system

Abstract

We provide the system. [Solution] A means of inputting legal document data into an information processing device, A means of analyzing the contract clauses included in the aforementioned data and identifying deficiencies and risks in those clauses, A means for automatically generating the optimal alternative clause based on the aforementioned risk, The means of presenting the aforementioned alternative clauses to the user device and modifying the legal document based on the input evaluation information, Means for associating the aforementioned correction process with electronic payment, A system that includes this.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the creation and conclusion of contracts, the labor and time of legal checks impose a significant burden. In addition, there are deficiencies and ambiguities in contract terms, which may lead to unfavorable situations for companies. As a result, there are problems such as delays in the contract process and an increase in legal risks. An object of the present invention is to provide a system that realizes risk reduction and efficiency improvement in such contract conclusion and supports quick and reliable contract conclusion.

Means for Solving the Problems

[0005] This invention provides a means for inputting contract document data into an information processing device and analyzing the contract clauses contained in that document data. This makes it possible to identify deficiencies and risks in the contract clauses. Furthermore, it provides a means for automatically generating favorable alternative clauses based on the identified risks, presenting these alternative clauses to the user device, and providing a system for revising the contract based on the input feedback. This reduces legal risks by utilizing past case law databases and successful contract examples, and enables a rapid contract process.

[0006] An "information processing device" is a device, including a computer, that has the function of inputting, processing, and outputting data.

[0007] A "contract" is a document that formalizes a legal agreement between two or more parties, and it is a document that describes the scope of rights and obligations.

[0008] "Document data" refers to contract information expressed as text, which is input into an information processing device.

[0009] "Contract clauses" refer to the various conditions and provisions written in a contract, which define the specific rights and obligations between the contracting parties.

[0010] "Deficiency" refers to any shortcomings or defects in the contract clauses, particularly those that are ambiguous or legally insufficient.

[0011] "Risk" refers to the possibility of contracting parties suffering losses or disadvantages due to deficiencies or ambiguities in the contract clauses.

[0012] An "alternative clause" is a clause used in place of an existing contract clause, and is created with the aim of providing more favorable terms.

[0013] "Automatic generation" refers to the process by which an information processing device creates new documents or clauses based on a program, without human intervention.

[0014] A "user device" is a device used by a user as an interface with an information processing device, and has means for input and output.

[0015] "Feedback" refers to evaluations and opinions that users provide regarding system suggestions and outputs, and this information is used to inform the system's responses and modifications. [Brief explanation of the drawing]

[0016] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. 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).

[0023] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] The 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.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention provides a system for efficiently performing legal checks and necessary revisions on contracts. To implement this system, it operates as follows:

[0038] The user uploads a draft of the contract from their terminal to the information processing device. This upload sends the contract document data to the server, where it is converted into a format that can be analyzed in text format.

[0039] The server analyzes the contract clauses based on this document data. The analysis utilizes AI and references a database of past case law to detect deficiencies and ambiguities in the contract clauses. This process identifies potential legal risks for each contract clause within the contract.

[0040] Once risks are identified, the server automatically generates favorable alternative clauses. This generation process optimizes clauses using past successful contract examples, proposing clauses that mitigate specific risks.

[0041] Users can view these alternative clauses on their devices. The server receives user feedback and modifies the generated clauses as needed. For example, if an applicable clause is misleading, the user can leave a comment.

[0042] This system not only streamlines communication between legal departments and users, but also enables the rapid creation of contracts with minimized legal risks. Furthermore, it reduces time and effort in this process. For example, if a supply contract lacks clear penalty clauses for delayed delivery, the system will suggest a clear alternative clause such as "In the event of a delay, a penalty of 5% of the delivery price can be claimed," thereby supporting the creation of a contract that is easy for both parties to understand.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user selects a draft contract file from their terminal and uploads it to the information processing system. The terminal then sends the selected contract file directly to the server.

[0046] Step 2:

[0047] The server converts the received contract file into text format. This makes the contract's contents analyzable by AI.

[0048] Step 3:

[0049] The server sends the text data to the AI ​​agent, which then begins analyzing the contract clauses. The AI ​​agent identifies each clause in the contract one by one, analyzing them while referring to past case precedents and legal databases.

[0050] Step 4:

[0051] The AI ​​agent identifies deficiencies and risks in contract clauses. This includes detecting ambiguities and legal shortcomings. It creates a list of deficiencies and assesses the corresponding risks.

[0052] Step 5:

[0053] The server automatically generates favorable alternative clauses based on risks identified by the AI ​​agent. This generation process is optimized by referencing past success stories.

[0054] Step 6:

[0055] The server sends the generated alternative clause to the user's device. The user can then view the presented clause on their device.

[0056] Step 7:

[0057] Users review the alternative clauses through their devices and provide feedback. This feedback includes approval, suggestions for revisions, and additional comments.

[0058] Step 8:

[0059] The server receives user feedback and makes revisions to alternative clauses if necessary. The revised content is then reviewed again for final confirmation.

[0060] Step 9:

[0061] The finalized contract is generated on the server and provided to the user in a downloadable format. The user can retrieve the completed contract from their device and complete the related processes.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Traditional contract drafting and legal review processes are time-consuming, labor-intensive, and prone to human error. Furthermore, it's difficult for individuals without specialized legal knowledge to properly evaluate and revise clauses. These challenges complicate communication between legal departments and users, reducing the efficiency of contract drafting.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for inputting an electronic document into an information processing device, means for analyzing clauses and identifying deficiencies and potential risks, and means for automatically generating favorable alternative expressions. This streamlines the legal review and revision of contracts, enabling the rapid creation of contracts with minimized legal risks.

[0067] An "information processing device" is a general term for hardware and software that inputs electronic documents and processes them as needed.

[0068] An "electronic document" is a document data stored in digital format and used to represent contracts or agreements.

[0069] A "clause" refers to individual provisions or conditions written in an electronic document, and is an element that forms the content of a contract.

[0070] "Deficiencies" refer to missing or incomplete sections in clauses or documents, which may cause problems in the execution of a contract.

[0071] "Potential risks" refer to uncertainties contained in the clauses and risks that may arise in the future.

[0072] A "favorable alternative wording" refers to a modified clause that avoids deficiencies or potential risks and presents more favorable terms for the contracting parties.

[0073] A "terminal" is an electronic device used by a user for communication and information processing.

[0074] "AI technology" refers to the scientific and technological advancements that enable computers to perform intelligent activities, with the aim of assisting in data analysis and decision-making.

[0075] "Natural language processing" is a technology that enables computers to understand human language, and involves the analysis and semantic understanding of text data.

[0076] A "reference case data set" is a collection of data from similar past cases and precedents, and serves as a source of information used as a standard for analysis and evaluation.

[0077] "Document examples" are documents created in the past that demonstrate the format and content of successful contracts and serve as references for optimization.

[0078] This invention relates to a system for efficiently performing legal checks and revisions on contracts. The system begins with a user uploading a draft contract using a terminal. The electronic document, in PDF or Word format, is then transmitted to an information processing device via a dedicated application on the terminal or a web interface.

[0079] The server converts received electronic documents into a format that can be analyzed using text. This conversion process may involve the use of software employing OCR (Optical Character Recognition) technology. Specifically, open-source OCR tools are utilized to perform highly accurate data extraction. The text data converted at this stage is then used in subsequent analysis processes.

[0080] Next, the server uses a generative AI model that performs natural language processing to analyze the clauses within the electronic document. In this step, the AI ​​identifies deficiencies and potential risks by comparing them with a dataset of past reference cases. The generative AI model is applied to the analysis, scrutinizing the document's structure and word usage. As a concrete example of the AI ​​model, industry-standard natural language processing tools are used.

[0081] The server automatically generates favorable alternative wording based on identified risks. This generation process utilizes successful document examples and is optimized accordingly. Favorable alternative wording is created based on prompt sentences generated by the AI. A specific example of a prompt sentence is, "Generate clear alternative clauses regarding penalties for delivery delays in supply contracts." This allows for document improvements tailored to specific situations.

[0082] Finally, there is a feedback function that allows users to review alternative expressions presented on their devices and provide feedback. The server receives user feedback and revises the generated clauses, making the contract clearer and more user-friendly. Implementing this system reduces legal risks and streamlines the contract drafting process.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user uploads a draft contract to the information processing device using a terminal. The input is a document in PDF or Word format, and the output is a notification that the transfer to the server is complete. This action allows the user to submit the contract to the system and prepare it for legal review.

[0086] Step 2:

[0087] The server converts received electronic documents into text format. In this process, the server applies OCR technology to process image data into text data. The input is image data, and the output is parsable text data. The server uses this conversion result to facilitate analysis in the next step.

[0088] Step 3:

[0089] The server uses a generation AI model to analyze the converted text data. Here, text data is taken as input, and a list of deficiencies and potential risks in the contract clauses is generated as output. The server employs natural language processing techniques to thoroughly examine the document content and identify problem areas.

[0090] Step 4:

[0091] The server generates favorable alternative statements based on identified potential risks. The input consists of risk information and referenced historical case data, while the output is a proposed alternative clause. The server uses prompt statements to instruct a generating AI model to construct the optimal clause.

[0092] Step 5:

[0093] Users can review alternative clauses on their devices and provide feedback. The input is the generated alternative clause, and the output is the user's comments and revision requests. This process allows users to examine the proposed clauses and reflect their own judgment.

[0094] Step 6:

[0095] The server incorporates user feedback and revises the final contract. The input is feedback information and generated clauses, and the output is the revised contract. This allows the server to create an improved contract that incorporates user input, thus supporting the creation of legally secure contracts.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] In the creation and review process of related legal documents and terms of service for electronic payments, there is a high possibility of deficiencies and ambiguous clauses that include legal risks, and measures are needed to prevent the troubles that arise from this. However, these tasks usually require a great deal of time and effort from experts, making it difficult to carry them out simply and efficiently.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for inputting data from legal documents, means for analyzing contract clauses and identifying deficiencies and risks in the clauses, and means for automatically generating optimal alternative clauses based on the risks. This enables the rapid and accurate creation of legal documents and risk management in electronic payments.

[0101] An "information processing device" is a computer device used to analyze and process input data.

[0102] A "legal document" refers to a legally binding document, such as a contract or terms of service.

[0103] "Data" refers to a collection of numbers or strings of characters that are input into an information processing device for analysis.

[0104] "Contractual clauses" refer to specific provisions and conditions included in a legal document.

[0105] "Deficiency" refers to a state in which the necessary requirements of a clause are not met.

[0106] "Risk" refers to a situation that could potentially lead to legal troubles in the future.

[0107] An "alternative clause" is an alternative contract clause proposed to mitigate deficiencies or risks.

[0108] "User equipment" refers to computer devices or terminals used by users.

[0109] "Evaluation information" refers to feedback and opinions that users provide regarding alternative terms.

[0110] "Electronic payment" refers to monetary transactions conducted using the internet and digital technology.

[0111] A "database" is an organized collection of information where data is stored and can be searched and used.

[0112] A "successful contract example" refers to an example of a contract that has been implemented in the past and successfully avoided legal risks.

[0113] "Optimization" refers to arranging a series of processes or states into an efficient and desirable form.

[0114] The embodiments for carrying out this invention will now be described in detail. First, legal document data is uploaded from the user's device to an information processing device. This information processing device is equipped with a natural language processing model built in, for example, Python (such as Tensorflow® or PyTorch). Upon receiving this data, the server first converts it to text format and analyzes the contract clauses. In the analysis, the server identifies deficiencies and risks in the clauses while referring to a database of accumulated case law.

[0115] Next, the server automatically generates the optimal alternative clauses using an AI model. These alternative clauses are optimized based on past successful contract examples. The generated alternative clauses are sent to the user's device, where the user reviews the content and inputs evaluation information.

[0116] This process reduces legal risks and allows for the creation of more secure and reliable contracts regarding electronic payments.

[0117] As a concrete example, imagine a company using this system to create terms of service for a new electronic payment service. Upon uploading the terms of service, the system would state that "the section regarding data sharing with third parties is unclear" and propose an alternative: "This service will only share data with third parties with the user's consent." The user can then either accept this alternative or add comments requesting revisions.

[0118] An example of a prompt input to the generating AI model is: "Analyze the following legal document, identify unclear sections, and propose alternatives: ○○ (text of the contract document)." This mechanism speeds up the creation of legal documents while improving their accuracy and reliability.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The user uploads legal document data from their terminal to the information processing device. The input document data is first converted to text format and sent to the server. This makes the document data ready for analysis.

