unit provides an easy-to-understand report to the user based on the results analyzed by the analysis unit. The report may, for example, use graphs and charts to visually display the summarized information. For example, but not limited to, the provision unit visually displays the summarized information in graphs and charts to make the information easier for the user to understand.As a result, the generation AI service of the embodiment can efficiently analyze, summarize, compare, and analyse the terms and conditions and regulations that a user is about to enter into, and provide an easy-to-understand report.

[0030] The summarization unit can use a generation AI to summarize the contract's key terms, constraints, fee structure, and cancellation conditions. Examples of generation AI include, but are not limited to, natural language generation models such as transformer-based models. The summarization unit can also use a generation AI to summarize the contract's key terms. For example, it can briefly summarize the contract period, payment terms, and penalty fees. The summarization unit can also use a generation AI to summarize the contract's constraints. For example, it can extract and summarize the contract's constraints and specific conditions. The summarization unit can also use a generation AI to summarize the contract's fee structure. For example, it can briefly summarize the fee breakdown and payment schedule. The summarization unit can also use a generation AI to summarize the contract's cancellation conditions. For example, it can briefly summarize the cancellation procedure and penalty fee terms. This allows the generation AI to efficiently summarize the contract's key terms, constraints, fee structure, cancellation conditions, and so on. Some or all of the above-described processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the contract's text data into a generation AI, which then generates a summary.

[0031] The comparison unit may compare the entered terms and conditions with a standard service contract template. Examples of standard service contract templates include, but are not limited to, industry-standard contracts and legally recognized templates. The comparison unit may identify the uniqueness and risks of the entered terms and conditions based on the standard service contract template. For example, the comparison unit may compare the entered terms and conditions with the standard service contract template to identify any differences in specific conditions or additional restrictions. The comparison unit may also evaluate the risks of the entered terms and conditions based on the standard service contract template. For example, the comparison unit may compare the entered terms and conditions with the standard service contract template to identify potential risks. This makes it easier to understand the uniqueness and risks of the entered terms and conditions by using a common service contract template as a reference. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit may input the standard service contract template and the entered terms and conditions into an AI model, and the AI ​​may output the comparison results.

[0032] The analysis unit can use the generative AI to refer to past precedents and legal documents to evaluate potential legal risks. Examples of generative AI include, but are not limited to, natural language generation models such as transformer-based models. The analysis unit can, for example, use the generative AI to evaluate potential legal risks in a contract based on past precedent data. For example, the generative AI can refer to court decisions, law books, and legal databases to identify legal risks associated with the contract. The analysis unit can also use the generative AI to refer to legal documents to evaluate potential legal risks in a contract. For example, the generative AI can evaluate legal risks associated with a contract based on past precedent data. This allows the generative AI to efficiently evaluate potential legal risks by referring to past precedents and legal documents. Some or all of the above-described processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs past precedent data into the generative AI, which then evaluates the legal risks.

[0033] The analysis unit can evaluate the fee structure and payment terms included in the contract. The fee structure and payment terms include, but are not limited to, payment schedules, discount terms, and penalty fees. The analysis unit, for example, evaluates the fee structure included in the contract. For example, it evaluates the fee breakdown and payment schedule to identify economic risks. The analysis unit can also evaluate the payment terms included in the contract. For example, it evaluates the payment schedule and discount terms to identify economic risks. The analysis unit can also evaluate penalty fee terms included in the contract. For example, it evaluates the amount of penalty fees and payment terms to identify economic risks. In this way, by evaluating the fee structure and payment terms included in the contract, economic risks can be grasped in advance. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the fee structure and payment terms of the contract into an AI model, and the AI ​​evaluates economic risks.

[0034] The providing unit can visually display the summarized information using a graph, chart, or the like. Examples of visual display methods include, but are not limited to, graphs, charts, dashboards, and the like. The providing unit, for example, displays the summarized information using a graph. For example, the main terms and constraints of the contract, the fee structure, the cancellation conditions, etc. are visually displayed using a graph. The providing unit can also display the summarized information using a chart. For example, the main terms and constraints of the contract, the fee structure, the cancellation conditions, etc. are visually displayed using a chart. The providing unit can also display the summarized information using a dashboard. For example, the main terms and constraints of the contract, the fee structure, the cancellation conditions, etc. are visually displayed using a dashboard. This visual display of the summarized information makes it easier for the user to understand the information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit inputs the summarized information into an AI model, and the AI ​​generates a graph or chart.

[0035] The providing unit can present specific information in an easy-to-understand manner using graphs and charts. Specific information includes, but is not limited to, contract terms, risk factors, and fee structures. The providing unit, for example, displays contract terms in a graph. For example, the main conditions and constraints of the contract, the fee structure, and cancellation conditions are visually displayed in a graph. The providing unit can also display risk factors in a chart. For example, potential legal and financial risks included in the contract are visually displayed in a chart. The providing unit can also display the fee structure in a dashboard. For example, the fee structure and payment terms of the contract are visually displayed in a dashboard. Thus, using graphs and charts makes important information visually easier to understand. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit inputs summarized information into an AI model, which then generates graphs and charts.

[0036] The reception unit can analyze the user's past contract history and select an appropriate input method. Past contract history includes, but is not limited to, past contracts, contract terms, and performance status. For example, if the user has previously preferred voice input, the reception unit suggests voice input. For example, if the user has previously used text input, the reception unit prioritizes text input. Furthermore, the reception unit can also suggest image input if the user has previously used image input. For example, if the user has previously used image input, the reception unit suggests image input. By analyzing the user's past contract history, the reception unit can provide the user with the optimal input method and improve input efficiency. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's past contract history data into an AI model, and the AI ​​selects the optimal input method.

[0037] When inputting the terms and conditions, the reception unit can perform filtering based on the user's current contract status and areas of interest. Examples of the current contract status and areas of interest include, but are not limited to, the type of current contract and the user's interests. For example, the reception unit can prioritize displaying terms and conditions related to the service to which the user currently subscribes. For example, the reception unit can prioritize displaying terms and conditions related to the user's areas of interest. The reception unit can also prioritize displaying highly relevant terms and conditions based on the user's past contract history. For example, the reception unit can prioritize displaying highly relevant terms and conditions based on the user's past contract history. In this way, filtering based on the user's current contract status and areas of interest can prioritize displaying highly relevant terms and conditions. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's current contract status and area of ​​interest data into an AI model, and the AI ​​performs filtering.

[0038] When inputting the terms and conditions, the reception unit can select the optimal input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit inputs the terms and conditions using voice recognition technology. For example, if the user selects text input, the reception unit provides a text box to input the terms and conditions. Furthermore, if the user selects image input, the reception unit can also input the terms and conditions using image recognition technology. For example, if the user selects image input, the reception unit inputs the terms and conditions using image recognition technology. This allows for selecting the optimal input means depending on the user's input method, thereby improving user convenience. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's input method data into an AI model, and the AI ​​selects the optimal input means.

[0039] When inputting the terms and conditions, the reception unit can prioritize inputting highly relevant terms and conditions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the reception unit prioritizes displaying terms and conditions related to that area. For example, when the user is traveling, the reception unit prioritizes displaying terms and conditions related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize displaying terms and conditions related to the user's home. For example, when the user is at home, the reception unit prioritizes displaying terms and conditions related to the user's home. This allows highly relevant terms and conditions to be displayed preferentially by taking the user's geographical location information into consideration, thereby improving user convenience. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information data into an AI model, and the AI ​​prioritizes displaying highly relevant terms and conditions.

[0040] When inputting the terms and conditions, the reception unit can analyze the user's social media activity and input the relevant terms and conditions. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit, for example, displays terms and conditions related to places where the user has checked in on social media. For example, the reception unit analyzes the content of the user's social media posts and displays the relevant terms and conditions. The reception unit can also display the relevant terms and conditions based on the activities of the user's friends on social media. For example, the reception unit displays the relevant terms and conditions based on the activities of the user's friends on social media. By analyzing the user's social media activity, the reception unit can prioritize the display of highly relevant terms and conditions, improving user convenience. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's social media activity data into an AI model, and the AI ​​displays the relevant terms and conditions.

[0041] When entering the terms and conditions, the reception unit can customize the input method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings, comments, and survey results. For example, if the user has previously preferred voice input, the reception unit suggests voice input. For example, if the user has previously used text input frequently, the reception unit prioritizes text input. The reception unit can also suggest image input if the user has previously used image input. For example, if the user has previously used image input, the reception unit suggests image input. By reflecting the user's past feedback, the reception unit can provide the user with the optimal input method and improve input efficiency. Some or all of the above-described processing by the reception unit may be performed using AI or without AI. For example, the reception unit inputs the user's past feedback data into an AI model, and the AI ​​customizes the input method.

