system

Abstract

Provide a system. 【Solution means】 A reception means for receiving contract information from a user, A recording means for storing past data, An AI generation means for generating a draft of a contract using the past data stored in the recording structure based on the contract information obtained by the reception structure, A display means for presenting the draft of the contract generated by the AI generation structure to the user, A correction means for allowing the user to correct the draft of the generated contract, A storage means for storing the corrected final document in the recording structure, An AI learning means for using the stored final document as re-learning data, A system including.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the current contract creation process, it may be difficult to find a contract that conforms to the format and content of the company's specific contract when referring to past cases. Also, it often takes a lot of time and effort to confirm various contract information with relevant departments within the company and obtain consent. For these reasons, an improvement in efficiency and accuracy is required in the process from contract creation to completion.

Means for Solving the Problems

[0005] This invention includes an input means for receiving contract-related information from a user, a storage means for storing past document data, a generation AI means for generating a draft contract using the stored data based on the input information, and a means for presenting the generated draft to the user. Furthermore, by providing a learning means for the generation AI to retrain by storing the final contract revised by the user in the storage means, the accuracy of subsequent draft generation is improved. In addition, by providing a communication means that enables two-way communication by sharing the contract draft with relevant departments within the company, the confirmation process is also streamlined. Through these means, it is possible to simultaneously achieve increased efficiency and improved accuracy in contract creation.

[0006] "User" refers to a person who operates this system, inputs information for contract creation, and receives the generated draft contract.

[0007] "Contract-related information" refers to information necessary for creating a contract, such as the contracting party, the contracting party, and the type of contract.

[0008] "Input means" refers to devices or software that provide an interface for users to input contract-related information.

[0009] "Means of storage" refers to databases and storage devices used to hold data on past contracts and final revised documents.

[0010] "Generative AI means" refers to an artificial intelligence process or system that automatically creates a draft contract based on input contract-related information and using past data stored in a memory means.

[0011] "Presentation means" refers to devices or software that display and notify users of the draft contract generated by the generation AI means.

[0012] "Learning method" refers to the process by which the generating AI learns new information using the data from the revised final contract.

[0013] "Communication methods" refer to network functions and systems that enable the sharing of draft contracts with relevant departments within the company and facilitate information exchange. [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] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[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 terms used in the following description will be explained.

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

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[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 is a system that streamlines the process from user input of contract-related information, through the generation of a draft contract using AI technology, to the confirmation and saving of the final contract. Specific embodiments are shown below.

[0036] System Configuration

[0037] 1. User terminal

[0038] Users utilize terminals equipped with a dedicated application or web interface for contract creation. Here, they can input necessary contract-related information such as the contracting party, contracting entity, and contract type. For security reasons, the user terminals also include a function to encrypt input data before sending it to the server.

[0039] 2. Server

[0040] The server receives contract-related information sent from user terminals. The server plays a central role in managing past contract data in conjunction with the database and running the generation AI that generates draft contracts.

[0041] 3. Generation AI

[0042] The AI ​​within the server uses the received contract-related information to compare it with stored past contract data and create a draft contract that reflects the specific wording and conditions unique to each company. This draft is quick, accurate, and carefully designed to align with the company's contract drafting practices.

[0043] 4. Presentation and revision of the draft.

[0044] The server returns the generated draft contract to the user's terminal, where the user reviews the draft. The user's terminal allows for editing of the draft's details and saving changes in real time. The user can then share this draft with relevant departments within the company for legal checks and necessary revisions.

[0045] 5. Saving the final version and AI training

[0046] The final version of the contract, after all revisions have been completed, will be uploaded to the server again. This final version's data will be used for new training by the generating AI, contributing to improved accuracy of future draft contracts.

[0047] Specific example

[0048] For example, consider a scenario where a user wants to enter into a service contract between "Company A" and "Company B." The user uses a terminal to input "Company A" as the contracting party, "Company B" as the contracting party, and "Service Contract" as the contract type. The server passes this information to the generating AI, which then references past service contract data to generate an optimal draft. The generated draft is suitable for the specific company's format and requirements and is presented to the user. This draft is then reviewed internally and revised as needed to finalize it. The final version is also saved on the server during this process and used for future AI training. This entire process saves time and allows for the creation of highly accurate contracts.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The user activates the terminal and accesses a dedicated application or web interface. Here, they enter contract-related information such as the contracting party, contracting entity, and contract type. The terminal verifies the entered data in real time and checks for errors in the input format.

[0052] Step 2:

[0053] The terminal encrypts verified contract-related information and sends it to the server using a secure communication protocol. Once the data transmission is complete, a notification of successful transmission is displayed to the user.

[0054] Step 3:

[0055] The server searches for past contract data based on the received data. It identifies appropriate datasets based on contract type and company name, and provides the data and input information to the generating AI.

[0056] Step 4:

[0057] The AI ​​generates a draft contract based on the input contract-related information and by referencing selected past contract data. This draft is created taking into account the company's specific format, expression, and contract terms.

[0058] Step 5:

[0059] The server sends the generated draft contract back to the terminal. The user receives the draft on the terminal and reviews its contents. If necessary, the user can edit the contents of the draft.

[0060] Step 6:

[0061] Users submit drafts to the company's legal department and other relevant departments, communicating to obtain confirmation and approval. The terminal records real-time comments and revision history from multiple users.

[0062] Step 7:

[0063] The revised final contract is uploaded from the user's device to the server. The final version of the contract is recorded as future training data.

[0064] Step 8:

[0065] The server uses newly uploaded contracts as training data for the generating AI, improving the accuracy of future contract drafts. The AI ​​retrains using past data, thereby improving the overall accuracy and efficiency of the system.

[0066] (Example 1)

[0067] 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."

[0068] In contract drafting, it is crucial to create contract documents quickly and accurately based on the diverse contract-related information held by the user. However, traditional methods require considerable effort and time for manual contract creation, and are prone to inconsistencies and errors. Furthermore, newly created contracts often do not reflect the organization's usage history. Therefore, there is a need for a system that can efficiently generate contracts and enable improvements based on past usage history.

[0069] 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.

[0070] In this invention, the server includes means for receiving information via a terminal device that accepts contract-related information from users, means for accessing a storage device that stores past document data and obtaining necessary data, and means for generating a draft contract document using a generative model based on the obtained data. This enables efficient generation of contracts and improved accuracy in document creation that reflects past usage history.

[0071] "User" refers to an individual or organization that is involved in the creation or modification of a contract and enters the necessary information.

[0072] "Terminal device" refers to a device used to input contract-related information or display draft contracts, and includes personal computers, tablets, and smartphones.

[0073] "Storage device" refers to a storage medium or system that stores past document data and usage history, and provides the data necessary for generating contracts.

[0074] A "generative model" refers to an artificial intelligence algorithm or system that automatically creates a draft contract document based on input contract-related information and data stored in a memory device.

[0075] A "display device" refers to a device equipped with a display that provides an interface for presenting a draft contract to the user and allowing them to make revisions or confirmations.

[0076] A "learning device" refers to a system equipped with a self-learning function that stores the final document modified by the user and utilizes that data to improve the performance of the generative model.

[0077] "Communication device" refers to a device or system equipped with network connectivity that a user uses to share and obtain confirmation of a draft contract with relevant departments within the company.

[0078] Modes for carrying out the invention

[0079] This invention is a system for efficiently generating contract documents by utilizing contract-related information. Specific embodiments thereof are described below.

[0080] To create a contract, users first use a terminal device equipped with a dedicated application or web interface. This terminal device may include a personal computer, tablet, or smartphone. Users input contract-related information, such as "contracting party," "contracting recipient," and "contract type." This information is encrypted for security purposes and transmitted to the server.

[0081] The server receives contract-related information transmitted from terminal devices and accesses storage devices for managing historical document data. These storage devices include cloud storage and databases on local servers. The server utilizes this historical contract data stored in these devices and automatically generates draft contract documents using a generative AI model. The generative AI model provides rapid and accurate drafts, taking into account the specific expressions and conditions of each company.

