system

Abstract

The system according to this embodiment aims to make the contents of a contract easier to understand, assess risks, and track the status of performance. [Solution] The system according to the embodiment comprises an extraction unit, a summarization unit, an evaluation unit, and a tracking unit. The extraction unit extracts the main points of the contract. The summarization unit summarizes the points extracted by the extraction unit. The evaluation unit evaluates the risks of the contract. The tracking unit tracks the status of contract performance.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for an individual to understand the content of a contract, evaluate risks, and track the performance status.

[0005] The system according to the embodiment aims to make it easier to understand the content of a contract, evaluate risks, and track the performance status.

Means for Solving the Problems

[0006] The system according to the embodiment includes an extraction unit, a summarization unit, an evaluation unit, and a tracking unit. The extraction unit extracts the main points of a contract. The summarization unit summarizes the points extracted by the extraction unit. The evaluation unit evaluates the risks of the contract. The tracking unit tracks the performance status of the contract. [Effects of the Invention]

[0007] The system according to this embodiment can make the contents of a contract easier to understand, assess risks, and track the status of performance. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A Fair Contract Agent according to an embodiment of the present invention is an AI agent for assisting individuals who are anxious about contracts. The Fair Contract Agent includes a contract analysis function that extracts key points from a contract and provides a concise summary. The contract analysis function explains complex legal terminology in an easy-to-understand manner, making the content easier for users to comprehend. The Fair Contract Agent includes a risk assessment function that evaluates the risks of a contract and highlights potential issues and clauses requiring attention. The Fair Contract Agent includes a performance history management function that tracks the status of contract performance and reminds users of deadlines and important milestones. The Fair Contract Agent includes a professional support connection function that provides the option to consult with lawyers and contract experts as needed. For example, the Fair Contract Agent extracts key points from a contract and provides a concise summary. For example, the Fair Contract Agent explains complex legal terminology in an easy-to-understand manner, making the content easier for users to comprehend. The Fair Contract Agent evaluates the risks of a contract and highlights potential issues and clauses requiring attention. For example, a fair contract agent tracks the progress of a contract and reminds clients of deadlines and key milestones. A fair contract agent also provides the option to consult with lawyers and contract specialists as needed. This allows individuals who are apprehensive about contracts to understand the contract, assess risks, manage their progress, and receive expert support when necessary. This eliminates contractual anxieties and allows clients to enter into contracts with confidence. Thus, a fair contract agent helps individuals who are apprehensive about contracts to understand the contract, assess risks, manage their progress, and receive expert support when necessary.

[0029] The fair contract agent according to this embodiment comprises an extraction unit, a summarization unit, an evaluation unit, and a tracking unit. The extraction unit extracts the main points of the contract. For example, the extraction unit extracts important clauses and high-risk parts of the contract. The extraction unit can analyze the content of the contract using AI and extract the main points. For example, the extraction unit can analyze the content of the contract using natural language processing technology and extract important information. The summarization unit summarizes the points extracted by the extraction unit. For example, the summarization unit concisely summarizes the extracted points. The summarization unit can generate a summary using AI. For example, the summarization unit summarizes the extracted points using generation AI. The evaluation unit evaluates the risks of the contract. For example, the evaluation unit evaluates the risks of the contract and highlights potential problems and clauses that require attention. The evaluation unit can evaluate the risks using AI. For example, the evaluation unit evaluates the risks of the contract using a risk evaluation algorithm. The tracking unit tracks the status of contract performance. The tracking unit, for example, reminds users of contract performance deadlines and important milestones. The tracking unit can track performance using AI. The tracking unit tracks contract performance using, for example, a performance status management system. This allows the Fair Contract Agent according to the embodiment to extract and summarize key points of the contract, assess risks, and track performance.

[0030] The extraction unit extracts the key points from the contract. Specifically, it identifies important clauses and high-risk sections of the contract and utilizes AI technology to extract this information. The AI ​​analyzes the content of the contract using natural language processing technology and extracts important information. For example, it automatically detects particularly important clauses, conditions, and high-risk sections within the contract and extracts this information. Natural language processing technology can understand the context of the contract and identify important keywords and phrases. This allows the extraction unit to quickly and accurately analyze the content of the contract and extract the key points. Furthermore, the extraction unit can continuously learn from the content of contracts and improve its analysis accuracy. For example, by training the AI ​​model using past contract data, it can extract key points from new contracts with high accuracy. As a result, the extraction unit can efficiently analyze contracts and extract information, significantly improving the efficiency of contract management.

[0031] The summarization unit summarizes the points extracted by the extraction unit. Specifically, it concisely summarizes the important extracted information, making it easier to grasp the overall picture of the contract. The summarization unit can generate summaries using AI. For example, it can use a generation AI to summarize the extracted points and concisely summarize the contents of the contract. The generation AI can concisely summarize the main points of the contract based on the extracted information. This allows the summarization unit to quickly grasp the contents of the contract and efficiently convey important information. Furthermore, the summarization unit can train its AI model using past summarization data to improve the accuracy of the summaries. This allows the summarization unit to accurately and concisely summarize the contents of contracts, improving the efficiency of contract management.

[0032] The evaluation department assesses the risks of a contract. Specifically, it evaluates the risks of the contract and highlights potential problems and clauses requiring attention. The evaluation department can use AI to assess risks. For example, it can use risk assessment algorithms to evaluate the risks of a contract and identify key risk factors. AI can analyze the content of a contract and identify high-risk clauses and conditions. This allows the evaluation department to quickly and accurately assess the risks of a contract and detect potential problems early. Furthermore, the evaluation department can train AI models using past risk assessment data to assess the risks of new contracts with high accuracy. This allows the evaluation department to efficiently assess the risks of contracts and improve the reliability of contract management.

[0033] The tracking unit tracks the performance of contracts. Specifically, it reminds users of contract deadlines and key milestones, and manages the progress of contracts. The tracking unit can use AI to track performance. For example, it can use a performance management system to track contract performance and remind users of key deadlines and milestones. The AI ​​can monitor contract progress in real time and automatically notify users of important events and deadlines. This allows the tracking unit to efficiently manage contract performance and reliably track contract progress. Furthermore, the tracking unit can train its AI model using historical performance data, enabling it to track the performance of new contracts with high accuracy. This allows the tracking unit to efficiently manage contract performance and improve the reliability of contract management.

