System

Abstract

An object of the system according to the embodiment is to allow a consumer to accurately understand contract content by analyzing the content of a contract and summarizing and presenting the contract content.SOLUTION: A system includes an analysis unit, a summarization unit, and a presentation unit. The analysis part analyzes contents of the contract document. The summarizing unit summarizes the content analyzed by the analyzing unit. The presentation unit presents the information summarized by the summarization unit to the user.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] Conventional technology has had the problem that it is difficult for consumers to accurately understand the contents of a contract, making it difficult for them to avoid unfavorable contracts.

[0005] The system according to the embodiment aims to enable consumers to accurately understand the contents of a contract by analyzing the contents of the contract and presenting a summary of the contents. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a summarization unit, and a presentation unit. The analysis unit analyzes the content of the contract. The summary unit summarizes the content analyzed by the analysis unit. The presentation unit presents the information summarized by the summary unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment analyzes the contents of the contract, summarizes them, and presents them to the consumer, thereby enabling the consumer to accurately understand the contents of the 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) A contract summarizing system according to an embodiment of the present invention analyzes and summarizes the contents of a contract and presents the summary to a user. The contract summarizing system analyzes the contents of a contract, extracts a summary and important points, and presents this information to a user to support contract decisions. For example, the contract summarizing system analyzes each clause of a contract in detail to identify important information and high-risk sections. The contract summarizing system then summarizes the analysis results and presents them to the user. The summary includes key points and important points of the contract. Furthermore, if the user requests a detailed explanation of a specific clause, the contract summarizing system provides a detailed explanation of that clause. The contract summarizing system also provides an interactive function to confirm the user's level of understanding. This allows consumers to enter into a contract with confidence, without having to worry about complex clauses or specialized knowledge. This allows consumers to enter into a contract with confidence, without having to worry about complex clauses or specialized knowledge. In addition, the party presenting the contract can efficiently explain the contract contents, which also leads to improved customer satisfaction.

[0029] A contract summarizing system according to an embodiment includes an analysis unit, a summarization unit, and a presentation unit. The analysis unit analyzes the content of a contract. For example, the analysis unit uses natural language processing technology to analyze each clause of the contract in detail and identify important information and high-risk sections. The analysis unit can also analyze the content of a contract using a machine learning algorithm. For example, the analysis unit highlights clauses in the contract that require particular attention or sections that contain terms that are unfavorable to consumers. The summarization unit summarizes the content analyzed by the analysis unit. For example, the summarization unit briefly summarizes the main points and points to note in the contract. The summarization unit performs summarization based on the length of the text and the importance of the information to be summarized. For example, the summarization unit briefly summarizes important information that consumers should understand, such as the purpose, duration, fees, and cancellation conditions of the contract. The presentation unit presents the information summarized by the summarization unit to a user. For example, if a user requests a detailed explanation of a specific clause, the presentation unit provides a detailed explanation of that clause. In addition, the presentation unit provides a feedback function in the form of a quiz or question to check the user's understanding. For example, the presentation unit introduces a feedback function in the form of a quiz or question to check whether the user has understood. This allows the contract summarizing system according to the embodiment to allow the user to accurately understand the contents of the contract and avoid entering into an unfavorable contract.

[0030] The analysis unit can analyze each clause of a contract and identify important information and high-risk parts. The analysis unit can use, for example, natural language processing technology to perform a detailed analysis of each clause of a contract. For example, the analysis unit can highlight clauses in a contract that require particular attention or parts that contain terms that are unfavorable to consumers. The analysis unit can also analyze the contents of a contract using a machine learning algorithm. For example, the analysis unit can analyze each clause of a contract and identify important information and high-risk parts. By identifying important information and high-risk parts in a contract, it is possible to clarify points that users should pay attention to.

[0031] The summary section can summarize the main points and points to note in the contract. For example, the summary section briefly summarizes the main points and points to note in the contract. For example, the summary section briefly summarizes important information that consumers should understand, such as the purpose, period, fees, and cancellation conditions of the contract. The summary section can also summarize based on the length of the text and the importance of the information to be summarized. For example, the summary section briefly summarizes the main points and points to note in the contract. This allows the user to quickly understand the contents of the contract by briefly summarizing the main points and points to note in the contract.

[0032] The presentation unit can provide an explanation of a specific provision when the user requests a detailed explanation of the provision. For example, when the user requests a detailed explanation of a specific provision, the presentation unit provides a detailed explanation of the provision. For example, when technical terms or legal expressions are included, the presentation unit provides an easy-to-understand explanation of these. The presentation unit can also provide a detailed explanation of a specific provision when the user requests a detailed explanation of the provision. For example, when technical terms or legal expressions are included, the presentation unit provides an easy-to-understand explanation of these. In this way, when the user requests a detailed explanation of a specific provision, the provision can be explained in detail, thereby deepening the user's understanding.

[0033] The presentation unit can provide a quiz- or question-style feedback function for checking the user's level of understanding. The presentation unit, for example, provides a quiz- or question-style feedback function for checking the user's level of understanding. For example, the presentation unit introduces a quiz- or question-style feedback function for checking whether the user has understood. The presentation unit can also provide a quiz- or question-style feedback function for checking the user's level of understanding. For example, the presentation unit introduces a quiz- or question-style feedback function for checking whether the user has understood. By providing a quiz- or question-style feedback function for checking the user's level of understanding, the user can gain a deeper understanding of the contract contents.