[0082] The generated draft contract is sent back from the server to the terminal device. The user can review the draft on the terminal device and edit its contents as needed. The user can then share the edited draft with relevant departments within the company and receive feedback. Once the final version of the contract document is completed, it is saved back to the server and used as training data for future AI-generated models.

[0083] For example, if a user wants to enter into a service contract between "Company A" and "Company B," they can enter the following prompt: "Please create a new service contract. Company A is the client, and Company B is the client." This enables the creation of efficient and accurate contract documents.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user enters contract-related information using a terminal device. The user accesses an application or web interface and enters information such as "contracting party," "contracting recipient," and "contract type" into a form. After completion, the terminal encrypts this data and securely sends it to the server. The output indicates that the entered data has been sent to the server.

[0087] Step 2:

[0088] The server receives contract-related information transmitted from the terminal. It decrypts the received data and searches for and retrieves past contract data stored in its storage device. Through these operations, the server prepares past data related to the entered contract conditions and obtains input for the generating AI model. This data becomes the input for the generating AI model.

[0089] Step 3:

[0090] A generative AI model running on the server generates a draft contract based on the information received in the previous step. The generative AI model applies the company's specific format and terminology to generate the draft, drawing on insights gained from past history. The generated draft contract becomes the output, and the server prepares to send it to the next step.

[0091] Step 4:

[0092] The server sends the generated contract draft to the user's terminal device. The user receives this draft on their terminal and reviews its contents. If necessary, the user edits the draft and saves the revised content. The revised draft becomes the output, ready for internal sharing and review.

[0093] Step 5:

[0094] The user uploads the final version of the contract, after completing the revisions, to the server. The server saves the final document to its storage device and uses it as training data for subsequent AI models. As a result, the saved final data becomes the output, forming the basis for the continuous improvement of the AI ​​model.

[0095] (Application Example 1)

[0096] 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."

[0097] The contract drafting process is problematic because it is time-consuming and labor-intensive. This is especially true in fields like electronic payment services, where numerous contracts and terms of service need to be generated appropriately and processed efficiently. However, current systems make these processes cumbersome, and efficiency improvements are needed.

[0098] 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.

[0099] In this invention, the server includes a receiving means for receiving contract information from a user, a recording means for storing past data, and an AI generation means for generating a draft contract based on the contract information acquired by the receiving structure and using the past data stored in the recording structure. This makes it possible for users to easily input contract information from a mobile device such as a smartphone, and to quickly and accurately generate, modify, and electronically submit and save a draft contract.

[0100] The "reception structure" is an input method for receiving contract information from users, and it has the function of safely and efficiently acquiring contract-related data.

[0101] A "record structure" refers to a database or storage medium used to store past document data, making the accumulated information easily accessible.

[0102] "AI-generated structure" refers to a method that uses AI technology to automatically generate draft contracts based on acquired contract information and utilizing past data.

[0103] The "display structure" refers to a means of presenting the generated draft contract to the user, allowing them to review its contents through a user interface.

[0104] "Revision structure" refers to a means of providing a function that allows users to edit and modify the generated draft contract.

[0105] A "archive structure" is a means of storing the final version of a contract, modified by the user, in a record structure and managing it so that it can be reused and referenced in the past.

[0106] An "AI learning structure" is a structure that uses the saved final document as retraining data and has the functionality to improve the AI's generation performance.

[0107] "Communication structure" refers to a function that includes communication means for users to share information with relevant departments and third parties, exchange opinions, and obtain confirmation.

[0108] An "electronic processing structure" is a structure that allows users to input contract information from a mobile device and generate, submit, and store contract documents electronically.

[0109] The system for implementing this invention mainly consists of a server, a user terminal, and related cloud AI services. The server has a reception structure for users to input contract information and a recording structure that records past document data as a cloud-based database. The AI ​​generation structure within the server automatically generates a draft contract based on the input information and past data.

[0110] The user terminal operates on a mobile device such as a smartphone or tablet and provides an interface for users to input contract information and view and modify the generated draft. This terminal also has a communication structure that allows it to send the revised final version of the contract to the server's storage structure and to share information with relevant departments.

[0111] The program uses Amazon Web Services' SageMaker and Google® Cloud AI Platform as infrastructure for AI generation. Secure protocols are used for data encryption and transmission to ensure data security.

[0112] As a concrete example, consider a scenario where a user is preparing a contract with a new business partner. The user uses their smartphone to input the contracting party, the contracting party, and the contract terms. This information is encrypted and sent to the server. The server's AI generation structure generates a draft contract based on this information and similar past data, and presents it to the user. The user reviews the draft via the smartphone interface, makes revisions as needed, and saves the final version.

[0113] An example of a prompt message would be: "Generate a draft contract. Contracting party: 'Company X', Contracting party: 'Company Y', Contract type: 'Sales contract', and include the special condition 'Shipping costs are the responsibility of the buyer'."

[0114] In this way, it is possible to streamline the entire process of generating, modifying, and saving contracts, thereby reducing the workload on users.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user launches a dedicated app on their smartphone and enters contract information such as the contracting party, contracting entity, and contract terms. The entered information is collected through the input method and securely transmitted to the server using an encryption protocol. The input here is manual by the user, and the output is encrypted data.

[0118] Step 2:

[0119] The server decrypts the contract information received from the user and stores it in the reception structure. Based on this information, it activates the AI ​​generation structure and selects the necessary contract format and template based on the input data. The input is the decrypted contract information, and the output is template selection data for the contract draft.

[0120] Step 3:

[0121] The AI-generated structure searches for similar past contract data from its memory structure according to a selected template and generates a draft contract. This process uses a generative AI model, performs data calculations based on the input data, and produces an optimally tailored draft contract. The output is the generated draft contract.

[0122] Step 4:

[0123] The server sends the generated draft contract to the terminal and displays it to the user. The user reviews the draft on the terminal and makes revisions as needed. Revisions are made in real time, and change data based on the user's input is generated, forming a new draft. The output is the draft contract with the user's revisions.

[0124] Step 5:

[0125] The user reviews the final version of the contract after completing the revisions and sends the final document to the server. The server stores this final version in a storage structure and adds it to the library as retraining data using a record structure. The input here is the final version of the contract, and the output is the stored document data.

[0126] Step 6:

[0127] The server's AI learning structure trains the generation AI model using saved final documents to improve the accuracy of subsequent contract generation processes. During this learning process, data calculations are performed using past data, and the AI ​​model is adjusted as an output.

[0128] 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.

[0129] This invention provides a system for smoothly and efficiently carrying out a series of processes for drafting contracts. This system includes a function to recognize the user's emotions and make adjustments accordingly, providing a more intuitive and user-friendly interface.

[0130] System Configuration

[0131] 1. User terminal

[0132] Users access a contract creation application or web interface using a terminal. They enter basic contract information and submit it to the system. This terminal is equipped with an interface for detecting the user's emotions.

[0133] 2. Server

[0134] The server receives input data sent from the terminal, and simultaneously uses past contract data to generate a draft contract using AI. The server also has an emotion engine built in, which allows it to receive feedback based on the user's emotions.

[0135] 3. Generative AI and Emotion Engines

[0136] The generative AI generates a draft contract based on the given information and historical data from the database. During this process, the emotion engine analyzes the user's emotions in real time and feeds that information back to the generative AI. For example, if it detects that the user is frustrated with an overly long document, the generative AI will suggest a more concise expression.

[0137] 4. Presentation and modification functions

[0138] The draft generated by the server is sent to the user's terminal. The user reviews and modifies this draft. During this time, the emotion engine analyzes the user's facial expressions and tone of voice, continuously monitoring the user's satisfaction level. If the user expresses dissatisfaction, the engine analyzes the cause and makes further suggestions.

[0139] 5. Final Agreement and Learning Process

[0140] Once the user completes the final contract, it is saved again on the server. The final contract data is used as training material for the AI, contributing to improvements in the quality of future processes. Feedback through the emotion engine is also incorporated into the learning process, enabling more accurate adjustments.

