system

Abstract

We provide the system. [Solution] A generation mechanism means for automatically generating documents based on transaction conditions received from an input device, An analytical mechanism means for detecting hazards and defects contained in the aforementioned document, A display mechanism means for presenting detected hazards and defects to the user, A dialogue mechanism for receiving questions regarding legal issues and generating appropriate responses, An interface means for displaying transaction-related information on a terminal device, 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 persona chatbot control method performed by at least one processor, including 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 contract operations, there is a problem that it takes a great deal of time and effort to create and confirm contract documents every time there is a new contract, change, cancellation, etc. Also, when a contract document contains potential risks, specialized knowledge and experience are required to detect them manually. Furthermore, in construction contracts, there are many legal questions and uncertainties, and there is a problem that the support system for quickly responding to them is insufficient.

Means for Solving the Problems

[0005] This invention provides a generation means for automatically generating contract documents based on contract terms received from an input means, and utilizes an analysis means to monitor the generated contract documents and detect risks and deficiencies, thereby reducing the workload associated with handling contract documents. Furthermore, it includes a presentation means for clearly presenting the detected risks and deficiencies to the user, thereby enabling the user to take appropriate action quickly. In addition, it provides a dialogue means for receiving legal questions and generating appropriate answers, enabling the provision of prompt legal support to the user.

[0006] "Input method" refers to the means by which a user provides contract terms and other necessary information to the system.

[0007] A "contract document" is a formal document that describes the terms of an agreement, and it is a detailed record of the matters agreed upon between the parties.

[0008] "Automated generation" refers to the process where a system creates contract documents with minimal human intervention.

[0009] "Generation means" refers to a function or module for automatically creating contract documents within the system.

[0010] "Risk" refers to potential problems or factors that cause future uncertainties included in a contract.

[0011] "Deficiency" refers to a situation in a contract where necessary items or matters are not adequately defined or described.

[0012] "Analysis tools" refer to functions or modules used to analyze contract documents and detect risks and deficiencies.

[0013] A "presentation means" is a function or interface for visually displaying detected risks or defects to the user.

[0014] A "dialogue mechanism" refers to a system function or module for responding to legal questions from users. [Brief explanation of the drawing]

[0015] [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a 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.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a 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, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention realizes a system that efficiently automates contract work. Specific embodiments of the system are shown below.

[0037] First, the user needs to enter the contract terms. The user uses a terminal to enter the basic contract details (e.g., subscriber name, contract period, and fee). This input method provides the necessary information to the system.

[0038] The server uses the received input information to utilize generation methods and automatically generates a draft of the contract document. This process recognizes different contract templates, uses the appropriate template based on pre-programmed rules, and incorporates the entered conditions.

[0039] The generated contract document is displayed to the user via the terminal. The user can review the document and make further edits if necessary.

[0040] Next, a risk analysis process takes place. The server analyzes the text of the contract document and uses analytical tools to identify potential risks and deficiencies. In this process, natural language processing technology is utilized to detect risks based on known risk patterns.

[0041] The analysis results are presented to the user via the device. The information presented includes details of risk areas and recommended countermeasures. The user can then adjust the contract terms based on this information.

[0042] Furthermore, a dialogue function is provided to answer legal questions. When a user enters a legal question on their terminal, the server processes the question using the dialogue mechanism and generates an appropriate answer based on the relevant information. The answer is displayed immediately on the terminal, allowing the user to obtain the necessary information instantly.

[0043] As a concrete example, consider a construction contract for a new base station. The user inputs basic conditions such as construction period, cost, and scope of responsibility. The server automatically generates a contract based on this information and displays it on the terminal. Meanwhile, the server can analyze the generated contract document and detect, for example, the risk of delivery delays or deficiencies regarding legal obligations. This allows the user to identify risks early and revise or negotiate the contract as needed.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user accesses an input form containing the contract terms and enters necessary basic information such as the contract holder's name, contract period, and amount into their device.

[0047] Step 2:

[0048] The terminal sends the entered contract terms to the server and requests the generation of the contract.

[0049] Step 3:

[0050] The server uses a generation mechanism to select an appropriate contract template based on the received contract terms and automatically generates the contract document. During this process, variables within the template are replaced according to the input information.

[0051] Step 4:

[0052] The terminal displays the generated contract document sent from the server to the user, allowing the user to review its contents.

[0053] Step 5:

[0054] Users can review the displayed contract document and make any necessary modifications on their device. Approval is also possible at this stage.

[0055] Step 6:

[0056] The user performs an action on their terminal to request a risk analysis of the contract document.

[0057] Step 7:

[0058] The terminal sends a risk analysis request to the server.

[0059] Step 8:

[0060] The server analyzes the text of the contract documents generated using analytical tools and employs natural language processing techniques to detect potential risks and deficiencies. Historical data and pattern recognition techniques are used to identify areas of deficiency and risk.

[0061] Step 9:

[0062] The server sends results to the terminal that detail the detected risks and deficiencies.

[0063] Step 10:

[0064] The terminal displays the analysis results in an easy-to-understand format for the user, and shows details of countermeasures and risks.

[0065] Step 11:

[0066] The user will review the presented risks and deficiencies and readjust the contract terms. In some cases, the contract terms may be redefined.

[0067] Step 12:

[0068] Users enter legal questions regarding the contract through the chat function on their device.

[0069] Step 13:

[0070] The device sends the user's question to the server and requests that it receive an appropriate answer.

[0071] Step 14:

[0072] The server analyzes the question using dialogue methods and collects information necessary for the generating AI to produce an appropriate answer. Based on this information, it creates an answer to the question.

[0073] Step 15:

[0074] The server sends the generated response to the terminal.

[0075] Step 16:

[0076] The terminal displays the submitted answers to the user and allows them to enter additional questions if needed.

[0077] This series of steps enables efficient automation of contract processes and provides legal support.

[0078] (Example 1)

[0079] 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."

[0080] Contract work often involves time-consuming and labor-intensive manual processes, making it inefficient and potentially leading to overlooked risks. Furthermore, answering legal questions about contracts quickly and accurately can be challenging. These challenges can result in delays and inaccurate contract execution.

[0081] 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.

[0082] In this invention, the server includes a generation function means for automatically generating contract documents based on contract terms obtained from an input function, an analysis function means for identifying risks and deficiencies contained in the contract documents, and a dialogue function means for receiving legal questions and creating appropriate responses. This enables efficient automation of contract operations, early detection of risks, and rapid response to legal questions.

[0083] The "input function" is a means of obtaining contract terms from the user.

[0084] The "generation function" is a means of automatically generating contract documents based on the acquired contract terms.

[0085] "Analysis function" refers to a means of identifying risks and deficiencies contained in contract documents.

[0086] A "display function" is a means of displaying identified risks and deficiencies to the user.

[0087] A "dialogue function" is a means of receiving legal questions and formulating appropriate responses to them.

[0088] An "information processing system" is a system that combines input, generation, analysis, display, and dialogue functions to efficiently automate contract work, enabling early detection of risks and prompt responses to legal questions.

[0089] This system utilizes specific hardware and software to efficiently automate contract management. Its specific form is described below.

[0090] The user enters basic information for creating a contract into the terminal. The terminal facilitates user operation by providing an intuitive interface for information input. This input information is essential as the basic data for generating contract documents in the system.

[0091] The server automatically generates contract documents using a generative AI model based on the entered contract terms. Specifically, it compares the acquired terms with several pre-prepared contract templates, selects the most appropriate template, and creates the document in natural language. The generative AI model customizes the contract documents based on templates appropriate for different contract types.

[0092] The generated contract document is displayed to the user via a terminal. The user can review and edit the contract document on the screen and make further adjustments as needed.

[0093] Next, the server uses natural language processing technology to analyze the contract document and identify potential risks and deficiencies. By comparing the text with known risk patterns, problems are automatically extracted. The analysis results are presented to the user via the terminal, allowing the user to consider the nature of the risks and recommended countermeasures.

[0094] Furthermore, when a user enters a legal question, the server uses its conversational function to generate an appropriate answer to the user's question. The generated answer is displayed on the terminal in real time, enabling quick information retrieval.

[0095] As a concrete example, consider a construction contract for a new base station. When a user inputs conditions such as construction period, cost, and scope of responsibility, the server generates a contract based on this information and displays it on the terminal. At that time, the server analyzes the generated contract to detect risks of delivery delays and deficiencies in legal obligations. This allows the user to obtain appropriate information for risk assessment and contract negotiation.

[0096] Example of a prompt:

[0097] "We are considering a construction contract for a new base station. Please prepare a contract with a construction period of 12 months, a total cost of 50 million yen, and the scope of responsibility being solely your company's. Also, please outline the potential risks and provide appropriate advice."

[0098] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0099] Step 1:

[0100] The user enters the contract terms through an input interface on their terminal. This information includes the subscriber's name, contract period, and fees. This information is received by the server and used as basic data for generating contract documents.

[0101] Step 2:

[0102] The server automatically generates contract documents using a generative AI model based on the received contract terms. First, it compares the acquired terms with various pre-prepared contract templates. It selects a template that matches the terms and generates a contract document in natural language. The output is a customized contract document based on the terms.

[0103] Step 3:

[0104] The terminal displays the generated contract document to the user. A screen is provided where the user can review the contract details and edit them manually as needed. The user's edits are then sent back to the server and saved as the final version of the contract document.

[0105] Step 4:

[0106] The server analyzes the final version of the contract document to identify potential risks and deficiencies. This process uses natural language processing techniques to compare the contract document with known risk patterns. As a result of the analysis, detailed risk information and recommended countermeasures are extracted.

[0107] Step 5:

[0108] The terminal displays analysis results from the server to the user. Risk information is displayed in a visually easy-to-understand format, allowing the user to consider adjusting or negotiating contract terms based on this information. This information provision enables users to grasp contract-related risks at an early stage.

[0109] Step 6:

[0110] When a user enters a legal question from their terminal, the server processes the question using a conversational function. Specifically, it analyzes the entered question and generates an appropriate answer using a generative AI model. The generated answer is immediately output to the terminal, allowing the user to quickly obtain the necessary legal information.

[0111] (Application Example 1)

[0112] 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."

[0113] In automating contract management, inefficiencies and inaccuracies exist, particularly in processes ranging from inputting transaction terms to generating documents, detecting risks, and responding to legal inquiries. This can make it difficult to quickly conclude contracts and properly identify potential risks.

[0114] 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.

[0115] In this invention, the server includes a generation mechanism for automatically generating documents based on transaction conditions received from an input device, an analysis mechanism for detecting risks and defects contained in the documents, and a display mechanism for presenting the detected risks and defects to the user. This enables increased efficiency in contract operations and optimized risk management.

[0116] An "input device" is a device that allows a user to input transaction conditions and provide those conditions to the system.

[0117] A "generation mechanism" is a means of automatically creating contract-related documents based on transaction terms received from an input device.

[0118] An "analysis mechanism" is a means of analyzing generated documents using natural language processing technology and comparing them with known risk patterns to identify risks and defects.

[0119] A "display mechanism" is a means of visually presenting to the user any dangers or defects detected by the analysis mechanism.

[0120] A "dialogue mechanism" is a means of generating and providing appropriate responses to questions users have about legal issues.

[0121] An "interface" is a means of displaying transaction-related information on a user's terminal device, enabling the user to interact with the system.

[0122] The system implementing this invention begins with the user inputting contract terms. The user inputs the terms of the transaction using an input device such as a smartphone or computer. This information is transmitted to a server via the internet. The server, through a generation mechanism for automatically generating contracts, selects an appropriate contract template based on this information and generates the necessary contract document.

[0123] The generated contract documents are analyzed by an analysis mechanism using natural language processing technology, and risks and defects are identified by comparing them with known risk patterns. This process uses software such as Google® NLP API. The detected risks and defects are provided to the user through a display mechanism, prompting appropriate risk-based action.

[0124] Furthermore, if the user has legal-related questions, they send these questions to the server via a dialogue mechanism. The server generates an appropriate response to the question using a pre-trained legal database and provides it to the user. This function utilizes a generative AI model.

[0125] As a concrete example, consider the process when a user enters into a new payment service agreement on an e-commerce site. Once the user enters the contract terms, the server automatically generates the contract, performs a risk analysis, and provides a risk warning based on the results. For example, a warning such as "These payment terms carry a risk of delivery delays" might be displayed.

[0126] An example of a prompt message to input into a generative AI model is as follows:

[0127] "Based on the contract terms entered by the user, automatically generate appropriate contract documents, detect deficiencies and risks using natural language processing, and then generate advice to clearly communicate the results to the user."

[0128] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0129] Step 1:

[0130] The user enters the transaction terms using an input device. The entered data includes the trader's name, duration, and fee. This data is transmitted to the server via a communication network.

[0131] Step 2:

[0132] The server generates a contract document using a generation mechanism based on the received transaction terms. The template selected here is automatically determined based on the received data to be the most suitable one. The generated document becomes the output, and the process proceeds to the next step.

[0133] Step 3:

[0134] The server feeds the generated contract documents into the analysis mechanism and applies natural language processing technology. The documents are analyzed as input, and risks are identified by comparing them with known risk patterns. The analysis results list the detected risks and deficiencies, which are then presented as output in the next step.

[0135] Step 4:

[0136] The server transmits the analysis results to the terminal via a display mechanism, visually presenting the user with detailed risk information. The user reviews the outputted risk information and makes any necessary adjustments.

[0137] Step 5:

[0138] The user sends legal-related questions to the server via an input device and a dialogue mechanism. The questions are sent to the server as input.