[0034] The analysis unit can improve the accuracy of the analysis by referring to data on similar past contracts. For example, the analysis unit identifies important clauses and high-risk parts based on data on similar contracts that have been analyzed in the past. For example, the analysis unit refers to data on similar past contracts to improve the reliability of the analysis results. The analysis unit can also apply an algorithm to improve the accuracy of the analysis by using data on similar past contracts. For example, the analysis unit identifies important clauses and high-risk parts based on data on similar past contracts. In this way, the accuracy of the analysis is improved by referring to data on similar past contracts.

[0035] The analysis unit can apply multiple analysis algorithms depending on the type of contract. For example, when analyzing a contract, the analysis unit applies different analysis algorithms depending on the type of contract (e.g., rental contract, sales contract). For example, in the case of a rental contract, the analysis unit applies an algorithm that focuses on analyzing clauses related to rent and contract period. In addition, in the case of a sales contract, the analysis unit can also apply an algorithm that focuses on analyzing clauses related to price and payment terms. In addition, in the case of a service contract, the analysis unit can apply an algorithm that focuses on analyzing clauses related to service content and cancellation conditions. In this way, by applying an analysis algorithm depending on the type of contract, the accuracy of the analysis is improved.

[0036] The analysis unit can perform analysis based on the attribute information of the contract submitter. For example, when analyzing a contract, the analysis unit performs the analysis while taking into account the attribute information of the contract submitter. For example, if the submitter is a corporation, the analysis unit can focus its analysis on clauses and risks specific to corporations. Furthermore, if the submitter is an individual, the analysis unit can also focus its analysis on clauses and risks related to individuals. Furthermore, the analysis unit can analyze clauses and risks specific to the business type or industry of the submitter. In this way, by taking into account the attribute information of the contract submitter, more appropriate analysis results can be provided.

[0037] The analysis unit can perform analysis based on the geographical scope of application of the contract. For example, when analyzing a contract, the analysis unit performs the analysis taking into account the geographical scope of application of the contract. For example, if the scope of application of the contract is limited to a specific region, the analysis unit performs the analysis taking into account the laws and regulations of that region. Furthermore, if the scope of application of the contract spans multiple regions, the analysis unit can also perform the analysis taking into account the laws and regulations of each region. Furthermore, if the scope of application of the contract is international, the analysis unit can also perform the analysis taking into account international law and the laws and regulations of each country. In this way, by taking into account the geographical scope of application of the contract, more appropriate analysis results can be provided.

[0038] The analysis unit can improve the accuracy of the analysis by referring to relevant laws and regulations. For example, when analyzing a contract, the analysis unit improves the accuracy of the analysis by referring to relevant laws and regulations. For example, if the provisions of the contract are based on a specific law, the analysis unit performs the analysis by referring to that law. Furthermore, if the provisions of the contract are related to multiple laws and regulations, the analysis unit can also perform the analysis by referring to each law. Furthermore, the analysis unit can check whether the provisions of the contract are based on the latest laws and regulations and perform the analysis. In this way, by referring to relevant laws and regulations, the analysis accuracy is improved.

[0039] The analysis unit can perform analysis based on the market value of the contract. For example, when analyzing a contract, the analysis unit performs the analysis taking into account the market value of the contract. For example, if the market value of the contract is high, the analysis unit will focus on analyzing high-risk clauses and important information. Also, if the market value of the contract is low, the analysis unit can focus on analyzing basic clauses and information. The analysis unit can also evaluate the market value of the contract and adjust the depth of the analysis based on that evaluation. In this way, by taking the market value of the contract into account, more appropriate analysis results can be provided.

[0040] The summarization unit can adjust the specificity of the summary based on the importance of the contract. For example, when generating a summary, the summarization unit adjusts the level of detail of the summary based on the importance of the contract. For example, for a contract with high importance, the summarization unit provides a detailed summary and emphasizes important points. For a contract with low importance, the summarization unit can also provide a basic summary and emphasize only the main points. The summarization unit can also evaluate the importance of the contract and adjust the level of detail of the summary based on the evaluation. In this way, by adjusting the level of detail of the summary based on the importance of the contract, a summary suitable for the user can be provided.

[0041] The summarization unit can apply multiple summarization algorithms depending on the category of the contract. For example, when generating a summary, the summarization unit applies different summarization algorithms depending on the category of the contract. For example, in the case of a rental contract, the summarization unit applies an algorithm that focuses on summarizing information related to rent and contract period. In addition, in the case of a sales contract, the summarization unit can also apply an algorithm that focuses on summarizing information related to price and payment terms. In addition, in the case of a service contract, the summarization unit can apply an algorithm that focuses on summarizing information related to service content and cancellation terms. In this way, the accuracy of the summary is improved by applying a summarization algorithm according to the category of the contract.

[0042] The summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. For example, when generating a summary, the summarization unit improves the accuracy of the summary by referring to the user's past summarization results. For example, the summarization unit improves the accuracy of the summary based on data of contracts that the user has previously summarized. The summarization unit can also improve the reliability of the summary by referring to the user's past summarization results. The summarization unit can also apply an algorithm to improve the accuracy of the summary by using the user's past summarization results. In this way, the accuracy of the summary is improved by referring to the user's past summarization results.

[0043] The summarization unit can determine the priority of summaries based on the submission time of the contract. For example, when generating a summary, the summarization unit determines the priority of summaries based on the submission time of the contract. For example, the summarization unit sets a high priority for a contract whose submission time is close. The summarization unit can also set a low priority for a contract whose submission time is far away. The summarization unit can also evaluate the submission time of the contract and determine the priority of summaries based on that evaluation. In this way, by determining the priority of summaries based on the submission time of the contract, it is possible to provide summaries that are suitable for the user.