[0034] The summary section can explain complex legal terminology in an easy-to-understand manner. For example, the summary section can concisely explain technical and legal terms. The summary section can analyze legal terminology using AI and explain it in an easy-to-understand way. For example, the summary section can generate explanations of legal terminology using generative AI. The summary section makes it easier for users to understand by showing the meaning and usage examples of legal terms. This makes it easier for users to understand the contents of contracts.

[0035] The evaluation unit can highlight potential problems and clauses requiring attention. For example, it can highlight ambiguous or high-risk clauses in a contract. The evaluation unit can use AI to assess the risks of a contract and identify problems. For example, it can use risk assessment algorithms to assess the risks of a contract and highlight clauses requiring attention. For example, it can highlight important deadlines or specific conditions in a contract. This clarifies the risks of the contract and helps users understand what they need to pay attention to.

[0036] The tracking unit can remind you of contract performance deadlines and important milestones. For example, it can remind you of the time elapsed since the contract signing date or specific dates. The tracking unit can use AI to track contract performance and remind you of important deadlines. For example, the tracking unit can use generative AI to manage contract performance deadlines and milestones. For example, the tracking unit can remind you of project progress stages and important events. This ensures proper management of contract performance and prevents important deadlines from being missed.

[0037] The Professional Support Connection provides options for consulting with lawyers and contract specialists as needed. For example, if a user faces a complex contract issue, the Professional Support Connection offers the option to consult with an expert. Using AI, the Professional Support Connection can select a specialist suited to the user's needs and support the consultation process. For example, the Professional Support Connection can provide consultations with lawyers and advice from contract specialists. The Professional Support Connection can also provide contact methods and reservation systems for users to receive support from specialists. This allows users to receive expert support for complex issues.

[0038] The extraction unit can apply extraction algorithms specific to particular industries or fields based on the content of the contract. For example, the extraction unit can focus on extracting technical elements from a contract in the IT industry. The extraction unit analyzes the content of the contract using generative AI and extracts points specific to particular industries or fields. For example, the extraction unit can focus on extracting legal compliance elements from a contract in the medical industry. For example, the extraction unit can focus on extracting property details and transaction terms from a contract in the real estate industry. This allows for the accurate extraction of points specific to particular industries or fields.

[0039] The extraction unit can refer to the contract's version history during extraction and highlight changes. For example, the extraction unit compares the latest contract with previous versions and highlights the changed clauses. The extraction unit analyzes the contract's version history using generational AI to identify changes. For example, the extraction unit notifies the user about important changes. For example, the extraction unit saves the change history and makes it available for reference as needed. This allows for a clear understanding of changes in the contract.

[0040] The extraction unit can improve the accuracy of extraction by considering the attribute information of the contract's creator during the extraction process. For example, if the creator is a legal professional, the extraction unit will focus on extracting legal points. The extraction unit uses generational AI to analyze the attribute information of the contract's creator and improve the accuracy of extraction. For example, if the creator is an engineer, the extraction unit will focus on extracting technical points. For example, if the creator is a business manager, the extraction unit will focus on extracting business points. In this way, the accuracy of extraction is improved by considering the attribute information of the contract's creator.

[0041] The extraction unit can improve the accuracy of its extraction by referring to relevant documents related to the contract during the extraction process. For example, the extraction unit can refer to relevant legal documents to accurately extract legal points. The extraction unit can analyze relevant documents related to the contract using generational AI to improve the accuracy of its extraction. For example, the extraction unit can refer to relevant technical documents to accurately extract technical points. For example, the extraction unit can refer to relevant business documents to accurately extract business points. In this way, the accuracy of the extraction is improved by referring to relevant documents.

[0042] The summarization unit can adjust the level of detail in the summary based on the importance of the contract during the summary generation process. For example, the summarization unit provides a detailed summary for important contracts. The summarization unit uses generation AI to analyze the importance of the contract and adjust the level of detail in the summary. For example, the summarization unit provides a concise summary for general contracts. For example, the summarization unit provides a rapid summary for urgent contracts. This allows the level of detail in the summary to be adjusted according to the importance of the contract.

[0043] The summarization unit can apply different summarization algorithms depending on the contract category when generating summaries. For example, the summarization unit applies a legal summarization algorithm to legal contracts. The summarization unit uses generation AI to analyze the contract category and apply the appropriate summarization algorithm. For example, the summarization unit applies a technical summarization algorithm to technology contracts. For example, the summarization unit applies a business summarization algorithm to business contracts. This allows the application of the appropriate summarization algorithm depending on the contract category.

[0044] The summarization unit can determine the priority of summaries based on the submission timing of contracts during the summarization process. For example, the summarization unit prioritizes the generation of summaries for urgent contracts. The summarization unit uses generation AI to analyze the submission timing of contracts and determine the priority of summaries. For example, the summarization unit quickly generates summaries for contracts with approaching submission deadlines. For example, the summarization unit applies the normal summarization process to contracts with ample time for submission. This allows for the determination of summary priority according to the submission timing of contracts.

[0045] The summarization unit can adjust the order of summaries based on the relevance of the contract during the summarization process. For example, the summarization unit will summarize important clauses first. The summarization unit will use generation AI to analyze the relevance of the contract and adjust the order of summaries. For example, the summarization unit will prioritize summarizing highly relevant clauses. For example, the summarization unit will postpone summarizing less relevant clauses. This allows the order of summaries to be adjusted according to the relevance of the contract.

[0046] The evaluation unit can improve the accuracy of its risk assessment by considering the interrelationships between contracts. For example, the evaluation unit improves the accuracy of its risk assessment by referring to relevant contracts. The evaluation unit improves the accuracy of its assessment by analyzing the interrelationships between contracts using generative AI. For example, the evaluation unit analyzes the interrelationships between contracts and reflects this in the risk assessment. For example, the evaluation unit identifies potential risks by considering the interrelationships between contracts. As a result, the accuracy of the risk assessment is improved by considering the interrelationships between contracts.

[0047] The evaluation department can consider the attribute information of the contract submitter when conducting risk assessments. For example, if the submitter is a legal professional, the evaluation department will focus on evaluating legal risks. The evaluation department uses generative AI to analyze the attribute information of the contract submitter and conduct risk assessments. For example, if the submitter is an engineer, the evaluation department will focus on evaluating technical risks. For example, if the submitter is a business manager, the evaluation department will focus on evaluating business risks. This improves the accuracy of risk assessments by considering the attribute information of the contract submitter.