[0042] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the terms and conditions. Examples of the importance of terms and conditions include, but are not limited to, legal risk, economic risk, and user interest. The summarization unit provides a detailed summary for important terms and conditions. For example, it summarizes important contract terms and risk factors in detail. The summarization unit can also provide a concise summary for less important terms and conditions. For example, it briefly summarizes less important contract terms and risk factors. The summarization unit can also provide a detailed summary for terms and conditions in which the user is particularly interested. For example, it summarizes contract terms and risk factors in which the user is most interested in detail. This allows the user to quickly grasp important information by adjusting the level of detail of the summary based on the importance of the terms and conditions. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the importance data of the terms and conditions into the generation AI, and the generation AI adjusts the level of detail of the summary.

[0043] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the terms and conditions. Examples of categories of terms and conditions include, but are not limited to, service contracts, sales contracts, and license agreements. For example, the summarization unit applies a specific summarization algorithm to the terms and conditions of a service contract. For example, a summary is generated using a summarization algorithm specialized for service contracts. Furthermore, the summarization unit can apply a different summarization algorithm to the terms and conditions of a purchase contract. For example, a summary is generated using a summarization algorithm specialized for purchase contracts. Furthermore, the summarization unit can apply yet another summarization algorithm to the terms and conditions of an insurance contract. For example, a summary is generated using a summarization algorithm specialized for insurance contracts. This allows for the application of different summarization algorithms depending on the category of the terms and conditions, thereby improving the accuracy of the summary. Some or all of the above-described processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs category data of the terms and conditions into the generation AI, which then applies an appropriate summarization algorithm.

[0044] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. Past summarization results include, but are not limited to, past summary sentences and summary evaluation results. For example, the summarization unit generates a summary by referring to a summarization style that the user previously preferred. For example, the summarization unit generates a summary based on a summarization style that the user previously found easy to understand. The summarization unit can also generate a summary by referring to summaries for which the user provided feedback in the past. For example, the summarization unit generates a summary based on feedback the user provided in the past. In this way, the accuracy of the summary can be improved by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the user's past summary result data into the generation AI, and the generation AI improves the accuracy of the summary.

[0045] When generating summaries, the summarization unit can determine the priority of summaries based on the submission date of the terms and conditions. The submission date of the terms and conditions includes, but is not limited to, the submission date, update date, etc. The summarization unit, for example, prioritizes summarization of recently submitted terms and conditions. For example, a concise summary is provided for terms and conditions that were submitted recently. The summarization unit can also provide a detailed summary for terms and conditions that have recently been submitted. For example, a detailed summary is provided for terms and conditions that have recently been submitted. In this way, by determining the priority of summaries based on the submission date of the terms and conditions, the latest information can be prioritized for summarization. Some or all of the above-mentioned processing in the summarization unit is performed using the generation AI. For example, the summarization unit inputs data on the submission date of the terms and conditions into the generation AI, and the generation AI determines the priority of summaries.

[0046] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the terms and conditions. Examples of the relevance of the terms and conditions include, but are not limited to, similarity of content and related laws and regulations. For example, the summarization unit prioritizes summarization of terms and conditions that are highly relevant to the user. For example, a concise summary is provided for terms and conditions that are less relevant to the user. The summarization unit can also prioritize summarization of terms and conditions related to the user's area of ​​interest. For example, terms and conditions related to the user's area of ​​interest are prioritized. This allows the user to quickly grasp important information by adjusting the order of summaries based on the relevance of the terms and conditions. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs relevance data of the terms and conditions into the generation AI, and the generation AI adjusts the order of the summaries.

[0047] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has technical expertise, the summarization unit provides a summary that uses a lot of technical terms. For example, if the user does not have technical expertise, the summarization unit provides a summary in simple language. The summarization unit can also provide a summary using appropriate technical terms according to the user's level of expertise. For example, a summary using appropriate technical terms is provided according to the user's level of expertise. This makes it easier for the user to understand the information by adjusting the use of technical terms in the summary according to the user's level of expertise. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the user's level of expertise data into the generation AI, which then adjusts the use of technical terms in the summary.

[0048] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between terms and conditions. Examples of interrelationships between terms and conditions include, but are not limited to, related clauses and interdependencies. For example, the comparison unit compares related terms and conditions by taking into account the interrelationships between terms and conditions. For example, it compares important points by taking into account the interrelationships between terms and conditions. The comparison unit can also compare high-risk points by taking into account the interrelationships between terms and conditions. For example, it compares high-risk points by taking into account the interrelationships between terms and conditions. In this way, the accuracy of the comparison can be improved by taking into account the interrelationships between terms and conditions. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit inputs data on the interrelationships between terms and conditions into an AI model, and the AI ​​improves the accuracy of the comparison.

[0049] When making a comparison, the comparison unit can take into account the attribute information of the person who submitted the terms and conditions. The attribute information of the person who submitted the terms and conditions includes, but is not limited to, company information, personal information, and past contract history. For example, the comparison unit compares related terms and conditions taking into account the attribute information of the person who submitted the terms and conditions. For example, it compares important points taking into account the attribute information of the person who submitted the terms and conditions. The comparison unit can also compare high-risk points taking into account the attribute information of the person who submitted the terms and conditions. For example, it compares high-risk points taking into account the attribute information of the person who submitted the terms and conditions. This allows the accuracy of the comparison to be improved by taking into account the attribute information of the person who submitted the terms and conditions. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit inputs the attribute information data of the person who submitted the terms and conditions into an AI model, and the AI ​​performs the comparison.

[0050] When comparing, the comparison unit can weight the comparison based on the frequency of submission of the terms and conditions. Examples of the frequency of submission include, but are not limited to, the number of submissions and the interval between submissions. For example, the comparison unit prioritizes comparison of terms and conditions that are submitted more frequently. For example, for terms and conditions that are submitted less frequently, a brief comparison is performed. The comparison unit can also compare important points based on the frequency of submission. For example, important points are compared based on the frequency of submission. By weighting the comparison based on the frequency of submission, important information can be prioritized in the comparison. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit inputs submission frequency data into an AI model, and the AI ​​weights the comparison.

[0051] The comparison unit may take into account the geographical distribution of the terms and conditions when making the comparison. Examples of geographical distribution include, but are not limited to, the number of contracts by region and the risks by region. For example, the comparison unit may prioritize comparing terms and conditions from geographically close regions. For example, a brief comparison may be made for terms and conditions from geographically distant regions. The comparison unit may also compare important points based on the geographical distribution. This allows for prioritized comparison of highly relevant information by taking the geographical distribution into consideration. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit may input geographical distribution data into an AI model, and the AI ​​will perform the comparison.

[0052] The comparison unit may improve the accuracy of the comparison by referring to related literature of the terms and conditions during the comparison. Examples of related literature include, but are not limited to, law books, case law collections, and legal databases. For example, the comparison unit may compare important points of the terms and conditions by referring to related literature. For example, the comparison unit may compare high-risk points of the terms and conditions by referring to related literature. The comparison unit may also compare interrelationships of the terms and conditions by referring to related literature. For example, the interrelationships of the terms and conditions may be compared by referring to related literature. In this way, the accuracy of the comparison can be improved by referring to related literature. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit inputs related literature data into an AI model, and the AI ​​performs the comparison.

[0053] The comparison unit may take into account the market value of the terms and conditions when making the comparison. Market value includes, but is not limited to, for example, the contract amount, the size of the transaction, and the industry average. For example, the comparison unit may prioritize the comparison of terms and conditions with high market value. For example, for terms and conditions with low market value, a brief comparison may be performed. The comparison unit may also compare important points based on market value. For example, important points may be compared based on market value. This allows important information to be compared preferentially by taking market value into consideration. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit inputs market value data into an AI model, and the AI ​​performs the comparison.

[0054] During analysis, the analysis unit can predict current risks by referring to past case law data. Past case law data includes, but is not limited to, court decisions, law books, and legal databases. The analysis unit, for example, predicts current legal risks based on past case law data. For example, it predicts current economic risks based on past case law data. The analysis unit can also predict current contract risks based on past case law data. For example, it predicts current contract risks based on past case law data. In this way, by referring to past case law data, current risks can be accurately predicted. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs past case law data into the generation AI, which then predicts current risks.

