system

Abstract

We provide the system. [Solution] A receiving means for receiving contract data, An analysis means for extracting text from the aforementioned contract data and performing preprocessing, A detection means that analyzes the contract data using natural language processing technology and detects errors and risks, A generation means for generating corrective proposals based on the detected errors and risks, A display means for presenting the user with the aforementioned errors, risks, and proposed corrections, A generation means for generating a new contract based on user input, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document ۱

Summary of the Invention

Problems to be Solved by the Invention

[0004] Regarding problems related to the creation and review of contracts, conventionally, a great deal of time and labor have been required, and it has been difficult to detect legal risks and errors. In addition, troubles caused by insufficient understanding of contract contents are likely to occur, and these have been factors hindering the efficiency improvement and quality improvement of contract management.

Means for Solving the Problems

[0005] The present invention provides means for receiving contract data and performing text analysis using natural language processing technology. Further, by detecting errors and risks in the contract text and generating amendments based thereon, clear guidelines are given to the user. In addition, a system is provided that refers to past successful cases and supports the rapid creation of a new contract through template generation, aiming to improve the efficiency of contract management.

[0006] "Contract data" refers to digital data that includes document information written in a contract.

[0007] "Receiving means" refers to the interface and process for importing contract data from outside the system.

[0008] "Analysis means" refers to a method or technique for extracting text from received contract data and preprocessing it to prepare the data for analysis.

[0009] "Natural language processing technology" refers to algorithms and methods that enable computers to understand and analyze human language.

[0010] "Detection methods" refer to the techniques and processes used to identify errors and legal risks based on analyzed contract data.

[0011] "Generation means" refers to a function and method that automatically creates appropriate revised drafts or new contracts, taking into account detected errors and risks.

[0012] "Display means" refers to an interface and functions for visually providing the user with the detected results and suggested modifications.

[0013] A "user" refers to a person or group that operates the system and performs tasks such as entering and confirming contract information. [Brief explanation of the drawing]

[0014] [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] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the language used in the following description will be explained.

[0017] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0018] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system for efficiently processing contract data. The system operates in cooperation with a server, terminals, and users to support the creation and verification of contracts.

[0036] The server first receives contract data from the user via the terminal. The received data is then analyzed using natural language processing techniques. Specifically, the server analyzes the text of the contract and applies natural language processing to understand its syntax and meaning.

[0037] Based on the analysis results, the server scrutinizes the contract and detects errors and legal risks. Using this analysis information, the server generates revised proposals for the user. These include specific suggestions for correcting errors and recommendations for clauses that should be improved.

[0038] The user visually confirms this information using their terminal. The terminal displays a list of proposed revisions and detailed analysis results of the contract sent from the server to the user. The user can review the proposed revisions and revise the contract as needed.

[0039] Furthermore, when a user creates a new contract, the server generates a new contract based on an existing template. In this process, the server considers past successes and selects the clauses that best suit the conditions specified by the user. Based on this, the user can quickly create a new contract.

[0040] As a concrete example, consider a scenario where a user creates a new sales contract. The user inputs the sales terms and agreements on their terminal. The server receives this information, selects an appropriate template, and automatically customizes the clauses. The final contract is completed after review and minor adjustments by the user.

[0041] Thus, the present invention significantly improves the efficiency of contract drafting and review, providing an environment in which users can confidently carry out contract work.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users select contract data using their terminals and upload it to the server. The system supports common document formats such as PDF and Word.

[0045] Step 2:

[0046] The server receives the contract data and extracts text from the file. The extracted text is preprocessed to remove unnecessary spaces and special characters.

[0047] Step 3:

[0048] The server analyzes the pre-processed text using natural language processing techniques. This includes morphological and syntactic analysis, and processing is performed to understand the structure and meaning of the sentences within the contract.

[0049] Step 4:

[0050] The server detects errors and potential legal risks based on the analysis results. It uses pre-configured rule-based systems and machine learning models to identify areas of risk.

[0051] Step 5:

[0052] The server generates proposed corrections based on the detection results. These corrections include suggested fixes for identified errors and recommended clause changes. It also refers to historical contract data to determine the optimal correction strategy.

[0053] Step 6:

[0054] The terminal displays errors, risks, and suggested solutions sent from the server to the user. The information is presented in an intuitive format using graphs and marked-up text.

[0055] Step 7:

[0056] The user reviews the proposed revisions via their device and applies the changes as needed, thereby finalizing the contract's structure.

[0057] Step 8:

[0058] When a user creates a new contract, the server generates a new contract using a template based on the requirements entered on the terminal. Appropriate clauses are automatically selected based on past success stories.

[0059] Step 9:

[0060] The generated contract is sent to the user's device, where the user reviews and edits it as needed. The completed contract is saved on the server as the final version.

[0061] (Example 1)

[0062] 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."

[0063] Traditional contract drafting and review processes relied heavily on manual work, requiring significant time and effort to identify errors and assess legal risks. This led to decreased efficiency in contract operations and ultimately impacted overall business productivity. Furthermore, the inability to fully utilize past examples in drafting new contracts resulted in inconsistent quality.

[0064] 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.

[0065] In this invention, the server includes communication means for receiving contract information, analysis means for extracting and pre-processing text from the contract information, and detection means for analyzing the contract information using natural language processing technology and detecting errors and risks. This enables the automatic and rapid detection of errors and risks in contracts by making full use of advanced natural language processing, allowing for the efficient and reliable execution of contract work.

[0066] "Contract information" refers to information consisting of all data and documents related to a contract, and includes the agreements and conditions between the parties.

[0067] "Communication methods" refer to the technical devices and protocols that servers use to exchange information with users and terminals.

[0068] "Text" refers to the elements that make up the text data within a contract, specifically used to express clauses and conditions.

[0069] "Analysis means" refers to technical mechanisms and devices for extracting necessary text from contract information and performing preprocessing.

[0070] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to detect errors and risks in the analysis of contract information.

[0071] "Detection means" refers to methods and devices that use natural language processing technology to discover errors and risks in contract information.

[0072] "Suggestion mechanism" refers to a system for creating and providing corrective suggestions to users based on detected errors and risks.

[0073] "Display means" refers to devices or methods for visually presenting errors, risks, and suggested corrections to the user.

[0074] "Document generation means" refers to a system or technology for creating new contracts based on user input.

[0075] "Optimization method" refers to technology for selecting and adjusting contract templates based on past cases, taking into account conditions entered by the user.

[0076] The system according to this invention is designed for the efficient processing of contract information and is operated in cooperation with a server, terminal, and user. The server receives contract information from the user via the terminal and is responsible for analyzing the information using natural language processing technology. Specifically, the server uses Python natural language processing libraries such as NLTK and SpaCy to analyze the text data of contracts using methods such as tokenization, morphological analysis, and syntactic analysis. This allows for the detection of errors and risks in contract content and supports efficient contract management.

[0077] The server also generates and proposes corrective measures based on the detected errors and risks. Users can then review and revise the contract based on these suggestions. The terminal visually displays detailed analysis results and proposed revisions, allowing users to intuitively evaluate the results. When creating a new contract according to user requests, the server automatically selects an appropriate template based on the input conditions and generates the final document. Users can customize the contract template and save it for future use as needed.

[0078] A concrete example is a scenario where a user attempts to create a new contract for the sale of goods. The user inputs the sales conditions and terms of conclusion via a terminal, and the server selects the most suitable template based on those conditions while incorporating the clauses requested by the user. Through this process, the user can finalize the contract more quickly and accurately.

[0079] An example of a prompt message would be: "Create a new sales contract. Sales conditions: Deliver 50 units of product A by the end of the year. Agreement: Penalties will be applied for delays in delivery." In this way, this system streamlines the contract creation process from start to finish, enabling users to perform their tasks reliably.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server receives contract information from the user via their terminal. The input is contract draft data sent by the user from their terminal. The server receives this data and stores it as text-based contract information. Specifically, the server retrieves the data using an HTTP request and stores it in a database.

[0083] Step 2:

[0084] The server extracts contract text from the received contract information and performs preprocessing. The input is contract information stored on the server. The server tokenizes this information for text analysis, performs noise reduction and formatting standardization, and outputs preprocessed data. Specifically, it executes a script written in Python to perform grammar checks and remove unnecessary symbols.

[0085] Step 3:

[0086] The server analyzes pre-processed contract information using natural language processing techniques. The input is tokenized and formatted data. The server uses natural language processing libraries (e.g., NLTK and SpaCy) to analyze the text, apply syntactic and semantic analysis, and output the analysis results. Specifically, it identifies keywords within the contract document and extracts structural information.

[0087] Step 4:

[0088] The server detects errors and risks based on the analysis results. The input is already analyzed data. The server compares this data with past cases to identify errors and legal risks and outputs that information. Specifically, it accesses a knowledge base and lists the relevant risks.

[0089] Step 5:

[0090] The server generates corrective action plans based on the errors and risks it detects. The input is the detection results, and the server uses this information to create a proposal document containing improvement suggestions and recommendations, which it then outputs. Specifically, it uses a template for corrections to write out corrective action plans that include specific wording.

[0091] Step 6:

[0092] The terminal presents the user with suggested corrections and analysis results sent from the server. The input is suggested correction data from the server. The terminal visually displays this data to the user, highlighting detected errors and risks. Dedicated display software is involved in the specific operations, presenting information with a user-friendly UI.

[0093] Step 7:

[0094] Users modify and review contract details via their terminals. Input consists of proposed revisions and draft contracts displayed on the terminal. Users make the necessary revisions based on this information and finally export the revised contract. Specifically, they use a text editor on the screen to directly make corrections and upload the updated version to the server.

[0095] Step 8:

[0096] When a user creates a new contract, the server completes the contract through an automated generation process. The input consists of data based on the user's requirements and choices. Based on the specified conditions, the server selects the most suitable template, customizes it as needed, and outputs the new contract. Specific operations include referencing and applying existing templates from a database.

[0097] (Application Example 1)

[0098] 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."

[0099] There is a need to improve the efficiency of contract processing, quickly generate complex contract documents, and minimize errors and risks. Furthermore, a system is required that allows end-users to easily input contract information and review optimized contract content. Additionally, a mechanism that utilizes cloud computing to securely process information while optimizing resources is needed.

[0100] 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.

[0101] In this invention, the server includes means for receiving contract information, means for analyzing and pre-processing language data, means for detecting errors and risks, means for generating data, means for displaying data, means for end users to input contract information using a smart device and process the data through cloud computing, and means for presenting alternatives and optimizing the contract document to suit the user. This enables end users to process contracts efficiently.

[0102] "Contract information" refers to data and information related to a contract, including transaction terms and details of the parties involved.

[0103] "Linguistic data" refers to text data written in natural language, specifically the documents used as part of a contract.

[0104] "Analysis means" refer to the technical elements used to analyze linguistic data and understand its structure and meaning.

[0105] "Detection means" refers to methods and functions for finding errors or risks from analyzed data.

[0106] "Generation means" refers to a function that creates new proposals or documents based on contract information and detected results.

[0107] "Display means" refers to interfaces and technologies used to visually present information to end users.

[0108] A "smart device" refers to a portable device equipped with advanced functions that users use to operate it, such as a smartphone or tablet.

[0109] "Cloud computing" is a technology that provides resources via the internet to efficiently process and store information.

[0110] An "alternative" is a choice or suggested correction offered to rectify an detected error or risk.

[0111] "Optimization" refers to the process of arranging contract information to best meet the user's requirements.

[0112] This invention is a system that efficiently processes contract information and automatically generates optimal contracts. The user starts by using a smart device to input the necessary contract information. This information is transmitted to a server via the cloud. The server uses natural language processing technology to analyze the contract information and preprocess the language data. Specific tools used include natural language processing libraries such as spaCy and NLTK. Based on this analysis, the server detects potential errors and risks in the contract content and automatically generates appropriate alternatives.

[0113] Cloud computing forms the foundation of the process, and information is centrally managed online. Users can review alternatives and suggested revisions presented on their smart device displays and make adjustments as needed. This allows users to easily create legally accurate and secure contracts without requiring advanced expertise.

[0114] For example, if a user wants to create new credit card terms and conditions, they open the smartphone app and enter their desired conditions. The server then automatically suggests the best conditions based on this information. The new contract is also created by referencing past successful contract examples. The generative AI model optimizes its suggestions by using prompts like the following.

[0115] Example prompt: "Analyze the contract, identify legal risks, and generate proposed revisions. The following contract terms are for the purchase of electronic equipment from Company X, with a down payment of 100,000 yen, monthly payments of 30,000 yen, and a loan term of 24 months."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] Users use their smart devices to enter the information required for the contract. This input includes contract terms and details of the parties involved. This data is sent from the device to the server. Users enter the information through an easy-to-use user interface and submit the data by pressing the submit button.

[0119] Step 2:

[0120] The server performs preprocessing to analyze the received contract information. The specific input is text information sent by the user, and the output is language data formatted for natural language processing. During this process, preprocessing such as noise reduction and tokenization is performed.

[0121] Step 3:

[0122] The server uses natural language processing techniques to analyze the formatted language data. The input is pre-processed language data, and the output is the analysis result for understanding the structure and meaning of the contract. At this stage, libraries such as spaCy and NLTK are used to structure the contract information and perform topic modeling.