[0139] Step 6:

[0140] The server uses a generative AI model to generate appropriate responses to input questions. Pre-trained legal data is used for response generation, and the output is the answer.

[0141] Step 7:

[0142] The server sends the generated response to the terminal, presenting it in a format easily understandable to the user. This allows the user to receive legal advice immediately.

[0143] 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.

[0144] This invention is a contract automation system that combines an emotion engine. It recognizes the user's emotional state and utilizes this information for contract document generation, risk detection, and legal support, thereby improving the user experience. The system is configured and performs the following specific operations.

[0145] First, the user enters the basic terms and conditions of the contract into the terminal. This information is sent to the server via the terminal, and the server automatically generates a contract document using a generation mechanism. The contract document is displayed to the user, and the emotion engine simultaneously analyzes the user's input data and dialogue history to recognize their emotional state.

[0146] If the user's emotions indicate anxiety or doubt, the server adjusts how the generated contract documents and risk information are presented. Specifically, it can provide more detailed explanations or highlight key risk points. This information is then appropriately presented to the user via the terminal.

[0147] Next, risks and deficiencies in the contract document are detected. The server analyzes the contract document using analytical tools, matching it with known risk patterns using natural language processing technology to identify potential risks and deficiencies. By utilizing an emotion engine, it is possible to dynamically change how the analysis results are presented and adjust the feedback to ensure the user feels reassured.

[0148] Furthermore, users can ask legal questions about the contract through the device's chat function. The server responds to these questions and generates appropriate answers using dialogue tools. The sentiment engine optimizes the level of detail and explanation in the answers according to the user's emotional state. The answers are provided to the user in real time to aid their understanding.

[0149] For example, if the emotion engine detects a stress response while a user is reviewing a contract, the server can provide further explanations of complex parts of the contract document or add explanations in more accessible language. This allows the user to understand the contract with confidence and take appropriate action quickly as needed.

[0150] Thus, the present invention not only highly automates contract work but also provides a flexible system that takes into account the user's emotional state by integrating emotion recognition technology.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] The user uses a terminal to input the basic conditions required for the contract and prepares the information needed to generate the contract.

[0154] Step 2:

[0155] The terminal sends the user's input data to the server and requests the automatic generation of the contract.

[0156] Step 3:

[0157] The server selects a contract template based on the received data and automatically generates the contract document using a generation mechanism. In this process, it replaces variables within the template based on the input information.

[0158] Step 4:

[0159] The server sends the generated contract document to the terminal and simultaneously passes the necessary input data to the emotion engine to recognize the user's emotional state.

[0160] Step 5:

[0161] The terminal displays contract documents to the user and adjusts the way the documents are presented based on the results of the emotion engine's recognition of the user's emotional state.

[0162] Step 6:

[0163] The user reviews the contract documents and proceeds with revisions and approvals as needed. At this point, if the emotion engine detects feelings of anxiety or doubt, more detailed explanations and highlighted information are provided.

[0164] Step 7:

[0165] The user enters information on the terminal to request a risk analysis based on the contract.

[0166] Step 8:

[0167] The terminal sends a risk analysis request to the server, initiating the specific risk detection process.

[0168] Step 9:

[0169] The server analyzes the contract document using analytical tools and identifies known risks and deficiencies using natural language processing technology. At the same time, it prepares feedback that corresponds to the user's emotions based on the results of the emotion engine.

[0170] Step 10:

[0171] The server sends the results of the risk and deficiency analysis to the terminal and delivers the results to the user in a presentation method that is appropriate to their emotional state.

[0172] Step 11:

[0173] The device informs the user of the details of risks and defects, providing the information in an easy-to-understand format. Furthermore, if the sentiment engine determines that additional explanation is needed, it will display the appropriate information.

[0174] Step 12:

[0175] Users can request legal support by entering legal questions related to the contract using the chat function on their device.

[0176] Step 13:

[0177] The terminal sends the received question to the server and prepares to receive the optimal answer through the dialogue mechanism.

[0178] Step 14:

[0179] The server processes user questions using dialogue mechanisms and generates appropriate answers. The level of detail in the answers is adjusted based on the results of the sentiment engine.

[0180] Step 15:

[0181] The server sends the generated response to the terminal, providing the user with information in real time.

[0182] Step 16:

[0183] The device displays answers to questions to the user and continues to accept any further questions. During this process, the explanation is dynamically optimized based on the evaluation of the emotion engine.

[0184] This series of steps allows users to receive a reassuring contract service that utilizes emotion recognition technology.

[0185] (Example 2)

[0186] 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 as the "terminal".

[0187] Traditional contract management systems have focused on automated document generation and risk identification, but have not addressed the emotional state of users. As a result, users may not fully understand the contract terms, leading to anxiety and delays in contract signing. The challenge lies in streamlining the contract process while providing flexible responses that accommodate users' emotional states.

[0188] 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.

[0189] In this invention, the server includes a generation device means for automatically generating documents based on contract terms received from an input means, a means for recognizing the user's emotional state using an emotion recognition device and adjusting the presentation method of documents and information according to that state, and an analysis device means for using natural language processing technology to identify potential risks and deficiencies contained in the documents. This makes it possible to streamline contract work and clarify risks in accordance with the user's emotions.

[0190] An "input means" is a device or interface that allows a user to provide information such as contract terms to the system.

[0191] A "generating device" is a device or program that has the function of automatically creating a document based on the received contract terms.

[0192] An "emotion recognition device" is a device or software that has the technology to identify the user's emotional state by analyzing the user's input and dialogue history.

[0193] An "analytical device" is a device or program that uses natural language processing technology to analyze documents and compare them with known risk patterns to identify potential risks and deficiencies.

[0194] A "display device" is a device or interface that clearly displays detected risks or defects to the user.

[0195] A "dialogue device" is a device or system that generates appropriate answers to legal questions from users and enables natural interaction with them.

[0196] The system in this invention is an automated contract management system that takes into account the user's emotional state. This system is mainly composed of an input means, a generation device, an emotion recognition device, an analysis device, a presentation device, and a dialogue device.

[0197] The user enters the necessary information for the contract using a terminal. This information is received via the input device and sent to the server. The server uses a generation device to automatically generate a document based on the received information. In this process, advanced natural language processing technology, known as a generation AI model, is utilized to generate a high-quality contract document.

[0198] The generated document is displayed to the user via the terminal, and simultaneously, an emotion recognition device analyzes the user's input behavior and dialogue history to recognize their emotional state. For example, the emotion recognition device can detect anxiety or questions the user may have while viewing the document. Based on this recognition information, the server dynamically adjusts how the document and risk information are presented, providing the information in a way that is easy for the user to understand.

[0199] Furthermore, the server's analysis system uses natural language processing technology to analyze the generated documents and compare them with risk patterns to identify potential risks and deficiencies. This detected information is displayed to the user via a terminal using a presentation device. To deepen the user's understanding and reduce anxiety, the presentation method is also adjusted according to their emotional state.

[0200] Furthermore, users can ask legal questions from their terminals, and the server's dialogue system uses a generative AI model to generate appropriate answers. The details and tone of the answers are adjusted according to the user's emotional state, ensuring they are easily understood.

[0201] As a concrete example, a prompt might be used in the form of, "Please briefly explain the key points of this contract. Please help alleviate any concerns by presenting it in a way that is easy for the user to understand."

[0202] In this way, the system provides flexible functions that comprehensively support everything from generating contract documents to presenting risk information and handling legal consultations, all in order to improve the user experience.

[0203] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0204] Step 1:

[0205] The user enters the necessary information for the contract into the terminal. This information includes the type, terms, and duration of the contract. This information is transmitted to the server via the terminal as basic data for generating the contract document.

[0206] Step 2:

[0207] The server receives the input contract information and automatically generates the document using a generation device. Utilizing a generation AI model, it converts the input information into contract text through natural language processing techniques. The output is a highly structured contract document.

[0208] Step 3:

[0209] The terminal displays the generated contract document to the user. Simultaneously, an emotion recognition device analyzes the user's emotional state based on terminal operations and input data. Inputs include the user's dialogue history and document viewing behavior, while output is information about the user's emotional state.

[0210] Step 4:

[0211] The server adjusts how the generated contract document is presented based on the user's emotional state. Specifically, if the user's emotions indicate anxiety or doubt, the server highlights important sections of the document or adds supplementary explanations. The resulting output is an adjusted document.

[0212] Step 5:

[0213] The server's analysis system analyzes the generated documents using natural language processing technology. The input is the generated document, which is then compared against known risk patterns to identify potential risks and deficiencies. This process yields risk information that should be provided to the user.

[0214] Step 6:

[0215] The detected risks and deficiencies are presented to the user via the terminal. The presentation device adjusts the content to ensure the user can understand it with confidence. In this process, emotion recognition information is utilized to add appropriate supplementary explanations regarding the risks.

[0216] Step 7:

[0217] The user asks legal questions from their terminal. In response, the server's dialogue system uses a generative AI model to generate appropriate answers. The content and level of detail of the answers are adjusted according to the user's emotions, and then communicated to the user as output.

[0218] Step 8:

[0219] Ultimately, the user reviews the contract and related information and takes any necessary additional actions. Based on the information provided by the system, the user can make decisions with confidence. This completes the contract process.

[0220] (Application Example 2)

[0221] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0222] In contract negotiations, users may feel anxious if they do not fully understand the contract terms. Furthermore, if potential risks or deficiencies exist in the contract document, users may proceed without noticing them. Additionally, while prompt and appropriate answers are required for legal questions, there is a lack of flexible responses that take into account the emotional state of the user. Improving this situation and building a system that allows users to enter into contracts with confidence is a key challenge.

[0223] 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.

[0224] In this invention, the server includes a generation function means for automatically generating a contract document based on contract terms received from an input device, an analysis function means for detecting risks and deficiencies contained in the contract document, and an emotion analysis function means for analyzing the user's emotions using emotion recognition technology and adjusting explanations according to the user's emotions. This makes it possible to automatically generate a contract document while taking into account the user's emotional state, and to detect risks and deficiencies and provide optimized legal support.

[0225] An "input device" refers to a terminal or equipment used by a user to input contract terms and questions.

[0226] "Contract terms" refer to the basic information and items that determine the content of a contract.

[0227] The "generation function" is a function that automatically creates contract documents based on the entered contract terms.

[0228] The "analysis function" is a function that analyzes contract documents to detect risks and deficiencies contained within them.

[0229] The "display function" is a feature that clearly displays detected risks and defects to the user.

[0230] The "dialogue function" is a feature that generates appropriate answers to legal questions from users and provides them in a conversational format.

[0231] The "emotion analysis function" is a feature that analyzes the user's emotions from their facial expressions and tone of voice, and adjusts the content and presentation method of the explanation according to the user's emotions.

[0232] "Language processing technology" is a general term for technologies used to process, analyze, and generate natural language.

[0233] This invention is a system that streamlines user contract management and takes emotional states into consideration. The system operates by combining an input device, a server, and emotion recognition technology.

[0234] The user submits the contract terms to the server via an input device. The server automatically generates a contract document based on the contract terms using a generative AI model and natural language processing technology. The generative AI model used is OpenAI's GPT-3®, ensuring natural document generation. In this process, the server selects the most suitable contract template based on the information provided by the user and generates a document that matches the conditions.

[0235] Next, the server analyzes the generated contract document and detects risk patterns and deficiencies using its analysis function. Here, the spaCy library is used as a language processing technique to compare it with known risk patterns. As a result, potential risks are identified and presented to the user.

[0236] Furthermore, the emotion analysis function utilizes emotion recognition technology based on data from the device's camera and microphone to understand the user's emotions. This uses Microsoft® Azure® Cognitive Services to extract emotional information from the user's facial expressions and voice. Depending on the user's emotional state, it provides supplementary explanations for difficult parts of the contract document, making it easier to understand.

[0237] When a user asks a legal question, the dialogue function is enabled, and appropriate answers are generated in real time using Google Dialogflow. These answers are tailored to the user's emotional state and presented in a more easily understandable format.

[0238] A concrete example is a scenario where a robot explains the important details of a rental agreement to a user. If the user expresses anxiety, the robot will gently and clearly explain the key points of the agreement, supporting the user so that they can proceed with the contract with confidence. The AI ​​model then responds based on the following example prompts:

[0239] "Users are expressing concerns about rental agreements. Please explain the important points of the contract in a safe and easy-to-understand manner, and reassure users."

[0240] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0241] Step 1:

[0242] The user enters the contract terms using an input device. This transmits the basic contract terms as data from the input device to the server. The server receives this data and prepares to generate the contract document.

[0243] Step 2:

[0244] The server uses a generative AI model to automatically generate contract documents based on the received contract terms. Specifically, it uses OpenAI GPT-3 to select a contract template that matches the conditions and creates the document. In this process, the input conditions are processed and output as a natural-sounding document.

[0245] Step 3:

[0246] The server analyzes the generated contract documents using natural language processing techniques. It utilizes the spaCy library to scan the documents and compare them against known risk patterns. This identifies potential risks and deficiencies within the contract documents, and risk information is output.

[0247] Step 4:

[0248] The server uses sentiment analysis capabilities to understand the user's emotions. Video and audio data acquired through the device's camera and microphone are analyzed using Microsoft Azure Cognitive Services to determine the user's emotional state. Based on the resulting emotional information, the server adjusts the presentation of contract documents.

[0249] Step 5:

[0250] If a user experiences anxiety or has questions while reviewing a contract document, the server, based on emotional information, will add detailed explanations to the difficult parts of the document. It will highlight and present particularly important points within the contract document in an easy-to-understand manner. This ensures that users receive information in a way that is easy to comprehend.