[0141] Specific example

[0142] For example, consider a scenario where a user creates a sales contract between "Company C" and "Company D." The user inputs contract data on a terminal, and the server uses this data to extract information from past sales contracts and generates a draft using AI. If the user shows dissatisfaction with the proposed clauses through facial expressions or tone of voice, the emotion engine analyzes this and notifies the AI. The AI ​​then uses this feedback to revise the clauses and proposes a new one. Finally, a satisfactory contract is completed, saved on the server, and used as data to support future processes. In this way, the feedback loop utilizing the emotion engine ensures a highly satisfying contract creation process.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user starts up the device and logs in by accessing a dedicated application or web interface. They enter necessary information such as the contracting company, contracting party, and contract type. The device verifies the format of the entered information before sending it to the server in a secure format.

[0146] Step 2:

[0147] The emotion engine installed in the device analyzes the user's facial expressions and voice in real time as they enter contract information, and temporarily stores this information on the device. When a change in emotion or a specific emotional state is detected, the information is sent to the server.

[0148] Step 3:

[0149] Based on the received contract-related information, the server searches its database of past contracts for similar data. Once a match is selected, the generation AI is activated and generates a draft contract using the stored data.

[0150] Step 4:

[0151] The server sends the generated draft to the user's terminal and simultaneously refers to feedback information from the emotion engine. Based on the emotion engine's analysis results, the generating AI may dynamically adjust the content of the draft.

[0152] Step 5:

[0153] Users can review a draft of the contract on their device and edit its contents as needed. While the user is reviewing the draft, the sentiment engine continues to monitor the user's reactions and notifies the server to make further adjustments if the user expresses dissatisfaction or confusion.

[0154] Step 6:

[0155] Once the user completes revisions to the draft, the final contract is uploaded to the server. This final version is stored in memory and used to retrain the generation AI. Sentiment data is also stored and used for future draft generation.

[0156] Step 7:

[0157] The server adjusts the generation AI and emotion engine based on the newly uploaded data to improve performance in the next process. This improves overall user satisfaction and generation accuracy.

[0158] (Example 2)

[0159] 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".

[0160] In the contract drafting process, it is difficult to make adjustments that reflect user sentiment in real time, and there is a lack of efficient means to generate contracts that increase user satisfaction. Furthermore, with conventional systems, there are limited ways for users to quickly review and revise contract contents within the organization.

[0161] 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.

[0162] In this invention, the server includes a terminal device that receives contract information from a user, an emotion detection means that analyzes the emotional state, a recording means that stores past contract data, an emotion recording means that holds the user's emotional data, a generation AI means that generates a draft contract based on the contract information and emotional data, and an adjustment means that performs adjustments taking the emotional data into consideration. This enables the efficient and highly satisfying generation of contracts based on the user's emotions, as well as rapid exchange of opinions and confirmation work within the organization.

[0163] A "terminal device" is a device used by users to input and transmit contract information, and it also has a function to detect emotional states.

[0164] "Emotion detection means" refers to technology that analyzes a user's facial expressions and voice to understand their emotional state in real time.

[0165] "Recording means" refers to a function that stores past contract data and emotional data, and retains information that serves as the basis for generating and adjusting contract drafts.

[0166] "Emotion recording means" refers to technology for storing emotional data obtained from users and utilizing it in subsequent processes.

[0167] "Generative AI means" refers to artificial intelligence technology used to create draft contracts based on recorded historical data.

[0168] A "modification mechanism" is a system that adjusts drafts generated while taking emotional data into consideration, in order to improve user satisfaction.

[0169] The "presentation method" refers to a function that provides users with a draft contract created by the generation AI method for review and revision.

[0170] A "correction suggestion method" is a technology that uses emotional feedback to show users the adjustments made by a generative AI.

[0171] "Learning method" refers to a function that saves the revised final document as retraining data and uses it to improve future contract generation processes.

[0172] A "communication function" is a system for sharing information with relevant departments within an organization, and it facilitates smooth exchange of opinions.

[0173] "Methods for exchanging opinions" refer to methods for sharing sentiment analysis results within an organization and improving cooperation among users and with related departments.

[0174] This invention is a system that efficiently collects contract information from users, makes adjustments based on emotions, and generates contracts. It is primarily built using servers, terminals, and AI technology.

[0175] The terminal is a device that provides an interface for users to input contract information. This device incorporates emotion detection functions such as a camera and microphone, which analyze the user's emotional state in real time through their facial expressions and voice. This data is transmitted to the server along with the user's input.

[0176] The server receives contract information and emotional data transmitted from the terminal. Based on this data, the server uses a generative AI to create a draft contract, referencing recorded past contract data. The emotional data is also analyzed by an emotional detection engine and used as feedback to the generative AI, which adjusts the document according to the user's emotional state. This is particularly useful when making adjustments such as simplifying the writing style or using technical terms appropriately.

[0177] As a concrete example, consider a case where a user creates a sales contract. The user uses a terminal to input sales conditions and product information, and sends it to a server via the business network. The server searches a database of past cases and, using similar contracts as references, generates a new draft using a generation AI. If the user expresses dissatisfaction during this process, emotional feedback is immediately conveyed to the AI, and the contract is adjusted to be more easily understood.

[0178] As an example of a prompt, instructions can be given to the generating AI model in the form of, "What improvements can be made to this draft sales contract? Please make clearer and more concise suggestions, taking into account the user's feelings." This process allows users to quickly and effectively create contracts that reflect their feelings and opinions.

[0179] This system combines generative AI models with sentiment analysis technology to provide users with an intuitive and highly satisfying contract creation process.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The user enters contract information through the terminal and presses the submit button. The information entered includes the type of contract, the names of the parties involved, and the contract date. The terminal also simultaneously records the user's facial expressions and voice, acquiring them as emotional data. The output of this process is basic contract data and emotional data.

[0183] Step 2:

[0184] The server receives contract information and sentiment data sent from the terminal. Using this data as input, the server searches the database for relevant past contract data. The database stores usage history and templates, and extracts appropriate information depending on the type of contract. The output of this process is past contract data.

[0185] Step 3:

[0186] The server's generating AI creates a draft contract based on received contract information and past contract data. The generating AI analyzes the contract information and extracted data from the database, and constructs the document using prompt sentences. It also receives feedback from the emotion engine and performs specific actions to adjust the tone and length of the text. The output of this process is a draft contract proposed to the user.

[0187] Step 4:

[0188] The server sends the generated draft contract to the user's terminal. The user reviews the draft on the terminal and suggests revisions based on their emotions and opinions. The terminal then analyzes the facial expressions and voice again and sends the emotional data back to the emotion engine. The output of this process is the user's feedback data.

[0189] Step 5:

[0190] The generation AI receives feedback data from users and readjusts the contract based on instructions from the emotion engine. Specifically, it makes edits such as simplifying wording and reducing redundant items. This adjustment process continues until the user is satisfied. The output of this process is a more optimized contract.

[0191] Step 6:

[0192] Once the user approves the final version of the contract, the server saves it to a database. This saved data is also used as training material for the generating AI model, contributing to improvements in the next contract generation process. The output of this process is the final contract and the training data.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0195] In the contract creation process, contracts are often generated using a uniform interface without considering the user's feelings, resulting in a decline in the quality of the user experience. Furthermore, there are limited ways to obtain information regarding user discomfort and satisfaction during online payments, resulting in a lack of means to provide a better user experience. This can cause users to feel stressed, leading to a decrease in the final transaction completion rate. Another challenge is the inability to utilize user sentiment information obtained after a transaction for future transactions.

[0196] 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.

[0197] In this invention, the server includes an input device means for receiving contract-related information from a user, a storage device means for storing past document information, an artificial intelligence generation device means for generating a draft contract using past document information stored in the storage device based on the contract-related information received by the input device, a presentation device means for presenting the draft contract generated by the artificial intelligence generation device to the user, and an emotion recognition device means for detecting the user's emotions and adjusting the transaction process. This makes it possible to detect the user's emotions in real time, dynamically adjust the contract creation and settlement processes, and provide a more satisfying user experience.

[0198] An "input device" is a device used to receive contract-related information from users.

[0199] A "storage device" is a medium used to store and manage historical document information.

[0200] An "artificial intelligence generation device" is a device that creates a draft contract based on input contract-related information and stored past document information.

[0201] A "presentation device" is a device equipped with the function of displaying a draft contract created by an artificial intelligence generation device to the user.