[0122] Step 2:

[0123] The server receives data converted to text format and analyzes the contract clauses using natural language processing techniques. The input is text data, and the output is detailed information on the identified clauses. In this process, the server analyzes the data, particularly by referring to case law information in a database to detect deficiencies and risks.

[0124] Step 3:

[0125] The server automatically generates alternative clauses to address deficiencies and risks using an AI model based on the analysis results. The input is information on deficiencies and risks identified in the analysis, and the output is the generated alternative clauses. This process optimizes the clauses based on past successful contract examples.

[0126] Step 4:

[0127] The server sends the generated alternative clauses to the user's device, and the user views them. The user reviews the displayed information and enters evaluation information as feedback. This input consists of user feedback and comments, which are used in the next step.

[0128] Step 5:

[0129] The server uses user feedback to revise alternative clauses if necessary. The input is user feedback, and the output is the revised legal document. This results in a highly reliable legal document, with the document associated with the electronic payment being the final form.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention aims to improve user satisfaction by combining a system that performs legal checks on contracts and makes necessary revisions with an emotion engine. This system can reflect the user's emotions in the process of analyzing contract document data and generating favorable alternative clauses.

[0132] First, the user uploads a draft of the contract to the information processing device using their terminal. The server converts the received contract into text data and begins AI analysis. The analysis proceeds by referring to a database of past case law to identify deficiencies and risks in the contract clauses. For identified deficiencies, the risks are assessed and favorable alternative clauses are automatically generated. Based on successful past contract cases, optimized clauses are proposed.

[0133] Furthermore, the server uses an emotion engine to collect user feedback. It recognizes emotions from user reactions and comments and adjusts the generated alternative clauses accordingly. For example, if a user is dissatisfied with the presented clause, the emotion engine detects this and provides feedback that guides the improvement of the alternative.

[0134] For example, if a user feels uneasy about a clause regarding delivery dates, the emotion engine can recognize that emotion and present alternative clauses that include more reassuring language. In this way, the present invention can customize the contract process according to the user's emotions, making the contract signing process smoother. This not only improves the efficiency of contract creation but also increases user satisfaction.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user selects a draft contract from their terminal and uploads it to the information processing device. The terminal then sends the contract file directly to the server.

[0138] Step 2:

[0139] The server converts the received contract file into text format, making it data that can be analyzed by an AI agent. During this process, contract clauses are identified.

[0140] Step 3:

[0141] The server sends the text data to an AI agent, which then begins analyzing the contract clauses. The AI ​​agent refers to commercial law and past case law databases to identify any deficiencies or risks in the clauses.

[0142] Step 4:

[0143] The AI ​​agent automatically generates favorable alternative clauses based on the risks it detects. This rapid response optimizes the contract terms.

[0144] Step 5:

[0145] The server utilizes an emotion engine to collect user feedback. It analyzes the user's emotions towards the presented alternative clauses and receives specific responses.

[0146] Step 6:

[0147] The server adjusts alternative clauses based on the analysis results from the emotion engine. Necessary modifications are made to ensure the content matches the user's emotions.

[0148] Step 7:

[0149] The user can review the revised alternative clauses again through their device. If the user is satisfied, they can approve the changes and the contract revisions will be completed.

[0150] Step 8:

[0151] The server generates the finalized contract and provides it to the user in a downloadable format. The user retrieves the contract from their terminal and proceeds with the related procedures.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0154] Traditional contract drafting systems can identify deficiencies and risks in contract clauses, but they cannot propose clauses that take into account the user's feelings and satisfaction, thus failing to fully meet user requirements. Furthermore, if the contract does not meet user expectations, frequent revisions become necessary, leading to time-consuming and laborious processes.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for inputting contract document data into an information processing device, means for analyzing contract clauses and identifying deficiencies and risks, means for automatically generating favorable alternative clauses based on the risks, and means for adjusting the alternative clauses based on emotional feedback. This makes it possible to generate flexible contract clauses that take into account the user's emotions while identifying and correcting deficiencies in the contract clauses.

[0157] An "information processing device" is an electronic device, including digital computers and servers, used for inputting, processing, storing, and outputting data.

[0158] "Document data" refers to digital data containing the contents of formal documents such as contracts, and includes formats such as text and PDF files.

[0159] "Contract clauses" are sections of a contract that describe specific arrangements and conditions, and define the content of the agreement between the parties.

[0160] A "deficiency" refers to any part of a contract clause that is missing, incomplete, or inaccurate, and which may cause problems in the performance of the contract.

[0161] "Risk" refers to elements that could potentially cause disadvantage to the parties in the performance of a contract, and relates to situations where contract clauses are considered legally or practically weak.

[0162] An "alternative clause" is a newly proposed clause that replaces an original contract clause and is a document created to make the contract terms more favorable.

[0163] A "user terminal" refers to a device such as a computer, smartphone, or tablet that is directly operated by the user, and is a device used to communicate with a server and send and receive data.

[0164] "Emotional feedback" refers to data that shows the emotions and opinions expressed by users regarding the terms of a contract, and serves as information to evaluate and improve the suitability of the clauses.

[0165] "Adjustment" refers to the process of modifying and optimizing the generated alternative clauses based on user feedback.

[0166] This invention is a system that performs legal checks on contracts and generates alternative clauses that reflect the user's feelings. Specifically, the server, terminal, and user cooperate with each other to process data.

[0167] First, the user uploads a draft of the contract to the information processing device using a terminal. The terminal used here can be a computer or smart device, and it can access the server via a network.

[0168] The server receives the uploaded contract and converts it into text data using OCR technology. Next, the server uses AI analysis software to analyze the contract clauses and identify deficiencies and risks. This analysis references a database of past case law.

[0169] If a risk is identified, the server uses a generative AI model to automatically generate favorable alternative clauses. The generative AI model is activated by a prompt message. For example, a prompt message such as "Please suggest improvements to the clause regarding delivery dates" might be used. This model outputs clauses appropriate to the context by referring to past successful cases.

[0170] If a user expresses dissatisfaction or concern with a particular contract clause, the server uses an emotion recognition engine to collect that feedback. The user's emotions are automatically analyzed from the entered comments. Based on this information, a generative AI model adjusts the alternative clauses.

[0171] For example, if a user enters "I'm worried about the delivery date," the emotion engine detects this anxiety, and the server resets the prompt to "Please suggest a delivery date that will put the user at ease," thereby generating a more suitable alternative clause.

[0172] In this way, by generating and proposing contract clauses that take user emotions into consideration, the contract process can be made smoother. This system makes it possible to create contracts efficiently and achieve high user satisfaction.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user uploads a draft of the contract to the information processing device using a terminal. The input is digital data of the contract (e.g., a PDF file). The terminal transmits this data to the server via the network. The server receives the contract file as output.

[0176] Step 2:

[0177] The server converts the received contract file into text data. OCR technology is used here to convert PDF and image-based data into text. The input is the digital data of the contract, and the output is the text data extracted by OCR.

[0178] Step 3:

[0179] The server uses AI analysis software to analyze contract clauses on text data. Text-based contracts are used as input. The analysis process identifies deficiencies and risks in the contract clauses by querying a database of past case law. The output is a list of identified deficiencies and risks.

[0180] Step 4:

[0181] The server uses a generative AI model to automatically generate favorable alternative clauses. The input is a list of deficiencies and risks, and includes a prompt in the form of "Generate proposals to address the deficiencies." The prompt is sent to the generative AI model, and the output is a proposed new alternative clause.

[0182] Step 5:

[0183] The server presents the generated alternative clauses to the user's terminal and collects emotional feedback. The input is the generated alternative clauses, and feedback regarding the user's emotions is obtained. The output is the user's feedback and its emotional data.

[0184] Step 6:

[0185] The server uses a sentiment engine to adjust alternative clauses based on user feedback. Sentimental feedback is used as input, and the sentiment engine generates guidelines for adjustment. The output is the final, adjusted contract clause.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0188] With the advancement of modern information and communication technology, documents such as contracts are being digitized, and many contracts are now concluded online. However, manually checking for deficiencies and risks in contract clauses that arise during this process is time-consuming and inefficient. Furthermore, for users to understand the contract content and conclude it with confidence, the clauses must be sensitive to the user's emotions and anxieties. Conventional technologies lack this emotional consideration, and users often proceed with the contract while feeling uneasy about its content. This invention aims to solve these problems.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes means for inputting document information of a document into an information processing device; means for analyzing contract clauses contained in the document information and identifying deficiencies and risks in the contract clauses; means for automatically generating favorable alternative expressions based on the risks; means for presenting the alternative expressions to a user device and modifying the contract content based on input feedback; and means for recognizing the user's emotions during the modification process and proposing appropriate alternative expressions corresponding to those emotions. This makes it possible to propose more optimal contract clauses that take the user's emotions into consideration during the contract process. Furthermore, efficient legal checks are performed, and the user's anxiety regarding the contract content is reduced.

[0191] An "information processing device" is an electronic device used to analyze digital documents and perform various calculations, and is a device that can efficiently handle data such as contracts.

[0192] "Document information" refers to text data such as contracts and agreements, and its content includes the terms and conditions of the contract.

[0193] "Contract clauses" are the individual conditions and agreements stated in a contract, and they clarify the rights and obligations under the contract.

[0194] "Deficiency" refers to a flaw or error in the contract clauses, meaning the contract is incomplete in its content.

[0195] "Risk" refers to any unexpected negative consequences or losses that may arise from specific contractual clauses.

[0196] "Alternative wording" refers to newly proposed wording in a document that modifies the original contract clause, and is used for the purpose of mitigating risk.

[0197] "User equipment" refers to terminals or devices used by the user, specifically equipment for reviewing proposed alternative terms and providing feedback.

[0198] "Feedback" refers to evaluations and comments made by users regarding proposed content, and is information used to improve and optimize the system.

[0199] "Emotions" refers to the psychological response that contractual clauses have to the user, encompassing emotional states such as anxiety or a sense of security.

[0200] A "proposal" is a new plan or solution presented to achieve a specific objective, and in this case, it refers to the optimal contract terms.

[0201] As a form for carrying out the invention, the system that realizes this application example consists of the following components to support a series of processes for legally reviewing and emotionally-based revising contracts.

[0202] The server, as an information processing device, is equipped with an interface for inputting contract document information. Through this interface, users can upload contract documents in digital format. Upon receiving the digitized document information, the server analyzes the document using a natural language processing tool (e.g., spaCy). During the analysis process, deficiencies and risks in the contract clauses are identified. Based on the identified risks, favorable alternative expressions are automatically generated using a generative AI model (e.g., GPT).

[0203] The generated alternative expressions are presented to the user's device, i.e., the terminal used by the user. At this time, the server utilizes the sentiment analysis capabilities of Azure Cognitive Services to recognize the user's emotions. When the user reviews the contract terms and provides feedback, appropriate alternative expressions tailored to those emotions are suggested again. Finally, these alternative expressions are revised into contract terms in a way that alleviates the user's anxiety.

[0204] As a concrete example, consider a scenario where a user, using a smartphone, checks the contract's shipping policy and feels uneasy about the return conditions. In this case, the server detects the user's anxiety using an emotion engine and presents a revised clause that provides reassurance. This entire process enhances understanding of the contract and streamlines the contract signing process.

[0205] An example of a prompt message that can be used to instruct a generative AI model is: "Assess the risks associated with this contract clause and propose appropriate alternative clauses based on sentiment recognition. Please include suggestions that reflect user feedback."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The user uploads the contract document information to the information processing device via their terminal. Here, the user inputs the contract in digital format, and that document data is sent to the server. The input data is stored in the format that the server receives.

[0209] Step 2:

[0210] The server analyzes the received document information. Specifically, it uses spaCy, a natural language processing tool, to analyze the document's text and extract the structure and key points of the contract clauses. This process identifies deficiencies and risks in the contract clauses. The input is digital contract data, and the output is a list of items for which risks have been identified.

[0211] Step 3:

[0212] The server generates favorable alternative wording based on identified risks. It automatically generates proposed amendments to contract clauses using a generative AI model (e.g., GPT). The input is information about the risky clauses, and the output is proposed clauses as alternatives. These alternatives are characterized by being optimized based on past successes.

[0213] Step 4:

[0214] The server presents the generated alternative representation to the user's terminal. The user reviews it and provides feedback through the interface. This feedback reflects the user's opinions and feelings. The input is the proposed revision, and the output is the user's feedback information.

[0215] Step 5:

[0216] The server uses Azure Cognitive Services to recognize emotions from user feedback. Based on the emotion analysis, it determines the degree to which the user felt anxious, dissatisfied, or reassured. In this process, the input is feedback data, and the output is a label of the user's emotional expression.