[0251] Step 6:

[0252] When a user enters a legal question, the server generates an appropriate answer via Google Dialogflow. This dialogue process adjusts the content and explanation of the answer based on the user's emotional state, and provides real-time responses.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] [Second Embodiment]

[0257] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0258] 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.

[0259] 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).

[0260] 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.

[0261] 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.

[0262] 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).

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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".

[0269] This invention realizes a system that efficiently automates contract work. Specific embodiments of the system are shown below.

[0270] First, the user needs to enter the contract terms. The user uses a terminal to enter the basic contract details (e.g., subscriber name, contract period, and fee). This input method provides the necessary information to the system.

[0271] The server uses the received input information to utilize generation methods and automatically generates a draft of the contract document. This process recognizes different contract templates, uses the appropriate template based on pre-programmed rules, and incorporates the entered conditions.

[0272] The generated contract document is displayed to the user via the terminal. The user can review the document and make further edits if necessary.

[0273] Next, a risk analysis process takes place. The server analyzes the text of the contract document and uses analytical tools to identify potential risks and deficiencies. In this process, natural language processing technology is utilized to detect risks based on known risk patterns.

[0274] The analysis results are presented to the user via the device. The information presented includes details of risk areas and recommended countermeasures. The user can then adjust the contract terms based on this information.

[0275] Furthermore, a dialogue function is provided to answer legal questions. When a user enters a legal question on their terminal, the server processes the question using the dialogue mechanism and generates an appropriate answer based on the relevant information. The answer is displayed immediately on the terminal, allowing the user to obtain the necessary information instantly.

[0276] As a concrete example, consider a construction contract for a new base station. The user inputs basic conditions such as construction period, cost, and scope of responsibility. The server automatically generates a contract based on this information and displays it on the terminal. Meanwhile, the server can analyze the generated contract document and detect, for example, the risk of delivery delays or deficiencies regarding legal obligations. This allows the user to identify risks early and revise or negotiate the contract as needed.

[0277] The following describes the processing flow.

[0278] Step 1:

[0279] The user accesses an input form that describes the contract terms and inputs necessary basic information such as the contractor name, contract period, amount, etc. into the terminal.

[0280] Step 2:

[0281] The terminal sends the input contract terms to the server and requests the generation of a contract document.

[0282] Step 3:

[0283] The server selects an appropriate contract template and automatically generates a contract document by utilizing the generation means based on the received contract terms. At this time, the variables in the template are replaced according to the input information.

[0284] Step 4:

[0285] The terminal displays the generated contract document sent from the server to the user so that the user can check the content.

[0286] Step 5:

[0287] The user checks the displayed contract document and can make corrections on the terminal if necessary. Approval is also possible at this stage.

[0288] Step 6:

[0289] The user performs an operation on the terminal to request a risk analysis of the contract document.

[0290] Step 7:

[0291] The terminal sends a request for risk analysis to the server.

[0292] Step 8:

[0293] The server analyzes the text of the contract documents generated using analytical tools and employs natural language processing techniques to detect potential risks and deficiencies. Historical data and pattern recognition techniques are used to identify areas of deficiency and risk.

[0294] Step 9:

[0295] The server sends results to the terminal that detail the detected risks and deficiencies.

[0296] Step 10:

[0297] The terminal displays the analysis results in an easy-to-understand format for the user, and shows details of countermeasures and risks.

[0298] Step 11:

[0299] The user will review the presented risks and deficiencies and readjust the contract terms. In some cases, the contract terms may be redefined.

[0300] Step 12:

[0301] Users enter legal questions regarding the contract through the chat function on their device.

[0302] Step 13:

[0303] The device sends the user's question to the server and requests that it receive an appropriate answer.

[0304] Step 14:

[0305] The server analyzes the question using dialogue methods and collects information necessary for the generating AI to produce an appropriate answer. Based on this information, it creates an answer to the question.

[0306] Step 15:

[0307] The server sends the generated response to the terminal.

[0308] Step 16:

[0309] The terminal displays the transmitted answer to the user and allows the user to re-enter if there are additional questions.

[0310] By this series of steps, efficient automation of contract operations and legal support are achieved.

[0311] (Example 1)

[0312] Next, Example 1 will be described.Hereinafter, in the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as "terminals".

[0313] In contract operations, manual processes that require a lot of time and effort are involved, resulting in low efficiency and the possibility of overlooking risks. In addition, it is difficult to quickly and accurately answer legal questions regarding contracts. These issues may lead to delays in operations and inaccurate contract conclusion.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0315] In this invention, the server includes a generation function means for automatically generating a contract document based on contract conditions obtained from an input function, an analysis function means for identifying risks and deficiencies included in the contract document, and an interaction function means for receiving legal questions and creating appropriate responses. Thereby, efficient automation of contract operations, early detection of risks, and prompt response to legal questions become possible.

[0316] The "input function" is a means for obtaining contract conditions from a user.

[0317] The "generation function" is a means for automatically generating a contract document based on the obtained contract conditions.

[0318] "Analysis function" refers to a means of identifying risks and deficiencies contained in contract documents.

[0319] A "display function" is a means of displaying identified risks and deficiencies to the user.

[0320] A "dialogue function" is a means of receiving legal questions and formulating appropriate responses to them.

[0321] An "information processing system" is a system that combines input, generation, analysis, display, and dialogue functions to efficiently automate contract work, enabling early detection of risks and prompt responses to legal questions.

[0322] This system utilizes specific hardware and software to efficiently automate contract management. Its specific form is described below.

[0323] The user enters basic information for creating a contract into the terminal. The terminal facilitates user operation by providing an intuitive interface for information input. This input information is essential as the basic data for generating contract documents in the system.

[0324] The server automatically generates contract documents using a generative AI model based on the entered contract terms. Specifically, it compares the acquired terms with several pre-prepared contract templates, selects the most appropriate template, and creates the document in natural language. The generative AI model customizes the contract documents based on templates appropriate for different contract types.

[0325] The generated contract document is displayed to the user via a terminal. The user can review and edit the contract document on the screen and make further adjustments as needed.

[0326] Next, the server uses natural language processing technology to analyze the contract document and identify potential risks and deficiencies. By comparing the text with known risk patterns, problems are automatically extracted. The analysis results are presented to the user via the terminal, allowing the user to consider the nature of the risks and recommended countermeasures.

[0327] Furthermore, when a user enters a legal question, the server uses its conversational function to generate an appropriate answer to the user's question. The generated answer is displayed on the terminal in real time, enabling quick information retrieval.

[0328] As a concrete example, consider a construction contract for a new base station. When a user inputs conditions such as construction period, cost, and scope of responsibility, the server generates a contract based on this information and displays it on the terminal. At that time, the server analyzes the generated contract to detect risks of delivery delays and deficiencies in legal obligations. This allows the user to obtain appropriate information for risk assessment and contract negotiation.

[0329] Example of a prompt:

[0330] "We are considering a construction contract for a new base station. Please prepare a contract with a construction period of 12 months, a total cost of 50 million yen, and the scope of responsibility being solely your company's. Also, please outline the potential risks and provide appropriate advice."

[0331] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0332] Step 1:

[0333] The user enters the contract terms through an input interface on their terminal. This information includes the subscriber's name, contract period, and fees. This information is received by the server and used as basic data for generating contract documents.

[0334] Step 2:

[0335] The server automatically generates contract documents using a generative AI model based on the received contract terms. First, it compares the acquired terms with various pre-prepared contract templates. It selects a template that matches the terms and generates a contract document in natural language. The output is a customized contract document based on the terms.

[0336] Step 3:

[0337] The terminal displays the generated contract document to the user. A screen is provided where the user can review the contract details and edit them manually as needed. The user's edits are then sent back to the server and saved as the final version of the contract document.

[0338] Step 4:

[0339] The server analyzes the final version of the contract document to identify potential risks and deficiencies. This process uses natural language processing techniques to compare the contract document with known risk patterns. As a result of the analysis, detailed risk information and recommended countermeasures are extracted.

[0340] Step 5:

[0341] The terminal displays analysis results from the server to the user. Risk information is displayed in a visually easy-to-understand format, allowing the user to consider adjusting or negotiating contract terms based on this information. This information provision enables users to grasp contract-related risks at an early stage.

[0342] Step 6:

[0343] When a user enters a legal question from their terminal, the server processes the question using a conversational function. Specifically, it analyzes the entered question and generates an appropriate answer using a generative AI model. The generated answer is immediately output to the terminal, allowing the user to quickly obtain the necessary legal information.

[0344] (Application Example 1)

[0345] 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."

[0346] In automating contract management, inefficiencies and inaccuracies exist, particularly in processes ranging from inputting transaction terms to generating documents, detecting risks, and responding to legal inquiries. This can make it difficult to quickly conclude contracts and properly identify potential risks.

[0347] 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.

[0348] In this invention, the server includes a generation mechanism for automatically generating documents based on transaction conditions received from an input device, an analysis mechanism for detecting risks and defects contained in the documents, and a display mechanism for presenting the detected risks and defects to the user. This enables increased efficiency in contract operations and optimized risk management.

[0349] An "input device" is a device that allows a user to input transaction conditions and provide those conditions to the system.

[0350] A "generation mechanism" is a means of automatically creating contract-related documents based on transaction terms received from an input device.

[0351] An "analysis mechanism" is a means of analyzing generated documents using natural language processing technology and comparing them with known risk patterns to identify risks and defects.

[0352] A "display mechanism" is a means of visually presenting to the user any dangers or defects detected by the analysis mechanism.

[0353] A "dialogue mechanism" is a means of generating and providing appropriate responses to questions users have about legal issues.

[0354] An "interface" is a means of displaying transaction-related information on a user's terminal device, enabling the user to interact with the system.

[0355] The system implementing this invention begins with the user inputting contract terms. The user inputs the terms of the transaction using an input device such as a smartphone or computer. This information is transmitted to a server via the internet. The server, through a generation mechanism for automatically generating contracts, selects an appropriate contract template based on this information and generates the necessary contract document.

[0356] The generated contract documents are analyzed by an analysis mechanism using natural language processing techniques, and risks and defects are identified by comparing them against known risk patterns. Software such as the Google NLP API is used in this process. The detected risks and defects are provided to the user through a display mechanism, prompting appropriate risk-based action.

[0357] Furthermore, if the user has legal-related questions, they send these questions to the server via a dialogue mechanism. The server generates an appropriate response to the question using a pre-trained legal database and provides it to the user. This function utilizes a generative AI model.

[0358] As a concrete example, consider the process when a user enters into a new payment service agreement on an e-commerce site. Once the user enters the contract terms, the server automatically generates the contract, performs a risk analysis, and provides a risk warning based on the results. For example, a warning such as "These payment terms carry a risk of delivery delays" might be displayed.

[0359] An example of a prompt message to input into a generative AI model is as follows:

[0360] "Based on the contract terms entered by the user, automatically generate appropriate contract documents, detect deficiencies and risks using natural language processing, and then generate advice to clearly communicate the results to the user."

[0361] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0362] Step 1:

[0363] The user enters the transaction terms using an input device. The entered data includes the trader's name, duration, and fee. This data is transmitted to the server via a communication network.

[0364] Step 2:

[0365] The server generates a contract document using a generation mechanism based on the received transaction terms. The template selected here is automatically determined based on the received data to be the most suitable one. The generated document becomes the output, and the process proceeds to the next step.

[0366] Step 3:

[0367] The server feeds the generated contract documents into the analysis mechanism and applies natural language processing technology. The documents are analyzed as input, and risks are identified by comparing them with known risk patterns. The analysis results list the detected risks and deficiencies, which are then presented as output in the next step.

[0368] Step 4:

[0369] The server transmits the analysis results to the terminal via a display mechanism, visually presenting the user with detailed risk information. The user reviews the outputted risk information and makes any necessary adjustments.

[0370] Step 5:

[0371] The user sends legal-related questions to the server via an input device and a dialogue mechanism. The questions are sent to the server as input.

[0372] Step 6:

[0373] The server uses a generative AI model to generate appropriate responses to input questions. Pre-trained legal data is used for response generation, and the output is the answer.

[0374] Step 7:

[0375] The server sends the generated response to the terminal, presenting it in a format easily understandable to the user. This allows the user to receive legal advice immediately.

[0376] 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.

[0377] This invention is a contract automation system that combines an emotion engine. It recognizes the user's emotional state and utilizes this information for contract document generation, risk detection, and legal support, thereby improving the user experience. The system is configured and performs the following specific operations.

[0378] First, the user enters the basic terms and conditions of the contract into the terminal. This information is sent to the server via the terminal, and the server automatically generates a contract document using a generation mechanism. The contract document is displayed to the user, and the emotion engine simultaneously analyzes the user's input data and dialogue history to recognize their emotional state.

[0379] If the user's emotions indicate anxiety or doubt, the server adjusts how the generated contract documents and risk information are presented. Specifically, it can provide more detailed explanations or highlight key risk points. This information is then appropriately presented to the user via the terminal.

[0380] Next, risks and deficiencies in the contract document are detected. The server analyzes the contract document using analytical tools, matching it with known risk patterns using natural language processing technology to identify potential risks and deficiencies. By utilizing an emotion engine, it is possible to dynamically change how the analysis results are presented and adjust the feedback to ensure the user feels reassured.

[0381] Furthermore, users can ask legal questions about the contract through the device's chat function. The server responds to these questions and generates appropriate answers using dialogue tools. The sentiment engine optimizes the level of detail and explanation in the answers according to the user's emotional state. The answers are provided to the user in real time to aid their understanding.