[0048] The evaluation unit can improve the accuracy of its risk assessments by referring to relevant documents in the contract. For example, the evaluation unit can accurately assess legal risks by referring to relevant legal documents. The evaluation unit can improve the accuracy of its assessments by analyzing relevant documents in the contract using generative AI. For example, the evaluation unit can accurately assess technical risks by referring to relevant technical documents. For example, the evaluation unit can accurately assess business risks by referring to relevant business documents. In this way, the accuracy of risk assessments is improved by referring to relevant documents.

[0049] The tracking unit can predict the current performance status by referring to past performance data when tracking performance status. For example, the tracking unit predicts the current performance status based on past performance data. The tracking unit predicts the current performance status by analyzing past performance data using generative AI. For example, the tracking unit predicts future performance status by analyzing trends in performance data. For example, the tracking unit identifies potential problems by referring to performance data. This allows for accurate prediction of the current performance status by referring to past performance data.

[0050] The tracking unit can apply different tracking methods depending on the contract category when tracking performance. For example, the tracking unit will focus on tracking the legal performance of legal contracts. The tracking unit will analyze the contract category using generative AI and apply the appropriate tracking method. For example, the tracking unit will focus on tracking the technical performance of technology contracts. For example, the tracking unit will focus on tracking the business performance of business contracts. This allows the appropriate tracking method to be applied according to the contract category.

[0051] The tracking unit can analyze changes in performance status based on the submission date of the contract when tracking performance status. For example, the tracking unit focuses its analysis on changes in performance status for contracts with an approaching submission date. The tracking unit uses generative AI to analyze the submission date of contracts and analyze changes in performance status. For example, the tracking unit tracks changes in performance status for contracts whose submission date has passed. For example, the tracking unit performs normal performance tracking for contracts with ample time before the submission date. This allows for accurate analysis of changes in performance status based on the submission date of the contract.

[0052] The tracking unit can analyze performance status by referring to relevant market data in the contract when tracking performance status. For example, the tracking unit can refer to relevant market data and analyze changes in performance status. The tracking unit can analyze relevant market data using generative AI and analyze performance status. For example, the tracking unit can predict performance status based on market data trends. For example, the tracking unit can refer to market data to identify potential problems. This allows for accurate analysis of changes in performance status by referring to relevant market data.

[0053] The Professional Support Connection Unit can select the most suitable expert by referring to past support history when providing expert support. For example, the Professional Support Connection Unit prioritizes selecting experts who have received high ratings in the past. The Professional Support Connection Unit analyzes past support history using generational AI to select the most suitable expert. For example, the Professional Support Connection Unit selects an expert that meets the user's needs based on past support history. For example, the Professional Support Connection Unit selects an expert strong in a specific field by referring to past support history. This allows for the selection of the most suitable expert by referring to past support history.

[0054] The Professional Support Connection Unit can customize expert advice based on the content of the contract during expert support sessions. For example, the Professional Support Connection Unit allows experts to provide specific advice based on the contract's content. The Professional Support Connection Unit uses generative AI to analyze the contract's content and customize expert advice. For example, the Professional Support Connection Unit communicates key points of the contract to the expert, allowing them to customize the advice. For example, the Professional Support Connection Unit communicates risk factors in the contract to the expert, enabling them to provide advice on those risks. This allows for the customization of expert advice based on the contract's content.

[0055] The Professional Support Connection Unit can select the most suitable expert when providing expert support, taking into account the user's geographical location. For example, the Professional Support Connection Unit prioritizes selecting experts located near the user. The Professional Support Connection Unit analyzes the user's geographical location using generative AI to select the most suitable expert. For example, the Professional Support Connection Unit selects the most suitable expert based on the user's geographical location. For example, the Professional Support Connection Unit selects an expert who can respond quickly, taking into account the user's geographical location. This allows for the selection of the most suitable expert by considering the user's geographical location.

[0056] The Professional Support Connection Unit can analyze a user's social media activity to select a suitable expert when providing support. For example, the Professional Support Connection Unit can analyze a user's social media activity and select an expert that meets their needs. The Professional Support Connection Unit can use generative AI to analyze a user's social media activity and select an expert. For example, the Professional Support Connection Unit can select an expert with strong expertise in a specific field based on the user's social media activity. For example, the Professional Support Connection Unit can refer to a user's social media activity to select the most suitable expert. This allows for the selection of the most suitable expert by analyzing the user's social media activity.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The extraction unit, when extracting key points from a contract, can refer to the user's past contract history to prioritize the extraction of particularly important points. For example, if the user has previously experienced problems with a specific clause, the unit will prioritize the extraction of points related to that clause. The extraction unit uses generational AI to analyze the user's past contract history and identify important points. For example, if the user has previously entered into high-risk contracts, the unit will focus on extracting points related to those risks. This allows the extraction of important points to be prioritized, taking into account the user's past contract history.

[0059] The summarization unit can adjust the level of detail in the summary during generation, taking into account the user's understanding of the contract. For example, if the user is unfamiliar with the contract, it will provide a detailed summary. The summarization unit uses generation AI to analyze the user's understanding and adjust the level of detail in the summary. For example, if the user is well-versed in the contract, it will provide a concise summary. This allows the level of detail in the summary to be adjusted according to the user's understanding.

[0060] The evaluation department can consider the past reliability of the contract submitter when conducting risk assessments. For example, if the submitter has a history of providing reliable contracts, the risk assessment will be mitigated. The evaluation department uses generative AI to analyze the submitter's past reliability and reflects this in the risk assessment. For example, if the submitter has a history of providing problematic contracts, the risk assessment will be made stricter. This allows for risk assessments to be conducted while considering the submitter's past reliability.

[0061] The tracking unit can analyze performance while considering the relevant legal requirements of the contract. For example, it can focus on tracking performance based on specific legal requirements. The tracking unit uses generative AI to analyze legal requirements and then analyzes performance. For example, if there is a possibility of violating a legal requirement, it will focus on tracking that performance. This allows for an accurate analysis of performance while considering legal requirements.