[0202] An "emotion recognition device" is a device that has the function of detecting the user's emotions and adjusting the transaction process accordingly.

[0203] This invention realizes a system that provides a contract creation and payment experience that takes user emotions into consideration. The system mainly consists of an input device, a storage device, an artificial intelligence generation device, a presentation device, and an emotion recognition device.

[0204] The server receives contract-related information through the user's input device. This input device can be a smartphone or smart glasses. The received information, along with past document data stored in the storage device, is used by an artificial intelligence generation system to generate a draft contract. This AI generation system utilizes generation AI technology.

[0205] The generated draft is presented to the user via a presentation device, which is a monitor or display. As the user reviews the draft, an emotion recognition device detects their facial expressions and tone of voice to understand their emotional state. For emotion recognition, for example, Microsoft® Azure®'s Emotion API is used. If the user expresses dissatisfaction or frustration, the emotion recognition device feeds this back to the artificial intelligence generation device, which then adjusts the content and interface of the contract.

[0206] A concrete example of this is a scenario involving electronic payments. When a user makes a payment to purchase goods, the system analyzes the user's facial expressions and voice using a camera and microphone, and an emotion recognition device then suggests the most suitable discount or simplifies the interface based on this analysis. This process improves user satisfaction and facilitates the smooth completion of transactions.

[0207] An example of a prompt would be, "Suggest strategies to provide a better experience when a user expresses dissatisfaction during online payment." Using this prompt, the generative AI model generates appropriate solutions tailored to the user's emotions.

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

[0209] Step 1:

[0210] The terminal accepts contract-related information from the user as input. The input data is acquired through the interface of a smartphone or smart glasses, and the basic information necessary for creating the contract is sent to the server.

[0211] Step 2:

[0212] The server combines the received contract-related information with past document information stored in its memory, performing database searches and comparisons. Based on this data, an artificial intelligence generation device generates a draft contract. The output is an initial draft contract.

[0213] Step 3:

[0214] The server sends a draft of the generated contract to the terminal, which then presents it to the user. The terminal uses a monitor or display to allow the user to visually review the presented draft.

[0215] Step 4:

[0216] While the user reviews the presented draft contract, the emotion recognition device uses a camera and microphone to capture the user's facial expressions and voice as input. Based on this data, it performs emotion analysis and provides the emotion recognition device with the user's emotional state as output.

[0217] Step 5:

[0218] The server determines whether the user is expressing dissatisfaction or frustration based on the output of the emotion recognition device. This determination is fed back to the artificial intelligence generation device as input, and the contract is adjusted. The adjusted contract is then generated as output.

[0219] Step 6:

[0220] The device will present the revised contract to the user again. If the user does not express any negative feelings towards this proposal, the user will review the final version.

[0221] Step 7:

[0222] The server receives the final version of the contract reviewed by the user as input and saves it to its storage device. This data is then output as retraining data for future contract creation.

[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 is a system that streamlines the process from user input of contract-related information, through the generation of a draft contract using AI technology, to the confirmation and saving of the final contract. Specific embodiments are shown below.

[0240] System Configuration

[0241] 1. User terminal

[0242] Users utilize terminals equipped with a dedicated application or web interface for contract creation. Here, they can input necessary contract-related information such as the contracting party, contracting entity, and contract type. For security reasons, the user terminals also include a function to encrypt input data before sending it to the server.

[0243] 2. Server

[0244] The server receives contract-related information sent from user terminals. The server plays a central role in managing past contract data in conjunction with the database and running the generation AI that generates draft contracts.

[0245] 3. Generation AI

[0246] The AI ​​within the server uses the received contract-related information to compare it with stored past contract data and create a draft contract that reflects the specific wording and conditions unique to each company. This draft is quick, accurate, and carefully designed to align with the company's contract drafting practices.

[0247] 4. Presentation and revision of the draft.

[0248] The server returns the generated draft contract to the user's terminal, where the user reviews the draft. The user's terminal allows for editing of the draft's details and saving changes in real time. The user can then share this draft with relevant departments within the company for legal checks and necessary revisions.

[0249] 5. Saving the final version and AI training

[0250] The final version of the contract, after all revisions have been completed, will be uploaded to the server again. This final version's data will be used for new training by the generating AI, contributing to improved accuracy of future draft contracts.

[0251] Specific example

[0252] For example, consider a scenario where a user wants to enter into a service contract between "Company A" and "Company B." The user uses a terminal to input "Company A" as the contracting party, "Company B" as the contracting party, and "Service Contract" as the contract type. The server passes this information to the generating AI, which then references past service contract data to generate an optimal draft. The generated draft is suitable for the specific company's format and requirements and is presented to the user. This draft is then reviewed internally and revised as needed to finalize it. The final version is also saved on the server during this process and used for future AI training. This entire process saves time and allows for the creation of highly accurate contracts.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The user activates the terminal and accesses a dedicated application or web interface. Here, they enter contract-related information such as the contracting party, contracting entity, and contract type. The terminal verifies the entered data in real time and checks for errors in the input format.

[0256] Step 2:

[0257] The terminal encrypts verified contract-related information and sends it to the server using a secure communication protocol. Once the data transmission is complete, a notification of successful transmission is displayed to the user.

[0258] Step 3:

[0259] The server searches for past contract data based on the received data. It identifies appropriate datasets based on contract type and company name, and provides the data and input information to the generating AI.

[0260] Step 4:

[0261] The AI ​​generates a draft contract based on the input contract-related information and by referencing selected past contract data. This draft is created taking into account the company's specific format, expression, and contract terms.

[0262] Step 5:

[0263] The server sends the generated draft contract back to the terminal. The user receives the draft on the terminal and reviews its contents. If necessary, the user can edit the contents of the draft.

[0264] Step 6:

[0265] Users submit drafts to the company's legal department and other relevant departments, communicating to obtain confirmation and approval. The terminal records real-time comments and revision history from multiple users.

[0266] Step 7:

[0267] The revised final contract is uploaded from the user's device to the server. The final version of the contract is recorded as future training data.

[0268] Step 8:

[0269] The server uses newly uploaded contracts as training data for the generating AI, improving the accuracy of future contract drafts. The AI ​​retrains using past data, thereby improving the overall accuracy and efficiency of the system.

[0270] (Example 1)

[0271] 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."

[0272] In contract drafting, it is crucial to create contract documents quickly and accurately based on the diverse contract-related information held by the user. However, traditional methods require considerable effort and time for manual contract creation, and are prone to inconsistencies and errors. Furthermore, newly created contracts often do not reflect the organization's usage history. Therefore, there is a need for a system that can efficiently generate contracts and enable improvements based on past usage history.

[0273] 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.

[0274] In this invention, the server includes means for receiving information via a terminal device that accepts contract-related information from users, means for accessing a storage device that stores past document data and obtaining necessary data, and means for generating a draft contract document using a generative model based on the obtained data. This enables efficient generation of contracts and improved accuracy in document creation that reflects past usage history.

[0275] "User" refers to an individual or organization that is involved in the creation or modification of a contract and enters the necessary information.

[0276] "Terminal device" refers to a device used to input contract-related information or display draft contracts, and includes personal computers, tablets, and smartphones.

[0277] "Storage device" refers to a storage medium or system that stores past document data and usage history, and provides the data necessary for generating contracts.

[0278] A "generative model" refers to an artificial intelligence algorithm or system that automatically creates a draft contract document based on input contract-related information and data stored in a memory device.

[0279] The "display device" refers to a device equipped with a display that provides an interface for presenting a draft contract to the user and allowing the user to make corrections and confirmations.

[0280] The "learning device" refers to a system equipped with a self-learning function that stores the final document modified by the user and utilizes the data to improve the performance of the generation model.

[0281] The "communication device" refers to a device or system equipped with a network connection function that the user uses to share a draft contract with relevant departments within the company and obtain confirmations.

[0282] Mode for Implementing the Invention

[0283] This invention is a system that efficiently generates a contract document by utilizing contract-related information. Hereinafter, specific embodiments thereof will be described.