[0055] During analysis, the analysis unit can apply different risk analysis methods to different categories of terms and conditions. Examples of categories of terms and conditions include, but are not limited to, service contracts, sales contracts, and license contracts. The analysis unit can apply a specific risk analysis method to service contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for service contracts. The analysis unit can also apply a different risk analysis method to purchase contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for purchase contracts. The analysis unit can also apply yet another risk analysis method to insurance contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for insurance contracts. This allows the application of different risk analysis methods to each category of terms and conditions, thereby improving the accuracy of the risk analysis. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs category data of the terms and conditions into the generation AI, which then applies an appropriate risk analysis method.

[0056] During analysis, the analysis unit can analyze risks by taking into account the attribute information of the person who submitted the terms and conditions. The attribute information of the person who submitted the terms and conditions includes, but is not limited to, corporate information, personal information, and past contract history. For example, the analysis unit analyzes legal risks by taking into account the attribute information of the person who submitted the terms and conditions. For example, it analyzes economic risks by taking into account the attribute information of the person who submitted the terms and conditions. The analysis unit can also analyze contract risks by taking into account the attribute information of the person who submitted the terms and conditions. For example, it analyzes contract risks by taking into account the attribute information of the person who submitted the terms and conditions. In this way, the accuracy of risk analysis can be improved by taking into account the attribute information of the person who submitted the terms and conditions. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs attribute information data of the person who submitted the terms and conditions into the generation AI, and the generation AI analyzes risks.

[0057] During analysis, the analysis unit can analyze changes in risk based on the submission date of the terms and conditions. The submission date of the terms and conditions includes, but is not limited to, for example, the submission date and the update date. For example, for recently submitted terms and conditions, the analysis unit analyzes the latest risks. For example, for older submitted terms and conditions, the analysis unit analyzes past risks. The analysis unit can also analyze changes in risk based on the submission date. For example, it analyzes changes in risk based on the submission date. In this way, by analyzing changes in risk based on the submission date of the terms and conditions, it is possible to accurately grasp the latest risks. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on the submission date of the terms and conditions into the generation AI, and the generation AI analyzes changes in risk.

[0058] During analysis, the analysis unit can analyze risks by referring to market data related to the terms and conditions. Examples of related market data include, but are not limited to, market research reports and industry statistical data. For example, the analysis unit analyzes economic risks based on the relevant market data. For example, it analyzes legal risks based on the relevant market data. The analysis unit can also analyze contract risks based on the relevant market data. For example, it analyzes contract risks based on the relevant market data. This makes it possible to accurately analyze economic and legal risks by referring to the relevant market data. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs relevant market data into the generation AI, which then analyzes the risks.

[0059] During analysis, the analysis unit can analyze risks by taking into account the technical maturity of the terms and conditions. Examples of technical maturity include, but are not limited to, the development stage of technology and its prevalence in the market. For example, the analysis unit analyzes terms and conditions with high technical maturity as having low risk. For example, the analysis unit analyzes terms and conditions with low technical maturity as having high risk. The analysis unit can also analyze risks based on technical maturity. For example, risk is analyzed based on technical maturity. In this way, the level of risk can be accurately evaluated by taking technical maturity into consideration. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs technical maturity data into the generation AI, and the generation AI analyzes the risk.

[0060] When providing a report, the providing unit can select an optimal display method by referring to the user's past operation history. Past operation history includes, but is not limited to, past click history, browsing history, etc. For example, the providing unit provides a report by referring to a display method that the user previously preferred. For example, the providing unit provides a report by referring to a display method that the user previously found easy to understand. The providing unit can also provide a report by referring to a display method for which the user previously provided feedback. For example, the providing unit provides a report based on feedback the user previously provided. In this way, by referring to the user's past operation history, the optimal display method can be provided to the user and understanding of the information can be promoted. Some or all of the above-mentioned processing in the providing unit may be performed using AI or without AI. For example, the providing unit inputs the user's past operation history data into an AI model, and the AI ​​selects the optimal display method.

[0061] When providing a report, the providing unit can customize the display content according to the user's current task. The current task includes, but is not limited to, for example, the current work content and priority. For example, the providing unit prioritizes displaying information related to the user's current task. For example, important information is displayed according to the user's current task. The providing unit can also customize the report content based on the user's current task. For example, the report content is customized based on the user's current task. This allows the user to quickly grasp the necessary information by customizing the display content according to the user's current task. Some or all of the above-mentioned processing in the providing unit may be performed using AI or without AI. For example, the providing unit inputs the user's current task data into an AI model, and the AI ​​customizes the display content.

[0062] The providing unit can improve the display method by reflecting user feedback when providing a report. User feedback includes, but is not limited to, survey results, comments, and ratings. The providing unit, for example, improves the display method based on user feedback. For example, important information is displayed preferentially based on user feedback. The providing unit can also customize the content of the report based on user feedback. For example, the content of the report is customized based on user feedback. This allows the display method to be improved by reflecting user feedback, thereby improving user convenience. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit inputs user feedback data into an AI model, and the AI ​​improves the display method.

[0063] When providing a report, the providing unit can select the optimal display method by taking into account the user's device information. Device information includes, but is not limited to, the device type, screen size, and OS. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, if the user is using a tablet, the providing unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible display method. This allows the user's device information to be taken into account to provide the optimal display method and promote understanding of the information. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit inputs the user's device information data into an AI model, and the AI ​​selects the optimal display method.

[0064] When providing a report, the providing unit can make the display content multilingual according to the user's language setting. Language settings include, but are not limited to, the user's browser settings and app language settings. For example, the providing unit automatically sets the report language based on the user's device language setting. For example, if the user uses multiple languages, the providing unit provides a language switching function. Furthermore, if the user selects a specific language, the providing unit can also provide the report in that language. For example, if the user selects a specific language, the report is provided in that language. This makes it easier for the user to understand the information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit inputs the user's language setting data into an AI model, and the AI ​​makes the display content multilingual.

[0065] When providing a report, the providing unit can visually display important information using graphs and charts. Examples of graphs and charts include, but are not limited to, bar graphs, pie charts, and line graphs. For example, the providing unit displays important information in a graph to make it easier to understand visually. For example, the providing unit displays important information in a chart to make it easier to understand visually. The providing unit can also provide customization options for visually displaying important information. For example, the providing unit provides customization options for visually displaying important information. As a result, using graphs and charts makes it easier to understand important information visually. Some or all of the above-described processing in the providing unit may be performed using AI or without AI. For example, the providing unit inputs summarized information into an AI model, and the AI ​​generates graphs and charts.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The reception unit can analyze the user's past contract history and select an appropriate input method. Past contract history includes, but is not limited to, past contracts, contract terms, and performance status. For example, if the user has previously preferred voice input, the reception unit suggests voice input. For example, if the user has previously used text input, the reception unit prioritizes text input. Furthermore, the reception unit can also suggest image input if the user has previously used image input. For example, if the user has previously used image input, the reception unit suggests image input. By analyzing the user's past contract history, the reception unit can provide the user with the optimal input method and improve input efficiency. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's past contract history data into an AI model, and the AI ​​selects the optimal input method.

[0068] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the terms and conditions. Examples of the importance of terms and conditions include, but are not limited to, legal risk, economic risk, and user interest. The summarization unit provides a detailed summary for important terms and conditions. For example, it summarizes important contract terms and risk factors in detail. The summarization unit can also provide a concise summary for less important terms and conditions. For example, it briefly summarizes less important contract terms and risk factors. The summarization unit can also provide a detailed summary for terms and conditions in which the user is particularly interested. For example, it summarizes contract terms and risk factors in which the user is most interested in detail. This allows the user to quickly grasp important information by adjusting the level of detail of the summary based on the importance of the terms and conditions. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the importance data of the terms and conditions into the generation AI, and the generation AI adjusts the level of detail of the summary.

[0069] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between terms and conditions. Examples of interrelationships between terms and conditions include, but are not limited to, related clauses and interdependencies. For example, the comparison unit compares related terms and conditions by taking into account the interrelationships between terms and conditions. For example, it compares important points by taking into account the interrelationships between terms and conditions. The comparison unit can also compare high-risk points by taking into account the interrelationships between terms and conditions. For example, it compares high-risk points by taking into account the interrelationships between terms and conditions. In this way, the accuracy of the comparison can be improved by taking into account the interrelationships between terms and conditions. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit inputs data on the interrelationships between terms and conditions into an AI model, and the AI ​​improves the accuracy of the comparison.

[0070] During analysis, the analysis unit can apply different risk analysis methods to different categories of terms and conditions. Examples of categories of terms and conditions include, but are not limited to, service contracts, sales contracts, and license contracts. The analysis unit can apply a specific risk analysis method to service contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for service contracts. The analysis unit can also apply a different risk analysis method to purchase contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for purchase contracts. The analysis unit can also apply yet another risk analysis method to insurance contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for insurance contracts. This allows the application of different risk analysis methods to each category of terms and conditions, thereby improving the accuracy of the risk analysis. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs category data of the terms and conditions into the generation AI, which then applies an appropriate risk analysis method.