[0123] Step 4:

[0124] The server detects potential errors and risks in the contract based on the analysis results. The input is the analysis results, and the output is the detection results regarding errors and risks. In this process, a generative AI model is used to automatically identify risks within the contract document.

[0125] Step 5:

[0126] The server generates corrective suggestions based on detected errors and risks. The specific input is the detection results regarding errors and risks, and the output is corrective suggestions and alternatives. The generation AI model generates appropriate proposals for improving the contract terms. In this process, it may refer to a database of past success stories.

[0127] Step 6:

[0128] The terminal displays generated modification suggestions and alternatives to the user. The input is the modification suggestions and alternatives, and the output is the user's visualization screen. The user can review this and, if necessary, adopt or fine-tune the suggested modifications. The display clearly shows the suggested items and their benefits.

[0129] Step 7:

[0130] The user reviews the final contract via their device and makes any necessary adjustments. The input is the revised proposal, and the output is the finalized contract. The user provides feedback through touchscreen operation or voice input. Once the contract is complete, it is formally confirmed by digital signature.

[0131] 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.

[0132] This invention provides a system that recognizes the user's emotional state and offers an optimal interface by incorporating an emotion engine into a contract management system. The system functions through the coordinated operation of the server, terminal, user, and emotion engine.

[0133] The server receives contract data from the user via the terminal. This contract data is extracted and preprocessed as text and analyzed using natural language processing technology. Based on the analysis results, the server detects errors and legal risks and generates proposed corrections. The generated proposed corrections and risk information are presented to the user through an emotion engine in a format appropriate to the user's emotional state.

[0134] The emotion engine detects changes in emotion in real time from user input and voice data. Based on this emotion data, the system estimates the user's stress level and comprehension level, and adjusts how contract information is presented. For example, if the user is feeling anxious, the information can be presented more clearly and in a hierarchical manner.

[0135] As a concrete example, consider a scenario where a user is reviewing a contract using their device. As the user considers the displayed revisions, the emotion engine analyzes the user's facial expressions and tone of voice to determine their level of understanding and emotional state. If the user appears confused, additional explanations and visual support are displayed on the device to provide further reassurance.

[0136] Furthermore, when a user creates a new contract, the server generates a contract template based on the specified conditions. At this time, the emotion engine re-analyzes the user's reactions and proposes the most suitable contract terms within a range that does not cause stress.

[0137] This system reduces the mental burden on users in contract work and allows them to efficiently and reliably review and create contract details.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user selects contract data using their device and uploads it to the server. The uploaded data is sent in document format as a contract.

[0141] Step 2:

[0142] The server extracts the received contract data into text format and performs preprocessing such as removing punctuation and irregular spaces. The preprocessed text is then prepared for analysis.

[0143] Step 3:

[0144] The server uses natural language processing techniques to analyze the pre-processed text. This analysis includes morphological analysis and dependency parsing, as well as a process to understand the sentence structure within the contract.

[0145] Step 4:

[0146] Based on the analysis results, the server identifies errors and legal risks in the contract. For risk detection, it applies established rule-based approaches and machine learning models.

[0147] Step 5:

[0148] The server automatically generates specific corrective suggestions based on the errors and risks it detects. These suggestions include parts that need to be corrected and clauses that need to be added.

[0149] Step 6:

[0150] The emotion engine analyzes the emotions a user expresses in real time while reviewing a contract. This analysis is based on the tone of voice the user uses when making voice inputs, as well as the speed and patterns of their text inputs.

[0151] Step 7:

[0152] The terminal displays information about errors and suggested corrections sent from the server, as well as information tailored to the user's emotional state, as analyzed by the emotion engine. The information is presented in a format optimized for the user's emotional state.

[0153] Step 8:

[0154] Users review the proposed revisions presented on their devices and, if necessary, provide feedback or decide whether to adopt the revisions. Throughout this process, user reactions are continuously monitored by the sentiment engine and reflected in the displayed content.

[0155] Step 9:

[0156] When a user creates a new contract, the server generates a draft of the new contract using past contract data and templates. The emotion engine adjusts the draft based on the user's input to reduce stress.

[0157] Step 10:

[0158] Finally, the contract reviewed and edited by the user is saved on the server, ready for later reference or submission to the legal department. Throughout the entire process, users can smoothly proceed with contract work in an emotionally supportive environment.

[0159] (Example 2)

[0160] 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".

[0161] Traditional contract management systems have faced challenges such as limited support for users to understand contract information and avoid errors and risks, as well as a lack of presentation and adjustment of contract information based on the user's emotional state. As a result, users often experience stress during contract work, potentially leading to misunderstandings and risks.

[0162] 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.

[0163] In this invention, the server includes receiving means for receiving contract information, analysis means for extracting and pre-processing character data, and detection means for analyzing the contract information using natural language processing technology and detecting errors and risks. This enables the presentation of information in a manner appropriate to the user's emotional state, facilitating understanding and risk avoidance.

[0164] A "receiving means" is a means that has the function of taking in contract information from users from an external source and supplying it to the system for processing.

[0165] An "analysis tool" is a means that has the function of converting received contract information into a format that is easy to process, extracting necessary information, and preparing it for the next processing stage.

[0166] "Natural language processing technology" is a technique that converts string data into a format that can be processed by a computer, and analyzes natural language written by humans to understand its meaning and structure.

[0167] A "detection means" is a means that automatically identifies errors and legal risks from contract information processed by an analysis means and notifies the user.

[0168] A "generation method" is a means that has the function of generating corrective suggestions to address detected errors and risks, and presenting them in a format that users can easily understand and adopt.

[0169] A "display means" is a means that presents contract information and proposed revisions in an appropriate format based on the user's emotional state, and has the function of helping the user understand the information.

[0170] An "emotion analysis tool" is a tool that detects the user's emotional state based on user input, voice, and video information, and reflects this in the system's operation.

[0171] This invention comprehensively provides a contract management system that includes methods for receiving, analyzing, detecting errors in, generating correction suggestions for, and displaying contract information. This system primarily consists of a server, terminals, and a sentiment analysis engine, all of which work in conjunction with each other.

[0172] The server receives contract information sent from the user via the terminal. The received information is preprocessed on the server as text data. This preprocessing includes noise reduction and character code conversion of the contract information, preparing it for smooth analysis. Specifically, important content is extracted from the contract information using natural language processing technology. This is done using commonly used libraries for text analysis software (e.g., spaCy).

[0173] When detecting errors or legal risks, the server performs detection processing based on the analysis results. This is a crucial step in ensuring the integrity of the contract and minimizing risks. Subsequently, correction suggestions for the detected errors are generated and presented to the user. These correction suggestions aim to further optimize the contract information and avoid legal risks.

[0174] Meanwhile, the device analyzes the user's emotional state in real time via an emotion analysis engine. This analysis includes facial expression analysis and voice tone analysis; for example, it uses OpenCV to track the user's facial expressions and a voice analysis library to evaluate the tone of their voice. The analysis results are shared with the server, and information is presented according to the user's emotional state.

[0175] For example, when a user is reviewing a contract, the device analyzes the user's emotional state and, if it determines the user is confused, provides detailed explanations and visual support. Furthermore, when a user generates a new contract, the server quickly generates a contract template based on pre-configured conditions and prompts the user for input.

[0176] Examples of prompts include, "What is the best way to point out errors in a contract?" and "How should information be organized and presented when the user is feeling stressed?"

[0177] This system reduces stress in contract work and allows users to efficiently and reliably verify and generate contract information.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The server receives contract information from the user via the terminal. The received contract information is imported into the system as text data. Contract information is obtained as input, and pre-processed text data is obtained as output. This pre-processing includes the removal of unnecessary spaces and special characters.

[0181] Step 2:

[0182] The server performs natural language processing on pre-processed text data. Using analysis software (e.g., spaCy), it extracts keywords related to important items and risks from the contract information. Pre-processed text data is used as input, and the extracted important information is output. Here, the grammatical consistency of the contract content is verified, and potential legal risks are identified.

[0183] Step 3:

[0184] The server generates corrective suggestions for errors and risks detected based on the analysis results. The generated suggestions are presented in a user-friendly format. The analysis results are used as input, and corrective suggestions are obtained as output. Specifically, the system automatically constructs suggestions based on predefined legal guidelines.

[0185] Step 4:

[0186] The device uses an emotion analysis engine to analyze the user's emotional state in real time. Here, the user's facial expressions and voice data are acquired as input, and information about the user's emotional state is obtained as output. Specifically, facial expression analysis software and a voice analysis library are used to evaluate the user's stress level and comprehension.

[0187] Step 5:

[0188] The server optimizes how information is presented to the user based on emotional state information obtained from the emotion analysis engine. Emotional state information is the input, and the adjusted information presentation is the output. If the user shows signs of tension or confusion, the server assists the user's understanding by displaying the information in a clearer, hierarchical manner.

[0189] Step 6:

[0190] The user reviews the proposed revisions and adjusts the contract terms as needed. Based on the user's interaction, a revised version of the contract is generated. The proposed revisions are the input, and the new contract terms are finalized as the output. Specifically, the user can monitor and reflect changes in real time using the interface on their device.

[0191] (Application Example 2)

[0192] 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".

[0193] Traditional contract management systems often fail to consider the user's level of understanding or emotional state when reviewing or revising contract details, potentially causing anxiety and stress. Furthermore, real-world contract procedures often lack sufficient user interaction to ensure a sense of security. In addition, inadequate support during the presentation of contract terms can prolong the contract process, leading to decreased efficiency and potentially hindering the successful completion of contracts.

[0194] 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.

[0195] In this invention, the server includes a receiving means, an analysis means, a detection means, a generation means, a display means, an emotion analysis means, and a dialogue support means. This allows for real-time analysis of the user's emotional state and adjustment of the appropriate method of presenting contract information, thereby providing the user with a sense of security and facilitating the smooth execution of contract procedures.

[0196] "Receiving means" refers to a device or method for acquiring contract data from an external terminal or network and importing it into the system.

[0197] "Analysis means" refers to a device or method that extracts received contract data as text and performs data processing or transformation.

[0198] "Detection means" refers to a device or method that uses natural language processing technology to analyze the content of contract data and identify errors or legal risks.

[0199] "Generating means" refers to an apparatus or method for creating appropriate revised drafts or new contracts based on detected errors or risks.

[0200] "Display means" refers to a device or method for visually presenting errors, risks, and suggested corrections to a user.

[0201] "Emotional analysis means" refers to a device or method that analyzes a user's facial expressions and tone of voice to determine the user's emotional state in real time.

[0202] A "dialogue support device" is a device or method that adjusts the interface to provide information in a more understandable way and give users a sense of security when they have anxieties or questions.

[0203] This invention incorporates an emotion engine and an interactive interface to improve the user experience in a contract management system. The system operates in conjunction with servers, terminals, and users.

[0204] First, the server uses a receiving means to acquire contract data from external devices or networks. The received data is extracted as text by an analysis means and preprocessed. Specifically, the analysis means uses natural language processing technology to detect errors and risks within the contract data. Based on the detected results, the generation means creates revised drafts or new contracts.

[0205] When generated revision proposals and risk information are presented to the user via display means, sentiment analysis means play a crucial role. The user's device captures the user's facial expressions and voice using a camera and microphone, and by analyzing this data, the system determines the user's emotional state in real time. This allows the system to estimate the user's stress level and level of understanding, and present information in an appropriate manner.

[0206] For example, if a user is using their smartphone to review contract details at a physical store, and sentiment analysis determines that the user is feeling anxious, additional explanations or visual guides will be displayed in response to the questions. This enhances the user's sense of security.

[0207] Examples of prompts generated using AI models include, "When purchasing a new smartphone, what contract terms would be most beneficial to the user?" and "Please explain these contract terms to the customer in the easiest way to understand." Such interface refinements are a means of smoothing real-world conversations and improving the user experience.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The server receives contract data from the user's terminal. The input here is the contract data uploaded by the user to the interface, and the server passes the received data to the next processing step for text extraction.

[0211] Step 2:

[0212] The server uses an analysis tool to extract text from the received contract data and performs preprocessing. The input is contract data, and the output is data in text format. Here, the text is cleaned and prepared for machine processing.

[0213] Step 3:

[0214] The server analyzes text data, which has been parsed using natural language processing technology, using detection methods to identify errors and risks in the contract. The input is pre-processed text data, and the output is a list of errors and risks. At this stage, it is checked whether the identified risks are due to a lack of legal requirements or inappropriate clauses.

[0215] Step 4:

[0216] The server generates revised proposals and a new contract using a generation mechanism based on the output of the detection mechanism. The input is a list of errors and risks, and the output is a revised proposal and a proposed document. At this stage, an AI model is used to automatically generate the optimal revision proposal.

[0217] Step 5:

[0218] The terminal presents the user with proposed revisions and risk information through its display mechanisms. This input is the generated proposed revisions, and the output is displayed on the interface in a format that is easy for the user to understand.

[0219] Step 6:

[0220] The device uses its camera and microphone to determine the user's emotional state in real time through emotion analysis. Input is the user's facial expressions and tone of voice, while output is data related to the user's emotional state. This data is used to estimate the user's understanding of the billing statement and their stress level.