[0062] The Professional Support Connection Unit can select the most suitable professional by referring to past feedback regarding the user's contract when providing support to a specialist. For example, it can prioritize selecting professionals who have received high ratings in the past. The Professional Support Connection Unit uses generative AI to analyze past feedback and select the most suitable professional. For example, it can select a professional who is strong in a specific field. In this way, the most suitable professional can be selected by referring to past feedback.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The extraction unit extracts the key points of the contract. For example, the extraction unit extracts important clauses and high-risk parts of the contract. The extraction unit can use AI to analyze the content of the contract and extract key points. For example, the extraction unit can use natural language processing technology to analyze the content of the contract and extract important information. Step 2: The summarization unit summarizes the points extracted by the extraction unit. For example, the summarization unit concisely summarizes the extracted points. The summarization unit can generate summaries using AI. For example, the summarization unit summarizes the points extracted using generation AI. Step 3: The evaluation department assesses the risks of the contract. The evaluation department, for example, assesses the risks of the contract and highlights potential problems and clauses that require attention. The evaluation department can use AI to assess risks. The evaluation department, for example, uses a risk assessment algorithm to assess the risks of the contract. Step 4: The tracking unit tracks the status of contract performance. The tracking unit reminds users of contract performance deadlines and important milestones, for example. The tracking unit can use AI to track performance. The tracking unit tracks contract performance using a performance management system, for example.

[0065] (Example of form 2) A Fair Contract Agent according to an embodiment of the present invention is an AI agent for assisting individuals who are anxious about contracts. The Fair Contract Agent includes a contract analysis function that extracts key points from a contract and provides a concise summary. The contract analysis function explains complex legal terminology in an easy-to-understand manner, making the content easier for users to comprehend. The Fair Contract Agent includes a risk assessment function that evaluates the risks of a contract and highlights potential issues and clauses requiring attention. The Fair Contract Agent includes a performance history management function that tracks the status of contract performance and reminds users of deadlines and important milestones. The Fair Contract Agent includes a professional support connection function that provides the option to consult with lawyers and contract experts as needed. For example, the Fair Contract Agent extracts key points from a contract and provides a concise summary. For example, the Fair Contract Agent explains complex legal terminology in an easy-to-understand manner, making the content easier for users to comprehend. The Fair Contract Agent evaluates the risks of a contract and highlights potential issues and clauses requiring attention. For example, a fair contract agent tracks the progress of a contract and reminds clients of deadlines and key milestones. A fair contract agent also provides the option to consult with lawyers and contract specialists as needed. This allows individuals who are apprehensive about contracts to understand the contract, assess risks, manage their progress, and receive expert support when necessary. This eliminates contractual anxieties and allows clients to enter into contracts with confidence. Thus, a fair contract agent helps individuals who are apprehensive about contracts to understand the contract, assess risks, manage their progress, and receive expert support when necessary.

[0066] The fair contract agent according to this embodiment comprises an extraction unit, a summarization unit, an evaluation unit, and a tracking unit. The extraction unit extracts the main points of the contract. For example, the extraction unit extracts important clauses and high-risk parts of the contract. The extraction unit can analyze the content of the contract using AI and extract the main points. For example, the extraction unit can analyze the content of the contract using natural language processing technology and extract important information. The summarization unit summarizes the points extracted by the extraction unit. For example, the summarization unit concisely summarizes the extracted points. The summarization unit can generate a summary using AI. For example, the summarization unit summarizes the extracted points using generation AI. The evaluation unit evaluates the risks of the contract. For example, the evaluation unit evaluates the risks of the contract and highlights potential problems and clauses that require attention. The evaluation unit can evaluate the risks using AI. For example, the evaluation unit evaluates the risks of the contract using a risk evaluation algorithm. The tracking unit tracks the status of contract performance. The tracking unit, for example, reminds users of contract performance deadlines and important milestones. The tracking unit can track performance using AI. The tracking unit tracks contract performance using, for example, a performance status management system. This allows the Fair Contract Agent according to the embodiment to extract and summarize key points of the contract, assess risks, and track performance.

[0067] The extraction unit extracts the key points from the contract. Specifically, it identifies important clauses and high-risk sections of the contract and utilizes AI technology to extract this information. The AI ​​analyzes the content of the contract using natural language processing technology and extracts important information. For example, it automatically detects particularly important clauses, conditions, and high-risk sections within the contract and extracts this information. Natural language processing technology can understand the context of the contract and identify important keywords and phrases. This allows the extraction unit to quickly and accurately analyze the content of the contract and extract the key points. Furthermore, the extraction unit can continuously learn from the content of contracts and improve its analysis accuracy. For example, by training the AI ​​model using past contract data, it can extract key points from new contracts with high accuracy. As a result, the extraction unit can efficiently analyze contracts and extract information, significantly improving the efficiency of contract management.

[0068] The summarization unit summarizes the points extracted by the extraction unit. Specifically, it concisely summarizes the important extracted information, making it easier to grasp the overall picture of the contract. The summarization unit can generate summaries using AI. For example, it can use a generation AI to summarize the extracted points and concisely summarize the contents of the contract. The generation AI can concisely summarize the main points of the contract based on the extracted information. This allows the summarization unit to quickly grasp the contents of the contract and efficiently convey important information. Furthermore, the summarization unit can train its AI model using past summarization data to improve the accuracy of the summaries. This allows the summarization unit to accurately and concisely summarize the contents of contracts, improving the efficiency of contract management.

[0069] The evaluation department assesses the risks of a contract. Specifically, it evaluates the risks of the contract and highlights potential problems and clauses requiring attention. The evaluation department can use AI to assess risks. For example, it can use risk assessment algorithms to evaluate the risks of a contract and identify key risk factors. AI can analyze the content of a contract and identify high-risk clauses and conditions. This allows the evaluation department to quickly and accurately assess the risks of a contract and detect potential problems early. Furthermore, the evaluation department can train AI models using past risk assessment data to assess the risks of new contracts with high accuracy. This allows the evaluation department to efficiently assess the risks of contracts and improve the reliability of contract management.

[0070] The tracking unit tracks the performance of contracts. Specifically, it reminds users of contract deadlines and key milestones, and manages the progress of contracts. The tracking unit can use AI to track performance. For example, it can use a performance management system to track contract performance and remind users of key deadlines and milestones. The AI ​​can monitor contract progress in real time and automatically notify users of important events and deadlines. This allows the tracking unit to efficiently manage contract performance and reliably track contract progress. Furthermore, the tracking unit can train its AI model using historical performance data, enabling it to track the performance of new contracts with high accuracy. This allows the tracking unit to efficiently manage contract performance and improve the reliability of contract management.