[0071] When providing a report, the providing unit can select an optimal display method by referring to the user's past operation history. Past operation history includes, but is not limited to, past click history, browsing history, etc. For example, the providing unit provides a report by referring to a display method that the user previously preferred. For example, the providing unit provides a report by referring to a display method that the user previously found easy to understand. The providing unit can also provide a report by referring to a display method for which the user previously provided feedback. For example, the providing unit provides a report based on feedback the user previously provided. In this way, by referring to the user's past operation history, the optimal display method can be provided to the user and understanding of the information can be promoted. Some or all of the above-mentioned processing in the providing unit may be performed using AI or without AI. For example, the providing unit inputs the user's past operation history data into an AI model, and the AI ​​selects the optimal display method.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit inputs the terms and conditions of the contract the user is about to enter into. The user can input terms and conditions such as service contracts, sales contracts, and license agreements in text or image format. If the input is in image format, it is converted into text data using OCR technology. Step 2: The summary section uses a generation AI to analyze the terms and conditions entered by the reception section and summarize the key points. The summary is based on the contract's main conditions and restrictions, fee structure, cancellation conditions, etc. Step 3: The comparison section compares the information summarized by the summary section with standard terms and conditions. The comparison is based on a template of a typical service contract. Step 4: The analysis unit uses the generative AI to analyze the legal and economic risks based on the results obtained by the comparison unit, with reference to past cases and legal documents. Step 5: The presentation unit provides the user with an easy-to-understand report based on the results of the analysis by the analysis unit. The report uses graphs and charts to visually display the summarized information.

[0074] (Example 2) The generation AI service according to an embodiment of the present invention is a system that efficiently analyzes, summarizes, compares, and analyzes the terms and conditions of a contract that a user is about to enter into, and provides an easy-to-understand report. The generation AI service inputs the terms and conditions of a contract that a user is about to enter into. The generation AI analyzes the content and summarizes key points. For example, it concisely summarizes the contract's main conditions and restrictions, fee structure, and cancellation conditions. Next, the generation AI service compares the input terms and conditions with standard terms and conditions. For example, it compares them with general service contracts to identify any differences in specific terms or additional restrictions. Furthermore, the generation AI service analyzes legal and economic risks. For example, it evaluates potential legal risks and economic burdens contained in the contract. The generation AI service also evaluates the validity of the contract based on the applicant's prior information. For example, it considers information such as the user's age, occupation, and income to determine whether the contract is appropriate. Finally, the generation AI service presents specific problems. For example, it points out any unclear points or unfavorable terms in the contract. This allows the generation AI service to easily understand complex terms and conditions and identify risks in advance. This allows the generative AI service to efficiently analyze, summarize, compare, and analyze the terms and conditions and regulations that users are about to enter into, and provide easy-to-understand reports.For example, by quickly and accurately analyzing the terms and conditions and regulations that users are about to enter into, and providing important information, it becomes possible to prevent problems at the time of signing and to conclude a safe and appropriate contract.

[0075] The generation AI service according to the embodiment includes a reception unit, a summarization unit, a comparison unit, an analysis unit, and a provision unit. The reception unit inputs the terms and conditions a user intends to enter into. The terms and conditions a user intends to enter into include, but are not limited to, service contracts, sales contracts, and license agreements. The reception unit inputs, for example, the terms and conditions a user intends to enter into in text format. The reception unit can also input the terms and conditions a user intends to enter into in image format and convert them into text data using OCR technology. The summarization unit uses a generation AI to analyze the terms and conditions input by the reception unit and summarize key points. The summarization is based on, for example, but is not limited to, the main conditions and constraints of the contract, the fee structure, and cancellation conditions. For example, the generation AI uses a text generation AI to concisely summarize the terms and conditions. The summarization unit can also use the generation AI to extract and summarize key points of the contract. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The comparison unit compares the information summarized by the summarization unit with standard terms and conditions. The comparison is performed, for example, but not limited to, with a standard service contract template. For example, the comparison unit identifies the uniqueness and risks of the entered terms and conditions and regulations based on a standard service contract template. The analysis unit uses the generation AI to analyze legal and economic risks based on the results obtained by the comparison unit. The analysis is performed, for example, but not limited to, with reference to past court cases and legal documents. For example, the generation AI evaluates potential legal risks contained in the contract based on past court case data. The analysis unit can also evaluate the fee structure and payment terms contained in the contract. For example, the generation AI evaluates economic risks based on the contract's fee structure and payment terms. The provision unit provides an easy-to-understand report to the user based on the results analyzed by the analysis unit. The report may, for example, use graphs and charts to visually display the summarized information. For example, but not limited to, the provision unit visually displays the summarized information in graphs and charts to make the information easier for the user to understand.As a result, the generation AI service of the embodiment can efficiently analyze, summarize, compare, and analyse the terms and conditions and regulations that a user is about to enter into, and provide an easy-to-understand report.

[0076] The summarization unit can use a generation AI to summarize the contract's key terms, constraints, fee structure, and cancellation conditions. Examples of generation AI include, but are not limited to, natural language generation models such as transformer-based models. The summarization unit can also use a generation AI to summarize the contract's key terms. For example, it can briefly summarize the contract period, payment terms, and penalty fees. The summarization unit can also use a generation AI to summarize the contract's constraints. For example, it can extract and summarize the contract's constraints and specific conditions. The summarization unit can also use a generation AI to summarize the contract's fee structure. For example, it can briefly summarize the fee breakdown and payment schedule. The summarization unit can also use a generation AI to summarize the contract's cancellation conditions. For example, it can briefly summarize the cancellation procedure and penalty fee terms. This allows the generation AI to efficiently summarize the contract's key terms, constraints, fee structure, cancellation conditions, and so on. Some or all of the above-described processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the contract's text data into a generation AI, which then generates a summary.

[0077] The comparison unit may compare the entered terms and conditions with a standard service contract template. Examples of standard service contract templates include, but are not limited to, industry-standard contracts and legally recognized templates. The comparison unit may identify the uniqueness and risks of the entered terms and conditions based on the standard service contract template. For example, the comparison unit may compare the entered terms and conditions with the standard service contract template to identify any differences in specific conditions or additional restrictions. The comparison unit may also evaluate the risks of the entered terms and conditions based on the standard service contract template. For example, the comparison unit may compare the entered terms and conditions with the standard service contract template to identify potential risks. This makes it easier to understand the uniqueness and risks of the entered terms and conditions by using a common service contract template as a reference. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit may input the standard service contract template and the entered terms and conditions into an AI model, and the AI ​​may output the comparison results.

[0078] The analysis unit can use the generative AI to refer to past precedents and legal documents to evaluate potential legal risks. Examples of generative AI include, but are not limited to, natural language generation models such as transformer-based models. The analysis unit can, for example, use the generative AI to evaluate potential legal risks in a contract based on past precedent data. For example, the generative AI can refer to court decisions, law books, and legal databases to identify legal risks associated with the contract. The analysis unit can also use the generative AI to refer to legal documents to evaluate potential legal risks in a contract. For example, the generative AI can evaluate legal risks associated with a contract based on past precedent data. This allows the generative AI to efficiently evaluate potential legal risks by referring to past precedents and legal documents. Some or all of the above-described processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs past precedent data into the generative AI, which then evaluates the legal risks.

[0079] The analysis unit can evaluate the fee structure and payment terms included in the contract. The fee structure and payment terms include, but are not limited to, payment schedules, discount terms, and penalty fees. The analysis unit, for example, evaluates the fee structure included in the contract. For example, it evaluates the fee breakdown and payment schedule to identify economic risks. The analysis unit can also evaluate the payment terms included in the contract. For example, it evaluates the payment schedule and discount terms to identify economic risks. The analysis unit can also evaluate penalty fee terms included in the contract. For example, it evaluates the amount of penalty fees and payment terms to identify economic risks. In this way, by evaluating the fee structure and payment terms included in the contract, economic risks can be grasped in advance. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the fee structure and payment terms of the contract into an AI model, and the AI ​​evaluates economic risks.