[0221] Step 7:

[0222] Based on the results of emotion analysis, the device adjusts the information presentation method using dialogue support mechanisms and displays additional explanations and visual guides. Input is the user's emotional state data, and output is an adjusted interface and additional information. This allows for quick responses when the user experiences anxiety or questions.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention provides a system for efficiently processing contract data. The system operates in cooperation with a server, terminals, and users to support the creation and verification of contracts.

[0240] The server first receives contract data from the user via the terminal. The received data is then analyzed using natural language processing techniques. Specifically, the server analyzes the text of the contract and applies natural language processing to understand its syntax and meaning.

[0241] Based on the analysis results, the server scrutinizes the contract and detects errors and legal risks. Using this analysis information, the server generates revised proposals for the user. These include specific suggestions for correcting errors and recommendations for clauses that should be improved.

[0242] The user visually confirms this information using their terminal. The terminal displays a list of proposed revisions and detailed analysis results of the contract sent from the server to the user. The user can review the proposed revisions and revise the contract as needed.

[0243] Furthermore, when a user creates a new contract, the server generates a new contract based on an existing template. In this process, the server considers past successes and selects the clauses that best suit the conditions specified by the user. Based on this, the user can quickly create a new contract.

[0244] As a concrete example, consider a scenario where a user creates a new sales contract. The user inputs the sales terms and agreements on their terminal. The server receives this information, selects an appropriate template, and automatically customizes the clauses. The final contract is completed after review and minor adjustments by the user.

[0245] Thus, the present invention significantly improves the efficiency of contract drafting and review, providing an environment in which users can confidently carry out contract work.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] Users select contract data using their terminals and upload it to the server. The system supports common document formats such as PDF and Word.

[0249] Step 2:

[0250] The server receives the contract data and extracts text from the file. The extracted text is preprocessed to remove unnecessary spaces and special characters.

[0251] Step 3:

[0252] The server analyzes the pre-processed text using natural language processing techniques. This includes morphological and syntactic analysis, and processing is performed to understand the structure and meaning of the sentences within the contract.

[0253] Step 4:

[0254] The server detects errors and potential legal risks based on the analysis results. It uses pre-configured rule-based systems and machine learning models to identify areas of risk.

[0255] Step 5:

[0256] The server generates proposed corrections based on the detection results. These corrections include suggested fixes for identified errors and recommended clause changes. It also refers to historical contract data to determine the optimal correction strategy.

[0257] Step 6:

[0258] The terminal displays errors, risks, and suggested solutions sent from the server to the user. The information is presented in an intuitive format using graphs and marked-up text.

[0259] Step 7:

[0260] The user reviews the proposed revisions via their device and applies the changes as needed, thereby finalizing the contract's structure.

[0261] Step 8:

[0262] When a user creates a new contract, the server generates a new contract using a template based on the requirements entered on the terminal. Appropriate clauses are automatically selected based on past success stories.

[0263] Step 9:

[0264] The generated contract is sent to the user's device, where the user reviews and edits it as needed. The completed contract is saved on the server as the final version.

[0265] (Example 1)

[0266] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] Traditional contract drafting and review processes relied heavily on manual work, requiring significant time and effort to identify errors and assess legal risks. This led to decreased efficiency in contract operations and ultimately impacted overall business productivity. Furthermore, the inability to fully utilize past examples in drafting new contracts resulted in inconsistent quality.

[0268] 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.

[0269] In this invention, the server includes communication means for receiving contract information, analysis means for extracting and pre-processing text from the contract information, and detection means for analyzing the contract information using natural language processing technology and detecting errors and risks. This enables the automatic and rapid detection of errors and risks in contracts by making full use of advanced natural language processing, allowing for the efficient and reliable execution of contract work.

[0270] "Contract information" refers to information consisting of all data and documents related to a contract, and includes the agreements and conditions between the parties.

[0271] "Communication methods" refer to the technical devices and protocols that servers use to exchange information with users and terminals.

[0272] "Text" refers to the elements that make up the text data within a contract, specifically used to express clauses and conditions.

[0273] "Analysis means" refers to technical mechanisms and devices for extracting necessary text from contract information and performing preprocessing.

[0274] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to detect errors and risks in the analysis of contract information.

[0275] "Detection means" refers to methods and devices that use natural language processing technology to discover errors and risks in contract information.

[0276] "Suggestion mechanism" refers to a system for creating and providing corrective suggestions to users based on detected errors and risks.

[0277] "Display means" refers to devices or methods for visually presenting errors, risks, and suggested corrections to the user.

[0278] "Document generation means" refers to a system or technology for creating new contracts based on user input.

[0279] "Optimization method" refers to technology for selecting and adjusting contract templates based on past cases, taking into account conditions entered by the user.

[0280] The system related to this invention is for efficiently processing contract information, and the server, terminal, and user operate in cooperation. The server receives contract information from the user via the terminal and plays a role in analyzing the information using natural language processing technology. Specifically, the server uses NLTK and SpaCy, which are natural language processing libraries in Python, to analyze the text data of the contract document using methods such as tokenization, morphological analysis, and syntactic analysis. Thereby, errors and risks in the contract content are detected, and efficient contract operations are supported.

[0281] The server also generates an amendment based on the detected errors and risks and proposes it to the user. Based on this, the user can confirm and correct the contract content. On the terminal, the detailed analysis results and amendments of the contract document are visually displayed, and the user can intuitively evaluate the results. When creating a new contract document according to the user's request, the server automatically selects an appropriate template based on the input conditions and generates the final document. The user can customize the contract document template and save the template for later use if necessary.

[0282] [[ID=捌]]As a specific example, a situation where a user attempts to create a new contract document regarding a commodity sales contract can be cited. The user inputs the sales conditions and conclusion conditions via the terminal, and the server selects the optimal template based on those conditions while incorporating the clauses desired by the user. Through this series of processes, the user can more quickly and accurately finalize the contract content.

[0283] As an example of the prompt sentence, a format such as "New creation of a sales contract. Sales conditions: Deliver 50 units of Product A within the year. Agreement items: Penalties will be applied for late delivery." can be considered. Thus, this system consistently improves the efficiency of the contract document creation work and enables reliable business performance for the user.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The server receives contract information from the user through the terminal. As input, there is the data of the contract draft sent by the user from the terminal. The server receives this and stores it as contract information in text format. As a specific operation, the server uses an HTTP request to obtain the data and stores it in a database.

[0287] Step 2:

[0288] The server extracts the text of the contract from the received contract information and performs preprocessing. The input is the contract information stored in the server. The server tokenizes this information for text analysis, performs noise removal and format unification, and outputs the preprocessed data. Specifically, it executes a script written in Python to perform grammar checking and remove unnecessary symbols.

[0289] Step 3:

[0290] The server analyzes the preprocessed contract information using natural language processing techniques. The input is the tokenized and formatted data. The server uses a natural language processing library (e.g., NLTK or SpaCy) to analyze the text, applies syntactic and semantic analysis to the text, and outputs the analysis results. Specifically, it identifies the keywords existing in the contract text and extracts the structural information.

[0291] Step 4:

[0292] The server detects errors and risks based on the analysis results. The input is the analyzed data. The server compares this data based on past cases, identifies errors and legal risks, and outputs that information. As a specific operation, it accesses a knowledge base and lists the corresponding risks.

[0293] Step 5:

[0294] The server generates corrective action plans based on the errors and risks it detects. The input is the detection results, and the server uses this information to create a proposal document containing improvement suggestions and recommendations, which it then outputs. Specifically, it uses a template for corrections to write out corrective action plans that include specific wording.

[0295] Step 6:

[0296] The terminal presents the user with suggested corrections and analysis results sent from the server. The input is suggested correction data from the server. The terminal visually displays this data to the user, highlighting detected errors and risks. Dedicated display software is involved in the specific operations, presenting information with a user-friendly UI.

[0297] Step 7:

[0298] Users modify and review contract details via their terminals. Input consists of proposed revisions and draft contracts displayed on the terminal. Users make the necessary revisions based on this information and finally export the revised contract. Specifically, they use a text editor on the screen to directly make corrections and upload the updated version to the server.

[0299] Step 8:

[0300] When a user creates a new contract, the server completes the contract through an automated generation process. The input consists of data based on the user's requirements and choices. Based on the specified conditions, the server selects the most suitable template, customizes it as needed, and outputs the new contract. Specific operations include referencing and applying existing templates from a database.

[0301] (Application Example 1)

[0302] 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."

[0303] There is a need to improve the efficiency of contract processing, quickly generate complex contract documents, and minimize errors and risks. In addition, there is a need for a system that allows end-users to easily input contract information and view optimized contract content. Furthermore, there is a need for a mechanism that utilizes cloud computing to process information securely while optimizing resources.

[0304] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0305] In this invention, the server includes means for receiving contract information, analysis means for extracting language data and performing preprocessing, detection means for detecting errors and risks, generation means, display means, means for end-users to input contract information using a smart device and process data through cloud computing, and means for presenting alternatives and optimizing compliant contract documents. As a result, end-users can efficiently perform contract processing.

[0306] "Contract information" refers to data and information related to a contract, including transaction conditions and details of the parties.

[0307] "Language data" refers to text data described in natural language and refers to the text that is treated as the contract content.

[0308] "Analysis means" refers to the technical elements for analyzing language data to understand its structure and meaning.

[0309] "Detection means" refers to the methods and functions for finding errors and risks from the analyzed data.

[0310] "Generation means" refers to the function of creating new proposals and documents based on contract information and the detected results.

[0311] "Display means" refers to interfaces and technologies used to visually present information to end users.

[0312] A "smart device" refers to a portable device equipped with advanced functions that users use to operate it, such as a smartphone or tablet.

[0313] "Cloud computing" is a technology that provides resources via the internet to efficiently process and store information.

[0314] An "alternative" is a choice or suggested correction offered to rectify an detected error or risk.

[0315] "Optimization" refers to the process of arranging contract information to best meet the user's requirements.

[0316] This invention is a system that efficiently processes contract information and automatically generates optimal contracts. The user starts by using a smart device to input the necessary contract information. This information is transmitted to a server via the cloud. The server uses natural language processing technology to analyze the contract information and preprocess the language data. Specific tools used include natural language processing libraries such as spaCy and NLTK. Based on this analysis, the server detects potential errors and risks in the contract content and automatically generates appropriate alternatives.

[0317] Cloud computing forms the foundation of the process, and information is centrally managed online. Users can review alternatives and suggested revisions presented on their smart device displays and make adjustments as needed. This allows users to easily create legally accurate and secure contracts without requiring advanced expertise.

[0318] For example, if a user wants to create new credit card terms and conditions, they open the smartphone app and enter their desired conditions. The server then automatically suggests the best conditions based on this information. The new contract is also created by referencing past successful contract examples. The generative AI model optimizes its suggestions by using prompts like the following.

[0319] Example prompt: "Analyze the contract, identify legal risks, and generate proposed revisions. The following contract terms are for the purchase of electronic equipment from Company X, with a down payment of 100,000 yen, monthly payments of 30,000 yen, and a loan term of 24 months."

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] Users use their smart devices to enter the information required for the contract. This input includes contract terms and details of the parties involved. This data is sent from the device to the server. Users enter the information through an easy-to-use user interface and submit the data by pressing the submit button.

[0323] Step 2:

[0324] The server performs preprocessing to analyze the received contract information. The specific input is text information sent by the user, and the output is language data formatted for natural language processing. During this process, preprocessing such as noise reduction and tokenization is performed.

[0325] Step 3:

[0326] The server uses natural language processing techniques to analyze the formatted language data. The input is pre-processed language data, and the output is the analysis result for understanding the structure and meaning of the contract. At this stage, libraries such as spaCy and NLTK are used to structure the contract information and perform topic modeling.

[0327] Step 4:

[0328] The server detects potential errors and risks in the contract based on the analysis results. The input is the analysis results, and the output is the detection results regarding errors and risks. In this process, a generative AI model is used to automatically identify risks within the contract document.

[0329] Step 5:

[0330] The server generates corrective suggestions based on detected errors and risks. The specific input is the detection results regarding errors and risks, and the output is corrective suggestions and alternatives. The generation AI model generates appropriate proposals for improving the contract terms. In this process, it may refer to a database of past success stories.

[0331] Step 6:

[0332] The terminal displays generated modification suggestions and alternatives to the user. The input is the modification suggestions and alternatives, and the output is the user's visualization screen. The user can review this and, if necessary, adopt or fine-tune the suggested modifications. The display clearly shows the suggested items and their benefits.

[0333] Step 7:

[0334] The user reviews the final contract via their device and makes any necessary adjustments. The input is the revised proposal, and the output is the finalized contract. The user provides feedback through touchscreen operation or voice input. Once the contract is complete, it is formally confirmed by digital signature.

[0335] 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.

[0336] This invention provides a system that recognizes the user's emotional state and offers an optimal interface by incorporating an emotion engine into a contract management system. The system functions through the coordinated operation of the server, terminal, user, and emotion engine.

[0337] The server receives contract data from the user via the terminal. This contract data is extracted and preprocessed as text and analyzed using natural language processing technology. Based on the analysis results, the server detects errors and legal risks and generates proposed corrections. The generated proposed corrections and risk information are presented to the user through an emotion engine in a format appropriate to the user's emotional state.