[0284] To create a contract, the user first uses a terminal device equipped with a dedicated application or web interface. This terminal device can be a personal computer, tablet, or smartphone, etc. The user inputs contract-related information, such as items like "contractor", "contractee", and "contract type". This information is designed to be encrypted in consideration of security and transmitted to the server.

[0285] The server receives the contract-related information transmitted from the terminal device and accesses a storage device for managing past document data. A database in cloud storage or a local server is used as the storage device. The server utilizes the past contract data stored in this storage device and automatically creates a draft of the contract document using a generation AI model. The generation AI model provides a draft quickly and accurately considering the expressions and conditions specific to each company.

[0286] The generated draft contract is sent back from the server to the terminal device. The user can check the draft on the terminal device and edit its content as needed. The user can share the edited draft with relevant departments within the company and obtain feedback. As a result, when the final version of the contract document is completed, it is saved to the server again and used as learning data for future generative AI models.

[0287] As a specific example, for instance, when a user wants to conclude a commission contract between "Company A" and "Company B", the following prompt text can be input. "Please create a new commission contract. The contract originator is Company A and the contract recipient is Company B." This enables the creation of an efficient and accurate contract document.

[0288] The flow of the specific process in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The user inputs contract-related information using the terminal device. The user accesses an application or web interface and enters information such as "contract originator", "contract recipient", and "contract type" into a form. After input completion, the terminal encrypts this data and securely transmits it to the server. The output is that the input data is transmitted to the server.

[0291] Step 2:

[0292] The server receives the contract-related information transmitted from the terminal. The server decrypts the received data and performs operations to search for and obtain past contract data in the storage device. Through these operations, the server prepares past data related to the input contract conditions and obtains input for the generative AI model. This data becomes the input to the generative AI model.

[0293] Step 3:

[0294] A generative AI model running on the server generates a draft contract based on the information received in the previous step. The generative AI model applies the company's specific format and terminology to generate the draft, drawing on insights gained from past history. The generated draft contract becomes the output, and the server prepares to send it to the next step.

[0295] Step 4:

[0296] The server sends the generated contract draft to the user's terminal device. The user receives this draft on their terminal and reviews its contents. If necessary, the user edits the draft and saves the revised content. The revised draft becomes the output, ready for internal sharing and review.

[0297] Step 5:

[0298] The user uploads the final version of the contract, after completing the revisions, to the server. The server saves the final document to its storage device and uses it as training data for subsequent AI models. As a result, the saved final data becomes the output, forming the basis for the continuous improvement of the AI ​​model.

[0299] (Application Example 1)

[0300] 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."

[0301] The contract drafting process is problematic because it is time-consuming and labor-intensive. This is especially true in fields like electronic payment services, where numerous contracts and terms of service need to be generated appropriately and processed efficiently. However, current systems make these processes cumbersome, and efficiency improvements are needed.

[0302] 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.

[0303] In this invention, the server includes a reception means for receiving contract information from a user, a recording means for storing past data, and an AI generation means for generating a draft of a contract document using the past data stored in the recording structure based on the contract information obtained by the reception structure. This enables the user to easily input contract information from a mobile device such as a smartphone, and quickly and accurately generate, modify, electronically submit, and save a draft of the contract document.

[0304] The "reception structure" is an input means for receiving contract information from a user, and has a function of safely and efficiently obtaining contract-related data.

[0305] The "recording structure" refers to a database or storage medium for storing past document data, and enables easy access to the information accumulated thereby.

[0306] The "AI generation structure" is a means using AI technology that utilizes past data based on the obtained contract information and automatically generates a draft of a contract document.

[0307] The "display structure" is a means for presenting the generated draft of the contract document to the user, and enables the content to be confirmed via a user interface.

[0308] The "modification structure" is a means for providing a function that allows the user to edit and modify the generated draft of the contract document.

[0309] The "storage structure" is a means for storing the final version of the contract document modified by the user in the recording structure and managing it so that it can be reused and referenced in the past.

[0310] The "AI learning structure" is a structure having a function for improving the generation performance of AI by using the stored final document as re-learning data.

[0311] "Communication structure" refers to a function that includes communication means for users to share information with relevant departments and third parties, exchange opinions, and obtain confirmation.

[0312] An "electronic processing structure" is a structure that allows users to input contract information from a mobile device and generate, submit, and store contract documents electronically.

[0313] The system for implementing this invention mainly consists of a server, a user terminal, and related cloud AI services. The server has a reception structure for users to input contract information and a recording structure that records past document data as a cloud-based database. The AI ​​generation structure within the server automatically generates a draft contract based on the input information and past data.

[0314] The user terminal operates on a mobile device such as a smartphone or tablet and provides an interface for users to input contract information and view and modify the generated draft. This terminal also has a communication structure that allows it to send the revised final version of the contract to the server's storage structure and to share information with relevant departments.

[0315] The program uses Amazon Web Services' SageMaker and Google Cloud AI Platform as infrastructure for AI generation. Secure protocols are used for data encryption and transmission to ensure data security.

[0316] As a concrete example, consider a scenario where a user is preparing a contract with a new business partner. The user uses their smartphone to input the contracting party, the contracting party, and the contract terms. This information is encrypted and sent to the server. The server's AI generation structure generates a draft contract based on this information and similar past data, and presents it to the user. The user reviews the draft via the smartphone interface, makes revisions as needed, and saves the final version.

[0317] An example of a prompt message would be: "Generate a draft contract. Contracting party: 'Company X', Contracting party: 'Company Y', Contract type: 'Sales contract', and include the special condition 'Shipping costs are the responsibility of the buyer'."

[0318] In this way, it is possible to streamline the entire process of generating, modifying, and saving contracts, thereby reducing the workload on users.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The user launches a dedicated app on their smartphone and enters contract information such as the contracting party, contracting entity, and contract terms. The entered information is collected through the input method and securely transmitted to the server using an encryption protocol. The input here is manual by the user, and the output is encrypted data.

[0322] Step 2:

[0323] The server decrypts the contract information received from the user and stores it in the reception structure. Based on this information, it activates the AI ​​generation structure and selects the necessary contract format and template based on the input data. The input is the decrypted contract information, and the output is template selection data for the contract draft.

[0324] Step 3:

[0325] The AI-generated structure searches for similar past contract data from its memory structure according to a selected template and generates a draft contract. This process uses a generative AI model, performs data calculations based on the input data, and produces an optimally tailored draft contract. The output is the generated draft contract.

[0326] Step 4:

[0327] The server sends the generated draft contract to the terminal and displays it to the user. The user reviews the draft on the terminal and makes revisions as needed. Revisions are made in real time, and change data based on the user's input is generated, forming a new draft. The output is the draft contract with the user's revisions.

[0328] Step 5:

[0329] The user reviews the final version of the contract after completing the revisions and sends the final document to the server. The server stores this final version in a storage structure and adds it to the library as retraining data using a record structure. The input here is the final version of the contract, and the output is the stored document data.

[0330] Step 6:

[0331] The server's AI learning structure trains the generation AI model using saved final documents to improve the accuracy of subsequent contract generation processes. During this learning process, data calculations are performed using past data, and the AI ​​model is adjusted as an output.

[0332] 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.

[0333] This invention provides a system for smoothly and efficiently carrying out a series of processes for drafting contracts. This system includes a function to recognize the user's emotions and make adjustments accordingly, providing a more intuitive and user-friendly interface.

[0334] System Configuration

[0335] 1. User terminal

[0336] Users access a contract creation application or web interface using a terminal. They enter basic contract information and submit it to the system. This terminal is equipped with an interface for detecting the user's emotions.

[0337] 2. Server

[0338] The server receives input data sent from the terminal, and simultaneously uses past contract data to generate a draft contract using AI. The server also has an emotion engine built in, which allows it to receive feedback based on the user's emotions.

[0339] 3. Generative AI and Emotion Engines

[0340] The generative AI generates a draft contract based on the given information and historical data from the database. During this process, the emotion engine analyzes the user's emotions in real time and feeds that information back to the generative AI. For example, if it detects that the user is frustrated with an overly long document, the generative AI will suggest a more concise expression.