[0071] The summary section can explain complex legal terminology in an easy-to-understand manner. For example, the summary section can concisely explain technical and legal terms. The summary section can analyze legal terminology using AI and explain it in an easy-to-understand way. For example, the summary section can generate explanations of legal terminology using generative AI. The summary section makes it easier for users to understand by showing the meaning and usage examples of legal terms. This makes it easier for users to understand the contents of contracts.

[0072] The evaluation unit can highlight potential problems and clauses requiring attention. For example, it can highlight ambiguous or high-risk clauses in a contract. The evaluation unit can use AI to assess the risks of a contract and identify problems. For example, it can use risk assessment algorithms to assess the risks of a contract and highlight clauses requiring attention. For example, it can highlight important deadlines or specific conditions in a contract. This clarifies the risks of the contract and helps users understand what they need to pay attention to.

[0073] The tracking unit can remind you of contract performance deadlines and important milestones. For example, it can remind you of the time elapsed since the contract signing date or specific dates. The tracking unit can use AI to track contract performance and remind you of important deadlines. For example, the tracking unit can use generative AI to manage contract performance deadlines and milestones. For example, the tracking unit can remind you of project progress stages and important events. This ensures proper management of contract performance and prevents important deadlines from being missed.

[0074] The Professional Support Connection provides options for consulting with lawyers and contract specialists as needed. For example, if a user faces a complex contract issue, the Professional Support Connection offers the option to consult with an expert. Using AI, the Professional Support Connection can select a specialist suited to the user's needs and support the consultation process. For example, the Professional Support Connection can provide consultations with lawyers and advice from contract specialists. The Professional Support Connection can also provide contact methods and reservation systems for users to receive support from specialists. This allows users to receive expert support for complex issues.

[0075] The extraction unit can estimate the user's emotions and determine the priority of points to extract based on the estimated emotions. For example, if the user is feeling anxious, the extraction unit will prioritize extracting important risk-related points. The extraction unit estimates the user's emotions using an emotion engine or generative AI and determines the priority of points to extract based on those emotions. For example, if the user is relaxed, the extraction unit will prioritize extracting the contract summary to help with overall understanding. For example, if the user is in a hurry, the extraction unit will extract only the most important points and provide them quickly. This allows for the priority extraction of important points according to the user's emotions.

[0076] The extraction unit can apply extraction algorithms specific to particular industries or fields based on the content of the contract. For example, the extraction unit can focus on extracting technical elements from a contract in the IT industry. The extraction unit analyzes the content of the contract using generative AI and extracts points specific to particular industries or fields. For example, the extraction unit can focus on extracting legal compliance elements from a contract in the medical industry. For example, the extraction unit can focus on extracting property details and transaction terms from a contract in the real estate industry. This allows for the accurate extraction of points specific to particular industries or fields.

[0077] The extraction unit can refer to the contract's version history during extraction and highlight changes. For example, the extraction unit compares the latest contract with previous versions and highlights the changed clauses. The extraction unit analyzes the contract's version history using generational AI to identify changes. For example, the extraction unit notifies the user about important changes. For example, the extraction unit saves the change history and makes it available for reference as needed. This allows for a clear understanding of changes in the contract.

[0078] The extraction unit can estimate the user's emotions and adjust the level of detail of the points extracted based on the estimated emotions. For example, if the user is feeling anxious, the extraction unit will extract points that include detailed explanations. The extraction unit estimates the user's emotions using an emotion engine or generative AI and adjusts the level of detail of the points extracted based on those emotions. For example, if the user is relaxed, the extraction unit will extract points that include concise explanations. For example, if the user is in a hurry, the extraction unit will extract only the essentials. This allows the level of detail of the points to be adjusted according to the user's emotions.

[0079] The extraction unit can improve the accuracy of extraction by considering the attribute information of the contract's creator during the extraction process. For example, if the creator is a legal professional, the extraction unit will focus on extracting legal points. The extraction unit uses generational AI to analyze the attribute information of the contract's creator and improve the accuracy of extraction. For example, if the creator is an engineer, the extraction unit will focus on extracting technical points. For example, if the creator is a business manager, the extraction unit will focus on extracting business points. In this way, the accuracy of extraction is improved by considering the attribute information of the contract's creator.

[0080] The extraction unit can improve the accuracy of its extraction by referring to relevant documents related to the contract during the extraction process. For example, the extraction unit can refer to relevant legal documents to accurately extract legal points. The extraction unit can analyze relevant documents related to the contract using generational AI to improve the accuracy of its extraction. For example, the extraction unit can refer to relevant technical documents to accurately extract technical points. For example, the extraction unit can refer to relevant business documents to accurately extract business points. In this way, the accuracy of the extraction is improved by referring to relevant documents.

[0081] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is feeling anxious, the summarization unit will use reassuring language. The summarization unit estimates the user's emotions using an emotion engine or generative AI, and adjusts the way the summary is presented based on those emotions. For example, if the user is relaxed, the summarization unit will use friendly language. For example, if the user is in a hurry, the summarization unit will use concise and clear language. This allows the summarization to be presented according to the user's emotions.

[0082] The summarization unit can adjust the level of detail in the summary based on the importance of the contract during the summary generation process. For example, the summarization unit provides a detailed summary for important contracts. The summarization unit uses generation AI to analyze the importance of the contract and adjust the level of detail in the summary. For example, the summarization unit provides a concise summary for general contracts. For example, the summarization unit provides a rapid summary for urgent contracts. This allows the level of detail in the summary to be adjusted according to the importance of the contract.

[0083] The summarization unit can apply different summarization algorithms depending on the contract category when generating summaries. For example, the summarization unit applies a legal summarization algorithm to legal contracts. The summarization unit uses generation AI to analyze the contract category and apply the appropriate summarization algorithm. For example, the summarization unit applies a technical summarization algorithm to technology contracts. For example, the summarization unit applies a business summarization algorithm to business contracts. This allows the application of the appropriate summarization algorithm depending on the contract category.

[0084] The summarization unit can estimate the user's emotions and adjust the length of the summary based on those emotions. For example, if the user is feeling anxious, the summarization unit will provide a detailed summary. The summarization unit estimates the user's emotions using an emotion engine or generative AI, and adjusts the length of the summary based on those emotions. For example, if the user is relaxed, the summarization unit will provide a concise summary. For example, if the user is in a hurry, the summarization unit will provide a short summary containing only the essential points. This allows the length of the summary to be adjusted according to the user's emotions.