[0382] For example, if the emotion engine detects a stress response while a user is reviewing a contract, the server can provide further explanations of complex parts of the contract document or add explanations in more accessible language. This allows the user to understand the contract with confidence and take appropriate action quickly as needed.

[0383] Thus, the present invention not only highly automates contract work but also provides a flexible system that takes into account the user's emotional state by integrating emotion recognition technology.

[0384] The following describes the processing flow.

[0385] Step 1:

[0386] The user uses a terminal to input the basic conditions required for the contract and prepares the information needed to generate the contract.

[0387] Step 2:

[0388] The terminal sends the user's input data to the server and requests the automatic generation of the contract.

[0389] Step 3:

[0390] The server selects a contract template based on the received data and automatically generates the contract document using a generation mechanism. In this process, it replaces variables within the template based on the input information.

[0391] Step 4:

[0392] The server sends the generated contract document to the terminal and simultaneously passes the necessary input data to the emotion engine to recognize the user's emotional state.

[0393] Step 5:

[0394] The terminal displays contract documents to the user and adjusts the way the documents are presented based on the results of the emotion engine's recognition of the user's emotional state.

[0395] Step 6:

[0396] The user reviews the contract documents and proceeds with revisions and approvals as needed. At this point, if the emotion engine detects feelings of anxiety or doubt, more detailed explanations and highlighted information are provided.

[0397] Step 7:

[0398] The user enters information on the terminal to request a risk analysis based on the contract.

[0399] Step 8:

[0400] The terminal sends a risk analysis request to the server, initiating the specific risk detection process.

[0401] Step 9:

[0402] The server analyzes the contract document using analytical tools and identifies known risks and deficiencies using natural language processing technology. At the same time, it prepares feedback that corresponds to the user's emotions based on the results of the emotion engine.

[0403] Step 10:

[0404] The server sends the results of the risk and deficiency analysis to the terminal and delivers the results to the user in a presentation method that is appropriate to their emotional state.

[0405] Step 11:

[0406] The device informs the user of the details of risks and defects, providing the information in an easy-to-understand format. Furthermore, if the sentiment engine determines that additional explanation is needed, it will display the appropriate information.

[0407] Step 12:

[0408] Users can request legal support by entering legal questions related to the contract using the chat function on their device.

[0409] Step 13:

[0410] The terminal sends the received question to the server and prepares to receive the optimal answer through the dialogue mechanism.

[0411] Step 14:

[0412] The server processes user questions using dialogue mechanisms and generates appropriate answers. The level of detail in the answers is adjusted based on the results of the sentiment engine.

[0413] Step 15:

[0414] The server sends the generated response to the terminal, providing the user with information in real time.

[0415] Step 16:

[0416] The device displays answers to questions to the user and continues to accept any further questions. During this process, the explanation is dynamically optimized based on the evaluation of the emotion engine.

[0417] This series of steps allows users to receive a reassuring contract service that utilizes emotion recognition technology.

[0418] (Example 2)

[0419] 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".

[0420] Traditional contract management systems have focused on automated document generation and risk identification, but have not addressed the emotional state of users. As a result, users may not fully understand the contract terms, leading to anxiety and delays in contract signing. The challenge lies in streamlining the contract process while providing flexible responses that accommodate users' emotional states.

[0421] 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.

[0422] In this invention, the server includes a generation device means for automatically generating documents based on contract terms received from an input means, a means for recognizing the user's emotional state using an emotion recognition device and adjusting the presentation method of documents and information according to that state, and an analysis device means for using natural language processing technology to identify potential risks and deficiencies contained in the documents. This makes it possible to streamline contract work and clarify risks in accordance with the user's emotions.

[0423] An "input means" is a device or interface that allows a user to provide information such as contract terms to the system.

[0424] A "generating device" is a device or program that has the function of automatically creating a document based on the received contract terms.

[0425] An "emotion recognition device" is a device or software that has the technology to identify the user's emotional state by analyzing the user's input and dialogue history.

[0426] An "analytical device" is a device or program that uses natural language processing technology to analyze documents and compare them with known risk patterns to identify potential risks and deficiencies.

[0427] A "display device" is a device or interface that clearly displays detected risks or defects to the user.

[0428] A "dialogue device" is a device or system that generates appropriate answers to legal questions from users and enables natural interaction with them.

[0429] The system in this invention is an automated contract management system that takes into account the user's emotional state. This system is mainly composed of an input means, a generation device, an emotion recognition device, an analysis device, a presentation device, and a dialogue device.

[0430] The user enters the necessary information for the contract using a terminal. This information is received via the input device and sent to the server. The server uses a generation device to automatically generate a document based on the received information. In this process, advanced natural language processing technology, known as a generation AI model, is utilized to generate a high-quality contract document.

[0431] The generated document is displayed to the user via the terminal, and simultaneously, an emotion recognition device analyzes the user's input behavior and dialogue history to recognize their emotional state. For example, the emotion recognition device can detect anxiety or questions the user may have while viewing the document. Based on this recognition information, the server dynamically adjusts how the document and risk information are presented, providing the information in a way that is easy for the user to understand.

[0432] Furthermore, the server's analysis system uses natural language processing technology to analyze the generated documents and compare them with risk patterns to identify potential risks and deficiencies. This detected information is displayed to the user via a terminal using a presentation device. To deepen the user's understanding and reduce anxiety, the presentation method is also adjusted according to their emotional state.

[0433] Furthermore, users can ask legal questions from their terminals, and the server's dialogue system uses a generative AI model to generate appropriate answers. The details and tone of the answers are adjusted according to the user's emotional state, ensuring they are easily understood.

[0434] As a concrete example, a prompt might be used in the form of, "Please briefly explain the key points of this contract. Please help alleviate any concerns by presenting it in a way that is easy for the user to understand."

[0435] In this way, the system provides flexible functions that comprehensively support everything from generating contract documents to presenting risk information and handling legal consultations, all in order to improve the user experience.

[0436] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0437] Step 1:

[0438] The user enters the necessary information for the contract into the terminal. This information includes the type, terms, and duration of the contract. This information is transmitted to the server via the terminal as basic data for generating the contract document.

[0439] Step 2:

[0440] The server receives the input contract information and automatically generates the document using a generation device. Utilizing a generation AI model, it converts the input information into contract text through natural language processing techniques. The output is a highly structured contract document.

[0441] Step 3:

[0442] The terminal displays the generated contract document to the user. Simultaneously, an emotion recognition device analyzes the user's emotional state based on terminal operations and input data. Inputs include the user's dialogue history and document viewing behavior, while output is information about the user's emotional state.

[0443] Step 4:

[0444] The server adjusts how the generated contract document is presented based on the user's emotional state. Specifically, if the user's emotions indicate anxiety or doubt, the server highlights important sections of the document or adds supplementary explanations. The resulting output is an adjusted document.

[0445] Step 5:

[0446] The server's analysis system analyzes the generated documents using natural language processing technology. The input is the generated document, which is then compared against known risk patterns to identify potential risks and deficiencies. This process yields risk information that should be provided to the user.

[0447] Step 6:

[0448] The detected risks and deficiencies are presented to the user via the terminal. The presentation device adjusts the content to ensure the user can understand it with confidence. In this process, emotion recognition information is utilized to add appropriate supplementary explanations regarding the risks.

[0449] Step 7:

[0450] The user asks legal questions from their terminal. In response, the server's dialogue system uses a generative AI model to generate appropriate answers. The content and level of detail of the answers are adjusted according to the user's emotions, and then communicated to the user as output.

[0451] Step 8:

[0452] Ultimately, the user reviews the contract and related information and takes any necessary additional actions. Based on the information provided by the system, the user can make decisions with confidence. This completes the contract process.

[0453] (Application Example 2)

[0454] 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 as the "terminal".

[0455] In contract negotiations, users may feel anxious if they do not fully understand the contract terms. Furthermore, if potential risks or deficiencies exist in the contract document, users may proceed without noticing them. Additionally, while prompt and appropriate answers are required for legal questions, there is a lack of flexible responses that take into account the emotional state of the user. Improving this situation and building a system that allows users to enter into contracts with confidence is a key challenge.

[0456] 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.

[0457] In this invention, the server includes a generation function means for automatically generating a contract document based on contract terms received from an input device, an analysis function means for detecting risks and deficiencies contained in the contract document, and an emotion analysis function means for analyzing the user's emotions using emotion recognition technology and adjusting explanations according to the user's emotions. This makes it possible to automatically generate a contract document while taking into account the user's emotional state, and to detect risks and deficiencies and provide optimized legal support.

[0458] An "input device" refers to a terminal or equipment used by a user to input contract terms and questions.

[0459] "Contract terms" refer to the basic information and items that determine the content of a contract.

[0460] The "generation function" is a function that automatically creates contract documents based on the entered contract terms.

[0461] The "analysis function" is a function that analyzes contract documents to detect risks and deficiencies contained within them.

[0462] The "display function" is a feature that clearly displays detected risks and defects to the user.

[0463] The "dialogue function" is a feature that generates appropriate answers to legal questions from users and provides them in a conversational format.

[0464] The "emotion analysis function" is a feature that analyzes the user's emotions from their facial expressions and tone of voice, and adjusts the content and presentation method of the explanation according to the user's emotions.

[0465] "Language processing technology" is a general term for technologies used to process, analyze, and generate natural language.

[0466] This invention is a system that streamlines user contract management and takes emotional states into consideration. The system operates by combining an input device, a server, and emotion recognition technology.

[0467] The user submits the contract terms to the server via an input device. The server automatically generates a contract document based on the contract terms using a generative AI model and natural language processing technology. OpenAI's GPT-3 is used for the generative AI model, resulting in natural-sounding document generation. In this process, the server selects the most suitable contract template based on the information provided by the user and generates a document that matches the conditions.

[0468] Next, the server analyzes the generated contract document and detects risk patterns and deficiencies using its analysis function. Here, the spaCy library is used as a language processing technique to compare it with known risk patterns. As a result, potential risks are identified and presented to the user.

[0469] Furthermore, the emotion analysis function utilizes emotion recognition technology based on data from the device's camera and microphone to understand the user's emotions. This uses Microsoft Azure Cognitive Services to extract emotional information from the user's facial expressions and voice. Depending on the user's emotional state, it provides supplementary explanations for difficult parts of the contract document, making it easier to understand.

[0470] When a user asks a legal question, the dialogue function is enabled, and appropriate answers are generated in real time using Google Dialogflow. These answers are tailored to the user's emotional state and presented in a more easily understandable format.

[0471] A concrete example is a scenario where a robot explains the important details of a rental agreement to a user. If the user expresses anxiety, the robot will gently and clearly explain the key points of the agreement, supporting the user so that they can proceed with the contract with confidence. The AI ​​model then responds based on the following example prompts:

[0472] "Users are expressing concerns about rental agreements. Please explain the important points of the contract in a safe and easy-to-understand manner, and reassure users."

[0473] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0474] Step 1:

[0475] The user enters the contract terms using an input device. This transmits the basic contract terms as data from the input device to the server. The server receives this data and prepares to generate the contract document.

[0476] Step 2:

[0477] The server uses a generative AI model to automatically generate contract documents based on the received contract terms. Specifically, it uses OpenAI GPT-3 to select a contract template that matches the conditions and creates the document. In this process, the input conditions are processed and output as a natural-sounding document.

[0478] Step 3:

[0479] The server analyzes the generated contract documents using natural language processing techniques. It utilizes the spaCy library to scan the documents and compare them against known risk patterns. This identifies potential risks and deficiencies within the contract documents, and risk information is output.

[0480] Step 4:

[0481] The server uses sentiment analysis capabilities to understand the user's emotions. Video and audio data acquired through the device's camera and microphone are analyzed using Microsoft Azure Cognitive Services to determine the user's emotional state. Based on the resulting emotional information, the server adjusts the presentation of contract documents.

[0482] Step 5:

[0483] If a user experiences anxiety or has questions while reviewing a contract document, the server, based on emotional information, will add detailed explanations to the difficult parts of the document. It will highlight and present particularly important points within the contract document in an easy-to-understand manner. This ensures that users receive information in a way that is easy to comprehend.

[0484] Step 6:

[0485] When a user enters a legal question, the server generates an appropriate answer via Google Dialogflow. This dialogue process adjusts the content and explanation of the answer based on the user's emotional state, and provides real-time responses.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] [Third Embodiment]

[0490] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0491] 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.

[0492] 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).

[0493] 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.

[0494] 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.

[0495] 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).

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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".

[0502] This invention realizes a system that efficiently automates contract work. Specific embodiments of the system are shown below.

[0503] First, the user needs to enter the contract terms. The user uses a terminal to enter the basic contract details (e.g., subscriber name, contract period, and fee). This input method provides the necessary information to the system.

[0504] The server uses the received input information to utilize generation methods and automatically generates a draft of the contract document. This process recognizes different contract templates, uses the appropriate template based on pre-programmed rules, and incorporates the entered conditions.

[0505] The generated contract document is displayed to the user via the terminal. The user can review the document and make further edits if necessary.

[0506] Next, a risk analysis process takes place. The server analyzes the text of the contract document and uses analytical tools to identify potential risks and deficiencies. In this process, natural language processing technology is utilized to detect risks based on known risk patterns.

[0507] The analysis results are presented to the user via the device. The information presented includes details of risk areas and recommended countermeasures. The user can then adjust the contract terms based on this information.

[0508] Furthermore, a dialogue function is provided to answer legal questions. When a user enters a legal question on their terminal, the server processes the question using the dialogue mechanism and generates an appropriate answer based on the relevant information. The answer is displayed immediately on the terminal, allowing the user to obtain the necessary information instantly.