[0044] The summarization unit can adjust the order of summaries based on the relevance of the contract documents. For example, when generating a summary, the summarization unit adjusts the order of summaries based on the relevance of the contract documents. For example, the summarization unit prioritizes the order of summaries for highly relevant contract documents. The summarization unit can also postpone the order of summaries for less relevant contract documents. The summarization unit can also evaluate the relevance of the contract documents and adjust the order of summaries based on the evaluation. In this way, by adjusting the order of summaries based on the relevance of the contract documents, it is possible to provide a summary that is suitable for the user.

[0045] The summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, when generating a summary, the summarization unit adjusts the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the summarization unit can provide a summary that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the summarization unit can provide a summary that avoids technical terms. The summarization unit can also evaluate the user's level of expertise and adjust the use of technical terms in the summary based on the evaluation. In this way, by adjusting the use of technical terms in the summary according to the user's level of expertise, a summary that is suitable for the user can be provided.

[0046] The presentation unit can adjust the specificity of the presentation based on the importance of the contract. For example, the presentation unit adjusts the level of detail of the presentation based on the importance of the contract when presenting the contract. For example, the presentation unit provides a detailed presentation for a contract with high importance and emphasizes important points. The presentation unit can also provide a basic presentation for a contract with low importance and emphasize only the main points. The presentation unit can also evaluate the importance of the contract and adjust the level of detail of the presentation based on the evaluation. In this way, by adjusting the level of detail of the presentation based on the importance of the contract, a presentation suitable for the user can be provided.

[0047] The presentation unit can apply multiple presentation algorithms depending on the category of the contract. For example, the presentation unit applies different presentation algorithms depending on the category of the contract when presenting. For example, in the case of a rental contract, the presentation unit applies an algorithm that focuses on presenting information related to rent and contract period. In addition, in the case of a sales contract, the presentation unit can also apply an algorithm that focuses on presenting information related to price and payment terms. In addition, in the case of a service contract, the presentation unit can apply an algorithm that focuses on presenting information related to service content and cancellation terms. In this way, by applying a presentation algorithm depending on the category of the contract, the accuracy of the presentation is improved.

[0048] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results. For example, the presentation unit improves the accuracy of presentation by referring to the user's past presentation results at the time of presentation. For example, the presentation unit improves the accuracy of presentation based on data of contracts previously presented to the user. The presentation unit can also improve the reliability of presentation by referring to the user's past presentation results. The presentation unit can also apply an algorithm to improve the accuracy of presentation by using the user's past presentation results. In this way, the accuracy of presentation is improved by referring to the user's past presentation results.

[0049] The presentation unit can determine the presentation priority based on the submission time of the contract. For example, the presentation unit determines the presentation priority based on the submission time of the contract at the time of presentation. For example, the presentation unit sets a high presentation priority for a contract whose submission time is close. The presentation unit can also set a low presentation priority for a contract whose submission time is further away. The presentation unit can also evaluate the submission time of the contract and determine the presentation priority based on the evaluation. In this way, by determining the presentation priority based on the submission time of the contract, it is possible to provide a presentation that is suitable for the user.

[0050] The presentation unit can adjust the order of presentation based on the relevance of the contracts. For example, the presentation unit adjusts the order of presentation based on the relevance of the contracts when presenting them. For example, the presentation unit prioritizes the order of presentation for highly relevant contracts. The presentation unit can also postpone the order of presentation for less relevant contracts. The presentation unit can also evaluate the relevance of the contracts and adjust the order of presentation based on the evaluation. In this way, by adjusting the order of presentation based on the relevance of the contracts, it is possible to provide a presentation that is suitable for the user.

[0051] The presentation unit can adjust the use of technical terms in the presentation according to the user's level of expertise. For example, the presentation unit adjusts the use of technical terms in the presentation according to the user's level of expertise at the time of presentation. For example, if the user has technical expertise, the presentation unit provides a presentation that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the presentation unit can also provide a presentation that avoids technical terms. Furthermore, the presentation unit can evaluate the user's level of expertise and adjust the use of technical terms in the presentation based on the evaluation. In this way, by adjusting the use of technical terms in the presentation according to the user's level of expertise, a presentation suitable for the user can be provided.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When analyzing the contents of a contract, the analysis unit can refer to the past behavioral history of the person submitting the contract. For example, the analysis unit can analyze what kind of contracts the submitter has submitted in the past and identify specific patterns and risks. The analysis unit can also compare the contents of contracts previously submitted by the submitter with the current contract to detect inconsistencies and discrepancies. Furthermore, the analysis unit can highlight clauses and risks that the submitter should pay particular attention to based on the submitter's past behavioral history. This makes it possible to provide more accurate analysis results by taking into account the submitter's past behavioral history.

[0054] The summarization unit can refer to the user's past feedback when generating a contract summary. For example, the summarization unit analyzes what types of summaries the user has preferred in the past and generates a summary that matches the user's preferences. The summarization unit can also improve the accuracy of the summary based on feedback provided by the user in the past. Furthermore, the summarization unit can highlight particularly important points or points to note based on the user's past feedback. In this way, by taking the user's past feedback into consideration, a summary that is more suitable for the user can be provided.

[0055] When analyzing the contents of a contract, the analysis unit can infer the intention of the contract's creator. For example, the analysis unit infers the creator's intention from the context and wording of the contract, and identifies important information and risks based on that intention. By inferring the creator's intention, the analysis unit can also clarify ambiguous or unclear parts of the contract. Furthermore, the analysis unit can provide appropriate advice to the user based on the creator's intention. This makes it possible to provide more accurate analysis results by taking the creator's intention into consideration.