[0080] The providing unit can visually display the summarized information using a graph, chart, or the like. Examples of visual display methods include, but are not limited to, graphs, charts, dashboards, and the like. The providing unit, for example, displays the summarized information using a graph. For example, the main terms and constraints of the contract, the fee structure, the cancellation conditions, etc. are visually displayed using a graph. The providing unit can also display the summarized information using a chart. For example, the main terms and constraints of the contract, the fee structure, the cancellation conditions, etc. are visually displayed using a chart. The providing unit can also display the summarized information using a dashboard. For example, the main terms and constraints of the contract, the fee structure, the cancellation conditions, etc. are visually displayed using a dashboard. This visual display of the summarized information makes it easier for the user to understand the information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit inputs the summarized information into an AI model, and the AI ​​generates a graph or chart.

[0081] The providing unit can present specific information in an easy-to-understand manner using graphs and charts. Specific information includes, but is not limited to, contract terms, risk factors, and fee structures. The providing unit, for example, displays contract terms in a graph. For example, the main conditions and constraints of the contract, the fee structure, and cancellation conditions are visually displayed in a graph. The providing unit can also display risk factors in a chart. For example, potential legal and financial risks included in the contract are visually displayed in a chart. The providing unit can also display the fee structure in a dashboard. For example, the fee structure and payment terms of the contract are visually displayed in a dashboard. Thus, using graphs and charts makes important information visually easier to understand. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit inputs summarized information into an AI model, which then generates graphs and charts.

[0082] The reception unit can estimate the user's emotions and dynamically adjust the timing of entering the terms and conditions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to enter the terms and conditions at a time when the user is able to relax. For example, if the user is concentrating, the reception unit can prompt the user to enter the terms and conditions immediately. Also, if the user is tired, the reception unit can prompt the user to enter the terms and conditions after a break. This adjusts the timing of entering the terms and conditions according to the user's emotions, thereby reducing the user's stress and promoting efficient input. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. For example, the reception unit inputs the user's facial expression data into a generation AI, which then estimates the user's emotions. Furthermore, the reception unit can analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the reception unit can monitor the user's facial expressions in real time and immediately detect changes in emotions. The reception unit can also accumulate the user's emotional data and analyze long-term emotional trends. For example, the reception unit can analyze the user's emotional data over time to identify patterns of emotional changes. This allows the user's emotions to be grasped in detail and the input timing to be dynamically adjusted.

[0083] The reception unit can analyze the user's past contract history and select an appropriate input method. Past contract history includes, but is not limited to, past contracts, contract terms, and performance status. For example, if the user has previously preferred voice input, the reception unit suggests voice input. For example, if the user has previously used text input, the reception unit prioritizes text input. Furthermore, the reception unit can also suggest image input if the user has previously used image input. For example, if the user has previously used image input, the reception unit suggests image input. By analyzing the user's past contract history, the reception unit can provide the user with the optimal input method and improve input efficiency. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's past contract history data into an AI model, and the AI ​​selects the optimal input method.

[0084] When inputting the terms and conditions, the reception unit can perform filtering based on the user's current contract status and areas of interest. Examples of the current contract status and areas of interest include, but are not limited to, the type of current contract and the user's interests. For example, the reception unit can prioritize displaying terms and conditions related to the service to which the user currently subscribes. For example, the reception unit can prioritize displaying terms and conditions related to the user's areas of interest. The reception unit can also prioritize displaying highly relevant terms and conditions based on the user's past contract history. For example, the reception unit can prioritize displaying highly relevant terms and conditions based on the user's past contract history. In this way, filtering based on the user's current contract status and areas of interest can prioritize displaying highly relevant terms and conditions. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's current contract status and area of ​​interest data into an AI model, and the AI ​​performs filtering.

[0085] When inputting the terms and conditions, the reception unit can select the optimal input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit inputs the terms and conditions using voice recognition technology. For example, if the user selects text input, the reception unit provides a text box to input the terms and conditions. Furthermore, if the user selects image input, the reception unit can also input the terms and conditions using image recognition technology. For example, if the user selects image input, the reception unit inputs the terms and conditions using image recognition technology. This allows for selecting the optimal input means depending on the user's input method, thereby improving user convenience. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's input method data into an AI model, and the AI ​​selects the optimal input means.

[0086] The reception unit can estimate the user's emotions and determine the priority of the terms and conditions to be input based on the estimated user emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the reception unit can prioritize displaying important terms and conditions. For example, if the user is relaxed, the reception unit can prioritize displaying detailed terms and conditions. The reception unit can also prioritize displaying concise terms and conditions if the user is in a hurry. For example, if the user is in a hurry, the reception unit can prioritize displaying concise terms and conditions. This allows the user to quickly grasp important information by determining the priority of the terms and conditions according to the user's emotions. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's emotion data into an AI model, and the AI ​​determines the priority of the terms and conditions.

[0087] When inputting the terms and conditions, the reception unit can prioritize inputting highly relevant terms and conditions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the reception unit prioritizes displaying terms and conditions related to that area. For example, when the user is traveling, the reception unit prioritizes displaying terms and conditions related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize displaying terms and conditions related to the user's home. For example, when the user is at home, the reception unit prioritizes displaying terms and conditions related to the user's home. This allows highly relevant terms and conditions to be displayed preferentially by taking the user's geographical location information into consideration, thereby improving user convenience. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information data into an AI model, and the AI ​​prioritizes displaying highly relevant terms and conditions.

[0088] When inputting the terms and conditions, the reception unit can analyze the user's social media activity and input the relevant terms and conditions. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit, for example, displays terms and conditions related to places where the user has checked in on social media. For example, the reception unit analyzes the content of the user's social media posts and displays the relevant terms and conditions. The reception unit can also display the relevant terms and conditions based on the activities of the user's friends on social media. For example, the reception unit displays the relevant terms and conditions based on the activities of the user's friends on social media. By analyzing the user's social media activity, the reception unit can prioritize the display of highly relevant terms and conditions, improving user convenience. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's social media activity data into an AI model, and the AI ​​displays the relevant terms and conditions.

[0089] When entering the terms and conditions, the reception unit can customize the input method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings, comments, and survey results. For example, if the user has previously preferred voice input, the reception unit suggests voice input. For example, if the user has previously used text input frequently, the reception unit prioritizes text input. The reception unit can also suggest image input if the user has previously used image input. For example, if the user has previously used image input, the reception unit suggests image input. By reflecting the user's past feedback, the reception unit can provide the user with the optimal input method and improve input efficiency. Some or all of the above-described processing by the reception unit may be performed using AI or without AI. For example, the reception unit inputs the user's past feedback data into an AI model, and the AI ​​customizes the input method.

[0090] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the summarization unit provides a concise and to-the-point summary. For example, if the user is relaxed, the summarization unit provides a detailed summary. Furthermore, if the user is in a hurry, the summarization unit can also provide a summary that can be understood in a short time. For example, if the user is in a hurry, the summarization unit provides a concise summary. This allows the user to easily understand the information by adjusting the way the summary is presented according to the user's emotions. Some or all of the above-described processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the user's emotion data into the generation AI, which then adjusts the way the summary is presented.

[0091] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the terms and conditions. Examples of the importance of terms and conditions include, but are not limited to, legal risk, economic risk, and user interest. The summarization unit provides a detailed summary for important terms and conditions. For example, it summarizes important contract terms and risk factors in detail. The summarization unit can also provide a concise summary for less important terms and conditions. For example, it briefly summarizes less important contract terms and risk factors. The summarization unit can also provide a detailed summary for terms and conditions in which the user is particularly interested. For example, it summarizes contract terms and risk factors in which the user is most interested in detail. This allows the user to quickly grasp important information by adjusting the level of detail of the summary based on the importance of the terms and conditions. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the importance data of the terms and conditions into the generation AI, and the generation AI adjusts the level of detail of the summary.

[0092] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the terms and conditions. Examples of categories of terms and conditions include, but are not limited to, service contracts, sales contracts, and license agreements. For example, the summarization unit applies a specific summarization algorithm to the terms and conditions of a service contract. For example, a summary is generated using a summarization algorithm specialized for service contracts. Furthermore, the summarization unit can apply a different summarization algorithm to the terms and conditions of a purchase contract. For example, a summary is generated using a summarization algorithm specialized for purchase contracts. Furthermore, the summarization unit can apply yet another summarization algorithm to the terms and conditions of an insurance contract. For example, a summary is generated using a summarization algorithm specialized for insurance contracts. This allows for the application of different summarization algorithms depending on the category of the terms and conditions, thereby improving the accuracy of the summary. Some or all of the above-described processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs category data of the terms and conditions into the generation AI, which then applies an appropriate summarization algorithm.