[0338] The emotion engine detects changes in emotion in real time from user input and voice data. Based on this emotion data, the system estimates the user's stress level and comprehension level, and adjusts how contract information is presented. For example, if the user is feeling anxious, the information can be presented more clearly and in a hierarchical manner.

[0339] As a concrete example, consider a scenario where a user is reviewing a contract using their device. As the user considers the displayed revisions, the emotion engine analyzes the user's facial expressions and tone of voice to determine their level of understanding and emotional state. If the user appears confused, additional explanations and visual support are displayed on the device to provide further reassurance.

[0340] Furthermore, when a user creates a new contract, the server generates a contract template based on the specified conditions. At this time, the emotion engine re-analyzes the user's reactions and proposes the most suitable contract terms within a range that does not cause stress.

[0341] This system reduces the mental burden on users in contract work and allows them to efficiently and reliably review and create contract details.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user selects contract data using their device and uploads it to the server. The uploaded data is sent in document format as a contract.

[0345] Step 2:

[0346] The server extracts the received contract data into text format and performs preprocessing such as removing punctuation and irregular spaces. The preprocessed text is then prepared for analysis.

[0347] Step 3:

[0348] The server uses natural language processing techniques to analyze the pre-processed text. This analysis includes morphological analysis and dependency parsing, as well as a process to understand the sentence structure within the contract.

[0349] Step 4:

[0350] Based on the analysis results, the server identifies errors and legal risks in the contract. For risk detection, it applies established rule-based approaches and machine learning models.

[0351] Step 5:

[0352] The server automatically generates specific corrective suggestions based on the errors and risks it detects. These suggestions include parts that need to be corrected and clauses that need to be added.

[0353] Step 6:

[0354] The emotion engine analyzes the emotions a user expresses in real time while reviewing a contract. This analysis is based on the tone of voice the user uses when making voice inputs, as well as the speed and patterns of their text inputs.

[0355] Step 7:

[0356] The terminal displays information about errors and suggested corrections sent from the server, as well as information tailored to the user's emotional state, as analyzed by the emotion engine. The information is presented in a format optimized for the user's emotional state.

[0357] Step 8:

[0358] Users review the proposed revisions presented on their devices and, if necessary, provide feedback or decide whether to adopt the revisions. Throughout this process, user reactions are continuously monitored by the sentiment engine and reflected in the displayed content.

[0359] Step 9:

[0360] When a user creates a new contract, the server generates a draft of the new contract using past contract data and templates. The emotion engine adjusts the draft based on the user's input to reduce stress.

[0361] Step 10:

[0362] Finally, the contract reviewed and edited by the user is saved on the server, ready for later reference or submission to the legal department. Throughout the entire process, users can smoothly proceed with contract work in an emotionally supportive environment.

[0363] (Example 2)

[0364] 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".

[0365] Traditional contract management systems have faced challenges such as limited support for users to understand contract information and avoid errors and risks, as well as a lack of presentation and adjustment of contract information based on the user's emotional state. As a result, users often experience stress during contract work, potentially leading to misunderstandings and risks.

[0366] 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.

[0367] In this invention, the server includes receiving means for receiving contract information, analysis means for extracting and pre-processing character data, and detection means for analyzing the contract information using natural language processing technology and detecting errors and risks. This enables the presentation of information in a manner appropriate to the user's emotional state, facilitating understanding and risk avoidance.

[0368] A "receiving means" is a means that has the function of taking in contract information from users from an external source and supplying it to the system for processing.

[0369] An "analysis tool" is a means that has the function of converting received contract information into a format that is easy to process, extracting necessary information, and preparing it for the next processing stage.

[0370] "Natural language processing technology" is a technique that converts string data into a format that can be processed by a computer, and analyzes natural language written by humans to understand its meaning and structure.

[0371] A "detection means" is a means that automatically identifies errors and legal risks from contract information processed by an analysis means and notifies the user.

[0372] A "generation method" is a means that has the function of generating corrective suggestions to address detected errors and risks, and presenting them in a format that users can easily understand and adopt.

[0373] A "display means" is a means that presents contract information and proposed revisions in an appropriate format based on the user's emotional state, and has the function of helping the user understand the information.

[0374] An "emotion analysis tool" is a tool that detects the user's emotional state based on user input, voice, and video information, and reflects this in the system's operation.

[0375] This invention comprehensively provides a contract management system that includes methods for receiving, analyzing, detecting errors in, generating correction suggestions for, and displaying contract information. This system primarily consists of a server, terminals, and a sentiment analysis engine, all of which work in conjunction with each other.

[0376] The server receives contract information sent from the user via the terminal. The received information is preprocessed on the server as text data. This preprocessing includes noise reduction and character code conversion of the contract information, preparing it for smooth analysis. Specifically, important content is extracted from the contract information using natural language processing technology. This is done using commonly used libraries for text analysis software (e.g., spaCy).

[0377] When detecting errors or legal risks, the server performs detection processing based on the analysis results. This is a crucial step in ensuring the integrity of the contract and minimizing risks. Subsequently, correction suggestions for the detected errors are generated and presented to the user. These correction suggestions aim to further optimize the contract information and avoid legal risks.

[0378] Meanwhile, the device analyzes the user's emotional state in real time via an emotion analysis engine. This analysis includes facial expression analysis and voice tone analysis; for example, it uses OpenCV to track the user's facial expressions and a voice analysis library to evaluate the tone of their voice. The analysis results are shared with the server, and information is presented according to the user's emotional state.

[0379] For example, when a user is reviewing a contract, the device analyzes the user's emotional state and, if it determines the user is confused, provides detailed explanations and visual support. Furthermore, when a user generates a new contract, the server quickly generates a contract template based on pre-configured conditions and prompts the user for input.

[0380] Examples of prompts include, "What is the best way to point out errors in a contract?" and "How should information be organized and presented when the user is feeling stressed?"

[0381] This system reduces stress in contract work and allows users to efficiently and reliably verify and generate contract information.

[0382] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0383] Step 1:

[0384] The server receives contract information from the user via the terminal. The received contract information is imported into the system as text data. Contract information is obtained as input, and pre-processed text data is obtained as output. This pre-processing includes the removal of unnecessary spaces and special characters.

[0385] Step 2:

[0386] The server performs natural language processing on pre-processed text data. Using analysis software (e.g., spaCy), it extracts keywords related to important items and risks from the contract information. Pre-processed text data is used as input, and the extracted important information is output. Here, the grammatical consistency of the contract content is verified, and potential legal risks are identified.

[0387] Step 3:

[0388] The server generates corrective suggestions for errors and risks detected based on the analysis results. The generated suggestions are presented in a user-friendly format. The analysis results are used as input, and corrective suggestions are obtained as output. Specifically, the system automatically constructs suggestions based on predefined legal guidelines.

[0389] Step 4:

[0390] The device uses an emotion analysis engine to analyze the user's emotional state in real time. Here, the user's facial expressions and voice data are acquired as input, and information about the user's emotional state is obtained as output. Specifically, facial expression analysis software and a voice analysis library are used to evaluate the user's stress level and comprehension.

[0391] Step 5:

[0392] The server optimizes how information is presented to the user based on emotional state information obtained from the emotion analysis engine. Emotional state information is the input, and the adjusted information presentation is the output. If the user shows signs of tension or confusion, the server assists the user's understanding by displaying the information in a clearer, hierarchical manner.

[0393] Step 6:

[0394] The user reviews the proposed revisions and adjusts the contract terms as needed. Based on the user's interaction, a revised version of the contract is generated. The proposed revisions are the input, and the new contract terms are finalized as the output. Specifically, the user can monitor and reflect changes in real time using the interface on their device.

[0395] (Application Example 2)

[0396] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0397] Traditional contract management systems often fail to consider the user's level of understanding or emotional state when reviewing or revising contract details, potentially causing anxiety and stress. Furthermore, real-world contract procedures often lack sufficient user interaction to ensure a sense of security. In addition, inadequate support during the presentation of contract terms can prolong the contract process, leading to decreased efficiency and potentially hindering the successful completion of contracts.

[0398] 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.

[0399] In this invention, the server includes a receiving means, an analysis means, a detection means, a generation means, a display means, an emotion analysis means, and a dialogue support means. This allows for real-time analysis of the user's emotional state and adjustment of the appropriate method of presenting contract information, thereby providing the user with a sense of security and facilitating the smooth execution of contract procedures.

[0400] "Receiving means" refers to a device or method for acquiring contract data from an external terminal or network and importing it into the system.

[0401] "Analysis means" refers to a device or method that extracts received contract data as text and performs data processing or transformation.

[0402] "Detection means" refers to a device or method that uses natural language processing technology to analyze the content of contract data and identify errors or legal risks.

[0403] "Generating means" refers to an apparatus or method for creating appropriate revised drafts or new contracts based on detected errors or risks.

[0404] "Display means" refers to a device or method for visually presenting errors, risks, and suggested corrections to a user.

[0405] "Emotional analysis means" refers to a device or method that analyzes a user's facial expressions and tone of voice to determine the user's emotional state in real time.

[0406] A "dialogue support device" is a device or method that adjusts the interface to provide information in a more understandable way and give users a sense of security when they have anxieties or questions.

[0407] This invention incorporates an emotion engine and an interactive interface to improve the user experience in a contract management system. The system operates in conjunction with servers, terminals, and users.

[0408] First, the server uses a receiving means to acquire contract data from external devices or networks. The received data is extracted as text by an analysis means and preprocessed. Specifically, the analysis means uses natural language processing technology to detect errors and risks within the contract data. Based on the detected results, the generation means creates revised drafts or new contracts.

[0409] When generated revision proposals and risk information are presented to the user via display means, sentiment analysis means play a crucial role. The user's device captures the user's facial expressions and voice using a camera and microphone, and by analyzing this data, the system determines the user's emotional state in real time. This allows the system to estimate the user's stress level and level of understanding, and present information in an appropriate manner.

[0410] For example, if a user is using their smartphone to review contract details at a physical store, and sentiment analysis determines that the user is feeling anxious, additional explanations or visual guides will be displayed in response to the questions. This enhances the user's sense of security.

[0411] Examples of prompts generated using AI models include, "When purchasing a new smartphone, what contract terms would be most beneficial to the user?" and "Please explain these contract terms to the customer in the easiest way to understand." Such interface refinements are a means of smoothing real-world conversations and improving the user experience.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The server receives contract data from the user's terminal. The input here is the contract data uploaded by the user to the interface, and the server passes the received data to the next processing step for text extraction.

[0415] Step 2:

[0416] The server uses an analysis tool to extract text from the received contract data and performs preprocessing. The input is contract data, and the output is data in text format. Here, the text is cleaned and prepared for machine processing.

[0417] Step 3:

[0418] The server analyzes text data, which has been parsed using natural language processing technology, using detection methods to identify errors and risks in the contract. The input is pre-processed text data, and the output is a list of errors and risks. At this stage, it is checked whether the identified risks are due to a lack of legal requirements or inappropriate clauses.

[0419] Step 4:

[0420] The server generates revised proposals and a new contract using a generation mechanism based on the output of the detection mechanism. The input is a list of errors and risks, and the output is a revised proposal and a proposed document. At this stage, an AI model is used to automatically generate the optimal revision proposal.

[0421] Step 5:

[0422] The terminal presents the user with proposed revisions and risk information through its display mechanisms. This input is the generated proposed revisions, and the output is displayed on the interface in a format that is easy for the user to understand.

[0423] Step 6:

[0424] The device uses its camera and microphone to determine the user's emotional state in real time through emotion analysis. Input is the user's facial expressions and tone of voice, while output is data related to the user's emotional state. This data is used to estimate the user's understanding of the billing statement and their stress level.

[0425] Step 7:

[0426] Based on the results of emotion analysis, the device adjusts the information presentation method using dialogue support mechanisms and displays additional explanations and visual guides. Input is the user's emotional state data, and output is an adjusted interface and additional information. This allows for quick responses when the user experiences anxiety or questions.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Third Embodiment]

[0431] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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".

[0443] This invention provides a system for efficiently processing contract data. The system operates in cooperation with a server, terminals, and users to support the creation and verification of contracts.

[0444] The server first receives contract data from the user via the terminal. The received data is then analyzed using natural language processing techniques. Specifically, the server analyzes the text of the contract and applies natural language processing to understand its syntax and meaning.

[0445] Based on the analysis results, the server scrutinizes the contract and detects errors and legal risks. Using this analysis information, the server generates revised proposals for the user. These include specific suggestions for correcting errors and recommendations for clauses that should be improved.

[0446] The user visually confirms this information using their terminal. The terminal displays a list of proposed revisions and detailed analysis results of the contract sent from the server to the user. The user can review the proposed revisions and revise the contract as needed.

[0447] Furthermore, when a user creates a new contract, the server generates a new contract based on an existing template. In this process, the server considers past successes and selects the clauses that best suit the conditions specified by the user. Based on this, the user can quickly create a new contract.

[0448] As a concrete example, consider a scenario where a user creates a new sales contract. The user inputs the sales terms and agreements on their terminal. The server receives this information, selects an appropriate template, and automatically customizes the clauses. The final contract is completed after review and minor adjustments by the user.

[0449] Thus, the present invention significantly improves the efficiency of contract drafting and review, providing an environment in which users can confidently carry out contract work.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] Users select contract data using their terminals and upload it to the server. The system supports common document formats such as PDF and Word.