[0056] When generating a contract summary, the summarization unit can adjust the format of the summary based on the content of the contract. For example, if the contract is a legal document, the summarization unit can provide a summary that uses a lot of legal terminology. If the contract is a technical document, the summarization unit can also provide a summary that uses a lot of technical terminology. Furthermore, if the contract is a general document, the summarization unit can also provide a summary that uses general language. In this way, by adjusting the format of the summary based on the content of the contract, a more appropriate summary can be provided.

[0057] The summarization unit can infer the intention of the person who submitted the contract when generating a summary of the contract. For example, the summarization unit infers the intention of the person who submitted the contract from the context and expressions of the contract, and highlights important points and points to note based on that intention. The summarization unit can also clarify ambiguous or unclear parts of the contract by inferring the intention of the person who submitted the contract. Furthermore, the summarization unit can provide appropriate advice to the user based on the intention of the person who submitted the contract. This makes it possible to provide a more accurate summary by taking the intention of the person who submitted the contract into consideration.

[0058] The presentation unit can refer to the user's past behavior history when presenting the contents of the contract. For example, the presentation unit can analyze what types of contracts the user has viewed in the past and identify specific patterns or preferences. The presentation unit can also compare the contents of contracts the user has viewed in the past with the current contract to provide highly relevant information. Furthermore, the presentation unit can highlight particularly important points or points to note based on the user's past behavior history. This makes it possible to present more appropriate information by taking the user's past behavior history into consideration.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The analysis unit analyzes the contents of the contract. The analysis unit uses natural language processing technology to perform a detailed analysis of each clause in the contract and identify important information and high-risk areas. The analysis unit can also use machine learning algorithms to analyze the contents of the contract. For example, the analysis unit can highlight clauses in the contract that require particular attention or parts that contain terms that are unfavorable to consumers. Step 2: The summary section summarizes the content analyzed by the analysis section. The summary section concisely summarizes the main points and important points of the contract. The summary is based on the length of the text and the importance of the information being summarized, and concisely summarizes important information that consumers need to understand, such as the purpose, duration, fees, and cancellation conditions of the contract. Step 3: The presentation unit presents the information summarized by the summary unit to the user. If the user requests a detailed explanation of a specific clause, the presentation unit provides a detailed explanation of that clause. The presentation unit also provides a quiz or question-style feedback function to check the user's level of understanding. This allows the user to accurately understand the contents of the contract and avoid unfavorable contracts.

[0061] (Example 2) A contract summarizing system according to an embodiment of the present invention analyzes and summarizes the contents of a contract and presents the summary to a user. The contract summarizing system analyzes the contents of a contract, extracts a summary and important points, and presents this information to a user to support contract decisions. For example, the contract summarizing system analyzes each clause of a contract in detail to identify important information and high-risk sections. The contract summarizing system then summarizes the analysis results and presents them to the user. The summary includes key points and important points of the contract. Furthermore, if the user requests a detailed explanation of a specific clause, the contract summarizing system provides a detailed explanation of that clause. The contract summarizing system also provides an interactive function to confirm the user's level of understanding. This allows consumers to enter into a contract with confidence, without having to worry about complex clauses or specialized knowledge. This allows consumers to enter into a contract with confidence, without having to worry about complex clauses or specialized knowledge. In addition, the party presenting the contract can efficiently explain the contract contents, which also leads to improved customer satisfaction.

[0062] A contract summarizing system according to an embodiment includes an analysis unit, a summarization unit, and a presentation unit. The analysis unit analyzes the content of a contract. For example, the analysis unit uses natural language processing technology to analyze each clause of the contract in detail and identify important information and high-risk sections. The analysis unit can also analyze the content of a contract using a machine learning algorithm. For example, the analysis unit highlights clauses in the contract that require particular attention or sections that contain terms that are unfavorable to consumers. The summarization unit summarizes the content analyzed by the analysis unit. For example, the summarization unit briefly summarizes the main points and points to note in the contract. The summarization unit performs summarization based on the length of the text and the importance of the information to be summarized. For example, the summarization unit briefly summarizes important information that consumers should understand, such as the purpose, duration, fees, and cancellation conditions of the contract. The presentation unit presents the information summarized by the summarization unit to a user. For example, if a user requests a detailed explanation of a specific clause, the presentation unit provides a detailed explanation of that clause. In addition, the presentation unit provides a feedback function in the form of a quiz or question to check the user's understanding. For example, the presentation unit introduces a feedback function in the form of a quiz or question to check whether the user has understood. This allows the contract summarizing system according to the embodiment to allow the user to accurately understand the contents of the contract and avoid entering into an unfavorable contract.

[0063] The analysis unit can analyze each clause of a contract and identify important information and high-risk parts. The analysis unit can use, for example, natural language processing technology to perform a detailed analysis of each clause of a contract. For example, the analysis unit can highlight clauses in a contract that require particular attention or parts that contain terms that are unfavorable to consumers. The analysis unit can also analyze the contents of a contract using a machine learning algorithm. For example, the analysis unit can analyze each clause of a contract and identify important information and high-risk parts. By identifying important information and high-risk parts in a contract, it is possible to clarify points that users should pay attention to.