[0217] Step 6:

[0218] The server readjusts alternative expressions and further optimizes the proposed content based on the recognized emotions. By presenting the readjusted alternatives to the user, contract terms that are more emotionally resonant are finalized. The input is the result of emotion recognition, and the output is the final proposed alternative expression.

[0219] 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.

[0220] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0226] 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.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0228] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] 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.

[0230] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0232] The 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.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0235] This invention provides a system for efficiently performing legal checks and necessary revisions on contracts. To implement this system, it operates as follows:

[0236] The user uploads a draft of the contract from their terminal to the information processing device. This upload sends the contract document data to the server, where it is converted into a format that can be analyzed in text format.

[0237] The server analyzes the contract clauses based on this document data. The analysis utilizes AI and references a database of past case law to detect deficiencies and ambiguities in the contract clauses. This process identifies potential legal risks for each contract clause within the contract.

[0238] Once risks are identified, the server automatically generates favorable alternative clauses. This generation process optimizes clauses using past successful contract examples, proposing clauses that mitigate specific risks.

[0239] Users can view these alternative clauses on their devices. The server receives user feedback and modifies the generated clauses as needed. For example, if an applicable clause is misleading, the user can leave a comment.

[0240] This system not only streamlines communication between legal departments and users, but also enables the rapid creation of contracts with minimized legal risks. Furthermore, it reduces time and effort in this process. For example, if a supply contract lacks clear penalty clauses for delayed delivery, the system will suggest a clear alternative clause such as "In the event of a delay, a penalty of 5% of the delivery price can be claimed," thereby supporting the creation of a contract that is easy for both parties to understand.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user selects a draft contract file from their terminal and uploads it to the information processing system. The terminal then sends the selected contract file directly to the server.

[0244] Step 2:

[0245] The server converts the received contract file into text format. This makes the contract's contents analyzable by AI.

[0246] Step 3:

[0247] The server sends the text data to the AI ​​agent, which then begins analyzing the contract clauses. The AI ​​agent identifies each clause in the contract one by one, analyzing them while referring to past case precedents and legal databases.

[0248] Step 4:

[0249] The AI ​​agent identifies deficiencies and risks in contract clauses. This includes detecting ambiguities and legal shortcomings. It creates a list of deficiencies and assesses the corresponding risks.

[0250] Step 5:

[0251] The server automatically generates favorable alternative clauses based on risks identified by the AI ​​agent. This generation process is optimized by referencing past success stories.

[0252] Step 6:

[0253] The server sends the generated alternative clause to the user's device. The user can then view the presented clause on their device.

[0254] Step 7:

[0255] Users review the alternative clauses through their devices and provide feedback. This feedback includes approval, suggestions for revisions, and additional comments.

[0256] Step 8:

[0257] The server receives user feedback and makes revisions to alternative clauses if necessary. The revised content is then reviewed again for final confirmation.

[0258] Step 9:

[0259] The finalized contract is generated on the server and provided to the user in a downloadable format. The user can retrieve the completed contract from their device and complete the related processes.

[0260] (Example 1)

[0261] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0262] Traditional contract drafting and legal review processes are time-consuming, labor-intensive, and prone to human error. Furthermore, it's difficult for individuals without specialized legal knowledge to properly evaluate and revise clauses. These challenges complicate communication between legal departments and users, reducing the efficiency of contract drafting.

[0263] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0264] In this invention, the server includes means for inputting an electronic document into an information processing device, means for analyzing clauses and identifying deficiencies and potential risks, and means for automatically generating favorable alternative expressions. This streamlines the legal review and revision of contracts, enabling the rapid creation of contracts with minimized legal risks.

[0265] An "information processing device" is a general term for hardware and software that inputs electronic documents and processes them as needed.

[0266] An "electronic document" is a document data stored in digital format and used to represent contracts or agreements.

[0267] A "clause" refers to individual provisions or conditions written in an electronic document, and is an element that forms the content of a contract.

[0268] "Deficiencies" refer to missing or incomplete sections in clauses or documents, which may cause problems in the execution of a contract.

[0269] "Potential risks" refer to uncertainties contained in the clauses and risks that may arise in the future.

[0270] A "favorable alternative wording" refers to a modified clause that avoids deficiencies or potential risks and presents more favorable terms for the contracting parties.

[0271] A "terminal" is an electronic device used by a user for communication and information processing.

[0272] "AI technology" refers to the scientific and technological advancements that enable computers to perform intelligent activities, with the aim of assisting in data analysis and decision-making.

[0273] "Natural language processing" is a technology that enables computers to understand human language, and involves the analysis and semantic understanding of text data.

[0274] A "reference case data set" is a collection of data from similar past cases and precedents, and serves as a source of information used as a standard for analysis and evaluation.

[0275] "Document examples" are documents created in the past that demonstrate the format and content of successful contracts and serve as references for optimization.

[0276] This invention relates to a system for efficiently performing legal checks and revisions on contracts. The system begins with a user uploading a draft contract using a terminal. The electronic document, in PDF or Word format, is then transmitted to an information processing device via a dedicated application on the terminal or a web interface.

[0277] The server converts received electronic documents into a format that can be analyzed using text. This conversion process may involve the use of software employing OCR (Optical Character Recognition) technology. Specifically, open-source OCR tools are utilized to perform highly accurate data extraction. The text data converted at this stage is then used in subsequent analysis processes.

[0278] Next, the server uses a generative AI model that performs natural language processing to analyze the clauses within the electronic document. In this step, the AI ​​identifies deficiencies and potential risks by comparing them with a dataset of past reference cases. The generative AI model is applied to the analysis, scrutinizing the document's structure and word usage. As a concrete example of the AI ​​model, industry-standard natural language processing tools are used.

[0279] The server automatically generates favorable alternative wording based on identified risks. This generation process utilizes successful document examples and is optimized accordingly. Favorable alternative wording is created based on prompt sentences generated by the AI. A specific example of a prompt sentence is, "Generate clear alternative clauses regarding penalties for delivery delays in supply contracts." This allows for document improvements tailored to specific situations.

[0280] Finally, there is a feedback function that allows users to review alternative expressions presented on their devices and provide feedback. The server receives user feedback and revises the generated clauses, making the contract clearer and more user-friendly. Implementing this system reduces legal risks and streamlines the contract drafting process.

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] The user uploads the contract draft to the information processing device using the terminal. The input is a document in PDF or Word format, and the output is a notification of successful transfer to the server. Through this operation, the user sends the contract to the system and gets ready to start the legal review.

[0284] Step 2:

[0285] The server converts the received electronic document into text format. In this process, the server applies OCR technology to process the image data into text data. The input is image data, and the output is analyzable text data. The server uses this conversion result to facilitate smooth analysis in the next step.

[0286] Step 3:

[0287] The server uses the generated AI model to analyze the converted text data. Here, it takes in the text data as input and generates a list of deficiencies and potential risks in the contract terms as output. The server makes full use of natural language processing technology to thoroughly investigate the document content and identify problem areas.

[0288] Step 4:

[0289] Based on the identified potential risks, the server generates advantageous alternative expressions. At this time, the input is the risk information and past case data for reference, and the output is a proposal for alternative clauses. The server uses prompt texts to instruct the generation AI model to construct optimal clauses.

[0290] Step 5:

[0291] The user checks the alternative clauses on the terminal and provides feedback. The input is the generated alternative clauses, and the output is the user's opinions and modification requests. Through this operation, the user can examine the proposed clauses and reflect their own judgments.

[0292] Step 6:

[0293] The server incorporates user feedback and revises the final contract. The input is feedback information and generated clauses, and the output is the revised contract. This allows the server to create an improved contract that incorporates user input, thus supporting the creation of legally secure contracts.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] In the creation and review process of related legal documents and terms of service for electronic payments, there is a high possibility of deficiencies and ambiguous clauses that include legal risks, and measures are needed to prevent the troubles that arise from this. However, these tasks usually require a great deal of time and effort from experts, making it difficult to carry them out simply and efficiently.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes means for inputting data from legal documents, means for analyzing contract clauses and identifying deficiencies and risks in the clauses, and means for automatically generating optimal alternative clauses based on the risks. This enables the rapid and accurate creation of legal documents and risk management in electronic payments.

[0299] An "information processing device" is a computer device used to analyze and process input data.

[0300] A "legal document" refers to a legally binding document, such as a contract or terms of service.

[0301] "Data" refers to a collection of numbers or strings of characters that are input into an information processing device for analysis.

[0302] "Contract terms" refers to individual regulations and conditions included in legal documents.

[0303] "Deficiency" indicates a state where the necessary requirements in the terms are not met.

[0304] "Risk" refers to a situation that may cause future legal troubles.

[0305] "Alternative terms" are alternative contract terms proposed to reduce deficiencies and risks.

[0306] "User device" refers to a computer device or terminal used by the user.

[0307] "Evaluation information" refers to feedback and opinions given by the user regarding alternative terms.

[0308] "Electronic payment" refers to the exchange of money conducted using the Internet and digital technologies.

[0309] "Database" is an information aggregate in which data is accumulated in an organized form and can be searched and utilized.

[0310] "Successful contract cases" refer to examples of contracts that have been implemented in the past and have succeeded in avoiding legal risks.

[0311] "Optimization" refers to arranging a series of processes or states in an efficient and desirable form.

[0312] The embodiments for implementing this invention will be specifically described. First, data of legal documents is uploaded from the user's user device to the information processing device. This information processing device is equipped with, for example, a natural language processing model (such as TensorFlow or PyTorch) constructed in Python. When the server receives this data, it first converts it into text format and analyzes the contract terms. In the analysis, while referring to a database that accumulates case laws, deficiencies and risks in the terms are identified.

[0313] Next, the server automatically generates the optimal alternative clauses using an AI model. These alternative clauses are optimized based on past successful contract examples. The generated alternative clauses are sent to the user's device, where the user reviews the content and inputs evaluation information.

[0314] This process reduces legal risks and allows for the creation of more secure and reliable contracts regarding electronic payments.

[0315] As a concrete example, imagine a company using this system to create terms of service for a new electronic payment service. Upon uploading the terms of service, the system would state that "the section regarding data sharing with third parties is unclear" and propose an alternative: "This service will only share data with third parties with the user's consent." The user can then either accept this alternative or add comments requesting revisions.

[0316] An example of a prompt input to the generating AI model is: "Analyze the following legal document, identify unclear sections, and propose alternatives: ○○ (text of the contract document)." This mechanism speeds up the creation of legal documents while improving their accuracy and reliability.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The user uploads legal document data from their terminal to the information processing device. The input document data is first converted to text format and sent to the server. This makes the document data ready for analysis.

[0320] Step 2:

[0321] The server receives data converted to text format and analyzes the contract clauses using natural language processing techniques. The input is text data, and the output is detailed information on the identified clauses. In this process, the server analyzes the data, particularly by referring to case law information in a database to detect deficiencies and risks.

[0322] Step 3:

[0323] The server automatically generates alternative clauses to address deficiencies and risks using an AI model based on the analysis results. The input is information on deficiencies and risks identified in the analysis, and the output is the generated alternative clauses. This process optimizes the clauses based on past successful contract examples.

[0324] Step 4:

[0325] The server sends the generated alternative clauses to the user's device, and the user views them. The user reviews the displayed information and enters evaluation information as feedback. This input consists of user feedback and comments, which are used in the next step.

[0326] Step 5:

[0327] The server uses user feedback to revise alternative clauses if necessary. The input is user feedback, and the output is the revised legal document. This results in a highly reliable legal document, with the document associated with the electronic payment being the final form.

[0328] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0329] This invention aims to improve user satisfaction by combining a system that performs legal checks on contracts and makes necessary revisions with an emotion engine. This system can reflect the user's emotions in the process of analyzing contract document data and generating favorable alternative clauses.

[0330] First, the user uploads a draft of the contract to the information processing device using their terminal. The server converts the received contract into text data and begins AI analysis. The analysis proceeds by referring to a database of past case law to identify deficiencies and risks in the contract clauses. For identified deficiencies, the risks are assessed and favorable alternative clauses are automatically generated. Based on successful past contract cases, optimized clauses are proposed.

[0331] Furthermore, the server uses an emotion engine to collect user feedback. It recognizes emotions from user reactions and comments and adjusts the generated alternative clauses accordingly. For example, if a user is dissatisfied with the presented clause, the emotion engine detects this and provides feedback that guides the improvement of the alternative.