[0453] Step 2:

[0454] The server receives the contract data and extracts text from the file. The extracted text is preprocessed to remove unnecessary spaces and special characters.

[0455] Step 3:

[0456] The server analyzes the pre-processed text using natural language processing techniques. This includes morphological and syntactic analysis, and processing is performed to understand the structure and meaning of the sentences within the contract.

[0457] Step 4:

[0458] The server detects errors and potential legal risks based on the analysis results. It uses pre-configured rule-based systems and machine learning models to identify areas of risk.

[0459] Step 5:

[0460] The server generates proposed corrections based on the detection results. These corrections include suggested fixes for identified errors and recommended clause changes. It also refers to historical contract data to determine the optimal correction strategy.

[0461] Step 6:

[0462] The terminal displays errors, risks, and suggested solutions sent from the server to the user. The information is presented in an intuitive format using graphs and marked-up text.

[0463] Step 7:

[0464] The user reviews the proposed revisions via their device and applies the changes as needed, thereby finalizing the contract's structure.

[0465] Step 8:

[0466] When a user creates a new contract, the server generates a new contract using a template based on the requirements entered on the terminal. Appropriate clauses are automatically selected based on past success stories.

[0467] Step 9:

[0468] The generated contract is sent to the user's device, where the user reviews and edits it as needed. The completed contract is saved on the server as the final version.

[0469] (Example 1)

[0470] 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."

[0471] Traditional contract drafting and review processes relied heavily on manual work, requiring significant time and effort to identify errors and assess legal risks. This led to decreased efficiency in contract operations and ultimately impacted overall business productivity. Furthermore, the inability to fully utilize past examples in drafting new contracts resulted in inconsistent quality.

[0472] 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.

[0473] In this invention, the server includes communication means for receiving contract information, analysis means for extracting and pre-processing text from the contract information, and detection means for analyzing the contract information using natural language processing technology and detecting errors and risks. This enables the automatic and rapid detection of errors and risks in contracts by making full use of advanced natural language processing, allowing for the efficient and reliable execution of contract work.

[0474] "Contract information" refers to information consisting of all data and documents related to a contract, and includes the agreements and conditions between the parties.

[0475] "Communication methods" refer to the technical devices and protocols that servers use to exchange information with users and terminals.

[0476] "Text" refers to the elements that make up the text data within a contract, specifically used to express clauses and conditions.

[0477] "Analysis means" refers to technical mechanisms and devices for extracting necessary text from contract information and performing preprocessing.

[0478] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to detect errors and risks in the analysis of contract information.

[0479] "Detection means" refers to methods and devices that use natural language processing technology to discover errors and risks in contract information.

[0480] "Suggestion mechanism" refers to a system for creating and providing corrective suggestions to users based on detected errors and risks.

[0481] "Display means" refers to devices or methods for visually presenting errors, risks, and suggested corrections to the user.

[0482] "Document generation means" refers to a system or technology for creating new contracts based on user input.

[0483] "Optimization method" refers to technology for selecting and adjusting contract templates based on past cases, taking into account conditions entered by the user.

[0484] The system according to this invention is designed for the efficient processing of contract information and is operated in cooperation with a server, terminal, and user. The server receives contract information from the user via the terminal and is responsible for analyzing the information using natural language processing technology. Specifically, the server uses Python natural language processing libraries such as NLTK and SpaCy to analyze the text data of contracts using methods such as tokenization, morphological analysis, and syntactic analysis. This allows for the detection of errors and risks in contract content and supports efficient contract management.

[0485] The server also generates and proposes corrective measures based on the detected errors and risks. Users can then review and revise the contract based on these suggestions. The terminal visually displays detailed analysis results and proposed revisions, allowing users to intuitively evaluate the results. When creating a new contract according to user requests, the server automatically selects an appropriate template based on the input conditions and generates the final document. Users can customize the contract template and save it for future use as needed.

[0486] A concrete example is a scenario where a user attempts to create a new contract for the sale of goods. The user inputs the sales conditions and terms of conclusion via a terminal, and the server selects the most suitable template based on those conditions while incorporating the clauses requested by the user. Through this process, the user can finalize the contract more quickly and accurately.

[0487] An example of a prompt message would be: "Create a new sales contract. Sales conditions: Deliver 50 units of product A by the end of the year. Agreement: Penalties will be applied for delays in delivery." In this way, this system streamlines the contract creation process from start to finish, enabling users to perform their tasks reliably.

[0488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0489] Step 1:

[0490] The server receives contract information from the user via their terminal. The input is contract draft data sent by the user from their terminal. The server receives this data and stores it as text-based contract information. Specifically, the server retrieves the data using an HTTP request and stores it in a database.

[0491] Step 2:

[0492] The server extracts contract text from the received contract information and performs preprocessing. The input is contract information stored on the server. The server tokenizes this information for text analysis, performs noise reduction and formatting standardization, and outputs preprocessed data. Specifically, it executes a script written in Python to perform grammar checks and remove unnecessary symbols.

[0493] Step 3:

[0494] The server analyzes pre-processed contract information using natural language processing techniques. The input is tokenized and formatted data. The server uses natural language processing libraries (e.g., NLTK and SpaCy) to analyze the text, apply syntactic and semantic analysis, and output the analysis results. Specifically, it identifies keywords within the contract document and extracts structural information.

[0495] Step 4:

[0496] The server detects errors and risks based on the analysis results. The input is already analyzed data. The server compares this data with past cases to identify errors and legal risks and outputs that information. Specifically, it accesses a knowledge base and lists the relevant risks.

[0497] Step 5:

[0498] The server generates corrective action plans based on the errors and risks it detects. The input is the detection results, and the server uses this information to create a proposal document containing improvement suggestions and recommendations, which it then outputs. Specifically, it uses a template for corrections to write out corrective action plans that include specific wording.

[0499] Step 6:

[0500] The terminal presents the user with suggested corrections and analysis results sent from the server. The input is suggested correction data from the server. The terminal visually displays this data to the user, highlighting detected errors and risks. Dedicated display software is involved in the specific operations, presenting information with a user-friendly UI.

[0501] Step 7:

[0502] Users modify and review contract details via their terminals. Input consists of proposed revisions and draft contracts displayed on the terminal. Users make the necessary revisions based on this information and finally export the revised contract. Specifically, they use a text editor on the screen to directly make corrections and upload the updated version to the server.

[0503] Step 8:

[0504] When a user creates a new contract, the server completes the contract through an automated generation process. The input consists of data based on the user's requirements and choices. Based on the specified conditions, the server selects the most suitable template, customizes it as needed, and outputs the new contract. Specific operations include referencing and applying existing templates from a database.

[0505] (Application Example 1)

[0506] 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."

[0507] There is a need to improve the efficiency of contract processing, quickly generate complex contract documents, and minimize errors and risks. Furthermore, a system is required that allows end-users to easily input contract information and review optimized contract content. Additionally, a mechanism that utilizes cloud computing to securely process information while optimizing resources is needed.

[0508] 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.

[0509] In this invention, the server includes means for receiving contract information, means for analyzing and pre-processing language data, means for detecting errors and risks, means for generating data, means for displaying data, means for end users to input contract information using a smart device and process the data through cloud computing, and means for presenting alternatives and optimizing the contract document to suit the user. This enables end users to process contracts efficiently.

[0510] "Contract information" refers to data and information related to a contract, including transaction terms and details of the parties involved.

[0511] "Linguistic data" refers to text data written in natural language, specifically the documents used as part of a contract.

[0512] "Analysis means" refer to the technical elements used to analyze linguistic data and understand its structure and meaning.

[0513] "Detection means" refers to methods and functions for finding errors or risks from analyzed data.

[0514] "Generation means" refers to a function that creates new proposals or documents based on contract information and detected results.

[0515] "Display means" refers to interfaces and technologies used to visually present information to end users.

[0516] A "smart device" refers to a portable device equipped with advanced functions that users use to operate it, such as a smartphone or tablet.

[0517] "Cloud computing" is a technology that provides resources via the internet to efficiently process and store information.

[0518] An "alternative" is a choice or suggested correction offered to rectify an detected error or risk.

[0519] "Optimization" refers to the process of arranging contract information to best meet the user's requirements.

[0520] This invention is a system that efficiently processes contract information and automatically generates optimal contracts. The user starts by using a smart device to input the necessary contract information. This information is transmitted to a server via the cloud. The server uses natural language processing technology to analyze the contract information and preprocess the language data. Specific tools used include natural language processing libraries such as spaCy and NLTK. Based on this analysis, the server detects potential errors and risks in the contract content and automatically generates appropriate alternatives.

[0521] Cloud computing forms the foundation of the process, and information is centrally managed online. Users can review alternatives and suggested revisions presented on their smart device displays and make adjustments as needed. This allows users to easily create legally accurate and secure contracts without requiring advanced expertise.

[0522] For example, if a user wants to create new credit card terms and conditions, they open the smartphone app and enter their desired conditions. The server then automatically suggests the best conditions based on this information. The new contract is also created by referencing past successful contract examples. The generative AI model optimizes its suggestions by using prompts like the following.

[0523] Example prompt: "Analyze the contract, identify legal risks, and generate proposed revisions. The following contract terms are for the purchase of electronic equipment from Company X, with a down payment of 100,000 yen, monthly payments of 30,000 yen, and a loan term of 24 months."

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] Users use their smart devices to enter the information required for the contract. This input includes contract terms and details of the parties involved. This data is sent from the device to the server. Users enter the information through an easy-to-use user interface and submit the data by pressing the submit button.

[0527] Step 2:

[0528] The server performs preprocessing to analyze the received contract information. The specific input is text information sent by the user, and the output is language data formatted for natural language processing. During this process, preprocessing such as noise reduction and tokenization is performed.

[0529] Step 3:

[0530] The server uses natural language processing techniques to analyze the formatted language data. The input is pre-processed language data, and the output is the analysis result for understanding the structure and meaning of the contract. At this stage, libraries such as spaCy and NLTK are used to structure the contract information and perform topic modeling.

[0531] Step 4:

[0532] The server detects potential errors and risks in the contract based on the analysis results. The input is the analysis results, and the output is the detection results regarding errors and risks. In this process, a generative AI model is used to automatically identify risks within the contract document.

[0533] Step 5:

[0534] The server generates corrective suggestions based on detected errors and risks. The specific input is the detection results regarding errors and risks, and the output is corrective suggestions and alternatives. The generation AI model generates appropriate proposals for improving the contract terms. In this process, it may refer to a database of past success stories.

[0535] Step 6:

[0536] The terminal displays generated modification suggestions and alternatives to the user. The input is the modification suggestions and alternatives, and the output is the user's visualization screen. The user can review this and, if necessary, adopt or fine-tune the suggested modifications. The display clearly shows the suggested items and their benefits.

[0537] Step 7:

[0538] The user reviews the final contract via their device and makes any necessary adjustments. The input is the revised proposal, and the output is the finalized contract. The user provides feedback through touchscreen operation or voice input. Once the contract is complete, it is formally confirmed by digital signature.

[0539] 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.

[0540] This invention provides a system that recognizes the user's emotional state and offers an optimal interface by incorporating an emotion engine into a contract management system. The system functions through the coordinated operation of the server, terminal, user, and emotion engine.

[0541] The server receives contract data from the user via the terminal. This contract data is extracted and preprocessed as text and analyzed using natural language processing technology. Based on the analysis results, the server detects errors and legal risks and generates proposed corrections. The generated proposed corrections and risk information are presented to the user through an emotion engine in a format appropriate to the user's emotional state.

[0542] The emotion engine detects changes in emotion in real time from user input and voice data. Based on this emotion data, the system estimates the user's stress level and comprehension level, and adjusts how contract information is presented. For example, if the user is feeling anxious, the information can be presented more clearly and in a hierarchical manner.

[0543] As a concrete example, consider a scenario where a user is reviewing a contract using their device. As the user considers the displayed revisions, the emotion engine analyzes the user's facial expressions and tone of voice to determine their level of understanding and emotional state. If the user appears confused, additional explanations and visual support are displayed on the device to provide further reassurance.

[0544] Furthermore, when a user creates a new contract, the server generates a contract template based on the specified conditions. At this time, the emotion engine re-analyzes the user's reactions and proposes the most suitable contract terms within a range that does not cause stress.

[0545] This system reduces the mental burden on users in contract work and allows them to efficiently and reliably review and create contract details.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] The user selects contract data using their device and uploads it to the server. The uploaded data is sent in document format as a contract.

[0549] Step 2:

[0550] The server extracts the received contract data into text format and performs preprocessing such as removing punctuation and irregular spaces. The preprocessed text is then prepared for analysis.

[0551] Step 3:

[0552] The server uses natural language processing techniques to analyze the pre-processed text. This analysis includes morphological analysis and dependency parsing, as well as a process to understand the sentence structure within the contract.

[0553] Step 4:

[0554] Based on the analysis results, the server identifies errors and legal risks in the contract. For risk detection, it applies established rule-based approaches and machine learning models.

[0555] Step 5:

[0556] The server automatically generates specific corrective suggestions based on the errors and risks it detects. These suggestions include parts that need to be corrected and clauses that need to be added.

[0557] Step 6:

[0558] The emotion engine analyzes the emotions a user expresses in real time while reviewing a contract. This analysis is based on the tone of voice the user uses when making voice inputs, as well as the speed and patterns of their text inputs.