[0509] As a concrete example, consider a construction contract for a new base station. The user inputs basic conditions such as construction period, cost, and scope of responsibility. The server automatically generates a contract based on this information and displays it on the terminal. Meanwhile, the server can analyze the generated contract document and detect, for example, the risk of delivery delays or deficiencies regarding legal obligations. This allows the user to identify risks early and revise or negotiate the contract as needed.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The user accesses an input form containing the contract terms and enters necessary basic information such as the contract holder's name, contract period, and amount into their device.

[0513] Step 2:

[0514] The terminal sends the entered contract terms to the server and requests the generation of the contract.

[0515] Step 3:

[0516] The server uses a generation mechanism to select an appropriate contract template based on the received contract terms and automatically generates the contract document. During this process, variables within the template are replaced according to the input information.

[0517] Step 4:

[0518] The terminal displays the generated contract document sent from the server to the user, allowing the user to review its contents.

[0519] Step 5:

[0520] Users can review the displayed contract document and make any necessary modifications on their device. Approval is also possible at this stage.

[0521] Step 6:

[0522] The user performs an action on their terminal to request a risk analysis of the contract document.

[0523] Step 7:

[0524] The terminal sends a risk analysis request to the server.

[0525] Step 8:

[0526] The server analyzes the text of the contract documents generated using analytical tools and employs natural language processing techniques to detect potential risks and deficiencies. Historical data and pattern recognition techniques are used to identify areas of deficiency and risk.

[0527] Step 9:

[0528] The server sends results to the terminal that detail the detected risks and deficiencies.

[0529] Step 10:

[0530] The terminal displays the analysis results in an easy-to-understand format for the user, and shows details of countermeasures and risks.

[0531] Step 11:

[0532] The user will review the presented risks and deficiencies and readjust the contract terms. In some cases, the contract terms may be redefined.

[0533] Step 12:

[0534] Users enter legal questions regarding the contract through the chat function on their device.

[0535] Step 13:

[0536] The device sends the user's question to the server and requests that it receive an appropriate answer.

[0537] Step 14:

[0538] The server analyzes the question using dialogue methods and collects information necessary for the generating AI to produce an appropriate answer. Based on this information, it creates an answer to the question.

[0539] Step 15:

[0540] The server sends the generated response to the terminal.

[0541] Step 16:

[0542] The terminal displays the submitted answers to the user and allows them to enter additional questions if needed.

[0543] This series of steps enables efficient automation of contract processes and provides legal support.

[0544] (Example 1)

[0545] 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."

[0546] Contract work often involves time-consuming and labor-intensive manual processes, making it inefficient and potentially leading to overlooked risks. Furthermore, answering legal questions about contracts quickly and accurately can be challenging. These challenges can result in delays and inaccurate contract execution.

[0547] 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.

[0548] In this invention, the server includes a generation function means for automatically generating contract documents based on contract terms obtained from an input function, an analysis function means for identifying risks and deficiencies contained in the contract documents, and a dialogue function means for receiving legal questions and creating appropriate responses. This enables efficient automation of contract operations, early detection of risks, and rapid response to legal questions.

[0549] The "input function" is a means of obtaining contract terms from the user.

[0550] The "generation function" is a means of automatically generating contract documents based on the acquired contract terms.

[0551] "Analysis function" refers to a means of identifying risks and deficiencies contained in contract documents.

[0552] A "display function" is a means of displaying identified risks and deficiencies to the user.

[0553] A "dialogue function" is a means of receiving legal questions and formulating appropriate responses to them.

[0554] An "information processing system" is a system that combines input, generation, analysis, display, and dialogue functions to efficiently automate contract work, enabling early detection of risks and prompt responses to legal questions.

[0555] This system utilizes specific hardware and software to efficiently automate contract management. Its specific form is described below.

[0556] The user enters basic information for creating a contract into the terminal. The terminal facilitates user operation by providing an intuitive interface for information input. This input information is essential as the basic data for generating contract documents in the system.

[0557] The server automatically generates contract documents using a generative AI model based on the entered contract terms. Specifically, it compares the acquired terms with several pre-prepared contract templates, selects the most appropriate template, and creates the document in natural language. The generative AI model customizes the contract documents based on templates appropriate for different contract types.

[0558] The generated contract document is displayed to the user via a terminal. The user can review and edit the contract document on the screen and make further adjustments as needed.

[0559] Next, the server uses natural language processing technology to analyze the contract document and identify potential risks and deficiencies. By comparing the text with known risk patterns, problems are automatically extracted. The analysis results are presented to the user via the terminal, allowing the user to consider the nature of the risks and recommended countermeasures.

[0560] Furthermore, when a user enters a legal question, the server uses its conversational function to generate an appropriate answer to the user's question. The generated answer is displayed on the terminal in real time, enabling quick information retrieval.

[0561] As a concrete example, consider a construction contract for a new base station. When a user inputs conditions such as construction period, cost, and scope of responsibility, the server generates a contract based on this information and displays it on the terminal. At that time, the server analyzes the generated contract to detect risks of delivery delays and deficiencies in legal obligations. This allows the user to obtain appropriate information for risk assessment and contract negotiation.

[0562] Example of a prompt:

[0563] "We are considering a construction contract for a new base station. Please prepare a contract with a construction period of 12 months, a total cost of 50 million yen, and the scope of responsibility being solely your company's. Also, please outline the potential risks and provide appropriate advice."

[0564] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0565] Step 1:

[0566] The user enters the contract terms through an input interface on their terminal. This information includes the subscriber's name, contract period, and fees. This information is received by the server and used as basic data for generating contract documents.

[0567] Step 2:

[0568] The server automatically generates contract documents using a generative AI model based on the received contract terms. First, it compares the acquired terms with various pre-prepared contract templates. It selects a template that matches the terms and generates a contract document in natural language. The output is a customized contract document based on the terms.

[0569] Step 3:

[0570] The terminal displays the generated contract document to the user. A screen is provided where the user can review the contract details and edit them manually as needed. The user's edits are then sent back to the server and saved as the final version of the contract document.

[0571] Step 4:

[0572] The server analyzes the final version of the contract document to identify potential risks and deficiencies. This process uses natural language processing techniques to compare the contract document with known risk patterns. As a result of the analysis, detailed risk information and recommended countermeasures are extracted.

[0573] Step 5:

[0574] The terminal displays analysis results from the server to the user. Risk information is displayed in a visually easy-to-understand format, allowing the user to consider adjusting or negotiating contract terms based on this information. This information provision enables users to grasp contract-related risks at an early stage.

[0575] Step 6:

[0576] When a user enters a legal question from their terminal, the server processes the question using a conversational function. Specifically, it analyzes the entered question and generates an appropriate answer using a generative AI model. The generated answer is immediately output to the terminal, allowing the user to quickly obtain the necessary legal information.

[0577] (Application Example 1)

[0578] 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."

[0579] In automating contract management, inefficiencies and inaccuracies exist, particularly in processes ranging from inputting transaction terms to generating documents, detecting risks, and responding to legal inquiries. This can make it difficult to quickly conclude contracts and properly identify potential risks.

[0580] 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.

[0581] In this invention, the server includes a generation mechanism for automatically generating documents based on transaction conditions received from an input device, an analysis mechanism for detecting risks and defects contained in the documents, and a display mechanism for presenting the detected risks and defects to the user. This enables increased efficiency in contract operations and optimized risk management.

[0582] An "input device" is a device that allows a user to input transaction conditions and provide those conditions to the system.

[0583] A "generation mechanism" is a means of automatically creating contract-related documents based on transaction terms received from an input device.

[0584] An "analysis mechanism" is a means of analyzing generated documents using natural language processing technology and comparing them with known risk patterns to identify risks and defects.

[0585] A "display mechanism" is a means of visually presenting to the user any dangers or defects detected by the analysis mechanism.

[0586] A "dialogue mechanism" is a means of generating and providing appropriate responses to questions users have about legal issues.

[0587] An "interface" is a means of displaying transaction-related information on a user's terminal device, enabling the user to interact with the system.

[0588] The system implementing this invention begins with the user inputting contract terms. The user inputs the terms of the transaction using an input device such as a smartphone or computer. This information is transmitted to a server via the internet. The server, through a generation mechanism for automatically generating contracts, selects an appropriate contract template based on this information and generates the necessary contract document.

[0589] The generated contract documents are analyzed by an analysis mechanism using natural language processing techniques, and risks and defects are identified by comparing them against known risk patterns. Software such as the Google NLP API is used in this process. The detected risks and defects are provided to the user through a display mechanism, prompting appropriate risk-based action.

[0590] Furthermore, if the user has legal-related questions, they send these questions to the server via a dialogue mechanism. The server generates an appropriate response to the question using a pre-trained legal database and provides it to the user. This function utilizes a generative AI model.

[0591] As a concrete example, consider the process when a user enters into a new payment service agreement on an e-commerce site. Once the user enters the contract terms, the server automatically generates the contract, performs a risk analysis, and provides a risk warning based on the results. For example, a warning such as "These payment terms carry a risk of delivery delays" might be displayed.

[0592] An example of a prompt message to input into a generative AI model is as follows:

[0593] "Based on the contract terms entered by the user, automatically generate appropriate contract documents, detect deficiencies and risks using natural language processing, and then generate advice to clearly communicate the results to the user."

[0594] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0595] Step 1:

[0596] The user enters the transaction terms using an input device. The entered data includes the trader's name, duration, and fee. This data is transmitted to the server via a communication network.

[0597] Step 2:

[0598] The server generates a contract document using a generation mechanism based on the received transaction terms. The template selected here is automatically determined based on the received data to be the most suitable one. The generated document becomes the output, and the process proceeds to the next step.

[0599] Step 3:

[0600] The server feeds the generated contract documents into the analysis mechanism and applies natural language processing technology. The documents are analyzed as input, and risks are identified by comparing them with known risk patterns. The analysis results list the detected risks and deficiencies, which are then presented as output in the next step.

[0601] Step 4:

[0602] The server transmits the analysis results to the terminal via a display mechanism, visually presenting the user with detailed risk information. The user reviews the outputted risk information and makes any necessary adjustments.

[0603] Step 5:

[0604] The user sends legal-related questions to the server via an input device and a dialogue mechanism. The questions are sent to the server as input.

[0605] Step 6:

[0606] The server uses a generative AI model to generate appropriate responses to input questions. Pre-trained legal data is used for response generation, and the output is the answer.

[0607] Step 7:

[0608] The server sends the generated response to the terminal, presenting it in a format easily understandable to the user. This allows the user to receive legal advice immediately.

[0609] 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.

[0610] This invention is a contract automation system that combines an emotion engine. It recognizes the user's emotional state and utilizes this information for contract document generation, risk detection, and legal support, thereby improving the user experience. The system is configured and performs the following specific operations.

[0611] First, the user enters the basic terms and conditions of the contract into the terminal. This information is sent to the server via the terminal, and the server automatically generates a contract document using a generation mechanism. The contract document is displayed to the user, and the emotion engine simultaneously analyzes the user's input data and dialogue history to recognize their emotional state.

[0612] If the user's emotions indicate anxiety or doubt, the server adjusts how the generated contract documents and risk information are presented. Specifically, it can provide more detailed explanations or highlight key risk points. This information is then appropriately presented to the user via the terminal.

[0613] Next, risks and deficiencies in the contract document are detected. The server analyzes the contract document using analytical tools, matching it with known risk patterns using natural language processing technology to identify potential risks and deficiencies. By utilizing an emotion engine, it is possible to dynamically change how the analysis results are presented and adjust the feedback to ensure the user feels reassured.

[0614] Furthermore, users can ask legal questions about the contract through the device's chat function. The server responds to these questions and generates appropriate answers using dialogue tools. The sentiment engine optimizes the level of detail and explanation in the answers according to the user's emotional state. The answers are provided to the user in real time to aid their understanding.

[0615] For example, if the emotion engine detects a stress response while a user is reviewing a contract, the server can provide further explanations of complex parts of the contract document or add explanations in more accessible language. This allows the user to understand the contract with confidence and take appropriate action quickly as needed.

[0616] Thus, the present invention not only highly automates contract work but also provides a flexible system that takes into account the user's emotional state by integrating emotion recognition technology.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] The user uses a terminal to input the basic conditions required for the contract and prepares the information needed to generate the contract.

[0620] Step 2:

[0621] The terminal sends the user's input data to the server and requests the automatic generation of the contract.

[0622] Step 3:

[0623] The server selects a contract template based on the received data and automatically generates the contract document using a generation mechanism. In this process, it replaces variables within the template based on the input information.

[0624] Step 4:

[0625] The server sends the generated contract document to the terminal and simultaneously passes the necessary input data to the emotion engine to recognize the user's emotional state.

[0626] Step 5:

[0627] The terminal displays contract documents to the user and adjusts the way the documents are presented based on the results of the emotion engine's recognition of the user's emotional state.

[0628] Step 6:

[0629] The user reviews the contract documents and proceeds with revisions and approvals as needed. At this point, if the emotion engine detects feelings of anxiety or doubt, more detailed explanations and highlighted information are provided.

[0630] Step 7:

[0631] The user enters information on the terminal to request a risk analysis based on the contract.

[0632] Step 8:

[0633] The terminal sends a risk analysis request to the server, initiating the specific risk detection process.

[0634] Step 9:

[0635] The server analyzes the contract document using analytical tools and identifies known risks and deficiencies using natural language processing technology. At the same time, it prepares feedback that corresponds to the user's emotions based on the results of the emotion engine.

[0636] Step 10:

[0637] The server sends the results of the risk and deficiency analysis to the terminal and delivers the results to the user in a presentation method that is appropriate to their emotional state.