[0332] For example, if a user feels uneasy about a clause regarding delivery dates, the emotion engine can recognize that emotion and present alternative clauses that include more reassuring language. In this way, the present invention can customize the contract process according to the user's emotions, making the contract signing process smoother. This not only improves the efficiency of contract creation but also increases user satisfaction.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The user selects a draft contract from their terminal and uploads it to the information processing device. The terminal then sends the contract file directly to the server.

[0336] Step 2:

[0337] The server converts the received contract file into text format, making it data that can be analyzed by an AI agent. During this process, contract clauses are identified.

[0338] Step 3:

[0339] The server sends the text data to an AI agent, which then begins analyzing the contract clauses. The AI ​​agent refers to commercial law and past case law databases to identify any deficiencies or risks in the clauses.

[0340] Step 4:

[0341] The AI ​​agent automatically generates favorable alternative clauses based on the risks it detects. This rapid response optimizes the contract terms.

[0342] Step 5:

[0343] The server utilizes an emotion engine to collect user feedback. It analyzes the user's emotions towards the presented alternative clauses and receives specific responses.

[0344] Step 6:

[0345] The server adjusts alternative clauses based on the analysis results from the emotion engine. Necessary modifications are made to ensure the content matches the user's emotions.

[0346] Step 7:

[0347] The user can review the revised alternative clauses again through their device. If the user is satisfied, they can approve the changes and the contract revisions will be completed.

[0348] Step 8:

[0349] The server generates the finalized contract and provides it to the user in a downloadable format. The user retrieves the contract from their terminal and proceeds with the related procedures.

[0350] (Example 2)

[0351] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0352] Traditional contract drafting systems can identify deficiencies and risks in contract clauses, but they cannot propose clauses that take into account the user's feelings and satisfaction, thus failing to fully meet user requirements. Furthermore, if the contract does not meet user expectations, frequent revisions become necessary, leading to time-consuming and laborious processes.

[0353] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0354] In this invention, the server includes means for inputting contract document data into an information processing device, means for analyzing contract clauses and identifying deficiencies and risks, means for automatically generating favorable alternative clauses based on the risks, and means for adjusting the alternative clauses based on emotional feedback. This makes it possible to generate flexible contract clauses that take into account the user's emotions while identifying and correcting deficiencies in the contract clauses.

[0355] An "information processing device" is an electronic device, including digital computers and servers, used for inputting, processing, storing, and outputting data.

[0356] "Document data" refers to digital data containing the contents of formal documents such as contracts, and includes formats such as text and PDF files.

[0357] "Contract clauses" are sections of a contract that describe specific arrangements and conditions, and define the content of the agreement between the parties.

[0358] A "deficiency" refers to any part of a contract clause that is missing, incomplete, or inaccurate, and which may cause problems in the performance of the contract.

[0359] "Risk" refers to elements that could potentially cause disadvantage to the parties in the performance of a contract, and relates to situations where contract clauses are considered legally or practically weak.

[0360] An "alternative clause" is a newly proposed clause that replaces an original contract clause and is a document created to make the contract terms more favorable.

[0361] A "user terminal" refers to a device such as a computer, smartphone, or tablet that is directly operated by the user, and is a device used to communicate with a server and send and receive data.

[0362] "Emotional feedback" refers to data that shows the emotions and opinions expressed by users regarding the terms of a contract, and serves as information to evaluate and improve the suitability of the clauses.

[0363] "Adjustment" refers to the process of modifying and optimizing the generated alternative clauses based on user feedback.

[0364] This invention is a system that performs legal checks on contracts and generates alternative clauses that reflect the user's feelings. Specifically, the server, terminal, and user cooperate with each other to process data.

[0365] First, the user uploads a draft of the contract to the information processing device using a terminal. The terminal used here can be a computer or smart device, and it can access the server via a network.

[0366] The server receives the uploaded contract and converts it into text data using OCR technology. Next, the server uses AI analysis software to analyze the contract clauses and identify deficiencies and risks. This analysis references a database of past case law.

[0367] If a risk is identified, the server uses a generative AI model to automatically generate favorable alternative clauses. The generative AI model is activated by a prompt message. For example, a prompt message such as "Please suggest improvements to the clause regarding delivery dates" might be used. This model outputs clauses appropriate to the context by referring to past successful cases.

[0368] If a user expresses dissatisfaction or concern with a particular contract clause, the server uses an emotion recognition engine to collect that feedback. The user's emotions are automatically analyzed from the entered comments. Based on this information, a generative AI model adjusts the alternative clauses.

[0369] For example, if a user enters "I'm worried about the delivery date," the emotion engine detects this anxiety, and the server resets the prompt to "Please suggest a delivery date that will put the user at ease," thereby generating a more suitable alternative clause.

[0370] In this way, by generating and proposing contract clauses that take user emotions into consideration, the contract process can be made smoother. This system makes it possible to create contracts efficiently and achieve high user satisfaction.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The user uploads a draft of the contract to the information processing device using a terminal. The input is digital data of the contract (e.g., a PDF file). The terminal transmits this data to the server via the network. The server receives the contract file as output.

[0374] Step 2:

[0375] The server converts the received contract file into text data. OCR technology is used here to convert PDF and image-based data into text. The input is the digital data of the contract, and the output is the text data extracted by OCR.

[0376] Step 3:

[0377] The server uses AI analysis software to analyze contract clauses on text data. Text-based contracts are used as input. The analysis process identifies deficiencies and risks in the contract clauses by querying a database of past case law. The output is a list of identified deficiencies and risks.

[0378] Step 4:

[0379] The server uses a generative AI model to automatically generate favorable alternative clauses. The input is a list of deficiencies and risks, and includes a prompt in the form of "Generate proposals to address the deficiencies." The prompt is sent to the generative AI model, and the output is a proposed new alternative clause.

[0380] Step 5:

[0381] The server presents the generated alternative clauses to the user's terminal and collects emotional feedback. The input is the generated alternative clauses, and feedback regarding the user's emotions is obtained. The output is the user's feedback and its emotional data.

[0382] Step 6:

[0383] The server uses a sentiment engine to adjust alternative clauses based on user feedback. Sentimental feedback is used as input, and the sentiment engine generates guidelines for adjustment. The output is the final, adjusted contract clause.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0386] With the advancement of modern information and communication technology, documents such as contracts are being digitized, and many contracts are now concluded online. However, manually checking for deficiencies and risks in contract clauses that arise during this process is time-consuming and inefficient. Furthermore, for users to understand the contract content and conclude it with confidence, the clauses must be sensitive to the user's emotions and anxieties. Conventional technologies lack this emotional consideration, and users often proceed with the contract while feeling uneasy about its content. This invention aims to solve these problems.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0388] In this invention, the server includes means for inputting document information of a document into an information processing device; means for analyzing contract clauses contained in the document information and identifying deficiencies and risks in the contract clauses; means for automatically generating favorable alternative expressions based on the risks; means for presenting the alternative expressions to a user device and modifying the contract content based on input feedback; and means for recognizing the user's emotions during the modification process and proposing appropriate alternative expressions corresponding to those emotions. This makes it possible to propose more optimal contract clauses that take the user's emotions into consideration during the contract process. Furthermore, efficient legal checks are performed, and the user's anxiety regarding the contract content is reduced.

[0389] An "information processing device" is an electronic device used to analyze digital documents and perform various calculations, and is a device that can efficiently handle data such as contracts.

[0390] "Document information" refers to text data such as contracts and agreements, and its content includes the terms and conditions of the contract.

[0391] "Contract clauses" are the individual conditions and agreements stated in a contract, and they clarify the rights and obligations under the contract.

[0392] "Deficiency" refers to a flaw or error in the contract clauses, meaning the contract is incomplete in its content.

[0393] "Risk" refers to any unexpected negative consequences or losses that may arise from specific contractual clauses.

[0394] "Alternative wording" refers to newly proposed wording in a document that modifies the original contract clause, and is used for the purpose of mitigating risk.

[0395] "User equipment" refers to terminals or devices used by the user, specifically equipment for reviewing proposed alternative terms and providing feedback.

[0396] "Feedback" refers to evaluations and comments made by users regarding proposed content, and is information used to improve and optimize the system.

[0397] "Emotions" refers to the psychological response that contractual clauses have to the user, encompassing emotional states such as anxiety or a sense of security.

[0398] A "proposal" is a new plan or solution presented to achieve a specific objective, and in this case, it refers to the optimal contract terms.

[0399] As a form for carrying out the invention, the system that realizes this application example consists of the following components to support a series of processes for legally reviewing and emotionally-based revising contracts.

[0400] The server, as an information processing device, is equipped with an interface for inputting contract document information. Through this interface, users can upload contract documents in digital format. Upon receiving the digitized document information, the server analyzes the document using a natural language processing tool (e.g., spaCy). During the analysis process, deficiencies and risks in the contract clauses are identified. Based on the identified risks, favorable alternative expressions are automatically generated using a generative AI model (e.g., GPT).

[0401] The generated alternative expressions are presented to the user's device, i.e., the terminal used by the user. At this time, the server utilizes Azure Cognitive Services' sentiment analysis capabilities to recognize the user's emotions. When the user reviews the contract terms and provides feedback, appropriate alternative expressions tailored to those emotions are suggested again. Finally, these alternative expressions are revised into contract terms in a way that alleviates the user's anxiety.

[0402] As a concrete example, consider a scenario where a user, using a smartphone, checks the contract's shipping policy and feels uneasy about the return conditions. In this case, the server detects the user's anxiety using an emotion engine and presents a revised clause that provides reassurance. This entire process enhances understanding of the contract and streamlines the contract signing process.

[0403] An example of a prompt message that can be used to instruct a generative AI model is: "Assess the risks associated with this contract clause and propose appropriate alternative clauses based on sentiment recognition. Please include suggestions that reflect user feedback."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user uploads the contract document information to the information processing device via their terminal. Here, the user inputs the contract in digital format, and that document data is sent to the server. The input data is stored in the format that the server receives.

[0407] Step 2:

[0408] The server analyzes the received document information. Specifically, it uses spaCy, a natural language processing tool, to analyze the document's text and extract the structure and key points of the contract clauses. This process identifies deficiencies and risks in the contract clauses. The input is digital contract data, and the output is a list of items for which risks have been identified.

[0409] Step 3:

[0410] The server generates favorable alternative wording based on identified risks. It automatically generates proposed amendments to contract clauses using a generative AI model (e.g., GPT). The input is information about the risky clauses, and the output is proposed clauses as alternatives. These alternatives are characterized by being optimized based on past successes.

[0411] Step 4:

[0412] The server presents the generated alternative representation to the user's terminal. The user reviews it and provides feedback through the interface. This feedback reflects the user's opinions and feelings. The input is the proposed revision, and the output is the user's feedback information.

[0413] Step 5:

[0414] The server uses Azure Cognitive Services to recognize emotions from user feedback. Based on the emotion analysis, it determines the degree to which the user felt anxious, dissatisfied, or reassured. In this process, the input is feedback data, and the output is a label of the user's emotional expression.

[0415] Step 6:

[0416] The server readjusts alternative expressions and further optimizes the proposed content based on the recognized emotions. By presenting the readjusted alternatives to the user, contract terms that are more emotionally resonant are finalized. The input is the result of emotion recognition, and the output is the final proposed alternative expression.

[0417] 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.

[0418] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0419] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0424] 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.

[0425] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0426] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0427] 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.

[0428] 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.

[0429] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0430] The 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.

[0431] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0432] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0433] This invention provides a system for efficiently performing legal checks and necessary revisions on contracts. To implement this system, it operates as follows:

[0434] The user uploads a draft of the contract from their terminal to the information processing device. This upload sends the contract document data to the server, where it is converted into a format that can be analyzed in text format.

[0435] The server analyzes the contract clauses based on this document data. The analysis utilizes AI and references a database of past case law to detect deficiencies and ambiguities in the contract clauses. This process identifies potential legal risks for each contract clause within the contract.

[0436] Once risks are identified, the server automatically generates favorable alternative clauses. This generation process optimizes clauses using past successful contract examples, proposing clauses that mitigate specific risks.

[0437] Users can view these alternative clauses on their devices. The server receives user feedback and modifies the generated clauses as needed. For example, if an applicable clause is misleading, the user can leave a comment.

[0438] This system not only streamlines communication between legal departments and users, but also enables the rapid creation of contracts with minimized legal risks. Furthermore, it reduces time and effort in this process. For example, if a supply contract lacks clear penalty clauses for delayed delivery, the system will suggest a clear alternative clause such as "In the event of a delay, a penalty of 5% of the delivery price can be claimed," thereby supporting the creation of a contract that is easy for both parties to understand.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The user selects a draft contract file from their terminal and uploads it to the information processing system. The terminal then sends the selected contract file directly to the server.