[0093] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. Past summarization results include, but are not limited to, past summary sentences and summary evaluation results. For example, the summarization unit generates a summary by referring to a summarization style that the user previously preferred. For example, the summarization unit generates a summary based on a summarization style that the user previously found easy to understand. The summarization unit can also generate a summary by referring to summaries for which the user provided feedback in the past. For example, the summarization unit generates a summary based on feedback the user provided in the past. In this way, the accuracy of the summary can be improved by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the user's past summary result data into the generation AI, and the generation AI improves the accuracy of the summary.

[0094] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The summarization unit can provide a short summary if the user is stressed, for example. If the user is relaxed, for example, it can provide a detailed summary. The summarization unit can also provide a concise summary if the user is in a hurry. For example, if the user is in a hurry, it can provide a concise summary. This allows the user to easily understand the information by adjusting the length of the summary according to the user's emotions. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the user's emotion data into the generation AI, which then adjusts the length of the summary.

[0095] When generating summaries, the summarization unit can determine the priority of summaries based on the submission date of the terms and conditions. The submission date of the terms and conditions includes, but is not limited to, the submission date, update date, etc. The summarization unit, for example, prioritizes summarization of recently submitted terms and conditions. For example, a concise summary is provided for terms and conditions that were submitted recently. The summarization unit can also provide a detailed summary for terms and conditions that have recently been submitted. For example, a detailed summary is provided for terms and conditions that have recently been submitted. In this way, by determining the priority of summaries based on the submission date of the terms and conditions, the latest information can be prioritized for summarization. Some or all of the above-mentioned processing in the summarization unit is performed using the generation AI. For example, the summarization unit inputs data on the submission date of the terms and conditions into the generation AI, and the generation AI determines the priority of summaries.

[0096] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the terms and conditions. Examples of the relevance of the terms and conditions include, but are not limited to, similarity of content and related laws and regulations. For example, the summarization unit prioritizes summarization of terms and conditions that are highly relevant to the user. For example, a concise summary is provided for terms and conditions that are less relevant to the user. The summarization unit can also prioritize summarization of terms and conditions related to the user's area of ​​interest. For example, terms and conditions related to the user's area of ​​interest are prioritized. This allows the user to quickly grasp important information by adjusting the order of summaries based on the relevance of the terms and conditions. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs relevance data of the terms and conditions into the generation AI, and the generation AI adjusts the order of the summaries.

[0097] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has technical expertise, the summarization unit provides a summary that uses a lot of technical terms. For example, if the user does not have technical expertise, the summarization unit provides a summary in simple language. The summarization unit can also provide a summary using appropriate technical terms according to the user's level of expertise. For example, a summary using appropriate technical terms is provided according to the user's level of expertise. This makes it easier for the user to understand the information by adjusting the use of technical terms in the summary according to the user's level of expertise. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the user's level of expertise data into the generation AI, which then adjusts the use of technical terms in the summary.

[0098] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated user emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, or survey results. For example, if the user is stressed, the comparison unit uses simple comparison criteria. For example, if the user is relaxed, the comparison unit uses detailed comparison criteria. Furthermore, if the user is in a hurry, the comparison unit can also use criteria that allow for quick comparison. This allows the user to easily understand information by adjusting the comparison criteria according to the user's emotions. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit inputs the user's emotion data into an AI model, and the AI ​​adjusts the comparison criteria.

[0099] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between terms and conditions. Examples of interrelationships between terms and conditions include, but are not limited to, related clauses and interdependencies. For example, the comparison unit compares related terms and conditions by taking into account the interrelationships between terms and conditions. For example, it compares important points by taking into account the interrelationships between terms and conditions. The comparison unit can also compare high-risk points by taking into account the interrelationships between terms and conditions. For example, it compares high-risk points by taking into account the interrelationships between terms and conditions. In this way, the accuracy of the comparison can be improved by taking into account the interrelationships between terms and conditions. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit inputs data on the interrelationships between terms and conditions into an AI model, and the AI ​​improves the accuracy of the comparison.

[0100] When making a comparison, the comparison unit can take into account the attribute information of the person who submitted the terms and conditions. The attribute information of the person who submitted the terms and conditions includes, but is not limited to, company information, personal information, and past contract history. For example, the comparison unit compares related terms and conditions taking into account the attribute information of the person who submitted the terms and conditions. For example, it compares important points taking into account the attribute information of the person who submitted the terms and conditions. The comparison unit can also compare high-risk points taking into account the attribute information of the person who submitted the terms and conditions. For example, it compares high-risk points taking into account the attribute information of the person who submitted the terms and conditions. This allows the accuracy of the comparison to be improved by taking into account the attribute information of the person who submitted the terms and conditions. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit inputs the attribute information data of the person who submitted the terms and conditions into an AI model, and the AI ​​performs the comparison.

[0101] When comparing, the comparison unit can weight the comparison based on the frequency of submission of the terms and conditions. Examples of the frequency of submission include, but are not limited to, the number of submissions and the interval between submissions. For example, the comparison unit prioritizes comparison of terms and conditions that are submitted more frequently. For example, for terms and conditions that are submitted less frequently, a brief comparison is performed. The comparison unit can also compare important points based on the frequency of submission. For example, important points are compared based on the frequency of submission. By weighting the comparison based on the frequency of submission, important information can be prioritized in the comparison. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit inputs submission frequency data into an AI model, and the AI ​​weights the comparison.

[0102] The comparison unit can estimate the user's emotions and adjust the order in which the comparison results are displayed based on the estimated user emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, or survey results. For example, if the user is feeling stressed, the comparison unit can prioritize displaying important results. For example, if the user is relaxed, the comparison unit can prioritize displaying detailed results. The comparison unit can also prioritize displaying concise results if the user is in a hurry. For example, if the user is in a hurry, the comparison unit can prioritize displaying concise results. This allows the user to quickly grasp important information by adjusting the order in which the comparison results are displayed according to the user's emotions. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit inputs the user's emotion data into an AI model, and the AI ​​adjusts the order in which the comparison results are displayed.

[0103] The comparison unit may take into account the geographical distribution of the terms and conditions when making the comparison. Examples of geographical distribution include, but are not limited to, the number of contracts by region and the risks by region. For example, the comparison unit may prioritize comparing terms and conditions from geographically close regions. For example, a brief comparison may be made for terms and conditions from geographically distant regions. The comparison unit may also compare important points based on the geographical distribution. This allows for prioritized comparison of highly relevant information by taking the geographical distribution into consideration. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit may input geographical distribution data into an AI model, and the AI ​​will perform the comparison.

[0104] The comparison unit may improve the accuracy of the comparison by referring to related literature of the terms and conditions during the comparison. Examples of related literature include, but are not limited to, law books, case law collections, and legal databases. For example, the comparison unit may compare important points of the terms and conditions by referring to related literature. For example, the comparison unit may compare high-risk points of the terms and conditions by referring to related literature. The comparison unit may also compare interrelationships of the terms and conditions by referring to related literature. For example, the interrelationships of the terms and conditions may be compared by referring to related literature. In this way, the accuracy of the comparison can be improved by referring to related literature. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit inputs related literature data into an AI model, and the AI ​​performs the comparison.

[0105] The comparison unit may take into account the market value of the terms and conditions when making the comparison. Market value includes, but is not limited to, for example, the contract amount, the size of the transaction, and the industry average. For example, the comparison unit may prioritize the comparison of terms and conditions with high market value. For example, for terms and conditions with low market value, a brief comparison may be performed. The comparison unit may also compare important points based on market value. For example, important points may be compared based on market value. This allows important information to be compared preferentially by taking market value into consideration. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit inputs market value data into an AI model, and the AI ​​performs the comparison.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the risk analysis based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The analysis unit provides a concise and highly visible risk analysis when the user is stressed, for example. For example, if the user is relaxed, it provides a detailed risk analysis. The analysis unit can also provide a risk analysis that focuses on the main points when the user is in a hurry. For example, if the user is in a hurry, it provides a risk analysis that focuses on the main points. This makes it easier for the user to understand the information by adjusting the display method of the risk analysis according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the display method of the risk analysis.

[0107] During analysis, the analysis unit can predict current risks by referring to past case law data. Past case law data includes, but is not limited to, court decisions, law books, and legal databases. The analysis unit, for example, predicts current legal risks based on past case law data. For example, it predicts current economic risks based on past case law data. The analysis unit can also predict current contract risks based on past case law data. For example, it predicts current contract risks based on past case law data. In this way, by referring to past case law data, current risks can be accurately predicted. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs past case law data into the generation AI, which then predicts current risks.