[0638] Step 11:

[0639] The device informs the user of the details of risks and defects, providing the information in an easy-to-understand format. Furthermore, if the sentiment engine determines that additional explanation is needed, it will display the appropriate information.

[0640] Step 12:

[0641] Users can request legal support by entering legal questions related to the contract using the chat function on their device.

[0642] Step 13:

[0643] The terminal sends the received question to the server and prepares to receive the optimal answer through the dialogue mechanism.

[0644] Step 14:

[0645] The server processes user questions using dialogue mechanisms and generates appropriate answers. The level of detail in the answers is adjusted based on the results of the sentiment engine.

[0646] Step 15:

[0647] The server sends the generated response to the terminal, providing the user with information in real time.

[0648] Step 16:

[0649] The device displays answers to questions to the user and continues to accept any further questions. During this process, the explanation is dynamically optimized based on the evaluation of the emotion engine.

[0650] This series of steps allows users to receive a reassuring contract service that utilizes emotion recognition technology.

[0651] (Example 2)

[0652] 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."

[0653] Traditional contract management systems have focused on automated document generation and risk identification, but have not addressed the emotional state of users. As a result, users may not fully understand the contract terms, leading to anxiety and delays in contract signing. The challenge lies in streamlining the contract process while providing flexible responses that accommodate users' emotional states.

[0654] 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.

[0655] In this invention, the server includes a generation device means for automatically generating documents based on contract terms received from an input means, a means for recognizing the user's emotional state using an emotion recognition device and adjusting the presentation method of documents and information according to that state, and an analysis device means for using natural language processing technology to identify potential risks and deficiencies contained in the documents. This makes it possible to streamline contract work and clarify risks in accordance with the user's emotions.

[0656] An "input means" is a device or interface that allows a user to provide information such as contract terms to the system.

[0657] A "generating device" is a device or program that has the function of automatically creating a document based on the received contract terms.

[0658] An "emotion recognition device" is a device or software that has the technology to identify the user's emotional state by analyzing the user's input and dialogue history.

[0659] An "analytical device" is a device or program that uses natural language processing technology to analyze documents and compare them with known risk patterns to identify potential risks and deficiencies.

[0660] A "display device" is a device or interface that clearly displays detected risks or defects to the user.

[0661] A "dialogue device" is a device or system that generates appropriate answers to legal questions from users and enables natural interaction with them.

[0662] The system in this invention is an automated contract management system that takes into account the user's emotional state. This system is mainly composed of an input means, a generation device, an emotion recognition device, an analysis device, a presentation device, and a dialogue device.

[0663] The user enters the necessary information for the contract using a terminal. This information is received via the input device and sent to the server. The server uses a generation device to automatically generate a document based on the received information. In this process, advanced natural language processing technology, known as a generation AI model, is utilized to generate a high-quality contract document.

[0664] The generated document is displayed to the user via the terminal, and simultaneously, an emotion recognition device analyzes the user's input behavior and dialogue history to recognize their emotional state. For example, the emotion recognition device can detect anxiety or questions the user may have while viewing the document. Based on this recognition information, the server dynamically adjusts how the document and risk information are presented, providing the information in a way that is easy for the user to understand.

[0665] Furthermore, the server's analysis system uses natural language processing technology to analyze the generated documents and compare them with risk patterns to identify potential risks and deficiencies. This detected information is displayed to the user via a terminal using a presentation device. To deepen the user's understanding and reduce anxiety, the presentation method is also adjusted according to their emotional state.

[0666] Furthermore, users can ask legal questions from their terminals, and the server's dialogue system uses a generative AI model to generate appropriate answers. The details and tone of the answers are adjusted according to the user's emotional state, ensuring they are easily understood.

[0667] As a concrete example, a prompt might be used in the form of, "Please briefly explain the key points of this contract. Please help alleviate any concerns by presenting it in a way that is easy for the user to understand."

[0668] In this way, the system provides flexible functions that comprehensively support everything from generating contract documents to presenting risk information and handling legal consultations, all in order to improve the user experience.

[0669] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0670] Step 1:

[0671] The user enters the necessary information for the contract into the terminal. This information includes the type, terms, and duration of the contract. This information is transmitted to the server via the terminal as basic data for generating the contract document.

[0672] Step 2:

[0673] The server receives the input contract information and automatically generates the document using a generation device. Utilizing a generation AI model, it converts the input information into contract text through natural language processing techniques. The output is a highly structured contract document.

[0674] Step 3:

[0675] The terminal displays the generated contract document to the user. Simultaneously, an emotion recognition device analyzes the user's emotional state based on terminal operations and input data. Inputs include the user's dialogue history and document viewing behavior, while output is information about the user's emotional state.

[0676] Step 4:

[0677] The server adjusts how the generated contract document is presented based on the user's emotional state. Specifically, if the user's emotions indicate anxiety or doubt, the server highlights important sections of the document or adds supplementary explanations. The resulting output is an adjusted document.

[0678] Step 5:

[0679] The server's analysis system analyzes the generated documents using natural language processing technology. The input is the generated document, which is then compared against known risk patterns to identify potential risks and deficiencies. This process yields risk information that should be provided to the user.

[0680] Step 6:

[0681] The detected risks and deficiencies are presented to the user via the terminal. The presentation device adjusts the content to ensure the user can understand it with confidence. In this process, emotion recognition information is utilized to add appropriate supplementary explanations regarding the risks.

[0682] Step 7:

[0683] The user asks legal questions from their terminal. In response, the server's dialogue system uses a generative AI model to generate appropriate answers. The content and level of detail of the answers are adjusted according to the user's emotions, and then communicated to the user as output.

[0684] Step 8:

[0685] Ultimately, the user reviews the contract and related information and takes any necessary additional actions. Based on the information provided by the system, the user can make decisions with confidence. This completes the contract process.

[0686] (Application Example 2)

[0687] 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."

[0688] In contract negotiations, users may feel anxious if they do not fully understand the contract terms. Furthermore, if potential risks or deficiencies exist in the contract document, users may proceed without noticing them. Additionally, while prompt and appropriate answers are required for legal questions, there is a lack of flexible responses that take into account the emotional state of the user. Improving this situation and building a system that allows users to enter into contracts with confidence is a key challenge.

[0689] 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.

[0690] In this invention, the server includes a generation function means for automatically generating a contract document based on contract terms received from an input device, an analysis function means for detecting risks and deficiencies contained in the contract document, and an emotion analysis function means for analyzing the user's emotions using emotion recognition technology and adjusting explanations according to the user's emotions. This makes it possible to automatically generate a contract document while taking into account the user's emotional state, and to detect risks and deficiencies and provide optimized legal support.

[0691] An "input device" refers to a terminal or equipment used by a user to input contract terms and questions.

[0692] "Contract terms" refer to the basic information and items that determine the content of a contract.

[0693] The "generation function" is a function that automatically creates contract documents based on the entered contract terms.

[0694] The "analysis function" is a function that analyzes contract documents to detect risks and deficiencies contained within them.

[0695] The "display function" is a feature that clearly displays detected risks and defects to the user.

[0696] The "dialogue function" is a feature that generates appropriate answers to legal questions from users and provides them in a conversational format.

[0697] The "emotion analysis function" is a feature that analyzes the user's emotions from their facial expressions and tone of voice, and adjusts the content and presentation method of the explanation according to the user's emotions.

[0698] "Language processing technology" is a general term for technologies used to process, analyze, and generate natural language.

[0699] This invention is a system that streamlines user contract management and takes emotional states into consideration. The system operates by combining an input device, a server, and emotion recognition technology.

[0700] The user submits the contract terms to the server via an input device. The server automatically generates a contract document based on the contract terms using a generative AI model and natural language processing technology. OpenAI's GPT-3 is used for the generative AI model, resulting in natural-sounding document generation. In this process, the server selects the most suitable contract template based on the information provided by the user and generates a document that matches the conditions.

[0701] Next, the server analyzes the generated contract document and detects risk patterns and deficiencies using its analysis function. Here, the spaCy library is used as a language processing technique to compare it with known risk patterns. As a result, potential risks are identified and presented to the user.

[0702] Furthermore, the emotion analysis function utilizes emotion recognition technology based on data from the device's camera and microphone to understand the user's emotions. This uses Microsoft Azure Cognitive Services to extract emotional information from the user's facial expressions and voice. Depending on the user's emotional state, it provides supplementary explanations for difficult parts of the contract document, making it easier to understand.

[0703] When a user asks a legal question, the dialogue function is enabled, and appropriate answers are generated in real time using Google Dialogflow. These answers are tailored to the user's emotional state and presented in a more easily understandable format.

[0704] A concrete example is a scenario where a robot explains the important details of a rental agreement to a user. If the user expresses anxiety, the robot will gently and clearly explain the key points of the agreement, supporting the user so that they can proceed with the contract with confidence. The AI ​​model then responds based on the following example prompts:

[0705] "Users are expressing concerns about rental agreements. Please explain the important points of the contract in a safe and easy-to-understand manner, and reassure users."

[0706] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0707] Step 1:

[0708] The user enters the contract terms using an input device. This transmits the basic contract terms as data from the input device to the server. The server receives this data and prepares to generate the contract document.

[0709] Step 2:

[0710] The server uses a generative AI model to automatically generate contract documents based on the received contract terms. Specifically, it uses OpenAI GPT-3 to select a contract template that matches the conditions and creates the document. In this process, the input conditions are processed and output as a natural-sounding document.

[0711] Step 3:

[0712] The server analyzes the generated contract documents using natural language processing techniques. It utilizes the spaCy library to scan the documents and compare them against known risk patterns. This identifies potential risks and deficiencies within the contract documents, and risk information is output.

[0713] Step 4:

[0714] The server uses sentiment analysis capabilities to understand the user's emotions. Video and audio data acquired through the device's camera and microphone are analyzed using Microsoft Azure Cognitive Services to determine the user's emotional state. Based on the resulting emotional information, the server adjusts the presentation of contract documents.

[0715] Step 5:

[0716] If a user experiences anxiety or has questions while reviewing a contract document, the server, based on emotional information, will add detailed explanations to the difficult parts of the document. It will highlight and present particularly important points within the contract document in an easy-to-understand manner. This ensures that users receive information in a way that is easy to comprehend.

[0717] Step 6:

[0718] When a user enters a legal question, the server generates an appropriate answer via Google Dialogflow. This dialogue process adjusts the content and explanation of the answer based on the user's emotional state, and provides real-time responses.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] [Fourth Embodiment]

[0723] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0724] 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.

[0725] 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).

[0726] 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.

[0727] 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.

[0728] 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).

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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".

[0736] This invention realizes a system that efficiently automates contract work. Specific embodiments of the system are shown below.

[0737] First, the user needs to enter the contract terms. The user uses a terminal to enter the basic contract details (e.g., subscriber name, contract period, and fee). This input method provides the necessary information to the system.

[0738] The server uses the received input information to utilize generation methods and automatically generates a draft of the contract document. This process recognizes different contract templates, uses the appropriate template based on pre-programmed rules, and incorporates the entered conditions.

[0739] The generated contract document is displayed to the user via the terminal. The user can review the document and make further edits if necessary.

[0740] Next, a risk analysis process takes place. The server analyzes the text of the contract document and uses analytical tools to identify potential risks and deficiencies. In this process, natural language processing technology is utilized to detect risks based on known risk patterns.

[0741] The analysis results are presented to the user via the device. The information presented includes details of risk areas and recommended countermeasures. The user can then adjust the contract terms based on this information.

[0742] Furthermore, a dialogue function is provided to answer legal questions. When a user enters a legal question on their terminal, the server processes the question using the dialogue mechanism and generates an appropriate answer based on the relevant information. The answer is displayed immediately on the terminal, allowing the user to obtain the necessary information instantly.

[0743] As a concrete example, consider a construction contract for a new base station. The user inputs basic conditions such as construction period, cost, and scope of responsibility. The server automatically generates a contract based on this information and displays it on the terminal. Meanwhile, the server can analyze the generated contract document and detect, for example, the risk of delivery delays or deficiencies regarding legal obligations. This allows the user to identify risks early and revise or negotiate the contract as needed.

[0744] The following describes the processing flow.

[0745] Step 1:

[0746] The user accesses an input form containing the contract terms and enters necessary basic information such as the contract holder's name, contract period, and amount into their device.

[0747] Step 2:

[0748] The terminal sends the entered contract terms to the server and requests the generation of the contract.

[0749] Step 3:

[0750] The server uses a generation mechanism to select an appropriate contract template based on the received contract terms and automatically generates the contract document. During this process, variables within the template are replaced according to the input information.

[0751] Step 4:

[0752] The terminal displays the generated contract document sent from the server to the user, allowing the user to review its contents.

[0753] Step 5:

[0754] Users can review the displayed contract document and make any necessary modifications on their device. Approval is also possible at this stage.

[0755] Step 6:

[0756] The user performs an action on their terminal to request a risk analysis of the contract document.

[0757] Step 7:

[0758] The terminal sends a risk analysis request to the server.

[0759] Step 8:

[0760] The server analyzes the text of the contract documents generated using analytical tools and employs natural language processing techniques to detect potential risks and deficiencies. Historical data and pattern recognition techniques are used to identify areas of deficiency and risk.

[0761] Step 9:

[0762] The server sends results to the terminal that detail the detected risks and deficiencies.

[0763] Step 10:

[0764] The terminal displays the analysis results in an easy-to-understand format for the user, and shows details of countermeasures and risks.

[0765] Step 11:

[0766] The user will review the presented risks and deficiencies and readjust the contract terms. In some cases, the contract terms may be redefined.