[0559] Step 7:

[0560] The terminal displays information about errors and suggested corrections sent from the server, as well as information tailored to the user's emotional state, as analyzed by the emotion engine. The information is presented in a format optimized for the user's emotional state.

[0561] Step 8:

[0562] Users review the proposed revisions presented on their devices and, if necessary, provide feedback or decide whether to adopt the revisions. Throughout this process, user reactions are continuously monitored by the sentiment engine and reflected in the displayed content.

[0563] Step 9:

[0564] When a user creates a new contract, the server generates a draft of the new contract using past contract data and templates. The emotion engine adjusts the draft based on the user's input to reduce stress.

[0565] Step 10:

[0566] Finally, the contract reviewed and edited by the user is saved on the server, ready for later reference or submission to the legal department. Throughout the entire process, users can smoothly proceed with contract work in an emotionally supportive environment.

[0567] (Example 2)

[0568] 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."

[0569] Traditional contract management systems have faced challenges such as limited support for users to understand contract information and avoid errors and risks, as well as a lack of presentation and adjustment of contract information based on the user's emotional state. As a result, users often experience stress during contract work, potentially leading to misunderstandings and risks.

[0570] 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.

[0571] In this invention, the server includes receiving means for receiving contract information, analysis means for extracting and pre-processing character data, and detection means for analyzing the contract information using natural language processing technology and detecting errors and risks. This enables the presentation of information in a manner appropriate to the user's emotional state, facilitating understanding and risk avoidance.

[0572] A "receiving means" is a means that has the function of taking in contract information from users from an external source and supplying it to the system for processing.

[0573] An "analysis tool" is a means that has the function of converting received contract information into a format that is easy to process, extracting necessary information, and preparing it for the next processing stage.

[0574] "Natural language processing technology" is a technique that converts string data into a format that can be processed by a computer, and analyzes natural language written by humans to understand its meaning and structure.

[0575] A "detection means" is a means that automatically identifies errors and legal risks from contract information processed by an analysis means and notifies the user.

[0576] A "generation method" is a means that has the function of generating corrective suggestions to address detected errors and risks, and presenting them in a format that users can easily understand and adopt.

[0577] A "display means" is a means that presents contract information and proposed revisions in an appropriate format based on the user's emotional state, and has the function of helping the user understand the information.

[0578] An "emotion analysis tool" is a tool that detects the user's emotional state based on user input, voice, and video information, and reflects this in the system's operation.

[0579] This invention comprehensively provides a contract management system that includes methods for receiving, analyzing, detecting errors in, generating correction suggestions for, and displaying contract information. This system primarily consists of a server, terminals, and a sentiment analysis engine, all of which work in conjunction with each other.

[0580] The server receives contract information sent from the user via the terminal. The received information is preprocessed on the server as text data. This preprocessing includes noise reduction and character code conversion of the contract information, preparing it for smooth analysis. Specifically, important content is extracted from the contract information using natural language processing technology. This is done using commonly used libraries for text analysis software (e.g., spaCy).

[0581] When detecting errors or legal risks, the server performs detection processing based on the analysis results. This is a crucial step in ensuring the integrity of the contract and minimizing risks. Subsequently, correction suggestions for the detected errors are generated and presented to the user. These correction suggestions aim to further optimize the contract information and avoid legal risks.

[0582] Meanwhile, the device analyzes the user's emotional state in real time via an emotion analysis engine. This analysis includes facial expression analysis and voice tone analysis; for example, it uses OpenCV to track the user's facial expressions and a voice analysis library to evaluate the tone of their voice. The analysis results are shared with the server, and information is presented according to the user's emotional state.

[0583] For example, when a user is reviewing a contract, the device analyzes the user's emotional state and, if it determines the user is confused, provides detailed explanations and visual support. Furthermore, when a user generates a new contract, the server quickly generates a contract template based on pre-configured conditions and prompts the user for input.

[0584] Examples of prompts include, "What is the best way to point out errors in a contract?" and "How should information be organized and presented when the user is feeling stressed?"

[0585] This system reduces stress in contract work and allows users to efficiently and reliably verify and generate contract information.

[0586] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0587] Step 1:

[0588] The server receives contract information from the user via the terminal. The received contract information is imported into the system as text data. Contract information is obtained as input, and pre-processed text data is obtained as output. This pre-processing includes the removal of unnecessary spaces and special characters.

[0589] Step 2:

[0590] The server performs natural language processing on pre-processed text data. Using analysis software (e.g., spaCy), it extracts keywords related to important items and risks from the contract information. Pre-processed text data is used as input, and the extracted important information is output. Here, the grammatical consistency of the contract content is verified, and potential legal risks are identified.

[0591] Step 3:

[0592] The server generates corrective suggestions for errors and risks detected based on the analysis results. The generated suggestions are presented in a user-friendly format. The analysis results are used as input, and corrective suggestions are obtained as output. Specifically, the system automatically constructs suggestions based on predefined legal guidelines.

[0593] Step 4:

[0594] The device uses an emotion analysis engine to analyze the user's emotional state in real time. Here, the user's facial expressions and voice data are acquired as input, and information about the user's emotional state is obtained as output. Specifically, facial expression analysis software and a voice analysis library are used to evaluate the user's stress level and comprehension.

[0595] Step 5:

[0596] The server optimizes how information is presented to the user based on emotional state information obtained from the emotion analysis engine. Emotional state information is the input, and the adjusted information presentation is the output. If the user shows signs of tension or confusion, the server assists the user's understanding by displaying the information in a clearer, hierarchical manner.

[0597] Step 6:

[0598] The user reviews the proposed revisions and adjusts the contract terms as needed. Based on the user's interaction, a revised version of the contract is generated. The proposed revisions are the input, and the new contract terms are finalized as the output. Specifically, the user can monitor and reflect changes in real time using the interface on their device.

[0599] (Application Example 2)

[0600] 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."

[0601] Traditional contract management systems often fail to consider the user's level of understanding or emotional state when reviewing or revising contract details, potentially causing anxiety and stress. Furthermore, real-world contract procedures often lack sufficient user interaction to ensure a sense of security. In addition, inadequate support during the presentation of contract terms can prolong the contract process, leading to decreased efficiency and potentially hindering the successful completion of contracts.

[0602] 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.

[0603] In this invention, the server includes a receiving means, an analysis means, a detection means, a generation means, a display means, an emotion analysis means, and a dialogue support means. This allows for real-time analysis of the user's emotional state and adjustment of the appropriate method of presenting contract information, thereby providing the user with a sense of security and facilitating the smooth execution of contract procedures.

[0604] "Receiving means" refers to a device or method for acquiring contract data from an external terminal or network and importing it into the system.

[0605] "Analysis means" refers to a device or method that extracts received contract data as text and performs data processing or transformation.

[0606] "Detection means" refers to a device or method that uses natural language processing technology to analyze the content of contract data and identify errors or legal risks.

[0607] "Generating means" refers to an apparatus or method for creating appropriate revised drafts or new contracts based on detected errors or risks.

[0608] "Display means" refers to a device or method for visually presenting errors, risks, and suggested corrections to a user.

[0609] "Emotional analysis means" refers to a device or method that analyzes a user's facial expressions and tone of voice to determine the user's emotional state in real time.

[0610] A "dialogue support device" is a device or method that adjusts the interface to provide information in a more understandable way and give users a sense of security when they have anxieties or questions.

[0611] This invention incorporates an emotion engine and an interactive interface to improve the user experience in a contract management system. The system operates in conjunction with servers, terminals, and users.

[0612] First, the server uses a receiving means to acquire contract data from external devices or networks. The received data is extracted as text by an analysis means and preprocessed. Specifically, the analysis means uses natural language processing technology to detect errors and risks within the contract data. Based on the detected results, the generation means creates revised drafts or new contracts.

[0613] When generated revision proposals and risk information are presented to the user via display means, sentiment analysis means play a crucial role. The user's device captures the user's facial expressions and voice using a camera and microphone, and by analyzing this data, the system determines the user's emotional state in real time. This allows the system to estimate the user's stress level and level of understanding, and present information in an appropriate manner.

[0614] For example, if a user is using their smartphone to review contract details at a physical store, and sentiment analysis determines that the user is feeling anxious, additional explanations or visual guides will be displayed in response to the questions. This enhances the user's sense of security.

[0615] Examples of prompts generated using AI models include, "When purchasing a new smartphone, what contract terms would be most beneficial to the user?" and "Please explain these contract terms to the customer in the easiest way to understand." Such interface refinements are a means of smoothing real-world conversations and improving the user experience.

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] The server receives contract data from the user's terminal. The input here is the contract data uploaded by the user to the interface, and the server passes the received data to the next processing step for text extraction.

[0619] Step 2:

[0620] The server uses an analysis tool to extract text from the received contract data and performs preprocessing. The input is contract data, and the output is data in text format. Here, the text is cleaned and prepared for machine processing.

[0621] Step 3:

[0622] The server analyzes text data, which has been parsed using natural language processing technology, using detection methods to identify errors and risks in the contract. The input is pre-processed text data, and the output is a list of errors and risks. At this stage, it is checked whether the identified risks are due to a lack of legal requirements or inappropriate clauses.

[0623] Step 4:

[0624] The server generates revised proposals and a new contract using a generation mechanism based on the output of the detection mechanism. The input is a list of errors and risks, and the output is a revised proposal and a proposed document. At this stage, an AI model is used to automatically generate the optimal revision proposal.

[0625] Step 5:

[0626] The terminal presents the user with proposed revisions and risk information through its display mechanisms. This input is the generated proposed revisions, and the output is displayed on the interface in a format that is easy for the user to understand.

[0627] Step 6:

[0628] The device uses its camera and microphone to determine the user's emotional state in real time through emotion analysis. Input is the user's facial expressions and tone of voice, while output is data related to the user's emotional state. This data is used to estimate the user's understanding of the billing statement and their stress level.

[0629] Step 7:

[0630] Based on the results of emotion analysis, the device adjusts the information presentation method using dialogue support mechanisms and displays additional explanations and visual guides. Input is the user's emotional state data, and output is an adjusted interface and additional information. This allows for quick responses when the user experiences anxiety or questions.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] [Fourth Embodiment]

[0635] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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".

[0648] This invention provides a system for efficiently processing contract data. The system operates in cooperation with a server, terminals, and users to support the creation and verification of contracts.

[0649] The server first receives contract data from the user via the terminal. The received data is then analyzed using natural language processing techniques. Specifically, the server analyzes the text of the contract and applies natural language processing to understand its syntax and meaning.

[0650] Based on the analysis results, the server scrutinizes the contract and detects errors and legal risks. Using this analysis information, the server generates revised proposals for the user. These include specific suggestions for correcting errors and recommendations for clauses that should be improved.

[0651] The user visually confirms this information using their terminal. The terminal displays a list of proposed revisions and detailed analysis results of the contract sent from the server to the user. The user can review the proposed revisions and revise the contract as needed.

[0652] Furthermore, when a user creates a new contract, the server generates a new contract based on an existing template. In this process, the server considers past successes and selects the clauses that best suit the conditions specified by the user. Based on this, the user can quickly create a new contract.

[0653] As a concrete example, consider a scenario where a user creates a new sales contract. The user inputs the sales terms and agreements on their terminal. The server receives this information, selects an appropriate template, and automatically customizes the clauses. The final contract is completed after review and minor adjustments by the user.

[0654] Thus, the present invention significantly improves the efficiency of contract drafting and review, providing an environment in which users can confidently carry out contract work.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] Users select contract data using their terminals and upload it to the server. The system supports common document formats such as PDF and Word.

[0658] Step 2:

[0659] The server receives the contract data and extracts text from the file. The extracted text is preprocessed to remove unnecessary spaces and special characters.

[0660] Step 3:

[0661] The server analyzes the pre-processed text using natural language processing techniques. This includes morphological and syntactic analysis, and processing is performed to understand the structure and meaning of the sentences within the contract.

[0662] Step 4:

[0663] The server detects errors and potential legal risks based on the analysis results. It uses pre-configured rule-based systems and machine learning models to identify areas of risk.

[0664] Step 5:

[0665] The server generates proposed corrections based on the detection results. These corrections include suggested fixes for identified errors and recommended clause changes. It also refers to historical contract data to determine the optimal correction strategy.

[0666] Step 6:

[0667] The terminal displays errors, risks, and suggested solutions sent from the server to the user. The information is presented in an intuitive format using graphs and marked-up text.

[0668] Step 7:

[0669] The user reviews the proposed revisions via their device and applies the changes as needed, thereby finalizing the contract's structure.

[0670] Step 8:

[0671] When a user creates a new contract, the server generates a new contract using a template based on the requirements entered on the terminal. Appropriate clauses are automatically selected based on past success stories.

[0672] Step 9:

[0673] The generated contract is sent to the user's device, where the user reviews and edits it as needed. The completed contract is saved on the server as the final version.

[0674] (Example 1)

[0675] 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".

[0676] Traditional contract drafting and review processes relied heavily on manual work, requiring significant time and effort to identify errors and assess legal risks. This led to decreased efficiency in contract operations and ultimately impacted overall business productivity. Furthermore, the inability to fully utilize past examples in drafting new contracts resulted in inconsistent quality.