[0341] 4. Presentation and modification functions

[0342] The draft generated by the server is sent to the user's terminal. The user reviews and modifies this draft. During this time, the emotion engine analyzes the user's facial expressions and tone of voice, continuously monitoring the user's satisfaction level. If the user expresses dissatisfaction, the engine analyzes the cause and makes further suggestions.

[0343] 5. Final Agreement and Learning Process

[0344] Once the user completes the final contract, it is saved again on the server. The final contract data is used as training material for the AI, contributing to improvements in the quality of future processes. Feedback through the emotion engine is also incorporated into the learning process, enabling more accurate adjustments.

[0345] Specific example

[0346] For example, consider a scenario where a user creates a sales contract between "Company C" and "Company D." The user inputs contract data on a terminal, and the server uses this data to extract information from past sales contracts and generates a draft using AI. If the user shows dissatisfaction with the proposed clauses through facial expressions or tone of voice, the emotion engine analyzes this and notifies the AI. The AI ​​then uses this feedback to revise the clauses and proposes a new one. Finally, a satisfactory contract is completed, saved on the server, and used as data to support future processes. In this way, the feedback loop utilizing the emotion engine ensures a highly satisfying contract creation process.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] The user starts up the device and logs in by accessing a dedicated application or web interface. They enter necessary information such as the contracting company, contracting party, and contract type. The device verifies the format of the entered information before sending it to the server in a secure format.

[0350] Step 2:

[0351] The emotion engine installed in the device analyzes the user's facial expressions and voice in real time as they enter contract information, and temporarily stores this information on the device. When a change in emotion or a specific emotional state is detected, the information is sent to the server.

[0352] Step 3:

[0353] Based on the received contract-related information, the server searches its database of past contracts for similar data. Once a match is selected, the generation AI is activated and generates a draft contract using the stored data.

[0354] Step 4:

[0355] The server sends the generated draft to the user's terminal and simultaneously refers to feedback information from the emotion engine. Based on the emotion engine's analysis results, the generating AI may dynamically adjust the content of the draft.

[0356] Step 5:

[0357] Users can review a draft of the contract on their device and edit its contents as needed. While the user is reviewing the draft, the sentiment engine continues to monitor the user's reactions and notifies the server to make further adjustments if the user expresses dissatisfaction or confusion.

[0358] Step 6:

[0359] Once the user completes revisions to the draft, the final contract is uploaded to the server. This final version is stored in memory and used to retrain the generation AI. Sentiment data is also stored and used for future draft generation.

[0360] Step 7:

[0361] The server adjusts the generation AI and emotion engine based on the newly uploaded data to improve performance in the next process. This improves overall user satisfaction and generation accuracy.

[0362] (Example 2)

[0363] 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".

[0364] In the contract drafting process, it is difficult to make adjustments that reflect user sentiment in real time, and there is a lack of efficient means to generate contracts that increase user satisfaction. Furthermore, with conventional systems, there are limited ways for users to quickly review and revise contract contents within the organization.

[0365] 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.

[0366] In this invention, the server includes a terminal device that receives contract information from a user, an emotion detection means that analyzes the emotional state, a recording means that stores past contract data, an emotion recording means that holds the user's emotional data, a generation AI means that generates a draft contract based on the contract information and emotional data, and an adjustment means that performs adjustments taking the emotional data into consideration. This enables the efficient and highly satisfying generation of contracts based on the user's emotions, as well as rapid exchange of opinions and confirmation work within the organization.

[0367] A "terminal device" is a device used by users to input and transmit contract information, and it also has a function to detect emotional states.

[0368] "Emotion detection means" refers to technology that analyzes a user's facial expressions and voice to understand their emotional state in real time.

[0369] "Recording means" refers to a function that stores past contract data and emotional data, and retains information that serves as the basis for generating and adjusting contract drafts.

[0370] "Emotion recording means" refers to technology for storing emotional data obtained from users and utilizing it in subsequent processes.

[0371] "Generative AI means" refers to artificial intelligence technology used to create draft contracts based on recorded historical data.

[0372] A "modification mechanism" is a system that adjusts drafts generated while taking emotional data into consideration, in order to improve user satisfaction.

[0373] The "presentation method" refers to a function that provides users with a draft contract created by the generation AI method for review and revision.

[0374] A "correction suggestion method" is a technology that uses emotional feedback to show users the adjustments made by a generative AI.

[0375] "Learning method" refers to a function that saves the revised final document as retraining data and uses it to improve future contract generation processes.

[0376] A "communication function" is a system for sharing information with relevant departments within an organization, and it facilitates smooth exchange of opinions.

[0377] "Methods for exchanging opinions" refer to methods for sharing sentiment analysis results within an organization and improving cooperation among users and with related departments.

[0378] This invention is a system that efficiently collects contract information from users, makes adjustments based on emotions, and generates contracts. It is primarily built using servers, terminals, and AI technology.

[0379] The terminal is a device that provides an interface for users to input contract information. This device incorporates emotion detection functions such as a camera and microphone, which analyze the user's emotional state in real time through their facial expressions and voice. This data is transmitted to the server along with the user's input.

[0380] The server receives contract information and emotional data transmitted from the terminal. Based on this data, the server uses a generative AI to create a draft contract, referencing recorded past contract data. The emotional data is also analyzed by an emotional detection engine and used as feedback to the generative AI, which adjusts the document according to the user's emotional state. This is particularly useful when making adjustments such as simplifying the writing style or using technical terms appropriately.

[0381] As a concrete example, consider a case where a user creates a sales contract. The user uses a terminal to input sales conditions and product information, and sends it to a server via the business network. The server searches a database of past cases and, using similar contracts as references, generates a new draft using a generation AI. If the user expresses dissatisfaction during this process, emotional feedback is immediately conveyed to the AI, and the contract is adjusted to be more easily understood.

[0382] As an example of a prompt, instructions can be given to the generating AI model in the form of, "What improvements can be made to this draft sales contract? Please make clearer and more concise suggestions, taking into account the user's feelings." This process allows users to quickly and effectively create contracts that reflect their feelings and opinions.

[0383] This system combines generative AI models with sentiment analysis technology to provide users with an intuitive and highly satisfying contract creation process.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1:

[0386] The user enters contract information through the terminal and presses the submit button. The information entered includes the type of contract, the names of the parties involved, and the contract date. The terminal also simultaneously records the user's facial expressions and voice, acquiring them as emotional data. The output of this process is basic contract data and emotional data.

[0387] Step 2:

[0388] The server receives contract information and sentiment data sent from the terminal. Using this data as input, the server searches the database for relevant past contract data. The database stores usage history and templates, and extracts appropriate information depending on the type of contract. The output of this process is past contract data.

[0389] Step 3:

[0390] The server's generating AI creates a draft contract based on received contract information and past contract data. The generating AI analyzes the contract information and extracted data from the database, and constructs the document using prompt sentences. It also receives feedback from the emotion engine and performs specific actions to adjust the tone and length of the text. The output of this process is a draft contract proposed to the user.

[0391] Step 4:

[0392] The server sends the generated draft contract to the user's terminal. The user reviews the draft on the terminal and suggests revisions based on their emotions and opinions. The terminal then analyzes the facial expressions and voice again and sends the emotional data back to the emotion engine. The output of this process is the user's feedback data.

[0393] Step 5:

[0394] The generation AI receives feedback data from users and readjusts the contract based on instructions from the emotion engine. Specifically, it makes edits such as simplifying wording and reducing redundant items. This adjustment process continues until the user is satisfied. The output of this process is a more optimized contract.

[0395] Step 6:

[0396] Once the user approves the final version of the contract, the server saves it to a database. This saved data is also used as training material for the generating AI model, contributing to improvements in the next contract generation process. The output of this process is the final contract and the training data.

[0397] (Application Example 2)

[0398] 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."

[0399] In the contract creation process, contracts are often generated using a uniform interface without considering the user's feelings, resulting in a decline in the quality of the user experience. Furthermore, there are limited ways to obtain information regarding user discomfort and satisfaction during online payments, resulting in a lack of means to provide a better user experience. This can cause users to feel stressed, leading to a decrease in the final transaction completion rate. Another challenge is the inability to utilize user sentiment information obtained after a transaction for future transactions.

[0400] 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.