[0064] The summary section can summarize the main points and points to note in the contract. For example, the summary section briefly summarizes the main points and points to note in the contract. For example, the summary section briefly summarizes important information that consumers should understand, such as the purpose, period, fees, and cancellation conditions of the contract. The summary section can also summarize based on the length of the text and the importance of the information to be summarized. For example, the summary section briefly summarizes the main points and points to note in the contract. This allows the user to quickly understand the contents of the contract by briefly summarizing the main points and points to note in the contract.

[0065] The presentation unit can provide an explanation of a specific provision when the user requests a detailed explanation of the provision. For example, when the user requests a detailed explanation of a specific provision, the presentation unit provides a detailed explanation of the provision. For example, when technical terms or legal expressions are included, the presentation unit provides an easy-to-understand explanation of these. The presentation unit can also provide a detailed explanation of a specific provision when the user requests a detailed explanation of the provision. For example, when technical terms or legal expressions are included, the presentation unit provides an easy-to-understand explanation of these. In this way, when the user requests a detailed explanation of a specific provision, the provision can be explained in detail, thereby deepening the user's understanding.

[0066] The presentation unit can provide a quiz- or question-style feedback function for checking the user's level of understanding. The presentation unit, for example, provides a quiz- or question-style feedback function for checking the user's level of understanding. For example, the presentation unit introduces a quiz- or question-style feedback function for checking whether the user has understood. The presentation unit can also provide a quiz- or question-style feedback function for checking the user's level of understanding. For example, the presentation unit introduces a quiz- or question-style feedback function for checking whether the user has understood. By providing a quiz- or question-style feedback function for checking the user's level of understanding, the user can gain a deeper understanding of the contract contents.

[0067] The analysis unit can estimate the user's emotions and adjust the scope of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can reduce the depth of the analysis and emphasize only the important points. If the user is relaxed, the analysis unit can perform a detailed analysis to provide an overall picture of the contract. If the user is in a hurry, the analysis unit can minimize the depth of the analysis and extract only the main points. By adjusting the depth of the analysis according to the user's emotions, it is possible to provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The analysis unit can improve the accuracy of the analysis by referring to data on similar past contracts. For example, the analysis unit identifies important clauses and high-risk parts based on data on similar contracts that have been analyzed in the past. For example, the analysis unit refers to data on similar past contracts to improve the reliability of the analysis results. The analysis unit can also apply an algorithm to improve the accuracy of the analysis by using data on similar past contracts. For example, the analysis unit identifies important clauses and high-risk parts based on data on similar past contracts. In this way, the accuracy of the analysis is improved by referring to data on similar past contracts.

[0069] The analysis unit can apply multiple analysis algorithms depending on the type of contract. For example, when analyzing a contract, the analysis unit applies different analysis algorithms depending on the type of contract (e.g., rental contract, sales contract). For example, in the case of a rental contract, the analysis unit applies an algorithm that focuses on analyzing clauses related to rent and contract period. In addition, in the case of a sales contract, the analysis unit can also apply an algorithm that focuses on analyzing clauses related to price and payment terms. In addition, in the case of a service contract, the analysis unit can apply an algorithm that focuses on analyzing clauses related to service content and cancellation conditions. In this way, by applying an analysis algorithm depending on the type of contract, the accuracy of the analysis is improved.

[0070] The analysis unit can perform analysis based on the attribute information of the contract submitter. For example, when analyzing a contract, the analysis unit performs the analysis while taking into account the attribute information of the contract submitter. For example, if the submitter is a corporation, the analysis unit can focus its analysis on clauses and risks specific to corporations. Furthermore, if the submitter is an individual, the analysis unit can also focus its analysis on clauses and risks related to individuals. Furthermore, the analysis unit can analyze clauses and risks specific to the business type or industry of the submitter. In this way, by taking into account the attribute information of the contract submitter, more appropriate analysis results can be provided.

[0071] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis results can be displayed in a simple, highly visible format. If the user is relaxed, the analysis unit can display detailed analysis results to provide an overall picture of the contract. If the user is in a hurry, the analysis unit can display analysis results that highlight only the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing a display method that is suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The analysis unit can perform analysis based on the geographical scope of application of the contract. For example, when analyzing a contract, the analysis unit performs the analysis taking into account the geographical scope of application of the contract. For example, if the scope of application of the contract is limited to a specific region, the analysis unit performs the analysis taking into account the laws and regulations of that region. Furthermore, if the scope of application of the contract spans multiple regions, the analysis unit can also perform the analysis taking into account the laws and regulations of each region. Furthermore, if the scope of application of the contract is international, the analysis unit can also perform the analysis taking into account international law and the laws and regulations of each country. In this way, by taking into account the geographical scope of application of the contract, more appropriate analysis results can be provided.

[0073] The analysis unit can improve the accuracy of the analysis by referring to relevant laws and regulations. For example, when analyzing a contract, the analysis unit improves the accuracy of the analysis by referring to relevant laws and regulations. For example, if the provisions of the contract are based on a specific law, the analysis unit performs the analysis by referring to that law. Furthermore, if the provisions of the contract are related to multiple laws and regulations, the analysis unit can also perform the analysis by referring to each law. Furthermore, the analysis unit can check whether the provisions of the contract are based on the latest laws and regulations and perform the analysis. In this way, by referring to relevant laws and regulations, the analysis accuracy is improved.

[0074] The analysis unit can perform analysis based on the market value of the contract. For example, when analyzing a contract, the analysis unit performs the analysis taking into account the market value of the contract. For example, if the market value of the contract is high, the analysis unit will focus on analyzing high-risk clauses and important information. Also, if the market value of the contract is low, the analysis unit can focus on analyzing basic clauses and information. The analysis unit can also evaluate the market value of the contract and adjust the depth of the analysis based on that evaluation. In this way, by taking the market value of the contract into account, more appropriate analysis results can be provided.