[0442] Step 2:

[0443] The server converts the received contract file into text format. This makes the contract's contents analyzable by AI.

[0444] Step 3:

[0445] The server sends the text data to the AI ​​agent, which then begins analyzing the contract clauses. The AI ​​agent identifies each clause in the contract one by one, analyzing them while referring to past case precedents and legal databases.

[0446] Step 4:

[0447] The AI ​​agent identifies deficiencies and risks in contract clauses. This includes detecting ambiguities and legal shortcomings. It creates a list of deficiencies and assesses the corresponding risks.

[0448] Step 5:

[0449] The server automatically generates favorable alternative clauses based on risks identified by the AI ​​agent. This generation process is optimized by referencing past success stories.

[0450] Step 6:

[0451] The server sends the generated alternative clause to the user's device. The user can then view the presented clause on their device.

[0452] Step 7:

[0453] Users review the alternative clauses through their devices and provide feedback. This feedback includes approval, suggestions for revisions, and additional comments.

[0454] Step 8:

[0455] The server receives user feedback and makes revisions to alternative clauses if necessary. The revised content is then reviewed again for final confirmation.

[0456] Step 9:

[0457] The finalized contract is generated on the server and provided to the user in a downloadable format. The user can retrieve the completed contract from their device and complete the related processes.

[0458] (Example 1)

[0459] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0460] Traditional contract drafting and legal review processes are time-consuming, labor-intensive, and prone to human error. Furthermore, it's difficult for individuals without specialized legal knowledge to properly evaluate and revise clauses. These challenges complicate communication between legal departments and users, reducing the efficiency of contract drafting.

[0461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0462] In this invention, the server includes means for inputting an electronic document into an information processing device, means for analyzing clauses and identifying deficiencies and potential risks, and means for automatically generating favorable alternative expressions. This streamlines the legal review and revision of contracts, enabling the rapid creation of contracts with minimized legal risks.

[0463] An "information processing device" is a general term for hardware and software that inputs electronic documents and processes them as needed.

[0464] An "electronic document" is a document data stored in digital format and used to represent contracts or agreements.

[0465] A "clause" refers to individual provisions or conditions written in an electronic document, and is an element that forms the content of a contract.

[0466] "Deficiencies" refer to missing or incomplete sections in clauses or documents, which may cause problems in the execution of a contract.

[0467] "Potential risks" refer to uncertainties contained in the clauses and risks that may arise in the future.

[0468] A "favorable alternative wording" refers to a modified clause that avoids deficiencies or potential risks and presents more favorable terms for the contracting parties.

[0469] A "terminal" is an electronic device used by a user for communication and information processing.

[0470] "AI technology" refers to the scientific and technological advancements that enable computers to perform intelligent activities, with the aim of assisting in data analysis and decision-making.

[0471] "Natural language processing" is a technology that enables computers to understand human language, and involves the analysis and semantic understanding of text data.

[0472] A "reference case data set" is a collection of data from similar past cases and precedents, and serves as a source of information used as a standard for analysis and evaluation.

[0473] "Document examples" are documents created in the past that demonstrate the format and content of successful contracts and serve as references for optimization.

[0474] This invention relates to a system for efficiently performing legal checks and revisions on contracts. The system begins with a user uploading a draft contract using a terminal. The electronic document, in PDF or Word format, is then transmitted to an information processing device via a dedicated application on the terminal or a web interface.

[0475] The server converts received electronic documents into a format that can be analyzed using text. This conversion process may involve the use of software employing OCR (Optical Character Recognition) technology. Specifically, open-source OCR tools are utilized to perform highly accurate data extraction. The text data converted at this stage is then used in subsequent analysis processes.

[0476] Next, the server uses a generative AI model that performs natural language processing to analyze the clauses within the electronic document. In this step, the AI ​​identifies deficiencies and potential risks by comparing them with a dataset of past reference cases. The generative AI model is applied to the analysis, scrutinizing the document's structure and word usage. As a concrete example of the AI ​​model, industry-standard natural language processing tools are used.

[0477] The server automatically generates favorable alternative wording based on identified risks. This generation process utilizes successful document examples and is optimized accordingly. Favorable alternative wording is created based on prompt sentences generated by the AI. A specific example of a prompt sentence is, "Generate clear alternative clauses regarding penalties for delivery delays in supply contracts." This allows for document improvements tailored to specific situations.

[0478] Finally, there is a feedback function that allows users to review alternative expressions presented on their devices and provide feedback. The server receives user feedback and revises the generated clauses, making the contract clearer and more user-friendly. Implementing this system reduces legal risks and streamlines the contract drafting process.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] The user uploads a draft contract to the information processing device using a terminal. The input is a document in PDF or Word format, and the output is a notification that the transfer to the server is complete. This action allows the user to submit the contract to the system and prepare it for legal review.

[0482] Step 2:

[0483] The server converts received electronic documents into text format. In this process, the server applies OCR technology to process image data into text data. The input is image data, and the output is parsable text data. The server uses this conversion result to facilitate analysis in the next step.

[0484] Step 3:

[0485] The server uses a generation AI model to analyze the converted text data. Here, text data is taken as input, and a list of deficiencies and potential risks in the contract clauses is generated as output. The server employs natural language processing techniques to thoroughly examine the document content and identify problem areas.

[0486] Step 4:

[0487] The server generates favorable alternative statements based on identified potential risks. The input consists of risk information and referenced historical case data, while the output is a proposed alternative clause. The server uses prompt statements to instruct a generating AI model to construct the optimal clause.

[0488] Step 5:

[0489] Users can review alternative clauses on their devices and provide feedback. The input is the generated alternative clause, and the output is the user's comments and revision requests. This process allows users to examine the proposed clauses and reflect their own judgment.

[0490] Step 6:

[0491] The server incorporates user feedback and revises the final contract. The input is feedback information and generated clauses, and the output is the revised contract. This allows the server to create an improved contract that incorporates user input, thus supporting the creation of legally secure contracts.

[0492] (Application Example 1)

[0493] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0494] In the creation and review process of related legal documents and terms of service for electronic payments, there is a high possibility of deficiencies and ambiguous clauses that include legal risks, and measures are needed to prevent the troubles that arise from this. However, these tasks usually require a great deal of time and effort from experts, making it difficult to carry them out simply and efficiently.

[0495] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0496] In this invention, the server includes means for inputting data from legal documents, means for analyzing contract clauses and identifying deficiencies and risks in the clauses, and means for automatically generating optimal alternative clauses based on the risks. This enables the rapid and accurate creation of legal documents and risk management in electronic payments.

[0497] An "information processing device" is a computer device used to analyze and process input data.

[0498] A "legal document" refers to a legally binding document, such as a contract or terms of service.

[0499] "Data" refers to a collection of numbers or strings of characters that are input into an information processing device for analysis.

[0500] "Contractual clauses" refer to specific provisions and conditions included in a legal document.

[0501] "Deficiency" refers to a state in which the necessary requirements of a clause are not met.

[0502] "Risk" refers to a situation that could potentially lead to legal troubles in the future.

[0503] An "alternative clause" is an alternative contract clause proposed to mitigate deficiencies or risks.

[0504] "User equipment" refers to computer devices or terminals used by users.

[0505] "Evaluation information" refers to feedback and opinions that users provide regarding alternative terms.

[0506] "Electronic payment" refers to monetary transactions conducted using the internet and digital technology.

[0507] A "database" is an organized collection of information where data is stored and can be searched and used.

[0508] A "successful contract example" refers to an example of a contract that has been implemented in the past and successfully avoided legal risks.

[0509] "Optimization" refers to arranging a series of processes or states into an efficient and desirable form.

[0510] The embodiments for carrying out this invention will now be described in detail. First, legal document data is uploaded from the user's device to an information processing device. This information processing device is equipped with a natural language processing model (such as TensorFlow or PyTorch) built in Python, for example. Upon receiving this data, the server first converts it to text format and analyzes the contract clauses. In the analysis, the server identifies deficiencies and risks in the clauses while referring to a database of accumulated case law.

[0511] Next, the server automatically generates the optimal alternative clauses using an AI model. These alternative clauses are optimized based on past successful contract examples. The generated alternative clauses are sent to the user's device, where the user reviews the content and inputs evaluation information.

[0512] This process reduces legal risks and allows for the creation of more secure and reliable contracts regarding electronic payments.

[0513] As a concrete example, imagine a company using this system to create terms of service for a new electronic payment service. Upon uploading the terms of service, the system would state that "the section regarding data sharing with third parties is unclear" and propose an alternative: "This service will only share data with third parties with the user's consent." The user can then either accept this alternative or add comments requesting revisions.

[0514] An example of a prompt input to the generating AI model is: "Analyze the following legal document, identify unclear sections, and propose alternatives: ○○ (text of the contract document)." This mechanism speeds up the creation of legal documents while improving their accuracy and reliability.

[0515] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0516] Step 1:

[0517] The user uploads legal document data from their terminal to the information processing device. The input document data is first converted to text format and sent to the server. This makes the document data ready for analysis.

[0518] Step 2:

[0519] The server receives data converted to text format and analyzes the contract clauses using natural language processing techniques. The input is text data, and the output is detailed information on the identified clauses. In this process, the server analyzes the data, particularly by referring to case law information in a database to detect deficiencies and risks.

[0520] Step 3:

[0521] The server automatically generates alternative clauses to address deficiencies and risks using an AI model based on the analysis results. The input is information on deficiencies and risks identified in the analysis, and the output is the generated alternative clauses. This process optimizes the clauses based on past successful contract examples.

[0522] Step 4:

[0523] The server sends the generated alternative clauses to the user's device, and the user views them. The user reviews the displayed information and enters evaluation information as feedback. This input consists of user feedback and comments, which are used in the next step.

[0524] Step 5:

[0525] The server uses user feedback to revise alternative clauses if necessary. The input is user feedback, and the output is the revised legal document. This results in a highly reliable legal document, with the document associated with the electronic payment being the final form.

[0526] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0527] This invention aims to improve user satisfaction by combining a system that performs legal checks on contracts and makes necessary revisions with an emotion engine. This system can reflect the user's emotions in the process of analyzing contract document data and generating favorable alternative clauses.

[0528] First, the user uploads a draft of the contract to the information processing device using their terminal. The server converts the received contract into text data and begins AI analysis. The analysis proceeds by referring to a database of past case law to identify deficiencies and risks in the contract clauses. For identified deficiencies, the risks are assessed and favorable alternative clauses are automatically generated. Based on successful past contract cases, optimized clauses are proposed.

[0529] Furthermore, the server uses an emotion engine to collect user feedback. It recognizes emotions from user reactions and comments and adjusts the generated alternative clauses accordingly. For example, if a user is dissatisfied with the presented clause, the emotion engine detects this and provides feedback that guides the improvement of the alternative.

[0530] For example, if a user feels uneasy about a clause regarding delivery dates, the emotion engine can recognize that emotion and present alternative clauses that include more reassuring language. In this way, the present invention can customize the contract process according to the user's emotions, making the contract signing process smoother. This not only improves the efficiency of contract creation but also increases user satisfaction.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The user selects a draft contract from their terminal and uploads it to the information processing device. The terminal then sends the contract file directly to the server.

[0534] Step 2:

[0535] The server converts the received contract file into text format, making it data that can be analyzed by an AI agent. During this process, contract clauses are identified.

[0536] Step 3:

[0537] The server sends the text data to an AI agent, which then begins analyzing the contract clauses. The AI ​​agent refers to commercial law and past case law databases to identify any deficiencies or risks in the clauses.

[0538] Step 4:

[0539] The AI ​​agent automatically generates favorable alternative clauses based on the risks it detects. This rapid response optimizes the contract terms.

[0540] Step 5:

[0541] The server utilizes an emotion engine to collect user feedback. It analyzes the user's emotions towards the presented alternative clauses and receives specific responses.

[0542] Step 6:

[0543] The server adjusts alternative clauses based on the analysis results from the emotion engine. Necessary modifications are made to ensure the content matches the user's emotions.

[0544] Step 7:

[0545] The user can review the revised alternative clauses again through their device. If the user is satisfied, they can approve the changes and the contract revisions will be completed.

[0546] Step 8:

[0547] The server generates the finalized contract and provides it to the user in a downloadable format. The user retrieves the contract from their terminal and proceeds with the related procedures.