[0401] In this invention, the server includes an input device means for receiving contract-related information from a user, a storage device means for storing past document information, an artificial intelligence generation device means for generating a draft contract using past document information stored in the storage device based on the contract-related information received by the input device, a presentation device means for presenting the draft contract generated by the artificial intelligence generation device to the user, and an emotion recognition device means for detecting the user's emotions and adjusting the transaction process. This makes it possible to detect the user's emotions in real time, dynamically adjust the contract creation and settlement processes, and provide a more satisfying user experience.

[0402] An "input device" is a device used to receive contract-related information from users.

[0403] A "storage device" is a medium used to store and manage historical document information.

[0404] An "artificial intelligence generation device" is a device that creates a draft contract based on input contract-related information and stored past document information.

[0405] A "presentation device" is a device equipped with the function of displaying a draft contract created by an artificial intelligence generation device to the user.

[0406] An "emotion recognition device" is a device that has the function of detecting the user's emotions and adjusting the transaction process accordingly.

[0407] This invention realizes a system that provides a contract creation and payment experience that takes user emotions into consideration. The system mainly consists of an input device, a storage device, an artificial intelligence generation device, a presentation device, and an emotion recognition device.

[0408] The server receives contract-related information through the user's input device. This input device can be a smartphone or smart glasses. The received information, along with past document data stored in the storage device, is used by an artificial intelligence generation system to generate a draft contract. This AI generation system utilizes generation AI technology.

[0409] The generated draft is presented to the user via a display device, such as a monitor or display. As the user reviews the draft, an emotion recognition device detects their facial expressions and tone of voice to understand their emotional state. For emotion recognition, for example, Microsoft Azure's Emotion API is used. If the user expresses dissatisfaction or frustration, the emotion recognition device feeds this back to the artificial intelligence generation device, which then adjusts the content and interface of the contract.

[0410] A concrete example of this is a scenario involving electronic payments. When a user makes a payment to purchase goods, the system analyzes the user's facial expressions and voice using a camera and microphone, and an emotion recognition device then suggests the most suitable discount or simplifies the interface based on this analysis. This process improves user satisfaction and facilitates the smooth completion of transactions.

[0411] An example of a prompt would be, "Suggest strategies to provide a better experience when a user expresses dissatisfaction during online payment." Using this prompt, the generative AI model generates appropriate solutions tailored to the user's emotions.

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

[0413] Step 1:

[0414] The terminal receives contract-related information from the user as input. The input data is acquired through the interface of a smartphone or smart glasses, and the basic information necessary for creating the contract is sent to the server.

[0415] Step 2:

[0416] The server combines the received contract-related information with past document information stored in its memory, performing database searches and comparisons. Based on this data, an artificial intelligence generation device generates a draft contract. The output is an initial draft contract.

[0417] Step 3:

[0418] The server sends a draft of the generated contract to the terminal, which then presents it to the user. The terminal uses a monitor or display to allow the user to visually review the presented draft.

[0419] Step 4:

[0420] While the user reviews the presented draft contract, the emotion recognition device uses a camera and microphone to capture the user's facial expressions and voice as input. Based on this data, it performs emotion analysis and provides the emotion recognition device with the user's emotional state as output.

[0421] Step 5:

[0422] The server determines whether the user is expressing dissatisfaction or frustration based on the output of the emotion recognition device. This determination is fed back to the artificial intelligence generation device as input, and the contract is adjusted. The adjusted contract is then generated as output.

[0423] Step 6:

[0424] The device will present the revised contract to the user again. If the user does not express any negative feelings towards this proposal, the user will review the final version.

[0425] Step 7:

[0426] The server receives the final version of the contract reviewed by the user as input and saves it to its storage device. This data is then output as retraining data for future contract creation.

[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 is a system that streamlines the process from user input of contract-related information, through the generation of a draft contract using AI technology, to the confirmation and saving of the final contract. Specific embodiments are shown below.

[0444] System Configuration

[0445] 1. User terminal

[0446] Users utilize terminals equipped with a dedicated application or web interface for contract creation. Here, they can input necessary contract-related information such as the contracting party, contracting entity, and contract type. For security reasons, the user terminals also include a function to encrypt input data before sending it to the server.

[0447] 2. Server

[0448] The server receives contract-related information sent from user terminals. The server plays a central role in managing past contract data in conjunction with the database and running the generation AI that generates draft contracts.

[0449] 3. Generation AI

[0450] The AI ​​within the server uses the received contract-related information to compare it with stored past contract data and create a draft contract that reflects the specific wording and conditions unique to each company. This draft is quick, accurate, and carefully designed to align with the company's contract drafting practices.

[0451] 4. Presentation and revision of the draft.

[0452] The server returns the generated draft contract to the user's terminal, where the user reviews the draft. The user's terminal allows for editing of the draft's details and saving changes in real time. The user can then share this draft with relevant departments within the company for legal checks and necessary revisions.

[0453] 5. Saving the final version and AI training

[0454] The final version of the contract, after all revisions have been completed, will be uploaded to the server again. This final version's data will be used for new training by the generating AI, contributing to improved accuracy of future draft contracts.

[0455] Specific example

[0456] For example, consider a scenario where a user wants to enter into a service contract between "Company A" and "Company B." The user uses a terminal to input "Company A" as the contracting party, "Company B" as the contracting party, and "Service Contract" as the contract type. The server passes this information to the generating AI, which then references past service contract data to generate an optimal draft. The generated draft is suitable for the specific company's format and requirements and is presented to the user. This draft is then reviewed internally and revised as needed to finalize it. The final version is also saved on the server during this process and used for future AI training. This entire process saves time and allows for the creation of highly accurate contracts.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] The user activates the terminal and accesses a dedicated application or web interface. Here, they enter contract-related information such as the contracting party, contracting entity, and contract type. The terminal verifies the entered data in real time and checks for errors in the input format.

[0460] Step 2:

[0461] The terminal encrypts verified contract-related information and sends it to the server using a secure communication protocol. Once the data transmission is complete, a notification of successful transmission is displayed to the user.

[0462] Step 3:

[0463] The server searches for past contract data based on the received data. It identifies appropriate datasets based on contract type and company name, and provides the data and input information to the generating AI.

[0464] Step 4:

[0465] The AI ​​generates a draft contract based on the input contract-related information and by referencing selected past contract data. This draft is created taking into account the company's specific format, expression, and contract terms.

[0466] Step 5:

[0467] The server sends the generated draft contract back to the terminal. The user receives the draft on the terminal and reviews its contents. If necessary, the user can edit the contents of the draft.

[0468] Step 6:

[0469] Users submit drafts to the company's legal department and other relevant departments, communicating to obtain confirmation and approval. The terminal records real-time comments and revision history from multiple users.

[0470] Step 7:

[0471] The revised final contract is uploaded from the user's device to the server. The final version of the contract is recorded as future training data.

[0472] Step 8:

[0473] The server uses newly uploaded contracts as training data for the generating AI, improving the accuracy of future contract drafts. The AI ​​retrains using past data, thereby improving the overall accuracy and efficiency of the system.

[0474] (Example 1)

[0475] 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."

[0476] In contract drafting, it is crucial to create contract documents quickly and accurately based on the diverse contract-related information held by the user. However, traditional methods require considerable effort and time for manual contract creation, and are prone to inconsistencies and errors. Furthermore, newly created contracts often do not reflect the organization's usage history. Therefore, there is a need for a system that can efficiently generate contracts and enable improvements based on past usage history.

[0477] 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.

[0478] In this invention, the server includes means for receiving information via a terminal device that accepts contract-related information from users, means for accessing a storage device that stores past document data and obtaining necessary data, and means for generating a draft contract document using a generative model based on the obtained data. This enables efficient generation of contracts and improved accuracy in document creation that reflects past usage history.

[0479] "User" refers to an individual or organization that is involved in the creation or modification of a contract and enters the necessary information.

[0480] "Terminal device" refers to a device used to input contract-related information or display draft contracts, and includes personal computers, tablets, and smartphones.

[0481] "Storage device" refers to a storage medium or system that stores past document data and usage history, and provides the data necessary for generating contracts.