[0108] During analysis, the analysis unit can apply different risk analysis methods to different categories of terms and conditions. Examples of categories of terms and conditions include, but are not limited to, service contracts, sales contracts, and license contracts. The analysis unit can apply a specific risk analysis method to service contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for service contracts. The analysis unit can also apply a different risk analysis method to purchase contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for purchase contracts. The analysis unit can also apply yet another risk analysis method to insurance contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for insurance contracts. This allows the application of different risk analysis methods to each category of terms and conditions, thereby improving the accuracy of the risk analysis. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs category data of the terms and conditions into the generation AI, which then applies an appropriate risk analysis method.

[0109] During analysis, the analysis unit can analyze risks by taking into account the attribute information of the person who submitted the terms and conditions. The attribute information of the person who submitted the terms and conditions includes, but is not limited to, corporate information, personal information, and past contract history. For example, the analysis unit analyzes legal risks by taking into account the attribute information of the person who submitted the terms and conditions. For example, it analyzes economic risks by taking into account the attribute information of the person who submitted the terms and conditions. The analysis unit can also analyze contract risks by taking into account the attribute information of the person who submitted the terms and conditions. For example, it analyzes contract risks by taking into account the attribute information of the person who submitted the terms and conditions. In this way, the accuracy of risk analysis can be improved by taking into account the attribute information of the person who submitted the terms and conditions. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs attribute information data of the person who submitted the terms and conditions into the generation AI, and the generation AI analyzes risks.

[0110] The analysis unit can estimate the user's emotions and adjust the importance of risks based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the analysis unit prioritizes displaying important risks. For example, if the user is relaxed, the analysis unit displays detailed risks. The analysis unit can also display concise risks if the user is in a hurry. For example, if the user is in a hurry, the analysis unit displays concise risks. This allows the user to quickly grasp important information by adjusting the importance of risks according to the user's emotions. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI, which then adjusts the importance of risks.

[0111] During analysis, the analysis unit can analyze changes in risk based on the submission date of the terms and conditions. The submission date of the terms and conditions includes, but is not limited to, for example, the submission date and the update date. For example, for recently submitted terms and conditions, the analysis unit analyzes the latest risks. For example, for older submitted terms and conditions, the analysis unit analyzes past risks. The analysis unit can also analyze changes in risk based on the submission date. For example, it analyzes changes in risk based on the submission date. In this way, by analyzing changes in risk based on the submission date of the terms and conditions, it is possible to accurately grasp the latest risks. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on the submission date of the terms and conditions into the generation AI, and the generation AI analyzes changes in risk.

[0112] During analysis, the analysis unit can analyze risks by referring to market data related to the terms and conditions. Examples of related market data include, but are not limited to, market research reports and industry statistical data. For example, the analysis unit analyzes economic risks based on the relevant market data. For example, it analyzes legal risks based on the relevant market data. The analysis unit can also analyze contract risks based on the relevant market data. For example, it analyzes contract risks based on the relevant market data. This makes it possible to accurately analyze economic and legal risks by referring to the relevant market data. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs relevant market data into the generation AI, which then analyzes the risks.

[0113] During analysis, the analysis unit can analyze risks by taking into account the technical maturity of the terms and conditions. Examples of technical maturity include, but are not limited to, the development stage of technology and its prevalence in the market. For example, the analysis unit analyzes terms and conditions with high technical maturity as having low risk. For example, the analysis unit analyzes terms and conditions with low technical maturity as having high risk. The analysis unit can also analyze risks based on technical maturity. For example, risk is analyzed based on technical maturity. In this way, the level of risk can be accurately evaluated by taking technical maturity into consideration. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs technical maturity data into the generation AI, and the generation AI analyzes the risk.

[0114] The providing unit can estimate the user's emotions and adjust the display method of the report based on the estimated user's emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the providing unit provides a concise and highly visible report. For example, if the user is relaxed, the providing unit provides a detailed report. The providing unit can also provide a report that focuses on the main points if the user is in a hurry. For example, if the user is in a hurry, the providing unit provides a report that focuses on the main points. This makes it easier for the user to understand the information by adjusting the display method of the report according to the user's emotions. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the display method of the report.

[0115] When providing a report, the providing unit can select an optimal display method by referring to the user's past operation history. Past operation history includes, but is not limited to, past click history, browsing history, etc. For example, the providing unit provides a report by referring to a display method that the user previously preferred. For example, the providing unit provides a report by referring to a display method that the user previously found easy to understand. The providing unit can also provide a report by referring to a display method for which the user previously provided feedback. For example, the providing unit provides a report based on feedback the user previously provided. In this way, by referring to the user's past operation history, the optimal display method can be provided to the user and understanding of the information can be promoted. Some or all of the above-mentioned processing in the providing unit may be performed using AI or without AI. For example, the providing unit inputs the user's past operation history data into an AI model, and the AI ​​selects the optimal display method.

[0116] When providing a report, the providing unit can customize the display content according to the user's current task. The current task includes, but is not limited to, for example, the current work content and priority. For example, the providing unit prioritizes displaying information related to the user's current task. For example, important information is displayed according to the user's current task. The providing unit can also customize the report content based on the user's current task. For example, the report content is customized based on the user's current task. This allows the user to quickly grasp the necessary information by customizing the display content according to the user's current task. Some or all of the above-mentioned processing in the providing unit may be performed using AI or without AI. For example, the providing unit inputs the user's current task data into an AI model, and the AI ​​customizes the display content.

[0117] The providing unit can improve the display method by reflecting user feedback when providing a report. User feedback includes, but is not limited to, survey results, comments, and ratings. The providing unit, for example, improves the display method based on user feedback. For example, important information is displayed preferentially based on user feedback. The providing unit can also customize the content of the report based on user feedback. For example, the content of the report is customized based on user feedback. This allows the display method to be improved by reflecting user feedback, thereby improving user convenience. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit inputs user feedback data into an AI model, and the AI ​​improves the display method.

[0118] The providing unit can estimate the user's emotions and adjust the report operation procedures based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The providing unit, for example, provides simple operation procedures when the user is stressed. For example, it provides detailed operation procedures when the user is relaxed. The providing unit can also provide procedures that allow quick operation when the user is in a hurry. For example, it provides procedures that allow quick operation when the user is in a hurry. This allows the user to quickly grasp information by adjusting the report operation procedures according to the user's emotions. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the report operation procedures.

[0119] When providing a report, the providing unit can select the optimal display method by taking into account the user's device information. Device information includes, but is not limited to, the device type, screen size, and OS. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, if the user is using a tablet, the providing unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible display method. This allows the user's device information to be taken into account to provide the optimal display method and promote understanding of the information. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit inputs the user's device information data into an AI model, and the AI ​​selects the optimal display method.

[0120] When providing a report, the providing unit can make the display content multilingual according to the user's language setting. Language settings include, but are not limited to, the user's browser settings and app language settings. For example, the providing unit automatically sets the report language based on the user's device language setting. For example, if the user uses multiple languages, the providing unit provides a language switching function. Furthermore, if the user selects a specific language, the providing unit can also provide the report in that language. For example, if the user selects a specific language, the report is provided in that language. This makes it easier for the user to understand the information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit inputs the user's language setting data into an AI model, and the AI ​​makes the display content multilingual.

[0121] When providing a report, the providing unit can visually display important information using graphs and charts. Examples of graphs and charts include, but are not limited to, bar graphs, pie charts, and line graphs. For example, the providing unit displays important information in a graph to make it easier to understand visually. For example, the providing unit displays important information in a chart to make it easier to understand visually. The providing unit can also provide customization options for visually displaying important information. For example, the providing unit provides customization options for visually displaying important information. As a result, using graphs and charts makes it easier to understand important information visually. Some or all of the above-described processing in the providing unit may be performed using AI or without AI. For example, the providing unit inputs summarized information into an AI model, and the AI ​​generates graphs and charts. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, summarization unit, comparison unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The summarization unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The comparison unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The reception unit can estimate the user's emotion and dynamically adjust the input timing of the terms and conditions based on the estimated user's emotion. For example, the user's emotion is estimated using the camera 42 or microphone 38B of the smart device 14, and is analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, summarization unit, comparison unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The summarization unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The comparison unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The reception unit can estimate the user's emotion and dynamically adjust the input timing of the terms and conditions based on the estimated user's emotion. For example, the estimation of the user's emotions is performed using the camera 42 and microphone 238 of the smart glasses 214 and analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, summarization unit, comparison unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The summarization unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The comparison unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The reception unit can estimate the user's emotion and dynamically adjust the input timing of the terms and conditions based on the estimated user's emotion. For example, the user's emotion is estimated using the camera 42 or microphone 238 of the headset terminal 314 and analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, summarization unit, comparison unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The summarization unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The comparison unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The reception unit can estimate the user's emotions and dynamically adjust the timing of inputting the terms and conditions based on the estimated user's emotions. For example, the user's emotions are estimated using the camera 42 or microphone 238 of the robot 414 and analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The reception unit can analyze the user's past contract history and select an appropriate input method. Past contract history includes, but is not limited to, past contracts, contract terms, and performance status. For example, if the user has previously preferred voice input, the reception unit suggests voice input. For example, if the user has previously used text input, the reception unit prioritizes text input. Furthermore, the reception unit can also suggest image input if the user has previously used image input. For example, if the user has previously used image input, the reception unit suggests image input. By analyzing the user's past contract history, the reception unit can provide the user with the optimal input method and improve input efficiency. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's past contract history data into an AI model, and the AI ​​selects the optimal input method.