[0075] The summarization unit can estimate the user's emotion and adjust the summary expression format based on the estimated user emotion. For example, the summarization unit can estimate the user's emotion and adjust the summary expression format based on the estimated user emotion. For example, if the user is feeling anxious, the summarization unit can provide a simple, highly visible summary. If the user is relaxed, the summarization unit can provide a detailed summary and an overall picture of the contract. If the user is in a hurry, the summarization unit can provide a summary that highlights only the main points. In this way, by adjusting the summary expression format according to the user's emotion, a summary suitable for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The summarization unit can adjust the specificity of the summary based on the importance of the contract. For example, when generating a summary, the summarization unit adjusts the level of detail of the summary based on the importance of the contract. For example, for a contract with high importance, the summarization unit provides a detailed summary and emphasizes important points. For a contract with low importance, the summarization unit can also provide a basic summary and emphasize only the main points. The summarization unit can also evaluate the importance of the contract and adjust the level of detail of the summary based on the evaluation. In this way, by adjusting the level of detail of the summary based on the importance of the contract, a summary suitable for the user can be provided.

[0077] The summarization unit can apply multiple summarization algorithms depending on the category of the contract. For example, when generating a summary, the summarization unit applies different summarization algorithms depending on the category of the contract. For example, in the case of a rental contract, the summarization unit applies an algorithm that focuses on summarizing information related to rent and contract period. In addition, in the case of a sales contract, the summarization unit can also apply an algorithm that focuses on summarizing information related to price and payment terms. In addition, in the case of a service contract, the summarization unit can apply an algorithm that focuses on summarizing information related to service content and cancellation terms. In this way, the accuracy of the summary is improved by applying a summarization algorithm according to the category of the contract.

[0078] The summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. For example, when generating a summary, the summarization unit improves the accuracy of the summary by referring to the user's past summarization results. For example, the summarization unit improves the accuracy of the summary based on data of contracts that the user has previously summarized. The summarization unit can also improve the reliability of the summary by referring to the user's past summarization results. The summarization unit can also apply an algorithm to improve the accuracy of the summary by using the user's past summarization results. In this way, the accuracy of the summary is improved by referring to the user's past summarization results.

[0079] The summarization unit can estimate the user's emotion and adjust the scope of the summary based on the estimated user emotion. For example, the summarization unit can estimate the user's emotion and adjust the length of the summary based on the estimated user emotion. For example, if the user is feeling anxious, the summarization unit can provide a short summary that covers the main points. If the user is relaxed, the summarization unit can provide a longer summary with detailed explanations. If the user is in a hurry, the summarization unit can provide a short summary that emphasizes only the main points. In this way, by adjusting the length of the summary according to the user's emotion, a summary that is appropriate for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The summarization unit can determine the priority of summaries based on the submission time of the contract. For example, when generating a summary, the summarization unit determines the priority of summaries based on the submission time of the contract. For example, the summarization unit sets a high priority for a contract whose submission time is close. The summarization unit can also set a low priority for a contract whose submission time is far away. The summarization unit can also evaluate the submission time of the contract and determine the priority of summaries based on that evaluation. In this way, by determining the priority of summaries based on the submission time of the contract, it is possible to provide summaries that are suitable for the user.

[0081] The summarization unit can adjust the order of summaries based on the relevance of the contract documents. For example, when generating a summary, the summarization unit adjusts the order of summaries based on the relevance of the contract documents. For example, the summarization unit prioritizes the order of summaries for highly relevant contract documents. The summarization unit can also postpone the order of summaries for less relevant contract documents. The summarization unit can also evaluate the relevance of the contract documents and adjust the order of summaries based on the evaluation. In this way, by adjusting the order of summaries based on the relevance of the contract documents, it is possible to provide a summary that is suitable for the user.

[0082] The summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, when generating a summary, the summarization unit adjusts the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the summarization unit can provide a summary that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the summarization unit can provide a summary that avoids technical terms. The summarization unit can also evaluate the user's level of expertise and adjust the use of technical terms in the summary based on the evaluation. In this way, by adjusting the use of technical terms in the summary according to the user's level of expertise, a summary that is suitable for the user can be provided.

[0083] The presentation unit can estimate the user's emotion and adjust the presentation format based on the estimated user's emotion. For example, the presentation unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. For example, if the user is feeling anxious, a simple, highly visible presentation method is provided. Furthermore, if the user is relaxed, the presentation unit can provide a presentation method including detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a presentation method that focuses on the main points. In this way, by adjusting the presentation method according to the user's emotion, a presentation method suitable for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The presentation unit can adjust the specificity of the presentation based on the importance of the contract. For example, the presentation unit adjusts the level of detail of the presentation based on the importance of the contract when presenting the contract. For example, the presentation unit provides a detailed presentation for a contract with high importance and emphasizes important points. The presentation unit can also provide a basic presentation for a contract with low importance and emphasize only the main points. The presentation unit can also evaluate the importance of the contract and adjust the level of detail of the presentation based on the evaluation. In this way, by adjusting the level of detail of the presentation based on the importance of the contract, a presentation suitable for the user can be provided.