[0085] The summarization unit can determine the priority of summaries based on the submission timing of contracts during the summarization process. For example, the summarization unit prioritizes the generation of summaries for urgent contracts. The summarization unit uses generation AI to analyze the submission timing of contracts and determine the priority of summaries. For example, the summarization unit quickly generates summaries for contracts with approaching submission deadlines. For example, the summarization unit applies the normal summarization process to contracts with ample time for submission. This allows for the determination of summary priority according to the submission timing of contracts.

[0086] The summarization unit can adjust the order of summaries based on the relevance of the contract during the summarization process. For example, the summarization unit will summarize important clauses first. The summarization unit will use generation AI to analyze the relevance of the contract and adjust the order of summaries. For example, the summarization unit will prioritize summarizing highly relevant clauses. For example, the summarization unit will postpone summarizing less relevant clauses. This allows the order of summaries to be adjusted according to the relevance of the contract.

[0087] The evaluation unit can estimate the user's emotions and adjust the risk assessment criteria based on those emotions. For example, if the user is feeling anxious, the evaluation unit will tighten the risk assessment criteria. The evaluation unit estimates the user's emotions using an emotion engine or generative AI and adjusts the risk assessment criteria based on those emotions. For example, if the user is relaxed, the evaluation unit will set the risk assessment criteria to normal. For example, if the user is in a hurry, the evaluation unit will perform a rapid risk assessment. This allows the risk assessment criteria to be adjusted according to the user's emotions.

[0088] The evaluation unit can improve the accuracy of its risk assessment by considering the interrelationships between contracts. For example, the evaluation unit improves the accuracy of its risk assessment by referring to relevant contracts. The evaluation unit improves the accuracy of its assessment by analyzing the interrelationships between contracts using generative AI. For example, the evaluation unit analyzes the interrelationships between contracts and reflects this in the risk assessment. For example, the evaluation unit identifies potential risks by considering the interrelationships between contracts. As a result, the accuracy of the risk assessment is improved by considering the interrelationships between contracts.

[0089] The evaluation department can consider the attribute information of the contract submitter when conducting risk assessments. For example, if the submitter is a legal professional, the evaluation department will focus on evaluating legal risks. The evaluation department uses generative AI to analyze the attribute information of the contract submitter and conduct risk assessments. For example, if the submitter is an engineer, the evaluation department will focus on evaluating technical risks. For example, if the submitter is a business manager, the evaluation department will focus on evaluating business risks. This improves the accuracy of risk assessments by considering the attribute information of the contract submitter.

[0090] The evaluation unit can improve the accuracy of its risk assessments by referring to relevant documents in the contract. For example, the evaluation unit can accurately assess legal risks by referring to relevant legal documents. The evaluation unit can improve the accuracy of its assessments by analyzing relevant documents in the contract using generative AI. For example, the evaluation unit can accurately assess technical risks by referring to relevant technical documents. For example, the evaluation unit can accurately assess business risks by referring to relevant business documents. In this way, the accuracy of risk assessments is improved by referring to relevant documents.

[0091] The tracking unit can estimate the user's emotions and adjust how the performance status is displayed based on the estimated emotions. For example, if the user is feeling anxious, the tracking unit will display detailed performance status. The tracking unit estimates the user's emotions using an emotion engine or generative AI and adjusts how the performance status is displayed based on those emotions. For example, if the user is relaxed, the tracking unit will display concise performance status. For example, if the user is in a hurry, the tracking unit will display only the essentials. This allows the display of performance status to be adjusted according to the user's emotions.

[0092] The tracking unit can predict the current performance status by referring to past performance data when tracking performance status. For example, the tracking unit predicts the current performance status based on past performance data. The tracking unit predicts the current performance status by analyzing past performance data using generative AI. For example, the tracking unit predicts future performance status by analyzing trends in performance data. For example, the tracking unit identifies potential problems by referring to performance data. This allows for accurate prediction of the current performance status by referring to past performance data.

[0093] The tracking unit can apply different tracking methods depending on the contract category when tracking performance. For example, the tracking unit will focus on tracking the legal performance of legal contracts. The tracking unit will analyze the contract category using generative AI and apply the appropriate tracking method. For example, the tracking unit will focus on tracking the technical performance of technology contracts. For example, the tracking unit will focus on tracking the business performance of business contracts. This allows the appropriate tracking method to be applied according to the contract category.

[0094] The tracking unit can estimate the user's emotions and adjust the importance of performance status based on the estimated emotions. For example, if the user is feeling anxious, the tracking unit will prioritize displaying important performance statuses. The tracking unit estimates the user's emotions using an emotion engine or generative AI, and adjusts the importance of performance statuses based on those emotions. For example, if the user is relaxed, the tracking unit will display all performance statuses in a balanced manner. For example, if the user is in a hurry, the tracking unit will display only the most important performance statuses. This allows the importance of performance statuses to be adjusted according to the user's emotions.

[0095] The tracking unit can analyze changes in performance status based on the submission date of the contract when tracking performance status. For example, the tracking unit focuses its analysis on changes in performance status for contracts with an approaching submission date. The tracking unit uses generative AI to analyze the submission date of contracts and analyze changes in performance status. For example, the tracking unit tracks changes in performance status for contracts whose submission date has passed. For example, the tracking unit performs normal performance tracking for contracts with ample time before the submission date. This allows for accurate analysis of changes in performance status based on the submission date of the contract.

[0096] The tracking unit can analyze performance status by referring to relevant market data in the contract when tracking performance status. For example, the tracking unit can refer to relevant market data and analyze changes in performance status. The tracking unit can analyze relevant market data using generative AI and analyze performance status. For example, the tracking unit can predict performance status based on market data trends. For example, the tracking unit can refer to market data to identify potential problems. This allows for accurate analysis of changes in performance status by referring to relevant market data.

[0097] The Professional Support Connection Unit can estimate the user's emotions and select a specialist based on those emotions. For example, if the user is feeling anxious, the Professional Support Connection Unit will select a specialist who can provide reassurance. The Professional Support Connection Unit estimates the user's emotions using an emotion engine or generative AI and selects a specialist based on those emotions. For example, if the user is relaxed, the Professional Support Connection Unit will select a friendly specialist. For example, if the user is in a hurry, the Professional Support Connection Unit will select a specialist who can respond quickly. This allows for the selection of the most suitable specialist according to the user's emotions.