[0548] (Example 2)

[0549] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0550] Traditional contract drafting systems can identify deficiencies and risks in contract clauses, but they cannot propose clauses that take into account the user's feelings and satisfaction, thus failing to fully meet user requirements. Furthermore, if the contract does not meet user expectations, frequent revisions become necessary, leading to time-consuming and laborious processes.

[0551] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0552] In this invention, the server includes means for inputting contract document data into an information processing device, means for analyzing contract clauses and identifying deficiencies and risks, means for automatically generating favorable alternative clauses based on the risks, and means for adjusting the alternative clauses based on emotional feedback. This makes it possible to generate flexible contract clauses that take into account the user's emotions while identifying and correcting deficiencies in the contract clauses.

[0553] An "information processing device" is an electronic device, including digital computers and servers, used for inputting, processing, storing, and outputting data.

[0554] "Document data" refers to digital data containing the contents of formal documents such as contracts, and includes formats such as text and PDF files.

[0555] "Contract clauses" are sections of a contract that describe specific arrangements and conditions, and define the content of the agreement between the parties.

[0556] A "deficiency" refers to any part of a contract clause that is missing, incomplete, or inaccurate, and which may cause problems in the performance of the contract.

[0557] "Risk" refers to elements that could potentially cause disadvantage to the parties in the performance of a contract, and relates to situations where contract clauses are considered legally or practically weak.

[0558] An "alternative clause" is a newly proposed clause that replaces an original contract clause and is a document created to make the contract terms more favorable.

[0559] A "user terminal" refers to a device such as a computer, smartphone, or tablet that is directly operated by the user, and is a device used to communicate with a server and send and receive data.

[0560] "Emotional feedback" refers to data that shows the emotions and opinions expressed by users regarding the terms of a contract, and serves as information to evaluate and improve the suitability of the clauses.

[0561] "Adjustment" refers to the process of modifying and optimizing the generated alternative clauses based on user feedback.

[0562] This invention is a system that performs legal checks on contracts and generates alternative clauses that reflect the user's feelings. Specifically, the server, terminal, and user cooperate with each other to process data.

[0563] First, the user uploads a draft of the contract to the information processing device using a terminal. The terminal used here can be a computer or smart device, and it can access the server via a network.

[0564] The server receives the uploaded contract and converts it into text data using OCR technology. Next, the server uses AI analysis software to analyze the contract clauses and identify deficiencies and risks. This analysis references a database of past case law.

[0565] If a risk is identified, the server uses a generative AI model to automatically generate favorable alternative clauses. The generative AI model is activated by a prompt message. For example, a prompt message such as "Please suggest improvements to the clause regarding delivery dates" might be used. This model outputs clauses appropriate to the context by referring to past successful cases.

[0566] If a user expresses dissatisfaction or concern with a particular contract clause, the server uses an emotion recognition engine to collect that feedback. The user's emotions are automatically analyzed from the entered comments. Based on this information, a generative AI model adjusts the alternative clauses.

[0567] For example, if a user enters "I'm worried about the delivery date," the emotion engine detects this anxiety, and the server resets the prompt to "Please suggest a delivery date that will put the user at ease," thereby generating a more suitable alternative clause.

[0568] In this way, by generating and proposing contract clauses that take user emotions into consideration, the contract process can be made smoother. This system makes it possible to create contracts efficiently and achieve high user satisfaction.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] The user uploads a draft of the contract to the information processing device using a terminal. The input is digital data of the contract (e.g., a PDF file). The terminal transmits this data to the server via the network. The server receives the contract file as output.

[0572] Step 2:

[0573] The server converts the received contract file into text data. OCR technology is used here to convert PDF and image-based data into text. The input is the digital data of the contract, and the output is the text data extracted by OCR.

[0574] Step 3:

[0575] The server uses AI analysis software to analyze contract clauses on text data. Text-based contracts are used as input. The analysis process identifies deficiencies and risks in the contract clauses by querying a database of past case law. The output is a list of identified deficiencies and risks.

[0576] Step 4:

[0577] The server uses a generative AI model to automatically generate favorable alternative clauses. The input is a list of deficiencies and risks, and includes a prompt in the form of "Generate proposals to address the deficiencies." The prompt is sent to the generative AI model, and the output is a proposed new alternative clause.

[0578] Step 5:

[0579] The server presents the generated alternative clauses to the user's terminal and collects emotional feedback. The input is the generated alternative clauses, and feedback regarding the user's emotions is obtained. The output is the user's feedback and its emotional data.

[0580] Step 6:

[0581] The server uses a sentiment engine to adjust alternative clauses based on user feedback. Sentimental feedback is used as input, and the sentiment engine generates guidelines for adjustment. The output is the final, adjusted contract clause.

[0582] (Application Example 2)

[0583] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0584] With the advancement of modern information and communication technology, documents such as contracts are being digitized, and many contracts are now concluded online. However, manually checking for deficiencies and risks in contract clauses that arise during this process is time-consuming and inefficient. Furthermore, for users to understand the contract content and conclude it with confidence, the clauses must be sensitive to the user's emotions and anxieties. Conventional technologies lack this emotional consideration, and users often proceed with the contract while feeling uneasy about its content. This invention aims to solve these problems.

[0585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0586] In this invention, the server includes means for inputting document information of a document into an information processing device; means for analyzing contract clauses contained in the document information and identifying deficiencies and risks in the contract clauses; means for automatically generating favorable alternative expressions based on the risks; means for presenting the alternative expressions to a user device and modifying the contract content based on input feedback; and means for recognizing the user's emotions during the modification process and proposing appropriate alternative expressions corresponding to those emotions. This makes it possible to propose more optimal contract clauses that take the user's emotions into consideration during the contract process. Furthermore, efficient legal checks are performed, and the user's anxiety regarding the contract content is reduced.

[0587] An "information processing device" is an electronic device used to analyze digital documents and perform various calculations, and is a device that can efficiently handle data such as contracts.

[0588] "Document information" refers to text data such as contracts and agreements, and its content includes the terms and conditions of the contract.

[0589] "Contract clauses" are the individual conditions and agreements stated in a contract, and they clarify the rights and obligations under the contract.

[0590] "Deficiency" refers to a flaw or error in the contract clauses, meaning the contract is incomplete in its content.

[0591] "Risk" refers to any unexpected negative consequences or losses that may arise from specific contractual clauses.

[0592] "Alternative wording" refers to newly proposed wording in a document that modifies the original contract clause, and is used for the purpose of mitigating risk.

[0593] "User equipment" refers to terminals or devices used by the user, specifically equipment for reviewing proposed alternative terms and providing feedback.

[0594] "Feedback" refers to evaluations and comments made by users regarding proposed content, and is information used to improve and optimize the system.

[0595] "Emotions" refers to the psychological response that contractual clauses have to the user, encompassing emotional states such as anxiety or a sense of security.

[0596] A "proposal" is a new plan or solution presented to achieve a specific objective, and in this case, it refers to the optimal contract terms.

[0597] As a form for carrying out the invention, the system that realizes this application example consists of the following components to support a series of processes for legally reviewing and emotionally-based revising contracts.

[0598] The server, as an information processing device, is equipped with an interface for inputting contract document information. Through this interface, users can upload contract documents in digital format. Upon receiving the digitized document information, the server analyzes the document using a natural language processing tool (e.g., spaCy). During the analysis process, deficiencies and risks in the contract clauses are identified. Based on the identified risks, favorable alternative expressions are automatically generated using a generative AI model (e.g., GPT).

[0599] The generated alternative expressions are presented to the user's device, i.e., the terminal used by the user. At this time, the server utilizes Azure Cognitive Services' sentiment analysis capabilities to recognize the user's emotions. When the user reviews the contract terms and provides feedback, appropriate alternative expressions tailored to those emotions are suggested again. Finally, these alternative expressions are revised into contract terms in a way that alleviates the user's anxiety.

[0600] As a concrete example, consider a scenario where a user, using a smartphone, checks the contract's shipping policy and feels uneasy about the return conditions. In this case, the server detects the user's anxiety using an emotion engine and presents a revised clause that provides reassurance. This entire process enhances understanding of the contract and streamlines the contract signing process.

[0601] An example of a prompt message that can be used to instruct a generative AI model is: "Assess the risks associated with this contract clause and propose appropriate alternative clauses based on sentiment recognition. Please include suggestions that reflect user feedback."

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The user uploads the contract document information to the information processing device via their terminal. Here, the user inputs the contract in digital format, and that document data is sent to the server. The input data is stored in the format that the server receives.

[0605] Step 2:

[0606] The server analyzes the received document information. Specifically, it uses spaCy, a natural language processing tool, to analyze the document's text and extract the structure and key points of the contract clauses. This process identifies deficiencies and risks in the contract clauses. The input is digital contract data, and the output is a list of items for which risks have been identified.

[0607] Step 3:

[0608] The server generates favorable alternative wording based on identified risks. It automatically generates proposed amendments to contract clauses using a generative AI model (e.g., GPT). The input is information about the risky clauses, and the output is proposed clauses as alternatives. These alternatives are characterized by being optimized based on past successes.

[0609] Step 4:

[0610] The server presents the generated alternative representation to the user's terminal. The user reviews it and provides feedback through the interface. This feedback reflects the user's opinions and feelings. The input is the proposed revision, and the output is the user's feedback information.

[0611] Step 5:

[0612] The server uses Azure Cognitive Services to recognize emotions from user feedback. Based on the emotion analysis, it determines the degree to which the user felt anxious, dissatisfied, or reassured. In this process, the input is feedback data, and the output is a label of the user's emotional expression.

[0613] Step 6:

[0614] The server readjusts alternative expressions and further optimizes the proposed content based on the recognized emotions. By presenting the readjusted alternatives to the user, contract terms that are more emotionally resonant are finalized. The input is the result of emotion recognition, and the output is the final proposed alternative expression.

[0615] 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.

[0616] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0617] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0622] 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.

[0623] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0624] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0625] 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.

[0626] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0627] 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.

[0628] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0629] The 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.

[0630] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0631] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] This invention provides a system for efficiently performing legal checks and necessary revisions on contracts. To implement this system, it operates as follows:

[0633] The user uploads a draft of the contract from their terminal to the information processing device. This upload sends the contract document data to the server, where it is converted into a format that can be analyzed in text format.

[0634] The server analyzes the contract clauses based on this document data. The analysis utilizes AI and references a database of past case law to detect deficiencies and ambiguities in the contract clauses. This process identifies potential legal risks for each contract clause within the contract.

[0635] Once risks are identified, the server automatically generates favorable alternative clauses. This generation process optimizes clauses using past successful contract examples, proposing clauses that mitigate specific risks.

[0636] Users can view these alternative clauses on their devices. The server receives user feedback and modifies the generated clauses as needed. For example, if an applicable clause is misleading, the user can leave a comment.

[0637] This system not only streamlines communication between legal departments and users, but also enables the rapid creation of contracts with minimized legal risks. Furthermore, it reduces time and effort in this process. For example, if a supply contract lacks clear penalty clauses for delayed delivery, the system will suggest a clear alternative clause such as "In the event of a delay, a penalty of 5% of the delivery price can be claimed," thereby supporting the creation of a contract that is easy for both parties to understand.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The user selects a draft contract file from their terminal and uploads it to the information processing system. The terminal then sends the selected contract file directly to the server.

[0641] Step 2:

[0642] The server converts the received contract file into text format. This makes the contract's contents analyzable by AI.

[0643] Step 3:

[0644] The server sends the text data to the AI ​​agent, which then begins analyzing the contract clauses. The AI ​​agent identifies each clause in the contract one by one, analyzing them while referring to past case precedents and legal databases.

[0645] Step 4:

[0646] The AI ​​agent identifies deficiencies and risks in contract clauses. This includes detecting ambiguities and legal shortcomings. It creates a list of deficiencies and assesses the corresponding risks.

[0647] Step 5:

[0648] The server automatically generates favorable alternative clauses based on risks identified by the AI ​​agent. This generation process is optimized by referencing past success stories.

[0649] Step 6:

[0650] The server sends the generated alternative clause to the user's device. The user can then view the presented clause on their device.

[0651] Step 7:

[0652] Users review the alternative clauses through their devices and provide feedback. This feedback includes approval, suggestions for revisions, and additional comments.

[0653] Step 8:

[0654] The server receives user feedback and makes revisions to alternative clauses if necessary. The revised content is then reviewed again for final confirmation.

[0655] Step 9:

[0656] The finalized contract is generated on the server and provided to the user in a downloadable format. The user can retrieve the completed contract from their device and complete the related processes.