[0124] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the terms and conditions. Examples of the importance of terms and conditions include, but are not limited to, legal risk, economic risk, and user interest. The summarization unit provides a detailed summary for important terms and conditions. For example, it summarizes important contract terms and risk factors in detail. The summarization unit can also provide a concise summary for less important terms and conditions. For example, it briefly summarizes less important contract terms and risk factors. The summarization unit can also provide a detailed summary for terms and conditions in which the user is particularly interested. For example, it summarizes contract terms and risk factors in which the user is most interested in detail. This allows the user to quickly grasp important information by adjusting the level of detail of the summary based on the importance of the terms and conditions. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the importance data of the terms and conditions into the generation AI, and the generation AI adjusts the level of detail of the summary.

[0125] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between terms and conditions. Examples of interrelationships between terms and conditions include, but are not limited to, related clauses and interdependencies. For example, the comparison unit compares related terms and conditions by taking into account the interrelationships between terms and conditions. For example, it compares important points by taking into account the interrelationships between terms and conditions. The comparison unit can also compare high-risk points by taking into account the interrelationships between terms and conditions. For example, it compares high-risk points by taking into account the interrelationships between terms and conditions. In this way, the accuracy of the comparison can be improved by taking into account the interrelationships between terms and conditions. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit inputs data on the interrelationships between terms and conditions into an AI model, and the AI ​​improves the accuracy of the comparison.

[0126] During analysis, the analysis unit can apply different risk analysis methods to different categories of terms and conditions. Examples of categories of terms and conditions include, but are not limited to, service contracts, sales contracts, and license contracts. The analysis unit can apply a specific risk analysis method to service contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for service contracts. The analysis unit can also apply a different risk analysis method to purchase contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for purchase contracts. The analysis unit can also apply yet another risk analysis method to insurance contract terms and conditions. For example, it can analyze risk using a risk analysis method specialized for insurance contracts. This allows the application of different risk analysis methods to each category of terms and conditions, thereby improving the accuracy of the risk analysis. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs category data of the terms and conditions into the generation AI, which then applies an appropriate risk analysis method.

[0127] When providing a report, the providing unit can select an optimal display method by referring to the user's past operation history. Past operation history includes, but is not limited to, past click history, browsing history, etc. For example, the providing unit provides a report by referring to a display method that the user previously preferred. For example, the providing unit provides a report by referring to a display method that the user previously found easy to understand. The providing unit can also provide a report by referring to a display method for which the user previously provided feedback. For example, the providing unit provides a report based on feedback the user previously provided. In this way, by referring to the user's past operation history, the optimal display method can be provided to the user and understanding of the information can be promoted. Some or all of the above-mentioned processing in the providing unit may be performed using AI or without AI. For example, the providing unit inputs the user's past operation history data into an AI model, and the AI ​​selects the optimal display method.

[0128] The reception unit can estimate the user's emotions and dynamically adjust the timing of entering the terms and conditions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to enter the terms and conditions at a time when the user is able to relax. For example, if the user is concentrating, the reception unit can prompt the user to enter the terms and conditions immediately. Also, if the user is tired, the reception unit can prompt the user to enter the terms and conditions after a break. This adjusts the timing of entering the terms and conditions according to the user's emotions, thereby reducing the user's stress and promoting efficient input. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. For example, the reception unit inputs the user's facial expression data into a generation AI, which then estimates the user's emotions. Furthermore, the reception unit can analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the reception unit can monitor the user's facial expressions in real time and immediately detect changes in emotions. The reception unit can also accumulate the user's emotional data and analyze long-term emotional trends. For example, the reception unit can analyze the user's emotional data over time to identify patterns of emotional changes. This allows the user's emotions to be grasped in detail and the input timing to be dynamically adjusted.

[0129] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the summarization unit provides a concise and to-the-point summary. For example, if the user is relaxed, the summarization unit provides a detailed summary. Furthermore, if the user is in a hurry, the summarization unit can also provide a summary that can be understood in a short time. For example, if the user is in a hurry, the summarization unit provides a concise summary. This allows the user to easily understand the information by adjusting the way the summary is presented according to the user's emotions. Some or all of the above-described processing in the summarization unit is performed using a generation AI. For example, the summarization unit inputs the user's emotion data into the generation AI, which then adjusts the way the summary is presented.

[0130] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated user emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, or survey results. For example, if the user is stressed, the comparison unit uses simple comparison criteria. For example, if the user is relaxed, the comparison unit uses detailed comparison criteria. Furthermore, if the user is in a hurry, the comparison unit can also use criteria that allow for quick comparison. This allows the user to easily understand information by adjusting the comparison criteria according to the user's emotions. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit inputs the user's emotion data into an AI model, and the AI ​​adjusts the comparison criteria.

[0131] The analysis unit can estimate the user's emotions and adjust the display method of the risk analysis based on the estimated user emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The analysis unit provides a concise and highly visible risk analysis when the user is stressed, for example. For example, if the user is relaxed, it provides a detailed risk analysis. The analysis unit can also provide a risk analysis that focuses on the main points when the user is in a hurry. For example, if the user is in a hurry, it provides a risk analysis that focuses on the main points. This makes it easier for the user to understand the information by adjusting the display method of the risk analysis according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the display method of the risk analysis.

[0132] The providing unit can estimate the user's emotions and adjust the display method of the report based on the estimated user's emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the providing unit provides a concise and highly visible report. For example, if the user is relaxed, the providing unit provides a detailed report. The providing unit can also provide a report that focuses on the main points if the user is in a hurry. For example, if the user is in a hurry, the providing unit provides a report that focuses on the main points. This makes it easier for the user to understand the information by adjusting the display method of the report according to the user's emotions. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the display method of the report.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The reception unit inputs the terms and conditions of the contract the user is about to enter into. The user can input terms and conditions such as service contracts, sales contracts, and license agreements in text or image format. If the input is in image format, it is converted into text data using OCR technology. Step 2: The summary section uses a generation AI to analyze the terms and conditions entered by the reception section and summarize the key points. The summary is based on the contract's main conditions and restrictions, fee structure, cancellation conditions, etc. Step 3: The comparison section compares the information summarized by the summary section with standard terms and conditions. The comparison is based on a template of a typical service contract. Step 4: The analysis unit uses the generative AI to analyze the legal and economic risks based on the results obtained by the comparison unit, with reference to past cases and legal documents. Step 5: The presentation unit provides the user with an easy-to-understand report based on the results of the analysis by the analysis unit. The report uses graphs and charts to visually display the summarized information.

[0135] 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.

[0136] 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.

[0137] 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.

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

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

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

[0142] The 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.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 for inputting the terms and conditions of the contract that the user intends to enter into; a summarizing unit that analyzes the terms and conditions and regulations input by the receiving unit and summarizes specific points; a comparison unit that compares the information summarized by the summary unit with standard terms and conditions; an analysis unit that analyzes specific legal risks and economic risks based on the results obtained by the comparison unit; a providing unit that provides a report that is easy to understand to a user based on the results of the analysis by the analyzing unit. A system characterized by:

2. The summary section Use generative AI to summarize the key terms, restrictions, pricing, and cancellation conditions of the contract 2. The system of claim 1.

3. The comparison unit Compare entered terms and conditions against a standard service contract template 2. The system of claim 1.

4. The analysis unit Uses generative AI to reference past cases and legal documents to assess potential legal risks 2. The system of claim 1.

5. The analysis unit Evaluate the pricing and payment terms included in the contract 2. The system of claim 1.

6. The providing unit Visually display summarized information in graphs, charts, etc.

2. The system of claim 1.

7. The providing unit Use graphs and charts to present specific information in an easy-to-understand way 2. The system of claim 1.

8. The reception unit Estimates the user's emotions and dynamically adjusts the timing of entering terms and conditions based on the estimated user emotions.

2. The system of claim 1.

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