[0098] The Professional Support Connection Unit can select the most suitable expert by referring to past support history when providing expert support. For example, the Professional Support Connection Unit prioritizes selecting experts who have received high ratings in the past. The Professional Support Connection Unit analyzes past support history using generational AI to select the most suitable expert. For example, the Professional Support Connection Unit selects an expert that meets the user's needs based on past support history. For example, the Professional Support Connection Unit selects an expert strong in a specific field by referring to past support history. This allows for the selection of the most suitable expert by referring to past support history.

[0099] The Professional Support Connection Unit can customize expert advice based on the content of the contract during expert support sessions. For example, the Professional Support Connection Unit allows experts to provide specific advice based on the contract's content. The Professional Support Connection Unit uses generative AI to analyze the contract's content and customize expert advice. For example, the Professional Support Connection Unit communicates key points of the contract to the expert, allowing them to customize the advice. For example, the Professional Support Connection Unit communicates risk factors in the contract to the expert, enabling them to provide advice on those risks. This allows for the customization of expert advice based on the contract's content.

[0100] The professional support connection unit can estimate the user's emotions and prioritize expert support based on those emotions. For example, if the user is feeling anxious, the professional support connection unit will prioritize providing expert support. The professional support connection unit estimates the user's emotions using an emotion engine or generative AI, and prioritizes expert support based on those emotions. For example, if the user is relaxed, the professional support connection unit will provide normal support. For example, if the user is in a hurry, the professional support connection unit will provide expert support quickly. This allows for prioritizing expert support according to the user's emotions.

[0101] The Professional Support Connection Unit can select the most suitable expert when providing expert support, taking into account the user's geographical location. For example, the Professional Support Connection Unit prioritizes selecting experts located near the user. The Professional Support Connection Unit analyzes the user's geographical location using generative AI to select the most suitable expert. For example, the Professional Support Connection Unit selects the most suitable expert based on the user's geographical location. For example, the Professional Support Connection Unit selects an expert who can respond quickly, taking into account the user's geographical location. This allows for the selection of the most suitable expert by considering the user's geographical location.

[0102] The Professional Support Connection Unit can analyze a user's social media activity to select a suitable expert when providing support. For example, the Professional Support Connection Unit can analyze a user's social media activity and select an expert that meets their needs. The Professional Support Connection Unit can use generative AI to analyze a user's social media activity and select an expert. For example, the Professional Support Connection Unit can select an expert with strong expertise in a specific field based on the user's social media activity. For example, the Professional Support Connection Unit can refer to a user's social media activity to select the most suitable expert. This allows for the selection of the most suitable expert by analyzing the user's social media activity.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The extraction unit, when extracting key points from a contract, can refer to the user's past contract history to prioritize the extraction of particularly important points. For example, if the user has previously experienced problems with a specific clause, the unit will prioritize the extraction of points related to that clause. The extraction unit uses generational AI to analyze the user's past contract history and identify important points. For example, if the user has previously entered into high-risk contracts, the unit will focus on extracting points related to those risks. This allows the extraction of important points to be prioritized, taking into account the user's past contract history.

[0105] The summarization unit can adjust the level of detail in the summary during generation, taking into account the user's understanding of the contract. For example, if the user is unfamiliar with the contract, it will provide a detailed summary. The summarization unit uses generation AI to analyze the user's understanding and adjust the level of detail in the summary. For example, if the user is well-versed in the contract, it will provide a concise summary. This allows the level of detail in the summary to be adjusted according to the user's understanding.

[0106] The evaluation department can consider the past reliability of the contract submitter when conducting risk assessments. For example, if the submitter has a history of providing reliable contracts, the risk assessment will be mitigated. The evaluation department uses generative AI to analyze the submitter's past reliability and reflects this in the risk assessment. For example, if the submitter has a history of providing problematic contracts, the risk assessment will be made stricter. This allows for risk assessments to be conducted while considering the submitter's past reliability.

[0107] The tracking unit can analyze performance while considering the relevant legal requirements of the contract. For example, it can focus on tracking performance based on specific legal requirements. The tracking unit uses generative AI to analyze legal requirements and then analyzes performance. For example, if there is a possibility of violating a legal requirement, it will focus on tracking that performance. This allows for an accurate analysis of performance while considering legal requirements.

[0108] The Professional Support Connection Unit can select the most suitable professional by referring to past feedback regarding the user's contract when providing support to a specialist. For example, it can prioritize selecting professionals who have received high ratings in the past. The Professional Support Connection Unit uses generative AI to analyze past feedback and select the most suitable professional. For example, it can select a professional who is strong in a specific field. In this way, the most suitable professional can be selected by referring to past feedback.

[0109] The extraction unit can estimate the user's emotions and determine the priority of points to extract based on those emotions. For example, if the user is feeling anxious, it will prioritize extracting important risk-related points. The extraction unit estimates the user's emotions using an emotion engine or generative AI, and determines the priority of points to extract based on those emotions. For example, if the user is relaxed, it will prioritize extracting the contract summary to help with overall understanding. This allows for the priority extraction of important points according to the user's emotions.

[0110] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is feeling anxious, it will use a reassuring style of expression. The summarization unit estimates the user's emotions using an emotion engine or generative AI, and adjusts the way the summary is presented based on those emotions. For example, if the user is relaxed, it will use a friendly style of expression. This allows the summary to be presented in accordance with the user's emotions.

[0111] The evaluation unit can estimate the user's emotions and adjust the risk assessment criteria based on those emotions. For example, if the user is feeling anxious, the risk assessment criteria can be made stricter. The evaluation unit can estimate the user's emotions using an emotion engine or generative AI, and adjust the risk assessment criteria based on those emotions. For example, if the user is relaxed, the risk assessment criteria can be set to normal. This allows the risk assessment criteria to be adjusted according to the user's emotions.

[0112] The tracking unit can estimate the user's emotions and adjust the display method of performance status based on the estimated emotions. For example, if the user is feeling anxious, it will display detailed performance status. The tracking unit estimates the user's emotions using an emotion engine or generative AI, and adjusts the display method of performance status based on those emotions. For example, if the user is relaxed, it will display a concise performance status. This allows the display method of performance status to be adjusted according to the user's emotions.