[0657] (Example 1)

[0658] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] Traditional contract drafting and legal review processes are time-consuming, labor-intensive, and prone to human error. Furthermore, it's difficult for individuals without specialized legal knowledge to properly evaluate and revise clauses. These challenges complicate communication between legal departments and users, reducing the efficiency of contract drafting.

[0660] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0661] In this invention, the server includes means for inputting an electronic document into an information processing device, means for analyzing clauses and identifying deficiencies and potential risks, and means for automatically generating favorable alternative expressions. This streamlines the legal review and revision of contracts, enabling the rapid creation of contracts with minimized legal risks.

[0662] An "information processing device" is a general term for hardware and software that inputs electronic documents and processes them as needed.

[0663] An "electronic document" is a document data stored in digital format and used to represent contracts or agreements.

[0664] A "clause" refers to individual provisions or conditions written in an electronic document, and is an element that forms the content of a contract.

[0665] "Deficiencies" refer to missing or incomplete sections in clauses or documents, which may cause problems in the execution of a contract.

[0666] "Potential risks" refer to uncertainties contained in the clauses and risks that may arise in the future.

[0667] A "favorable alternative wording" refers to a modified clause that avoids deficiencies or potential risks and presents more favorable terms for the contracting parties.

[0668] A "terminal" is an electronic device used by a user for communication and information processing.

[0669] "AI technology" refers to the scientific and technological advancements that enable computers to perform intelligent activities, with the aim of assisting in data analysis and decision-making.

[0670] "Natural language processing" is a technology that enables computers to understand human language, and involves the analysis and semantic understanding of text data.

[0671] A "reference case data set" is a collection of data from similar past cases and precedents, and serves as a source of information used as a standard for analysis and evaluation.

[0672] "Document examples" are documents created in the past that demonstrate the format and content of successful contracts and serve as references for optimization.

[0673] This invention relates to a system for efficiently performing legal checks and revisions on contracts. The system begins with a user uploading a draft contract using a terminal. The electronic document, in PDF or Word format, is then transmitted to an information processing device via a dedicated application on the terminal or a web interface.

[0674] The server converts received electronic documents into a format that can be analyzed using text. This conversion process may involve the use of software employing OCR (Optical Character Recognition) technology. Specifically, open-source OCR tools are utilized to perform highly accurate data extraction. The text data converted at this stage is then used in subsequent analysis processes.

[0675] Next, the server uses a generative AI model that performs natural language processing to analyze the clauses within the electronic document. In this step, the AI ​​identifies deficiencies and potential risks by comparing them with a dataset of past reference cases. The generative AI model is applied to the analysis, scrutinizing the document's structure and word usage. As a concrete example of the AI ​​model, industry-standard natural language processing tools are used.

[0676] The server automatically generates favorable alternative wording based on identified risks. This generation process utilizes successful document examples and is optimized accordingly. Favorable alternative wording is created based on prompt sentences generated by the AI. A specific example of a prompt sentence is, "Generate clear alternative clauses regarding penalties for delivery delays in supply contracts." This allows for document improvements tailored to specific situations.

[0677] Finally, there is a feedback function that allows users to review alternative expressions presented on their devices and provide feedback. The server receives user feedback and revises the generated clauses, making the contract clearer and more user-friendly. Implementing this system reduces legal risks and streamlines the contract drafting process.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The user uploads a draft contract to the information processing device using a terminal. The input is a document in PDF or Word format, and the output is a notification that the transfer to the server is complete. This action allows the user to submit the contract to the system and prepare it for legal review.

[0681] Step 2:

[0682] The server converts received electronic documents into text format. In this process, the server applies OCR technology to process image data into text data. The input is image data, and the output is parsable text data. The server uses this conversion result to facilitate analysis in the next step.

[0683] Step 3:

[0684] The server uses a generation AI model to analyze the converted text data. Here, text data is taken as input, and a list of deficiencies and potential risks in the contract clauses is generated as output. The server employs natural language processing techniques to thoroughly examine the document content and identify problem areas.

[0685] Step 4:

[0686] The server generates favorable alternative statements based on identified potential risks. The input consists of risk information and referenced historical case data, while the output is a proposed alternative clause. The server uses prompt statements to instruct a generating AI model to construct the optimal clause.

[0687] Step 5:

[0688] Users can review alternative clauses on their devices and provide feedback. The input is the generated alternative clause, and the output is the user's comments and revision requests. This process allows users to examine the proposed clauses and reflect their own judgment.

[0689] Step 6:

[0690] The server incorporates user feedback and revises the final contract. The input is feedback information and generated clauses, and the output is the revised contract. This allows the server to create an improved contract that incorporates user input, thus supporting the creation of legally secure contracts.

[0691] (Application Example 1)

[0692] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] In the creation and review process of related legal documents and terms of service for electronic payments, there is a high possibility of deficiencies and ambiguous clauses that include legal risks, and measures are needed to prevent the troubles that arise from this. However, these tasks usually require a great deal of time and effort from experts, making it difficult to carry them out simply and efficiently.

[0694] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0695] In this invention, the server includes means for inputting data from legal documents, means for analyzing contract clauses and identifying deficiencies and risks in the clauses, and means for automatically generating optimal alternative clauses based on the risks. This enables the rapid and accurate creation of legal documents and risk management in electronic payments.

[0696] An "information processing device" is a computer device used to analyze and process input data.

[0697] A "legal document" refers to a legally binding document, such as a contract or terms of service.

[0698] "Data" refers to a collection of numbers or strings of characters that are input into an information processing device for analysis.

[0699] "Contractual clauses" refer to specific provisions and conditions included in a legal document.

[0700] "Deficiency" refers to a state in which the necessary requirements of a clause are not met.

[0701] "Risk" refers to a situation that could potentially lead to legal troubles in the future.

[0702] An "alternative clause" is an alternative contract clause proposed to mitigate deficiencies or risks.

[0703] "User equipment" refers to computer devices or terminals used by users.

[0704] "Evaluation information" refers to feedback and opinions that users provide regarding alternative terms.

[0705] "Electronic payment" refers to monetary transactions conducted using the internet and digital technology.

[0706] A "database" is an organized collection of information where data is stored and can be searched and used.

[0707] A "successful contract example" refers to an example of a contract that has been implemented in the past and successfully avoided legal risks.

[0708] "Optimization" refers to arranging a series of processes or states into an efficient and desirable form.

[0709] The embodiments for carrying out this invention will now be described in detail. First, legal document data is uploaded from the user's device to an information processing device. This information processing device is equipped with a natural language processing model (such as TensorFlow or PyTorch) built in Python, for example. Upon receiving this data, the server first converts it to text format and analyzes the contract clauses. In the analysis, the server identifies deficiencies and risks in the clauses while referring to a database of accumulated case law.

[0710] Next, the server automatically generates the optimal alternative clauses using an AI model. These alternative clauses are optimized based on past successful contract examples. The generated alternative clauses are sent to the user's device, where the user reviews the content and inputs evaluation information.

[0711] This process reduces legal risks and allows for the creation of more secure and reliable contracts regarding electronic payments.

[0712] As a concrete example, imagine a company using this system to create terms of service for a new electronic payment service. Upon uploading the terms of service, the system would state that "the section regarding data sharing with third parties is unclear" and propose an alternative: "This service will only share data with third parties with the user's consent." The user can then either accept this alternative or add comments requesting revisions.

[0713] An example of a prompt input to the generating AI model is: "Analyze the following legal document, identify unclear sections, and propose alternatives: ○○ (text of the contract document)." This mechanism speeds up the creation of legal documents while improving their accuracy and reliability.

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The user uploads legal document data from their terminal to the information processing device. The input document data is first converted to text format and sent to the server. This makes the document data ready for analysis.

[0717] Step 2:

[0718] The server receives data converted to text format and analyzes the contract clauses using natural language processing techniques. The input is text data, and the output is detailed information on the identified clauses. In this process, the server analyzes the data, particularly by referring to case law information in a database to detect deficiencies and risks.

[0719] Step 3:

[0720] The server automatically generates alternative clauses to address deficiencies and risks using an AI model based on the analysis results. The input is information on deficiencies and risks identified in the analysis, and the output is the generated alternative clauses. This process optimizes the clauses based on past successful contract examples.

[0721] Step 4:

[0722] The server sends the generated alternative clauses to the user's device, and the user views them. The user reviews the displayed information and enters evaluation information as feedback. This input consists of user feedback and comments, which are used in the next step.

[0723] Step 5:

[0724] The server uses user feedback to revise alternative clauses if necessary. The input is user feedback, and the output is the revised legal document. This results in a highly reliable legal document, with the document associated with the electronic payment being the final form.

[0725] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0726] This invention aims to improve user satisfaction by combining a system that performs legal checks on contracts and makes necessary revisions with an emotion engine. This system can reflect the user's emotions in the process of analyzing contract document data and generating favorable alternative clauses.

[0727] First, the user uploads a draft of the contract to the information processing device using their terminal. The server converts the received contract into text data and begins AI analysis. The analysis proceeds by referring to a database of past case law to identify deficiencies and risks in the contract clauses. For identified deficiencies, the risks are assessed and favorable alternative clauses are automatically generated. Based on successful past contract cases, optimized clauses are proposed.

[0728] Furthermore, the server uses an emotion engine to collect user feedback. It recognizes emotions from user reactions and comments and adjusts the generated alternative clauses accordingly. For example, if a user is dissatisfied with the presented clause, the emotion engine detects this and provides feedback that guides the improvement of the alternative.

[0729] For example, if a user feels uneasy about a clause regarding delivery dates, the emotion engine can recognize that emotion and present alternative clauses that include more reassuring language. In this way, the present invention can customize the contract process according to the user's emotions, making the contract signing process smoother. This not only improves the efficiency of contract creation but also increases user satisfaction.

[0730] The following describes the processing flow.

[0731] Step 1:

[0732] The user selects a draft contract from their terminal and uploads it to the information processing device. The terminal then sends the contract file directly to the server.

[0733] Step 2:

[0734] The server converts the received contract file into text format, making it data that can be analyzed by an AI agent. During this process, contract clauses are identified.

[0735] Step 3:

[0736] The server sends the text data to an AI agent, which then begins analyzing the contract clauses. The AI ​​agent refers to commercial law and past case law databases to identify any deficiencies or risks in the clauses.

[0737] Step 4:

[0738] The AI ​​agent automatically generates favorable alternative clauses based on the risks it detects. This rapid response optimizes the contract terms.

[0739] Step 5:

[0740] The server utilizes an emotion engine to collect user feedback. It analyzes the user's emotions towards the presented alternative clauses and receives specific responses.

[0741] Step 6:

[0742] The server adjusts alternative clauses based on the analysis results from the emotion engine. Necessary modifications are made to ensure the content matches the user's emotions.

[0743] Step 7:

[0744] The user can review the revised alternative clauses again through their device. If the user is satisfied, they can approve the changes and the contract revisions will be completed.

[0745] Step 8:

[0746] The server generates the finalized contract and provides it to the user in a downloadable format. The user retrieves the contract from their terminal and proceeds with the related procedures.

[0747] (Example 2)

[0748] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0749] Traditional contract drafting systems can identify deficiencies and risks in contract clauses, but they cannot propose clauses that take into account the user's feelings and satisfaction, thus failing to fully meet user requirements. Furthermore, if the contract does not meet user expectations, frequent revisions become necessary, leading to time-consuming and laborious processes.

[0750] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0751] In this invention, the server includes means for inputting contract document data into an information processing device, means for analyzing contract clauses and identifying deficiencies and risks, means for automatically generating favorable alternative clauses based on the risks, and means for adjusting the alternative clauses based on emotional feedback. This makes it possible to generate flexible contract clauses that take into account the user's emotions while identifying and correcting deficiencies in the contract clauses.

[0752] An "information processing device" is an electronic device, including digital computers and servers, used for inputting, processing, storing, and outputting data.

[0753] "Document data" refers to digital data containing the contents of formal documents such as contracts, and includes formats such as text and PDF files.

[0754] "Contract clauses" are sections of a contract that describe specific arrangements and conditions, and define the content of the agreement between the parties.

[0755] A "deficiency" refers to any part of a contract clause that is missing, incomplete, or inaccurate, and which may cause problems in the performance of the contract.

[0756] "Risk" refers to elements that could potentially cause disadvantage to the parties in the performance of a contract, and relates to situations where contract clauses are considered legally or practically weak.

[0757] An "alternative clause" is a newly proposed clause that replaces an original contract clause and is a document created to make the contract terms more favorable.