[0085] The presentation unit can apply multiple presentation algorithms depending on the category of the contract. For example, the presentation unit applies different presentation algorithms depending on the category of the contract when presenting. For example, in the case of a rental contract, the presentation unit applies an algorithm that focuses on presenting information related to rent and contract period. In addition, in the case of a sales contract, the presentation unit can also apply an algorithm that focuses on presenting information related to price and payment terms. In addition, in the case of a service contract, the presentation unit can apply an algorithm that focuses on presenting information related to service content and cancellation terms. In this way, by applying a presentation algorithm depending on the category of the contract, the accuracy of the presentation is improved.

[0086] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results. For example, the presentation unit improves the accuracy of presentation by referring to the user's past presentation results at the time of presentation. For example, the presentation unit improves the accuracy of presentation based on data of contracts previously presented to the user. The presentation unit can also improve the reliability of presentation by referring to the user's past presentation results. The presentation unit can also apply an algorithm to improve the accuracy of presentation by using the user's past presentation results. In this way, the accuracy of presentation is improved by referring to the user's past presentation results.

[0087] The presentation unit can estimate the user's emotion and adjust the range of the presentation based on the estimated user's emotion. The presentation unit, for example, estimates the user's emotion and adjusts the length of the presentation based on the estimated user's emotion. For example, if the user is feeling anxious, the presentation unit can provide a short, to-the-point presentation. If the user is relaxed, the presentation unit can also provide a longer presentation with detailed explanations. If the user is in a hurry, the presentation unit can also provide a short presentation that emphasizes only the main points. In this way, by adjusting the length of the presentation according to the user's emotion, a presentation appropriate for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The presentation unit can determine the presentation priority based on the submission time of the contract. For example, the presentation unit determines the presentation priority based on the submission time of the contract at the time of presentation. For example, the presentation unit sets a high presentation priority for a contract whose submission time is close. The presentation unit can also set a low presentation priority for a contract whose submission time is further away. The presentation unit can also evaluate the submission time of the contract and determine the presentation priority based on the evaluation. In this way, by determining the presentation priority based on the submission time of the contract, it is possible to provide a presentation that is suitable for the user.

[0089] The presentation unit can adjust the order of presentation based on the relevance of the contracts. For example, the presentation unit adjusts the order of presentation based on the relevance of the contracts when presenting them. For example, the presentation unit prioritizes the order of presentation for highly relevant contracts. The presentation unit can also postpone the order of presentation for less relevant contracts. The presentation unit can also evaluate the relevance of the contracts and adjust the order of presentation based on the evaluation. In this way, by adjusting the order of presentation based on the relevance of the contracts, it is possible to provide a presentation that is suitable for the user.

[0090] The presentation unit can adjust the use of technical terms in the presentation according to the user's level of expertise. For example, the presentation unit adjusts the use of technical terms in the presentation according to the user's level of expertise at the time of presentation. For example, if the user has technical expertise, the presentation unit provides a presentation that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the presentation unit can also provide a presentation that avoids technical terms. Furthermore, the presentation unit can evaluate the user's level of expertise and adjust the use of technical terms in the presentation based on the evaluation. In this way, by adjusting the use of technical terms in the presentation according to the user's level of expertise, a presentation suitable for the user can be provided. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, summarization unit, and presentation unit, described above, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and uses natural language processing technology to analyze each clause of the contract in detail and identify important information and high-risk sections. The summarization unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and concisely summarizes the main points and important points of the contract. The presentation unit is implemented, for example, by the control unit 46A of the smart device 14, and presents summarized information to the user and provides detailed explanations of specific clauses. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, summarization unit, and presentation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and uses natural language processing technology to analyze each clause of the contract in detail and identify important information and high-risk sections. The summarization unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and concisely summarizes the main points and important points of the contract. The presentation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and presents summarized information to the user and provides detailed explanations of specific clauses. === Hard Collateral 1-3 === Each of the multiple elements, including the analysis unit, summarization unit, and presentation unit, described above, is implemented, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is implemented in at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and uses natural language processing technology to analyze each clause of the contract in detail and identify important information and high-risk sections. The summarization unit is implemented, 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, and concisely summarizes the main points and important points of the contract. The presentation unit is implemented, for example, by the control unit 46A of the headset type terminal 314, and presents summarized information to the user and provides detailed explanations of specific clauses. === Hard Collateral 1-4 === Each of the multiple elements, including the analysis unit, summarization unit, and presentation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and uses natural language processing technology to analyze each clause of the contract in detail and identify important information and high-risk sections. 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, and concisely summarizes the main points and important points of the contract. The presentation unit is realized, for example, by the control unit 46A of the robot 414, and presents summarized information to the user and provides detailed explanations of specific clauses.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When analyzing the contents of a contract, the analysis unit can refer to the past behavioral history of the person submitting the contract. For example, the analysis unit can analyze what kind of contracts the submitter has submitted in the past and identify specific patterns and risks. The analysis unit can also compare the contents of contracts previously submitted by the submitter with the current contract to detect inconsistencies and discrepancies. Furthermore, the analysis unit can highlight clauses and risks that the submitter should pay particular attention to based on the submitter's past behavioral history. This makes it possible to provide more accurate analysis results by taking into account the submitter's past behavioral history.

[0093] The summarization unit can refer to the user's past feedback when generating a contract summary. For example, the summarization unit analyzes what types of summaries the user has preferred in the past and generates a summary that matches the user's preferences. The summarization unit can also improve the accuracy of the summary based on feedback provided by the user in the past. Furthermore, the summarization unit can highlight particularly important points or points to note based on the user's past feedback. In this way, by taking the user's past feedback into consideration, a summary that is more suitable for the user can be provided.