[0113] The professional support connection unit can estimate the user's emotions and select a specialist based on those emotions. For example, if the user is feeling anxious, it will select a specialist who can provide reassurance. The professional support connection unit estimates the user's emotions using an emotion engine or generative AI, and selects a specialist based on those emotions. For example, if the user is relaxed, it will select a friendly specialist. This allows for the selection of the most suitable specialist according to the user's emotions.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The extraction unit extracts the key points of the contract. For example, the extraction unit extracts important clauses and high-risk parts of the contract. The extraction unit can use AI to analyze the content of the contract and extract key points. For example, the extraction unit can use natural language processing technology to analyze the content of the contract and extract important information. Step 2: The summarization unit summarizes the points extracted by the extraction unit. For example, the summarization unit concisely summarizes the extracted points. The summarization unit can generate summaries using AI. For example, the summarization unit summarizes the points extracted using generation AI. Step 3: The evaluation department assesses the risks of the contract. The evaluation department, for example, assesses the risks of the contract and highlights potential problems and clauses that require attention. The evaluation department can use AI to assess risks. The evaluation department, for example, uses a risk assessment algorithm to assess the risks of the contract. Step 4: The tracking unit tracks the status of contract performance. The tracking unit reminds users of contract performance deadlines and important milestones, for example. The tracking unit can use AI to track performance. The tracking unit tracks contract performance using a performance management system, for example.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the functions of the Fair Contract Agent described above is implemented by at least one of the smart device 14 and the data processing device 12. For example, the contract analysis function is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The risk assessment function is implemented by the specific processing unit 290 of the data processing device 12. The performance history management function is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The professional support connection function is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the functions of the Fair Contract Agent described above is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the contract analysis function is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The risk assessment function is implemented by the specific processing unit 290 of the data processing device 12. The performance history management function is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The professional support connection function is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the functions of the Fair Contract Agent described above is implemented by at least one of the headset terminal 314 and the data processing device 12. For example, the contract analysis function is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The risk assessment function is implemented by the specific processing unit 290 of the data processing device 12. The performance history management function is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The professional support connection function is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the functions of the Fair Contract Agent described above is implemented by, for example, at least one of the robot 414 and the data processing device 12. For example, the contract analysis function is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The risk assessment function is implemented by, for example, the specific processing unit 290 of the data processing device 12. The performance history management function is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The professional support connection function is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0169] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] 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.

[0179] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) An extraction section that extracts the main points of the contract, A summarization unit that summarizes the points extracted by the extraction unit, The evaluation department assesses the risks of the contract, A tracking unit for tracking the status of contract performance is provided. A system characterized by the following features. (Note 2) The summary section above is, Explaining complex legal terms in an easy-to-understand way. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, Highlight potential issues and clauses that require attention. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is Remind us of contract deadlines and important milestones. The system described in Appendix 1, characterized by the features described herein. (Note 5) Equipped with a professional support connection, We offer the option to consult with lawyers and contract specialists as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The extraction unit is The system estimates the user's emotions and determines the priority of points to extract based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The extraction unit is Based on the contract, we apply an extraction algorithm that is specialized for a particular industry or field. The system described in Appendix 1, characterized by the features described herein. (Note 8) The extraction unit is During extraction, refer to the contract's version history and highlight the changes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The extraction unit is It estimates the user's emotions and adjusts the level of detail of the points extracted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The extraction unit is During extraction, the accuracy of the extraction is improved by considering the attribute information of the contract creator. The system described in Appendix 1, characterized by the features described herein. (Note 11) The extraction unit is During extraction, we improve the accuracy of the extraction by referring to relevant documents in the contract. The system described in Appendix 1, characterized by the features described herein. (Note 12) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, When generating summaries, the priority of summaries is determined based on the submission date of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, When generating summaries, adjust the order of the summaries based on the relevance of the contracts. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, We estimate user sentiment and adjust risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, When conducting risk assessments, consider the interrelationships between contracts to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, When conducting a risk assessment, the attribute information of the person submitting the contract should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, When conducting risk assessments, referencing relevant documents in the contract improves the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tracking unit is The system estimates the user's emotions and adjusts how performance status is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tracking unit is When tracking performance, historical performance data is used to predict the current performance status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tracking unit is When tracking performance, different tracking methods are applied for each category of contract. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tracking unit is It estimates the user's emotions and adjusts the importance of performance status based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tracking unit is When tracking performance, analyze changes in performance based on when the contract was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tracking unit is When tracking performance, analyze performance by referring to relevant market data in the contract. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned professional support connection unit is We estimate the user's emotions and select experts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned professional support connection unit is When seeking expert support, we select the most suitable expert by referring to past support history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned professional support connection unit is When receiving support from an expert, customize their advice based on the terms of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned professional support connection unit is The system estimates the user's emotions and prioritizes expert support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned professional support connection unit is When providing expert support, the system selects the most suitable expert by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned professional support connection unit is When providing expert support, we analyze the user's social media activity to select the appropriate expert. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An extraction section that extracts the main points of the contract, A summarization unit that summarizes the points extracted by the extraction unit, The evaluation department assesses the risks of the contract, A tracking unit for tracking the status of contract performance is provided. A system characterized by the following features.

2. The summary section above is, Explaining complex legal terms in an easy-to-understand way. The system according to feature 1.

3. The evaluation unit, Highlight potential issues and clauses that require attention. The system according to feature 1.

4. The aforementioned tracking unit is Remind us of contract deadlines and important milestones. The system according to feature 1.

5. Equipped with a professional support connection, We offer the option to consult with lawyers and contract specialists as needed. The system according to feature 1.

6. The extraction unit is The system estimates the user's emotions and determines the priority of points to extract based on those estimated emotions. The system according to feature 1.

7. The extraction unit is Based on the contract, we apply an extraction algorithm that is specialized for a particular industry or field. The system according to feature 1.

8. The extraction unit is During extraction, refer to the contract's version history and highlight the changes. The system according to feature 1.

9. The extraction unit is It estimates the user's emotions and adjusts the level of detail of the points extracted based on the estimated user emotions. The system according to feature 1.

10. The extraction unit is During extraction, the accuracy of the extraction is improved by considering the attribute information of the contract creator. The system according to feature 1.

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