[0767] Step 12:

[0768] Users enter legal questions regarding the contract through the chat function on their device.

[0769] Step 13:

[0770] The device sends the user's question to the server and requests that it receive an appropriate answer.

[0771] Step 14:

[0772] The server analyzes the question using dialogue methods and collects information necessary for the generating AI to produce an appropriate answer. Based on this information, it creates an answer to the question.

[0773] Step 15:

[0774] The server sends the generated response to the terminal.

[0775] Step 16:

[0776] The terminal displays the submitted answers to the user and allows them to enter additional questions if needed.

[0777] This series of steps enables efficient automation of contract processes and provides legal support.

[0778] (Example 1)

[0779] 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".

[0780] Contract work often involves time-consuming and labor-intensive manual processes, making it inefficient and potentially leading to overlooked risks. Furthermore, answering legal questions about contracts quickly and accurately can be challenging. These challenges can result in delays and inaccurate contract execution.

[0781] 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.

[0782] In this invention, the server includes a generation function means for automatically generating contract documents based on contract terms obtained from an input function, an analysis function means for identifying risks and deficiencies contained in the contract documents, and a dialogue function means for receiving legal questions and creating appropriate responses. This enables efficient automation of contract operations, early detection of risks, and rapid response to legal questions.

[0783] The "input function" is a means of obtaining contract terms from the user.

[0784] The "generation function" is a means of automatically generating contract documents based on the acquired contract terms.

[0785] "Analysis function" refers to a means of identifying risks and deficiencies contained in contract documents.

[0786] A "display function" is a means of displaying identified risks and deficiencies to the user.

[0787] A "dialogue function" is a means of receiving legal questions and formulating appropriate responses to them.

[0788] An "information processing system" is a system that combines input, generation, analysis, display, and dialogue functions to efficiently automate contract work, enabling early detection of risks and prompt responses to legal questions.

[0789] This system utilizes specific hardware and software to efficiently automate contract management. Its specific form is described below.

[0790] The user enters basic information for creating a contract into the terminal. The terminal facilitates user operation by providing an intuitive interface for information input. This input information is essential as the basic data for generating contract documents in the system.

[0791] The server automatically generates contract documents using a generative AI model based on the entered contract terms. Specifically, it compares the acquired terms with several pre-prepared contract templates, selects the most appropriate template, and creates the document in natural language. The generative AI model customizes the contract documents based on templates appropriate for different contract types.

[0792] The generated contract document is displayed to the user via a terminal. The user can review and edit the contract document on the screen and make further adjustments as needed.

[0793] Next, the server uses natural language processing technology to analyze the contract document and identify potential risks and deficiencies. By comparing the text with known risk patterns, problems are automatically extracted. The analysis results are presented to the user via the terminal, allowing the user to consider the nature of the risks and recommended countermeasures.

[0794] Furthermore, when a user enters a legal question, the server uses its conversational function to generate an appropriate answer to the user's question. The generated answer is displayed on the terminal in real time, enabling quick information retrieval.

[0795] As a concrete example, consider a construction contract for a new base station. When a user inputs conditions such as construction period, cost, and scope of responsibility, the server generates a contract based on this information and displays it on the terminal. At that time, the server analyzes the generated contract to detect risks of delivery delays and deficiencies in legal obligations. This allows the user to obtain appropriate information for risk assessment and contract negotiation.

[0796] Example of a prompt:

[0797] "We are considering a construction contract for a new base station. Please prepare a contract with a construction period of 12 months, a total cost of 50 million yen, and the scope of responsibility being solely your company's. Also, please outline the potential risks and provide appropriate advice."

[0798] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0799] Step 1:

[0800] The user enters the contract terms through an input interface on their terminal. This information includes the subscriber's name, contract period, and fees. This information is received by the server and used as basic data for generating contract documents.

[0801] Step 2:

[0802] The server automatically generates contract documents using a generative AI model based on the received contract terms. First, it compares the acquired terms with various pre-prepared contract templates. It selects a template that matches the terms and generates a contract document in natural language. The output is a customized contract document based on the terms.

[0803] Step 3:

[0804] The terminal displays the generated contract document to the user. A screen is provided where the user can review the contract details and edit them manually as needed. The user's edits are then sent back to the server and saved as the final version of the contract document.

[0805] Step 4:

[0806] The server analyzes the final version of the contract document to identify potential risks and deficiencies. This process uses natural language processing techniques to compare the contract document with known risk patterns. As a result of the analysis, detailed risk information and recommended countermeasures are extracted.

[0807] Step 5:

[0808] The terminal displays analysis results from the server to the user. Risk information is displayed in a visually easy-to-understand format, allowing the user to consider adjusting or negotiating contract terms based on this information. This information provision enables users to grasp contract-related risks at an early stage.

[0809] Step 6:

[0810] When a user enters a legal question from their terminal, the server processes the question using a conversational function. Specifically, it analyzes the entered question and generates an appropriate answer using a generative AI model. The generated answer is immediately output to the terminal, allowing the user to quickly obtain the necessary legal information.

[0811] (Application Example 1)

[0812] 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".

[0813] In automating contract management, inefficiencies and inaccuracies exist, particularly in processes ranging from inputting transaction terms to generating documents, detecting risks, and responding to legal inquiries. This can make it difficult to quickly conclude contracts and properly identify potential risks.

[0814] 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.

[0815] In this invention, the server includes a generation mechanism for automatically generating documents based on transaction conditions received from an input device, an analysis mechanism for detecting risks and defects contained in the documents, and a display mechanism for presenting the detected risks and defects to the user. This enables increased efficiency in contract operations and optimized risk management.

[0816] An "input device" is a device that allows a user to input transaction conditions and provide those conditions to the system.

[0817] A "generation mechanism" is a means of automatically creating contract-related documents based on transaction terms received from an input device.

[0818] An "analysis mechanism" is a means of analyzing generated documents using natural language processing technology and comparing them with known risk patterns to identify risks and defects.

[0819] A "display mechanism" is a means of visually presenting to the user any dangers or defects detected by the analysis mechanism.

[0820] A "dialogue mechanism" is a means of generating and providing appropriate responses to questions users have about legal issues.

[0821] An "interface" is a means of displaying transaction-related information on a user's terminal device, enabling the user to interact with the system.

[0822] The system implementing this invention begins with the user inputting contract terms. The user inputs the terms of the transaction using an input device such as a smartphone or computer. This information is transmitted to a server via the internet. The server, through a generation mechanism for automatically generating contracts, selects an appropriate contract template based on this information and generates the necessary contract document.

[0823] The generated contract documents are analyzed by an analysis mechanism using natural language processing techniques, and risks and defects are identified by comparing them against known risk patterns. Software such as the Google NLP API is used in this process. The detected risks and defects are provided to the user through a display mechanism, prompting appropriate risk-based action.

[0824] Furthermore, if the user has legal-related questions, they send these questions to the server via a dialogue mechanism. The server generates an appropriate response to the question using a pre-trained legal database and provides it to the user. This function utilizes a generative AI model.

[0825] As a concrete example, consider the process when a user enters into a new payment service agreement on an e-commerce site. Once the user enters the contract terms, the server automatically generates the contract, performs a risk analysis, and provides a risk warning based on the results. For example, a warning such as "These payment terms carry a risk of delivery delays" might be displayed.

[0826] An example of a prompt message to input into a generative AI model is as follows:

[0827] "Based on the contract terms entered by the user, automatically generate appropriate contract documents, detect deficiencies and risks using natural language processing, and then generate advice to clearly communicate the results to the user."

[0828] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0829] Step 1:

[0830] The user enters the transaction terms using an input device. The entered data includes the trader's name, duration, and fee. This data is transmitted to the server via a communication network.

[0831] Step 2:

[0832] The server generates a contract document using a generation mechanism based on the received transaction terms. The template selected here is automatically determined based on the received data to be the most suitable one. The generated document becomes the output, and the process proceeds to the next step.

[0833] Step 3:

[0834] The server feeds the generated contract documents into the analysis mechanism and applies natural language processing technology. The documents are analyzed as input, and risks are identified by comparing them with known risk patterns. The analysis results list the detected risks and deficiencies, which are then presented as output in the next step.

[0835] Step 4:

[0836] The server transmits the analysis results to the terminal via a display mechanism, visually presenting the user with detailed risk information. The user reviews the outputted risk information and makes any necessary adjustments.

[0837] Step 5:

[0838] The user sends legal-related questions to the server via an input device and a dialogue mechanism. The questions are sent to the server as input.

[0839] Step 6:

[0840] The server uses a generative AI model to generate appropriate responses to input questions. Pre-trained legal data is used for response generation, and the output is the answer.

[0841] Step 7:

[0842] The server sends the generated response to the terminal, presenting it in a format easily understandable to the user. This allows the user to receive legal advice immediately.

[0843] 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.

[0844] This invention is a contract automation system that combines an emotion engine. It recognizes the user's emotional state and utilizes this information for contract document generation, risk detection, and legal support, thereby improving the user experience. The system is configured and performs the following specific operations.

[0845] First, the user enters the basic terms and conditions of the contract into the terminal. This information is sent to the server via the terminal, and the server automatically generates a contract document using a generation mechanism. The contract document is displayed to the user, and the emotion engine simultaneously analyzes the user's input data and dialogue history to recognize their emotional state.

[0846] If the user's emotions indicate anxiety or doubt, the server adjusts how the generated contract documents and risk information are presented. Specifically, it can provide more detailed explanations or highlight key risk points. This information is then appropriately presented to the user via the terminal.

[0847] Next, risks and deficiencies in the contract document are detected. The server analyzes the contract document using analytical tools, matching it with known risk patterns using natural language processing technology to identify potential risks and deficiencies. By utilizing an emotion engine, it is possible to dynamically change how the analysis results are presented and adjust the feedback to ensure the user feels reassured.

[0848] Furthermore, users can ask legal questions about the contract through the device's chat function. The server responds to these questions and generates appropriate answers using dialogue tools. The sentiment engine optimizes the level of detail and explanation in the answers according to the user's emotional state. The answers are provided to the user in real time to aid their understanding.

[0849] For example, if the emotion engine detects a stress response while a user is reviewing a contract, the server can provide further explanations of complex parts of the contract document or add explanations in more accessible language. This allows the user to understand the contract with confidence and take appropriate action quickly as needed.

[0850] Thus, the present invention not only highly automates contract work but also provides a flexible system that takes into account the user's emotional state by integrating emotion recognition technology.

[0851] The following describes the processing flow.

[0852] Step 1:

[0853] The user uses a terminal to input the basic conditions required for the contract and prepares the information needed to generate the contract.

[0854] Step 2:

[0855] The terminal sends the user's input data to the server and requests the automatic generation of the contract.

[0856] Step 3:

[0857] The server selects a contract template based on the received data and automatically generates the contract document using a generation mechanism. In this process, it replaces variables within the template based on the input information.

[0858] Step 4:

[0859] The server sends the generated contract document to the terminal and simultaneously passes the necessary input data to the emotion engine to recognize the user's emotional state.

[0860] Step 5:

[0861] The terminal displays contract documents to the user and adjusts the way the documents are presented based on the results of the emotion engine's recognition of the user's emotional state.

[0862] Step 6:

[0863] The user reviews the contract documents and proceeds with revisions and approvals as needed. At this point, if the emotion engine detects feelings of anxiety or doubt, more detailed explanations and highlighted information are provided.

[0864] Step 7:

[0865] The user enters information on the terminal to request a risk analysis based on the contract.

[0866] Step 8:

[0867] The terminal sends a risk analysis request to the server, initiating the specific risk detection process.

[0868] Step 9:

[0869] The server analyzes the contract document using analytical tools and identifies known risks and deficiencies using natural language processing technology. At the same time, it prepares feedback that corresponds to the user's emotions based on the results of the emotion engine.

[0870] Step 10:

[0871] The server sends the results of the risk and deficiency analysis to the terminal and delivers the results to the user in a presentation method that is appropriate to their emotional state.

[0872] Step 11:

[0873] The device informs the user of the details of risks and defects, providing the information in an easy-to-understand format. Furthermore, if the sentiment engine determines that additional explanation is needed, it will display the appropriate information.

[0874] Step 12:

[0875] Users can request legal support by entering legal questions related to the contract using the chat function on their device.

[0876] Step 13:

[0877] The terminal sends the received question to the server and prepares to receive the optimal answer through the dialogue mechanism.

[0878] Step 14:

[0879] The server processes user questions using dialogue mechanisms and generates appropriate answers. The level of detail in the answers is adjusted based on the results of the sentiment engine.

[0880] Step 15:

[0881] The server sends the generated response to the terminal, providing the user with information in real time.

[0882] Step 16:

[0883] The device displays answers to questions to the user and continues to accept any further questions. During this process, the explanation is dynamically optimized based on the evaluation of the emotion engine.

[0884] This series of steps allows users to receive a reassuring contract service that utilizes emotion recognition technology.

[0885] (Example 2)

[0886] 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".

[0887] Traditional contract management systems have focused on automated document generation and risk identification, but have not addressed the emotional state of users. As a result, users may not fully understand the contract terms, leading to anxiety and delays in contract signing. The challenge lies in streamlining the contract process while providing flexible responses that accommodate users' emotional states.

[0888] 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.

[0889] In this invention, the server includes a generation device means for automatically generating documents based on contract terms received from an input means, a means for recognizing the user's emotional state using an emotion recognition device and adjusting the presentation method of documents and information according to that state, and an analysis device means for using natural language processing technology to identify potential risks and deficiencies contained in the documents. This makes it possible to streamline contract work and clarify risks in accordance with the user's emotions.