[0677] 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.

[0678] In this invention, the server includes communication means for receiving contract information, analysis means for extracting and pre-processing text from the contract information, and detection means for analyzing the contract information using natural language processing technology and detecting errors and risks. This enables the automatic and rapid detection of errors and risks in contracts by making full use of advanced natural language processing, allowing for the efficient and reliable execution of contract work.

[0679] "Contract information" refers to information consisting of all data and documents related to a contract, and includes the agreements and conditions between the parties.

[0680] "Communication methods" refer to the technical devices and protocols that servers use to exchange information with users and terminals.

[0681] "Text" refers to the elements that make up the text data within a contract, specifically used to express clauses and conditions.

[0682] "Analysis means" refers to technical mechanisms and devices for extracting necessary text from contract information and performing preprocessing.

[0683] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to detect errors and risks in the analysis of contract information.

[0684] "Detection means" refers to methods and devices that use natural language processing technology to discover errors and risks in contract information.

[0685] "Suggestion mechanism" refers to a system for creating and providing corrective suggestions to users based on detected errors and risks.

[0686] "Display means" refers to devices or methods for visually presenting errors, risks, and suggested corrections to the user.

[0687] "Document generation means" refers to a system or technology for creating new contracts based on user input.

[0688] "Optimization method" refers to technology for selecting and adjusting contract templates based on past cases, taking into account conditions entered by the user.

[0689] The system according to this invention is designed for the efficient processing of contract information and is operated in cooperation with a server, terminal, and user. The server receives contract information from the user via the terminal and is responsible for analyzing the information using natural language processing technology. Specifically, the server uses Python natural language processing libraries such as NLTK and SpaCy to analyze the text data of contracts using methods such as tokenization, morphological analysis, and syntactic analysis. This allows for the detection of errors and risks in contract content and supports efficient contract management.

[0690] The server also generates and proposes corrective measures based on the detected errors and risks. Users can then review and revise the contract based on these suggestions. The terminal visually displays detailed analysis results and proposed revisions, allowing users to intuitively evaluate the results. When creating a new contract according to user requests, the server automatically selects an appropriate template based on the input conditions and generates the final document. Users can customize the contract template and save it for future use as needed.

[0691] A concrete example is a scenario where a user attempts to create a new contract for the sale of goods. The user inputs the sales conditions and terms of conclusion via a terminal, and the server selects the most suitable template based on those conditions while incorporating the clauses requested by the user. Through this process, the user can finalize the contract more quickly and accurately.

[0692] An example of a prompt message would be: "Create a new sales contract. Sales conditions: Deliver 50 units of product A by the end of the year. Agreement: Penalties will be applied for delays in delivery." In this way, this system streamlines the contract creation process from start to finish, enabling users to perform their tasks reliably.

[0693] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0694] Step 1:

[0695] The server receives contract information from the user via their terminal. The input is contract draft data sent by the user from their terminal. The server receives this data and stores it as text-based contract information. Specifically, the server retrieves the data using an HTTP request and stores it in a database.

[0696] Step 2:

[0697] The server extracts contract text from the received contract information and performs preprocessing. The input is contract information stored on the server. The server tokenizes this information for text analysis, performs noise reduction and formatting standardization, and outputs preprocessed data. Specifically, it executes a script written in Python to perform grammar checks and remove unnecessary symbols.

[0698] Step 3:

[0699] The server analyzes pre-processed contract information using natural language processing techniques. The input is tokenized and formatted data. The server uses natural language processing libraries (e.g., NLTK and SpaCy) to analyze the text, apply syntactic and semantic analysis, and output the analysis results. Specifically, it identifies keywords within the contract document and extracts structural information.

[0700] Step 4:

[0701] The server detects errors and risks based on the analysis results. The input is already analyzed data. The server compares this data with past cases to identify errors and legal risks and outputs that information. Specifically, it accesses a knowledge base and lists the relevant risks.

[0702] Step 5:

[0703] The server generates corrective action plans based on the errors and risks it detects. The input is the detection results, and the server uses this information to create a proposal document containing improvement suggestions and recommendations, which it then outputs. Specifically, it uses a template for corrections to write out corrective action plans that include specific wording.

[0704] Step 6:

[0705] The terminal presents the user with suggested corrections and analysis results sent from the server. The input is suggested correction data from the server. The terminal visually displays this data to the user, highlighting detected errors and risks. Dedicated display software is involved in the specific operations, presenting information with a user-friendly UI.

[0706] Step 7:

[0707] Users modify and review contract details via their terminals. Input consists of proposed revisions and draft contracts displayed on the terminal. Users make the necessary revisions based on this information and finally export the revised contract. Specifically, they use a text editor on the screen to directly make corrections and upload the updated version to the server.

[0708] Step 8:

[0709] When a user creates a new contract, the server completes the contract through an automated generation process. The input consists of data based on the user's requirements and choices. Based on the specified conditions, the server selects the most suitable template, customizes it as needed, and outputs the new contract. Specific operations include referencing and applying existing templates from a database.

[0710] (Application Example 1)

[0711] 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".

[0712] There is a need to improve the efficiency of contract processing, quickly generate complex contract documents, and minimize errors and risks. Furthermore, a system is required that allows end-users to easily input contract information and review optimized contract content. Additionally, a mechanism that utilizes cloud computing to securely process information while optimizing resources is needed.

[0713] 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.

[0714] In this invention, the server includes means for receiving contract information, means for analyzing and pre-processing language data, means for detecting errors and risks, means for generating data, means for displaying data, means for end users to input contract information using a smart device and process the data through cloud computing, and means for presenting alternatives and optimizing the contract document to suit the user. This enables end users to process contracts efficiently.

[0715] "Contract information" refers to data and information related to a contract, including transaction terms and details of the parties involved.

[0716] "Linguistic data" refers to text data written in natural language, specifically the documents used as part of a contract.

[0717] "Analysis means" refer to the technical elements used to analyze linguistic data and understand its structure and meaning.

[0718] "Detection means" refers to methods and functions for finding errors or risks from analyzed data.

[0719] "Generation means" refers to a function that creates new proposals or documents based on contract information and detected results.

[0720] "Display means" refers to interfaces and technologies used to visually present information to end users.

[0721] A "smart device" refers to a portable device equipped with advanced functions that users use to operate it, such as a smartphone or tablet.

[0722] "Cloud computing" is a technology that provides resources via the internet to efficiently process and store information.

[0723] An "alternative" is a choice or suggested correction offered to rectify an detected error or risk.

[0724] "Optimization" refers to the process of arranging contract information to best meet the user's requirements.

[0725] This invention is a system that efficiently processes contract information and automatically generates optimal contracts. The user starts by using a smart device to input the necessary contract information. This information is transmitted to a server via the cloud. The server uses natural language processing technology to analyze the contract information and preprocess the language data. Specific tools used include natural language processing libraries such as spaCy and NLTK. Based on this analysis, the server detects potential errors and risks in the contract content and automatically generates appropriate alternatives.

[0726] Cloud computing forms the foundation of the process, and information is centrally managed online. Users can review alternatives and suggested revisions presented on their smart device displays and make adjustments as needed. This allows users to easily create legally accurate and secure contracts without requiring advanced expertise.

[0727] For example, if a user wants to create new credit card terms and conditions, they open the smartphone app and enter their desired conditions. The server then automatically suggests the best conditions based on this information. The new contract is also created by referencing past successful contract examples. The generative AI model optimizes its suggestions by using prompts like the following.

[0728] Example prompt: "Analyze the contract, identify legal risks, and generate proposed revisions. The following contract terms are for the purchase of electronic equipment from Company X, with a down payment of 100,000 yen, monthly payments of 30,000 yen, and a loan term of 24 months."

[0729] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0730] Step 1:

[0731] Users use their smart devices to enter the information required for the contract. This input includes contract terms and details of the parties involved. This data is sent from the device to the server. Users enter the information through an easy-to-use user interface and submit the data by pressing the submit button.

[0732] Step 2:

[0733] The server performs preprocessing to analyze the received contract information. The specific input is text information sent by the user, and the output is language data formatted for natural language processing. During this process, preprocessing such as noise reduction and tokenization is performed.

[0734] Step 3:

[0735] The server uses natural language processing techniques to analyze the formatted language data. The input is pre-processed language data, and the output is the analysis result for understanding the structure and meaning of the contract. At this stage, libraries such as spaCy and NLTK are used to structure the contract information and perform topic modeling.

[0736] Step 4:

[0737] The server detects potential errors and risks in the contract based on the analysis results. The input is the analysis results, and the output is the detection results regarding errors and risks. In this process, a generative AI model is used to automatically identify risks within the contract document.

[0738] Step 5:

[0739] The server generates corrective suggestions based on detected errors and risks. The specific input is the detection results regarding errors and risks, and the output is corrective suggestions and alternatives. The generation AI model generates appropriate proposals for improving the contract terms. In this process, it may refer to a database of past success stories.

[0740] Step 6:

[0741] The terminal displays generated modification suggestions and alternatives to the user. The input is the modification suggestions and alternatives, and the output is the user's visualization screen. The user can review this and, if necessary, adopt or fine-tune the suggested modifications. The display clearly shows the suggested items and their benefits.

[0742] Step 7:

[0743] The user reviews the final contract via their device and makes any necessary adjustments. The input is the revised proposal, and the output is the finalized contract. The user provides feedback through touchscreen operation or voice input. Once the contract is complete, it is formally confirmed by digital signature.

[0744] 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.

[0745] This invention provides a system that recognizes the user's emotional state and offers an optimal interface by incorporating an emotion engine into a contract management system. The system functions through the coordinated operation of the server, terminal, user, and emotion engine.

[0746] The server receives contract data from the user via the terminal. This contract data is extracted and preprocessed as text and analyzed using natural language processing technology. Based on the analysis results, the server detects errors and legal risks and generates proposed corrections. The generated proposed corrections and risk information are presented to the user through an emotion engine in a format appropriate to the user's emotional state.

[0747] The emotion engine detects changes in emotion in real time from user input and voice data. Based on this emotion data, the system estimates the user's stress level and comprehension level, and adjusts how contract information is presented. For example, if the user is feeling anxious, the information can be presented more clearly and in a hierarchical manner.

[0748] As a concrete example, consider a scenario where a user is reviewing a contract using their device. As the user considers the displayed revisions, the emotion engine analyzes the user's facial expressions and tone of voice to determine their level of understanding and emotional state. If the user appears confused, additional explanations and visual support are displayed on the device to provide further reassurance.

[0749] Furthermore, when a user creates a new contract, the server generates a contract template based on the specified conditions. At this time, the emotion engine re-analyzes the user's reactions and proposes the most suitable contract terms within a range that does not cause stress.

[0750] This system reduces the mental burden on users in contract work and allows them to efficiently and reliably review and create contract details.

[0751] The following describes the processing flow.

[0752] Step 1:

[0753] The user selects contract data using their device and uploads it to the server. The uploaded data is sent in document format as a contract.

[0754] Step 2:

[0755] The server extracts the received contract data into text format and performs preprocessing such as removing punctuation and irregular spaces. The preprocessed text is then prepared for analysis.

[0756] Step 3:

[0757] The server uses natural language processing techniques to analyze the pre-processed text. This analysis includes morphological analysis and dependency parsing, as well as a process to understand the sentence structure within the contract.

[0758] Step 4:

[0759] Based on the analysis results, the server identifies errors and legal risks in the contract. For risk detection, it applies established rule-based approaches and machine learning models.

[0760] Step 5:

[0761] The server automatically generates specific corrective suggestions based on the errors and risks it detects. These suggestions include parts that need to be corrected and clauses that need to be added.

[0762] Step 6:

[0763] The emotion engine analyzes the emotions a user expresses in real time while reviewing a contract. This analysis is based on the tone of voice the user uses when making voice inputs, as well as the speed and patterns of their text inputs.

[0764] Step 7:

[0765] The terminal displays information about errors and suggested corrections sent from the server, as well as information tailored to the user's emotional state, as analyzed by the emotion engine. The information is presented in a format optimized for the user's emotional state.

[0766] Step 8:

[0767] Users review the proposed revisions presented on their devices and, if necessary, provide feedback or decide whether to adopt the revisions. Throughout this process, user reactions are continuously monitored by the sentiment engine and reflected in the displayed content.

[0768] Step 9:

[0769] When a user creates a new contract, the server generates a draft of the new contract using past contract data and templates. The emotion engine adjusts the draft based on the user's input to reduce stress.

[0770] Step 10:

[0771] Finally, the contract reviewed and edited by the user is saved on the server, ready for later reference or submission to the legal department. Throughout the entire process, users can smoothly proceed with contract work in an emotionally supportive environment.

[0772] (Example 2)

[0773] 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".

[0774] Traditional contract management systems have faced challenges such as limited support for users to understand contract information and avoid errors and risks, as well as a lack of presentation and adjustment of contract information based on the user's emotional state. As a result, users often experience stress during contract work, potentially leading to misunderstandings and risks.

[0775] 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.

[0776] In this invention, the server includes receiving means for receiving contract information, analysis means for extracting and pre-processing character data, and detection means for analyzing the contract information using natural language processing technology and detecting errors and risks. This enables the presentation of information in a manner appropriate to the user's emotional state, facilitating understanding and risk avoidance.