[0758] A "user terminal" refers to a device such as a computer, smartphone, or tablet that is directly operated by the user, and is a device used to communicate with a server and send and receive data.

[0759] "Emotional feedback" refers to data that shows the emotions and opinions expressed by users regarding the terms of a contract, and serves as information to evaluate and improve the suitability of the clauses.

[0760] "Adjustment" refers to the process of modifying and optimizing the generated alternative clauses based on user feedback.

[0761] This invention is a system that performs legal checks on contracts and generates alternative clauses that reflect the user's feelings. Specifically, the server, terminal, and user cooperate with each other to process data.

[0762] First, the user uploads a draft of the contract to the information processing device using a terminal. The terminal used here can be a computer or smart device, and it can access the server via a network.

[0763] The server receives the uploaded contract and converts it into text data using OCR technology. Next, the server uses AI analysis software to analyze the contract clauses and identify deficiencies and risks. This analysis references a database of past case law.

[0764] If a risk is identified, the server uses a generative AI model to automatically generate favorable alternative clauses. The generative AI model is activated by a prompt message. For example, a prompt message such as "Please suggest improvements to the clause regarding delivery dates" might be used. This model outputs clauses appropriate to the context by referring to past successful cases.

[0765] If a user expresses dissatisfaction or concern with a particular contract clause, the server uses an emotion recognition engine to collect that feedback. The user's emotions are automatically analyzed from the entered comments. Based on this information, a generative AI model adjusts the alternative clauses.

[0766] For example, if a user enters "I'm worried about the delivery date," the emotion engine detects this anxiety, and the server resets the prompt to "Please suggest a delivery date that will put the user at ease," thereby generating a more suitable alternative clause.

[0767] In this way, by generating and proposing contract clauses that take user emotions into consideration, the contract process can be made smoother. This system makes it possible to create contracts efficiently and achieve high user satisfaction.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The user uploads a draft of the contract to the information processing device using a terminal. The input is digital data of the contract (e.g., a PDF file). The terminal transmits this data to the server via the network. The server receives the contract file as output.

[0771] Step 2:

[0772] The server converts the received contract file into text data. OCR technology is used here to convert PDF and image-based data into text. The input is the digital data of the contract, and the output is the text data extracted by OCR.

[0773] Step 3:

[0774] The server uses AI analysis software to analyze contract clauses on text data. Text-based contracts are used as input. The analysis process identifies deficiencies and risks in the contract clauses by querying a database of past case law. The output is a list of identified deficiencies and risks.

[0775] Step 4:

[0776] The server uses a generative AI model to automatically generate favorable alternative clauses. The input is a list of deficiencies and risks, and includes a prompt in the form of "Generate proposals to address the deficiencies." The prompt is sent to the generative AI model, and the output is a proposed new alternative clause.

[0777] Step 5:

[0778] The server presents the generated alternative clauses to the user's terminal and collects emotional feedback. The input is the generated alternative clauses, and feedback regarding the user's emotions is obtained. The output is the user's feedback and its emotional data.

[0779] Step 6:

[0780] The server uses a sentiment engine to adjust alternative clauses based on user feedback. Sentimental feedback is used as input, and the sentiment engine generates guidelines for adjustment. The output is the final, adjusted contract clause.

[0781] (Application Example 2)

[0782] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] With the advancement of modern information and communication technology, documents such as contracts are being digitized, and many contracts are now concluded online. However, manually checking for deficiencies and risks in contract clauses that arise during this process is time-consuming and inefficient. Furthermore, for users to understand the contract content and conclude it with confidence, the clauses must be sensitive to the user's emotions and anxieties. Conventional technologies lack this emotional consideration, and users often proceed with the contract while feeling uneasy about its content. This invention aims to solve these problems.

[0784] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0785] In this invention, the server includes means for inputting document information of a document into an information processing device; means for analyzing contract clauses contained in the document information and identifying deficiencies and risks in the contract clauses; means for automatically generating favorable alternative expressions based on the risks; means for presenting the alternative expressions to a user device and modifying the contract content based on input feedback; and means for recognizing the user's emotions during the modification process and proposing appropriate alternative expressions corresponding to those emotions. This makes it possible to propose more optimal contract clauses that take the user's emotions into consideration during the contract process. Furthermore, efficient legal checks are performed, and the user's anxiety regarding the contract content is reduced.

[0786] An "information processing device" is an electronic device used to analyze digital documents and perform various calculations, and is a device that can efficiently handle data such as contracts.

[0787] "Document information" refers to text data such as contracts and agreements, and its content includes the terms and conditions of the contract.

[0788] "Contract clauses" are the individual conditions and agreements stated in a contract, and they clarify the rights and obligations under the contract.

[0789] "Deficiency" refers to a flaw or error in the contract clauses, meaning the contract is incomplete in its content.

[0790] "Risk" refers to any unexpected negative consequences or losses that may arise from specific contractual clauses.

[0791] "Alternative wording" refers to newly proposed wording in a document that modifies the original contract clause, and is used for the purpose of mitigating risk.

[0792] "User equipment" refers to terminals or devices used by the user, specifically equipment for reviewing proposed alternative terms and providing feedback.

[0793] "Feedback" refers to evaluations and comments made by users regarding proposed content, and is information used to improve and optimize the system.

[0794] "Emotions" refers to the psychological response that contractual clauses have to the user, encompassing emotional states such as anxiety or a sense of security.

[0795] A "proposal" is a new plan or solution presented to achieve a specific objective, and in this case, it refers to the optimal contract terms.

[0796] As a form for carrying out the invention, the system that realizes this application example consists of the following components to support a series of processes for legally reviewing and emotionally-based revising contracts.

[0797] The server, as an information processing device, is equipped with an interface for inputting contract document information. Through this interface, users can upload contract documents in digital format. Upon receiving the digitized document information, the server analyzes the document using a natural language processing tool (e.g., spaCy). During the analysis process, deficiencies and risks in the contract clauses are identified. Based on the identified risks, favorable alternative expressions are automatically generated using a generative AI model (e.g., GPT).

[0798] The generated alternative expressions are presented to the user's device, i.e., the terminal used by the user. At this time, the server utilizes Azure Cognitive Services' sentiment analysis capabilities to recognize the user's emotions. When the user reviews the contract terms and provides feedback, appropriate alternative expressions tailored to those emotions are suggested again. Finally, these alternative expressions are revised into contract terms in a way that alleviates the user's anxiety.

[0799] As a concrete example, consider a scenario where a user, using a smartphone, checks the contract's shipping policy and feels uneasy about the return conditions. In this case, the server detects the user's anxiety using an emotion engine and presents a revised clause that provides reassurance. This entire process enhances understanding of the contract and streamlines the contract signing process.

[0800] An example of a prompt message that can be used to instruct a generative AI model is: "Assess the risks associated with this contract clause and propose appropriate alternative clauses based on sentiment recognition. Please include suggestions that reflect user feedback."

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The user uploads the contract document information to the information processing device via their terminal. Here, the user inputs the contract in digital format, and that document data is sent to the server. The input data is stored in the format that the server receives.

[0804] Step 2:

[0805] The server analyzes the received document information. Specifically, it uses spaCy, a natural language processing tool, to analyze the document's text and extract the structure and key points of the contract clauses. This process identifies deficiencies and risks in the contract clauses. The input is digital contract data, and the output is a list of items for which risks have been identified.

[0806] Step 3:

[0807] The server generates favorable alternative wording based on identified risks. It automatically generates proposed amendments to contract clauses using a generative AI model (e.g., GPT). The input is information about the risky clauses, and the output is proposed clauses as alternatives. These alternatives are characterized by being optimized based on past successes.

[0808] Step 4:

[0809] The server presents the generated alternative representation to the user's terminal. The user reviews it and provides feedback through the interface. This feedback reflects the user's opinions and feelings. The input is the proposed revision, and the output is the user's feedback information.

[0810] Step 5:

[0811] The server uses Azure Cognitive Services to recognize emotions from user feedback. Based on the emotion analysis, it determines the degree to which the user felt anxious, dissatisfied, or reassured. In this process, the input is feedback data, and the output is a label of the user's emotional expression.

[0812] Step 6:

[0813] The server readjusts alternative expressions and further optimizes the proposed content based on the recognized emotions. By presenting the readjusted alternatives to the user, contract terms that are more emotionally resonant are finalized. The input is the result of emotion recognition, and the output is the final proposed alternative expression.

[0814] 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.

[0815] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0816] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0817] 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.

[0818] Figure 9 shows an 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.

[0819] 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.

[0820] 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.

[0821] 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, motorcycles, etc., 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, for example, based 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.

[0822] 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."

[0823] 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.

[0824] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0825] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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 the like 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means of inputting contract document data into an information processing device,

[0838] A means for analyzing the contract clauses contained in the aforementioned document data and identifying deficiencies and risks in the said contract clauses,

[0839] A means for automatically generating favorable alternative clauses based on the aforementioned risks,

[0840] A means of presenting the aforementioned alternative clauses to the user device and modifying the contract based on the input feedback,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for referring to a database of past case law when evaluating the deficiencies and risks of the aforementioned contract clauses.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for optimizing the clauses by utilizing past successful contract examples when generating the aforementioned alternative clauses.

[0846] "Example 1"

[0847] (Claim 1)

[0848] A means of inputting electronic documents into an information processing device,

[0849] Means for analyzing the clauses contained in the aforementioned electronic document and identifying deficiencies and potential risks in the said clauses,

[0850] A means for automatically generating favorable alternative expressions based on the aforementioned potential risks,

[0851] A means for presenting the aforementioned alternative expression to the terminal and modifying the electronic document based on the inputted opinion,

[0852] A means of detecting ambiguity in clauses by performing natural language processing using AI technology,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising means for referring to a set of past reference case data when evaluating the deficiencies and potential risks of the aforementioned clause.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for optimizing the expression by utilizing past successful document examples when generating the alternative expression.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] A means of inputting legal document data into an information processing device,

[0861] A means of analyzing the contract clauses included in the aforementioned data and identifying deficiencies and risks in those clauses,

[0862] A means for automatically generating the optimal alternative clause based on the aforementioned risk,

[0863] The means of presenting the aforementioned alternative clauses to the user device and modifying the legal document based on the input evaluation information,

[0864] Means for associating the aforementioned correction process with electronic payment,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, further comprising means for referring to a database of past case precedents when evaluating the deficiencies and risks of the aforementioned contract clauses.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising means for optimizing the clauses by utilizing past successful contract examples when generating the aforementioned alternative clauses.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means of inputting contract document data into an information processing device,

[0873] A means for analyzing the contract clauses contained in the aforementioned document data and identifying deficiencies and risks in the said contract clauses,

[0874] A means for automatically generating favorable alternative clauses based on the aforementioned risks,

[0875] A means for presenting the aforementioned alternative clause to the user terminal and collecting the inputted emotional feedback,

[0876] Means for adjusting alternative clauses based on the aforementioned emotional feedback,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for referring to a database of past case law when evaluating the deficiencies and risks of the aforementioned contract clauses.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for optimizing the clauses by utilizing past successful contract examples when generating the aforementioned alternative clauses.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] A means of inputting document information from a document into an information processing device,

[0885] A means for analyzing the contract clauses contained in the aforementioned document information and identifying deficiencies and risks in the said contract clauses,

[0886] A means for automatically generating advantageous alternative expressions based on the aforementioned risks,

[0887] A means of presenting the aforementioned alternative expression to the user device and modifying the contract content based on the input feedback,

[0888] In the aforementioned revision process, means are provided to recognize the user's emotions and propose appropriate alternative expressions corresponding to those emotions.

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, further comprising means for referring to a past case law information base when evaluating the deficiencies and risks of the aforementioned contract clauses.

[0892] (Claim 3)

[0893] The system according to claim 1, further comprising means for optimizing the expression by utilizing past successful contract examples when generating the alternative expression. [Explanation of Symbols]

[0894] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting legal document data into an information processing device, A means of analyzing the contract clauses included in the aforementioned data and identifying deficiencies and risks in those clauses, A means for automatically generating the optimal alternative clause based on the aforementioned risk, The means of presenting the aforementioned alternative clauses to the user device and modifying the legal document based on the input evaluation information, Means for associating the aforementioned correction process with electronic payment, A system that includes this.

2. The system according to claim 1, further comprising means for referring to a database of past case precedents when evaluating the deficiencies and risks of the aforementioned contract clauses.

3. The system according to claim 1, further comprising means for optimizing the clauses by utilizing past successful contract examples when generating the aforementioned alternative clauses.

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