[0890] An "input means" is a device or interface that allows a user to provide information such as contract terms to the system.

[0891] A "generating device" is a device or program that has the function of automatically creating a document based on the received contract terms.

[0892] An "emotion recognition device" is a device or software that has the technology to identify the user's emotional state by analyzing the user's input and dialogue history.

[0893] An "analytical device" is a device or program that uses natural language processing technology to analyze documents and compare them with known risk patterns to identify potential risks and deficiencies.

[0894] A "display device" is a device or interface that clearly displays detected risks or defects to the user.

[0895] A "dialogue device" is a device or system that generates appropriate answers to legal questions from users and enables natural interaction with them.

[0896] The system in this invention is an automated contract management system that takes into account the user's emotional state. This system is mainly composed of an input means, a generation device, an emotion recognition device, an analysis device, a presentation device, and a dialogue device.

[0897] The user enters the necessary information for the contract using a terminal. This information is received via the input device and sent to the server. The server uses a generation device to automatically generate a document based on the received information. In this process, advanced natural language processing technology, known as a generation AI model, is utilized to generate a high-quality contract document.

[0898] The generated document is displayed to the user via the terminal, and simultaneously, an emotion recognition device analyzes the user's input behavior and dialogue history to recognize their emotional state. For example, the emotion recognition device can detect anxiety or questions the user may have while viewing the document. Based on this recognition information, the server dynamically adjusts how the document and risk information are presented, providing the information in a way that is easy for the user to understand.

[0899] Furthermore, the server's analysis system uses natural language processing technology to analyze the generated documents and compare them with risk patterns to identify potential risks and deficiencies. This detected information is displayed to the user via a terminal using a presentation device. To deepen the user's understanding and reduce anxiety, the presentation method is also adjusted according to their emotional state.

[0900] Furthermore, users can ask legal questions from their terminals, and the server's dialogue system uses a generative AI model to generate appropriate answers. The details and tone of the answers are adjusted according to the user's emotional state, ensuring they are easily understood.

[0901] As a concrete example, a prompt might be used in the form of, "Please briefly explain the key points of this contract. Please help alleviate any concerns by presenting it in a way that is easy for the user to understand."

[0902] In this way, the system provides flexible functions that comprehensively support everything from generating contract documents to presenting risk information and handling legal consultations, all in order to improve the user experience.

[0903] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0904] Step 1:

[0905] The user enters the necessary information for the contract into the terminal. This information includes the type, terms, and duration of the contract. This information is transmitted to the server via the terminal as basic data for generating the contract document.

[0906] Step 2:

[0907] The server receives the input contract information and automatically generates the document using a generation device. Utilizing a generation AI model, it converts the input information into contract text through natural language processing techniques. The output is a highly structured contract document.

[0908] Step 3:

[0909] The terminal displays the generated contract document to the user. Simultaneously, an emotion recognition device analyzes the user's emotional state based on terminal operations and input data. Inputs include the user's dialogue history and document viewing behavior, while output is information about the user's emotional state.

[0910] Step 4:

[0911] The server adjusts how the generated contract document is presented based on the user's emotional state. Specifically, if the user's emotions indicate anxiety or doubt, the server highlights important sections of the document or adds supplementary explanations. The resulting output is an adjusted document.

[0912] Step 5:

[0913] The server's analysis system analyzes the generated documents using natural language processing technology. The input is the generated document, which is then compared against known risk patterns to identify potential risks and deficiencies. This process yields risk information that should be provided to the user.

[0914] Step 6:

[0915] The detected risks and deficiencies are presented to the user via the terminal. The presentation device adjusts the content to ensure the user can understand it with confidence. In this process, emotion recognition information is utilized to add appropriate supplementary explanations regarding the risks.

[0916] Step 7:

[0917] The user asks legal questions from their terminal. In response, the server's dialogue system uses a generative AI model to generate appropriate answers. The content and level of detail of the answers are adjusted according to the user's emotions, and then communicated to the user as output.

[0918] Step 8:

[0919] Ultimately, the user reviews the contract and related information and takes any necessary additional actions. Based on the information provided by the system, the user can make decisions with confidence. This completes the contract process.

[0920] (Application Example 2)

[0921] 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".

[0922] In contract negotiations, users may feel anxious if they do not fully understand the contract terms. Furthermore, if potential risks or deficiencies exist in the contract document, users may proceed without noticing them. Additionally, while prompt and appropriate answers are required for legal questions, there is a lack of flexible responses that take into account the emotional state of the user. Improving this situation and building a system that allows users to enter into contracts with confidence is a key challenge.

[0923] 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.

[0924] In this invention, the server includes a generation function means for automatically generating a contract document based on contract terms received from an input device, an analysis function means for detecting risks and deficiencies contained in the contract document, and an emotion analysis function means for analyzing the user's emotions using emotion recognition technology and adjusting explanations according to the user's emotions. This makes it possible to automatically generate a contract document while taking into account the user's emotional state, and to detect risks and deficiencies and provide optimized legal support.

[0925] An "input device" refers to a terminal or equipment used by a user to input contract terms and questions.

[0926] "Contract terms" refer to the basic information and items that determine the content of a contract.

[0927] The "generation function" is a function that automatically creates contract documents based on the entered contract terms.

[0928] The "analysis function" is a function that analyzes contract documents to detect risks and deficiencies contained within them.

[0929] The "display function" is a feature that clearly displays detected risks and defects to the user.

[0930] The "dialogue function" is a feature that generates appropriate answers to legal questions from users and provides them in a conversational format.

[0931] The "emotion analysis function" is a feature that analyzes the user's emotions from their facial expressions and tone of voice, and adjusts the content and presentation method of the explanation according to the user's emotions.

[0932] "Language processing technology" is a general term for technologies used to process, analyze, and generate natural language.

[0933] This invention is a system that streamlines user contract management and takes emotional states into consideration. The system operates by combining an input device, a server, and emotion recognition technology.

[0934] The user submits the contract terms to the server via an input device. The server automatically generates a contract document based on the contract terms using a generative AI model and natural language processing technology. OpenAI's GPT-3 is used for the generative AI model, resulting in natural-sounding document generation. In this process, the server selects the most suitable contract template based on the information provided by the user and generates a document that matches the conditions.

[0935] Next, the server analyzes the generated contract document and detects risk patterns and deficiencies using its analysis function. Here, the spaCy library is used as a language processing technique to compare it with known risk patterns. As a result, potential risks are identified and presented to the user.

[0936] Furthermore, the emotion analysis function utilizes emotion recognition technology based on data from the device's camera and microphone to understand the user's emotions. This uses Microsoft Azure Cognitive Services to extract emotional information from the user's facial expressions and voice. Depending on the user's emotional state, it provides supplementary explanations for difficult parts of the contract document, making it easier to understand.

[0937] When a user asks a legal question, the dialogue function is enabled, and appropriate answers are generated in real time using Google Dialogflow. These answers are tailored to the user's emotional state and presented in a more easily understandable format.

[0938] A concrete example is a scenario where a robot explains the important details of a rental agreement to a user. If the user expresses anxiety, the robot will gently and clearly explain the key points of the agreement, supporting the user so that they can proceed with the contract with confidence. The AI ​​model then responds based on the following example prompts:

[0939] "Users are expressing concerns about rental agreements. Please explain the important points of the contract in a safe and easy-to-understand manner, and reassure users."

[0940] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0941] Step 1:

[0942] The user enters the contract terms using an input device. This transmits the basic contract terms as data from the input device to the server. The server receives this data and prepares to generate the contract document.

[0943] Step 2:

[0944] The server uses a generative AI model to automatically generate contract documents based on the received contract terms. Specifically, it uses OpenAI GPT-3 to select a contract template that matches the conditions and creates the document. In this process, the input conditions are processed and output as a natural-sounding document.

[0945] Step 3:

[0946] The server analyzes the generated contract documents using natural language processing techniques. It utilizes the spaCy library to scan the documents and compare them against known risk patterns. This identifies potential risks and deficiencies within the contract documents, and risk information is output.

[0947] Step 4:

[0948] The server uses sentiment analysis capabilities to understand the user's emotions. Video and audio data acquired through the device's camera and microphone are analyzed using Microsoft Azure Cognitive Services to determine the user's emotional state. Based on the resulting emotional information, the server adjusts the presentation of contract documents.

[0949] Step 5:

[0950] If a user experiences anxiety or has questions while reviewing a contract document, the server, based on emotional information, will add detailed explanations to the difficult parts of the document. It will highlight and present particularly important points within the contract document in an easy-to-understand manner. This ensures that users receive information in a way that is easy to comprehend.

[0951] Step 6:

[0952] When a user enters a legal question, the server generates an appropriate answer via Google Dialogflow. This dialogue process adjusts the content and explanation of the answer based on the user's emotional state, and provides real-time responses.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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.

[0957] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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."

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] The following is further disclosed regarding the embodiments described above.

[0975] (Claim 1)

[0976] A generation means for automatically generating a contract document based on contract terms received from an input means,

[0977] An analytical means for detecting risks and deficiencies contained in the aforementioned contract document,

[0978] A means of presenting detected risks and deficiencies to the user,

[0979] A dialogue mechanism for receiving legal questions and generating appropriate answers,

[0980] A system that includes this.

[0981] (Claim 2)

[0982] The contract document generation means is a system according to claim 1 that generates a document by selecting different types of contract templates and corresponding them to received conditions.

[0983] (Claim 3)

[0984] The system according to claim 1, wherein the analysis means analyzes the contract document using natural language processing and identifies risks by comparing it with known risk patterns.

[0985] "Example 1"

[0986] (Claim 1)

[0987] A generation function means that automatically generates contract documents based on contract terms obtained from the input function,

[0988] Analytical means for identifying risks and deficiencies contained in contract documents,

[0989] Display function means for displaying identified risks and deficiencies to the user,

[0990] A dialogue function for receiving legal questions and formulating appropriate responses,

[0991] An information processing system that includes this.

[0992] (Claim 2)

[0993] The information processing system according to claim 1, wherein the contract document generation function generates a document by selecting various types of contract templates and applying them to acquired conditions.

[0994] (Claim 3)

[0995] The information processing system according to claim 1, wherein the analysis function uses natural language processing to analyze contract documents and identifies risks by comparing them with known risk patterns.

[0996] "Application Example 1"

[0997] (Claim 1)

[0998] A generation mechanism means for automatically generating documents based on transaction conditions received from an input device,

[0999] An analytical mechanism means for detecting hazards and defects contained in the aforementioned document,

[1000] A display mechanism means for presenting detected hazards and defects to the user,

[1001] A dialogue mechanism for receiving questions regarding legal issues and generating appropriate responses,

[1002] An interface means for displaying transaction-related information on a terminal device,

[1003] A system that includes this.

[1004] (Claim 2)

[1005] The system according to claim 1, wherein the document generation mechanism generates a document by selecting different types of transaction templates and corresponding them to received conditions.

[1006] (Claim 3)

[1007] The system according to claim 1, wherein the analysis mechanism analyzes a document using natural language processing technology and identifies a risk by matching it with known risk patterns.

[1008] "Example 2 of combining an emotion engine"

[1009] (Claim 1)

[1010] A generation device means that automatically generates a document based on contract terms received from an input means,

[1011] A means for recognizing the user's emotional state using an emotion recognition device and adjusting the method of presenting documents and information according to that state,

[1012] To identify potential risks and deficiencies contained in the aforementioned document, an analytical apparatus using natural language processing technology is provided.

[1013] A presentation device means that presents detected risks and deficiencies to the user and dynamically adjusts the presentation method to provide a sense of security,

[1014] A dialogue device that receives legal questions and generates appropriate answers, and a means for adjusting the answers based on the user's emotional state,

[1015] A system that includes this.

[1016] (Claim 2)

[1017] The document generation device is a system according to claim 1 that generates a document by selecting multiple contract templates and corresponding them to received conditions.

[1018] (Claim 3)

[1019] The system according to claim 1, wherein the analytical device analyzes a document using natural language processing technology and identifies risks by comparing it with known risk patterns.

[1020] "Application example 2 when combining with an emotional engine"

[1021] (Claim 1)

[1022] A generation function means for automatically generating a contract document based on contract terms received from an input device,

[1023] An analytical function means for detecting risks and deficiencies contained in the aforementioned contract document,

[1024] A presentation function means for presenting detected risks and deficiencies to the user,

[1025] A dialogue function for receiving legal questions and generating appropriate answers,

[1026] An emotion analysis function means for analyzing the user's emotions using emotion recognition technology and adjusting the explanation according to the user's emotions,

[1027] A system that includes this.

[1028] (Claim 2)

[1029] The system according to claim 1, wherein the contract document generation function generates a document by selecting different types of contract templates and corresponding them to received conditions.

[1030] (Claim 3)

[1031] The system according to claim 1, wherein the analysis function analyzes contract documents using language processing technology and identifies risks by comparing them with known risk patterns. [Explanation of Symbols]

[1032] 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 generation mechanism means for automatically generating documents based on transaction conditions received from an input device, An analytical mechanism means for detecting hazards and defects contained in the aforementioned document, A display mechanism means for presenting detected hazards and defects to the user, A dialogue mechanism for receiving questions regarding legal issues and generating appropriate responses, An interface means for displaying transaction-related information on a terminal device, A system that includes this.

2. The system according to claim 1, wherein the document generation mechanism generates a document by selecting different types of transaction templates and corresponding them to received conditions.

3. The system according to claim 1, wherein the analysis mechanism analyzes a document using natural language processing technology and identifies a risk by matching it with known risk patterns.

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