[0777] A "receiving means" is a means that has the function of taking in contract information from users from an external source and supplying it to the system for processing.

[0778] An "analysis tool" is a means that has the function of converting received contract information into a format that is easy to process, extracting necessary information, and preparing it for the next processing stage.

[0779] "Natural language processing technology" is a technique that converts string data into a format that can be processed by a computer, and analyzes natural language written by humans to understand its meaning and structure.

[0780] A "detection means" is a means that automatically identifies errors and legal risks from contract information processed by an analysis means and notifies the user.

[0781] A "generation method" is a means that has the function of generating corrective suggestions to address detected errors and risks, and presenting them in a format that users can easily understand and adopt.

[0782] A "display means" is a means that presents contract information and proposed revisions in an appropriate format based on the user's emotional state, and has the function of helping the user understand the information.

[0783] An "emotion analysis tool" is a tool that detects the user's emotional state based on user input, voice, and video information, and reflects this in the system's operation.

[0784] This invention comprehensively provides a contract management system that includes methods for receiving, analyzing, detecting errors in, generating correction suggestions for, and displaying contract information. This system primarily consists of a server, terminals, and a sentiment analysis engine, all of which work in conjunction with each other.

[0785] The server receives contract information sent from the user via the terminal. The received information is preprocessed on the server as text data. This preprocessing includes noise reduction and character code conversion of the contract information, preparing it for smooth analysis. Specifically, important content is extracted from the contract information using natural language processing technology. This is done using commonly used libraries for text analysis software (e.g., spaCy).

[0786] When detecting errors or legal risks, the server performs detection processing based on the analysis results. This is a crucial step in ensuring the integrity of the contract and minimizing risks. Subsequently, correction suggestions for the detected errors are generated and presented to the user. These correction suggestions aim to further optimize the contract information and avoid legal risks.

[0787] Meanwhile, the device analyzes the user's emotional state in real time via an emotion analysis engine. This analysis includes facial expression analysis and voice tone analysis; for example, it uses OpenCV to track the user's facial expressions and a voice analysis library to evaluate the tone of their voice. The analysis results are shared with the server, and information is presented according to the user's emotional state.

[0788] For example, when a user is reviewing a contract, the device analyzes the user's emotional state and, if it determines the user is confused, provides detailed explanations and visual support. Furthermore, when a user generates a new contract, the server quickly generates a contract template based on pre-configured conditions and prompts the user for input.

[0789] Examples of prompts include, "What is the best way to point out errors in a contract?" and "How should information be organized and presented when the user is feeling stressed?"

[0790] This system reduces stress in contract work and allows users to efficiently and reliably verify and generate contract information.

[0791] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0792] Step 1:

[0793] The server receives contract information from the user via the terminal. The received contract information is imported into the system as text data. Contract information is obtained as input, and pre-processed text data is obtained as output. This pre-processing includes the removal of unnecessary spaces and special characters.

[0794] Step 2:

[0795] The server performs natural language processing on pre-processed text data. Using analysis software (e.g., spaCy), it extracts keywords related to important items and risks from the contract information. Pre-processed text data is used as input, and the extracted important information is output. Here, the grammatical consistency of the contract content is verified, and potential legal risks are identified.

[0796] Step 3:

[0797] The server generates corrective suggestions for errors and risks detected based on the analysis results. The generated suggestions are presented in a user-friendly format. The analysis results are used as input, and corrective suggestions are obtained as output. Specifically, the system automatically constructs suggestions based on predefined legal guidelines.

[0798] Step 4:

[0799] The device uses an emotion analysis engine to analyze the user's emotional state in real time. Here, the user's facial expressions and voice data are acquired as input, and information about the user's emotional state is obtained as output. Specifically, facial expression analysis software and a voice analysis library are used to evaluate the user's stress level and comprehension.

[0800] Step 5:

[0801] The server optimizes how information is presented to the user based on emotional state information obtained from the emotion analysis engine. Emotional state information is the input, and the adjusted information presentation is the output. If the user shows signs of tension or confusion, the server assists the user's understanding by displaying the information in a clearer, hierarchical manner.

[0802] Step 6:

[0803] The user reviews the proposed revisions and adjusts the contract terms as needed. Based on the user's interaction, a revised version of the contract is generated. The proposed revisions are the input, and the new contract terms are finalized as the output. Specifically, the user can monitor and reflect changes in real time using the interface on their device.

[0804] (Application Example 2)

[0805] 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".

[0806] Traditional contract management systems often fail to consider the user's level of understanding or emotional state when reviewing or revising contract details, potentially causing anxiety and stress. Furthermore, real-world contract procedures often lack sufficient user interaction to ensure a sense of security. In addition, inadequate support during the presentation of contract terms can prolong the contract process, leading to decreased efficiency and potentially hindering the successful completion of contracts.

[0807] 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.

[0808] In this invention, the server includes a receiving means, an analysis means, a detection means, a generation means, a display means, an emotion analysis means, and a dialogue support means. This allows for real-time analysis of the user's emotional state and adjustment of the appropriate method of presenting contract information, thereby providing the user with a sense of security and facilitating the smooth execution of contract procedures.

[0809] "Receiving means" refers to a device or method for acquiring contract data from an external terminal or network and importing it into the system.

[0810] "Analysis means" refers to a device or method that extracts received contract data as text and performs data processing or transformation.

[0811] "Detection means" refers to a device or method that uses natural language processing technology to analyze the content of contract data and identify errors or legal risks.

[0812] "Generating means" refers to an apparatus or method for creating appropriate revised drafts or new contracts based on detected errors or risks.

[0813] "Display means" refers to a device or method for visually presenting errors, risks, and suggested corrections to a user.

[0814] "Emotional analysis means" refers to a device or method that analyzes a user's facial expressions and tone of voice to determine the user's emotional state in real time.

[0815] A "dialogue support device" is a device or method that adjusts the interface to provide information in a more understandable way and give users a sense of security when they have anxieties or questions.

[0816] This invention incorporates an emotion engine and an interactive interface to improve the user experience in a contract management system. The system operates in conjunction with servers, terminals, and users.

[0817] First, the server uses a receiving means to acquire contract data from external devices or networks. The received data is extracted as text by an analysis means and preprocessed. Specifically, the analysis means uses natural language processing technology to detect errors and risks within the contract data. Based on the detected results, the generation means creates revised drafts or new contracts.

[0818] When generated revision proposals and risk information are presented to the user via display means, sentiment analysis means play a crucial role. The user's device captures the user's facial expressions and voice using a camera and microphone, and by analyzing this data, the system determines the user's emotional state in real time. This allows the system to estimate the user's stress level and level of understanding, and present information in an appropriate manner.

[0819] For example, if a user is using their smartphone to review contract details at a physical store, and sentiment analysis determines that the user is feeling anxious, additional explanations or visual guides will be displayed in response to the questions. This enhances the user's sense of security.

[0820] Examples of prompts generated using AI models include, "When purchasing a new smartphone, what contract terms would be most beneficial to the user?" and "Please explain these contract terms to the customer in the easiest way to understand." Such interface refinements are a means of smoothing real-world conversations and improving the user experience.

[0821] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0822] Step 1:

[0823] The server receives contract data from the user's terminal. The input here is the contract data uploaded by the user to the interface, and the server passes the received data to the next processing step for text extraction.

[0824] Step 2:

[0825] The server uses an analysis tool to extract text from the received contract data and performs preprocessing. The input is contract data, and the output is data in text format. Here, the text is cleaned and prepared for machine processing.

[0826] Step 3:

[0827] The server analyzes text data, which has been parsed using natural language processing technology, using detection methods to identify errors and risks in the contract. The input is pre-processed text data, and the output is a list of errors and risks. At this stage, it is checked whether the identified risks are due to a lack of legal requirements or inappropriate clauses.

[0828] Step 4:

[0829] The server generates revised proposals and a new contract using a generation mechanism based on the output of the detection mechanism. The input is a list of errors and risks, and the output is a revised proposal and a proposed document. At this stage, an AI model is used to automatically generate the optimal revision proposal.

[0830] Step 5:

[0831] The terminal presents the user with proposed revisions and risk information through its display mechanisms. This input is the generated proposed revisions, and the output is displayed on the interface in a format that is easy for the user to understand.

[0832] Step 6:

[0833] The device uses its camera and microphone to determine the user's emotional state in real time through emotion analysis. Input is the user's facial expressions and tone of voice, while output is data related to the user's emotional state. This data is used to estimate the user's understanding of the billing statement and their stress level.

[0834] Step 7:

[0835] Based on the results of emotion analysis, the device adjusts the information presentation method using dialogue support mechanisms and displays additional explanations and visual guides. Input is the user's emotional state data, and output is an adjusted interface and additional information. This allows for quick responses when the user experiences anxiety or questions.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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."

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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 as being incorporated by reference.

[0857] The following is further disclosed regarding the embodiments described above.

[0858] (Claim 1)

[0859] A receiving means for receiving contract data,

[0860] An analysis means for extracting text from the aforementioned contract data and performing preprocessing,

[0861] A detection means that analyzes the contract data using natural language processing technology and detects errors and risks,

[0862] A generation means for generating corrective proposals based on the detected errors and risks,

[0863] A display means for presenting the user with the aforementioned errors, risks, and proposed corrections,

[0864] A generation means for generating a new contract based on user input,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein the analysis means identifies clauses and wording by comparing contract data with past contract data.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the generation means generates a contract template by referring to past successful cases.

[0870] "Example 1"

[0871] (Claim 1)

[0872] A means of receiving contract information,

[0873] An analysis means for extracting text from the aforementioned contract information and performing preprocessing,

[0874] A detection means that analyzes the contract information using natural language processing technology and detects errors and risks,

[0875] A proposal means for generating corrective measures based on the detected errors and risks,

[0876] A means of displaying the aforementioned errors, dangers, and proposed corrections to the user,

[0877] A document generation means that generates a new contract based on user input,

[0878] An optimization method that optimizes contract templates based on conditions entered by the user and taking into account past cases,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, wherein the analysis means identifies clauses and sentences by comparing contract information with past contract information.

[0882] (Claim 3)

[0883] The system according to claim 1, wherein the optimization means creates a contract template by referring to successful cases.

[0884] "Application Example 1"

[0885] (Claim 1)

[0886] Means for receiving contract information,

[0887] An analysis means for extracting and preprocessing language data from the aforementioned contract information,

[0888] A detection means that analyzes the contract information using natural language processing technology and detects errors and risks,

[0889] A generation means for generating correction suggestions based on the detected errors and risks,

[0890] A means for displaying the aforementioned errors, hazards, and suggested corrections to the end user,

[0891] A generation means for generating a new contract document based on end-user input,

[0892] A means by which end users input contract information using smart devices and process the data through cloud computing,

[0893] A means of presenting alternatives and optimizing the contract document to suit the requirements,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, wherein the analysis means identifies content and phrases by comparing contract information with existing contract information.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein the generation means generates a template for a contract document by referring to existing examples.

[0899] "Example 2 of combining an emotion engine"

[0900] (Claim 1)

[0901] A means of receiving contract information,

[0902] An analysis means for extracting and pre-processing character data from the aforementioned contract information,

[0903] A detection means that analyzes the contract information using natural language processing technology and detects errors and risks,

[0904] A generation means for generating correction suggestions based on the detected errors and risks,

[0905] A display means that analyzes the user's emotional state and presents errors, risks, and corrective suggestions in a format appropriate to the said emotional state,

[0906] A sentiment analysis method for collecting and analyzing user sentiment data,

[0907] A generation means for generating a new document based on user input,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, wherein the analysis means identifies documents and literature by comparing contract information with past contract information.

[0911] (Claim 3)

[0912] The system according to claim 1, wherein the generation means generates a document template by referring to past successful cases.

[0913] "Application example 2 when combining with an emotional engine"

[0914] (Claim 1)

[0915] A receiving means for receiving contract data,

[0916] An analysis means for extracting text from the aforementioned contract data and performing preprocessing,

[0917] A detection means that analyzes the contract data using natural language processing technology and detects errors and risks,

[0918] A generation means for generating corrective proposals based on the detected errors and risks,

[0919] A display means for presenting the user with the aforementioned errors, risks, and proposed corrections,

[0920] A generation means for generating a new contract based on user input,

[0921] A sentiment analysis tool that analyzes user emotions in real time and adjusts the method of presenting appropriate contract information,

[0922] A dialogue support system that enhances a sense of security by providing additional explanations and visual support through display means when engaging in dialogue in the real world,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, wherein the analysis means includes means for identifying clauses and wording by comparing contract data with past contract data.

[0926] (Claim 3)

[0927] The system according to claim 1, wherein the generation means comprises means for generating a contract template by referring to past successful cases. [Explanation of Symbols]

[0928] 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 receiving means for receiving contract data, An analysis means for extracting text from the aforementioned contract data and performing preprocessing, A detection means that analyzes the contract data using natural language processing technology and detects errors and risks, A generation means for generating corrective proposals based on the detected errors and risks, A display means for presenting the user with the aforementioned errors, risks, and proposed corrections, A generation means for generating a new contract based on user input, A system that includes this.

2. The system according to claim 1, wherein the analysis means identifies clauses and wording by comparing contract data with past contract data.

3. The system according to claim 1, wherein the generation means generates a contract template by referring to past successful cases.

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