[0094] The presentation unit can take into account the user's current situation when presenting the contents of the contract. For example, if the user is in a hurry, the presentation unit can provide a concise presentation that emphasizes only the main points. Alternatively, if the user is relaxed, the presentation unit can provide a presentation that includes detailed information. Furthermore, if the user is in a specific situation, the presentation unit can provide appropriate information according to that situation. This allows more appropriate information to be presented by taking the user's current situation into consideration.

[0095] When analyzing the contents of a contract, the analysis unit can infer the intention of the contract's creator. For example, the analysis unit infers the creator's intention from the context and wording of the contract, and identifies important information and risks based on that intention. By inferring the creator's intention, the analysis unit can also clarify ambiguous or unclear parts of the contract. Furthermore, the analysis unit can provide appropriate advice to the user based on the creator's intention. This makes it possible to provide more accurate analysis results by taking the creator's intention into consideration.

[0096] When generating a contract summary, the summarization unit can adjust the format of the summary based on the content of the contract. For example, if the contract is a legal document, the summarization unit can provide a summary that uses a lot of legal terminology. If the contract is a technical document, the summarization unit can also provide a summary that uses a lot of technical terminology. Furthermore, if the contract is a general document, the summarization unit can also provide a summary that uses general language. In this way, by adjusting the format of the summary based on the content of the contract, a more appropriate summary can be provided.

[0097] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit will prioritize analyzing important points and high-risk parts. If the user is relaxed, the analysis unit can also perform a detailed analysis and provide an overall picture of the contract. Furthermore, if the user is in a hurry, the analysis unit can extract only the main points and provide analysis results quickly. In this way, by adjusting the analysis priority according to the user's emotions, it is possible to provide analysis results that are appropriate for the user.

[0098] The summarization unit can infer the intention of the person who submitted the contract when generating a summary of the contract. For example, the summarization unit infers the intention of the person who submitted the contract from the context and expressions of the contract, and highlights important points and points to note based on that intention. The summarization unit can also clarify ambiguous or unclear parts of the contract by inferring the intention of the person who submitted the contract. Furthermore, the summarization unit can provide appropriate advice to the user based on the intention of the person who submitted the contract. This makes it possible to provide a more accurate summary by taking the intention of the person who submitted the contract into consideration.

[0099] The presentation unit can refer to the user's past behavior history when presenting the contents of the contract. For example, the presentation unit can analyze what types of contracts the user has viewed in the past and identify specific patterns or preferences. The presentation unit can also compare the contents of contracts the user has viewed in the past with the current contract to provide highly relevant information. Furthermore, the presentation unit can highlight particularly important points or points to note based on the user's past behavior history. This makes it possible to present more appropriate information by taking the user's past behavior history into consideration.

[0100] When analyzing the contents of a contract, the analysis unit can estimate the emotions of the person submitting the contract. For example, the analysis unit can estimate the emotions of the person submitting the contract from the submitter's writing style and expressions, and identify important information and risks based on those emotions. The analysis unit can also clarify ambiguous or unclear parts of the contract by estimating the submitter's emotions. Furthermore, the analysis unit can provide appropriate advice to the user based on the submitter's emotions. In this way, by taking the submitter's emotions into consideration, more accurate analysis results can be provided.

[0101] When generating a summary of a contract, the summarization unit can estimate the user's emotions and adjust the priority of summaries based on the estimated user emotions. For example, if the user is feeling anxious, the summarization unit can prioritize summarizing important points and high-risk parts. If the user is relaxed, the summarization unit can provide a detailed summary and provide an overall picture of the contract. Furthermore, if the user is in a hurry, the summarization unit can provide a summary that emphasizes only the main points. In this way, by adjusting the priority of summaries according to the user's emotions, a summary that is suitable for the user can be provided.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The analysis unit analyzes the contents of the contract. The analysis unit uses natural language processing technology to perform a detailed analysis of each clause in the contract and identify important information and high-risk areas. The analysis unit can also use machine learning algorithms to analyze the contents of the contract. For example, the analysis unit can highlight clauses in the contract that require particular attention or parts that contain terms that are unfavorable to consumers. Step 2: The summary section summarizes the content analyzed by the analysis section. The summary section concisely summarizes the main points and important points of the contract. The summary is based on the length of the text and the importance of the information being summarized, and concisely summarizes important information that consumers need to understand, such as the purpose, duration, fees, and cancellation conditions of the contract. Step 3: The presentation unit presents the information summarized by the summary unit to the user. If the user requests a detailed explanation of a specific clause, the presentation unit provides a detailed explanation of that clause. The presentation unit also provides a quiz or question-style feedback function to check the user's level of understanding. This allows the user to accurately understand the contents of the contract and avoid unfavorable contracts.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. an analysis unit that analyzes the contents of the contract; a summarizing unit that summarizes the content analyzed by the analyzing unit; a presentation unit that presents the information summarized by the summarization unit to a user; Equipped with A system characterized by:

2. The analysis unit Analyze each clause of the contract to identify areas of information and high risk 2. The system of claim 1.

3. The summary section Summarize the main points and points to note in the contract 2. The system of claim 1.

4. The presentation unit If the user requests a detailed explanation of a particular provision, explain that provision.

2. The system of claim 1.

5. The presentation unit Provide quizzes or feedback in the form of questions to check user comprehension 2. The system of claim 1.

6. The analysis unit Inferring user emotions and adjusting the scope of analysis based on the estimated user emotions 2. The system of claim 1.

7. The analysis unit Improve the accuracy of analysis by referencing data on similar past contracts 2. The system of claim 1.

8. The analysis unit Applying multiple analysis algorithms depending on the type of contract 2. The system of claim 1.

Images