[0482] A "generative model" refers to an artificial intelligence algorithm or system that automatically creates a draft contract document based on input contract-related information and data stored in a memory device.

[0483] A "display device" refers to a device equipped with a display that provides an interface for presenting a draft contract to the user and allowing them to make revisions or confirmations.

[0484] A "learning device" refers to a system equipped with a self-learning function that stores the final document modified by the user and utilizes that data to improve the performance of the generative model.

[0485] "Communication device" refers to a device or system equipped with network connectivity that a user uses to share and obtain confirmation of a draft contract with relevant departments within the company.

[0486] Modes for carrying out the invention

[0487] This invention is a system for efficiently generating contract documents by utilizing contract-related information. Specific embodiments thereof are described below.

[0488] To create a contract, users first use a terminal device equipped with a dedicated application or web interface. This terminal device may include a personal computer, tablet, or smartphone. Users input contract-related information, such as "contracting party," "contracting recipient," and "contract type." This information is encrypted for security purposes and transmitted to the server.

[0489] The server receives contract-related information transmitted from terminal devices and accesses storage devices for managing historical document data. These storage devices include cloud storage and databases on local servers. The server utilizes this historical contract data stored in these devices and automatically generates draft contract documents using a generative AI model. The generative AI model provides rapid and accurate drafts, taking into account the specific expressions and conditions of each company.

[0490] The generated draft contract is sent back from the server to the terminal device. The user can review the draft on the terminal device and edit its contents as needed. The user can then share the edited draft with relevant departments within the company and receive feedback. Once the final version of the contract document is completed, it is saved back to the server and used as training data for future AI-generated models.

[0491] For example, if a user wants to enter into a service contract between "Company A" and "Company B," they can enter the following prompt: "Please create a new service contract. Company A is the client, and Company B is the client." This enables the creation of efficient and accurate contract documents.

[0492] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0493] Step 1:

[0494] The user enters contract-related information using a terminal device. The user accesses an application or web interface and enters information such as "contracting party," "contracting recipient," and "contract type" into a form. After completion, the terminal encrypts this data and securely sends it to the server. The output indicates that the entered data has been sent to the server.

[0495] Step 2:

[0496] The server receives contract-related information transmitted from the terminal. It decrypts the received data and searches for and retrieves past contract data stored in its storage device. Through these operations, the server prepares past data related to the entered contract conditions and obtains input for the generating AI model. This data becomes the input for the generating AI model.

[0497] Step 3:

[0498] A generative AI model running on the server generates a draft contract based on the information received in the previous step. The generative AI model applies the company's specific format and terminology to generate the draft, drawing on insights gained from past history. The generated draft contract becomes the output, and the server prepares to send it to the next step.

[0499] Step 4:

[0500] The server sends the generated contract draft to the user's terminal device. The user receives this draft on their terminal and reviews its contents. If necessary, the user edits the draft and saves the revised content. The revised draft becomes the output, ready for internal sharing and review.

[0501] Step 5:

[0502] The user uploads the final version of the contract, after completing the revisions, to the server. The server saves the final document to its storage device and uses it as training data for subsequent AI models. As a result, the saved final data becomes the output, forming the basis for the continuous improvement of the AI ​​model.

[0503] (Application Example 1)

[0504] 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."

[0505] The contract drafting process is problematic because it is time-consuming and labor-intensive. This is especially true in fields like electronic payment services, where numerous contracts and terms of service need to be generated appropriately and processed efficiently. However, current systems make these processes cumbersome, and efficiency improvements are needed.

[0506] 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.

[0507] In this invention, the server includes a receiving means for receiving contract information from a user, a recording means for storing past data, and an AI generation means for generating a draft contract based on the contract information acquired by the receiving structure and using the past data stored in the recording structure. This makes it possible for users to easily input contract information from a mobile device such as a smartphone, and to quickly and accurately generate, modify, and electronically submit and save a draft contract.

[0508] The "reception structure" is an input method for receiving contract information from users, and it has the function of safely and efficiently acquiring contract-related data.

[0509] A "record structure" refers to a database or storage medium used to store past document data, making the accumulated information easily accessible.

[0510] "AI-generated structure" refers to a method that uses AI technology to automatically generate draft contracts based on acquired contract information and utilizing past data.

[0511] The "display structure" refers to a means of presenting the generated draft contract to the user, allowing them to review its contents through a user interface.

[0512] "Revision structure" refers to a means of providing a function that allows users to edit and modify the generated draft contract.

[0513] A "archive structure" is a means of storing the final version of a contract, modified by the user, in a record structure and managing it so that it can be reused and referenced in the past.

[0514] An "AI learning structure" is a structure that uses the saved final document as retraining data and has the functionality to improve the AI's generation performance.

[0515] "Communication structure" refers to a function that includes communication means for users to share information with relevant departments and third parties, exchange opinions, and obtain confirmation.

[0516] An "electronic processing structure" is a structure that allows users to input contract information from a mobile device and generate, submit, and store contract documents electronically.

[0517] The system for implementing this invention mainly consists of a server, a user terminal, and related cloud AI services. The server has a reception structure for users to input contract information and a recording structure that records past document data as a cloud-based database. The AI ​​generation structure within the server automatically generates a draft contract based on the input information and past data.

[0518] The user terminal operates on a mobile device such as a smartphone or tablet and provides an interface for users to input contract information and view and modify the generated draft. This terminal also has a communication structure that allows it to send the revised final version of the contract to the server's storage structure and to share information with relevant departments.

[0519] The program uses Amazon Web Services' SageMaker and Google Cloud AI Platform as infrastructure for AI generation. Secure protocols are used for data encryption and transmission to ensure data security.

[0520] As a concrete example, consider a scenario where a user is preparing a contract with a new business partner. The user uses their smartphone to input the contracting party, the contracting party, and the contract terms. This information is encrypted and sent to the server. The server's AI generation structure generates a draft contract based on this information and similar past data, and presents it to the user. The user reviews the draft via the smartphone interface, makes revisions as needed, and saves the final version.

[0521] An example of a prompt message would be: "Generate a draft contract. Contracting party: 'Company X', Contracting party: 'Company Y', Contract type: 'Sales contract', and include the special condition 'Shipping costs are the responsibility of the buyer'."

[0522] In this way, it is possible to streamline the entire process of generating, modifying, and saving contracts, thereby reducing the workload on users.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The user launches a dedicated app on their smartphone and enters contract information such as the contracting party, contracting entity, and contract terms. The entered information is collected through the input method and securely transmitted to the server using an encryption protocol. The input here is manual by the user, and the output is encrypted data.

[0526] Step 2:

[0527] The server decrypts the contract information received from the user and stores it in the reception structure. Based on this information, it activates the AI ​​generation structure and selects the necessary contract format and template based on the input data. The input is the decrypted contract information, and the output is template selection data for the contract draft.

[0528] Step 3:

[0529] The AI-generated structure searches for similar past contract data from its memory structure according to a selected template and generates a draft contract. This process uses a generative AI model, performs data calculations based on the input data, and produces an optimally tailored draft contract. The output is the generated draft contract.

[0530] Step 4:

[0531] The server sends the generated draft contract to the terminal and displays it to the user. The user reviews the draft on the terminal and makes revisions as needed. Revisions are made in real time, and change data based on the user's input is generated, forming a new draft. The output is the draft contract with the user's revisions.

[0532] Step 5:

[0533] The user reviews the final version of the contract after completing the revisions and sends the final document to the server. The server stores this final version in a storage structure and adds it to the library as retraining data using a record structure. The input here is the final version of the contract, and the output is the stored document data.

[0534] Step 6:

[0535] The server's AI learning structure trains the generation AI model using saved final documents to improve the accuracy of subsequent contract generation processes. During this learning process, data calculations are performed using past data, and the AI ​​model is adjusted as an output.

[0536] 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.

[0537] This invention provides a system for smoothly and efficiently carrying out a series of processes for drafting contracts. This system includes a function to recognize the user's emotions and make adjustments accordingly, providing a more intuitive and user-friendly interface.

[0538] System Configuration

[0539] 1. User terminal

[0540] Users access a contract creation application or web interface using a terminal. They enter basic contract information and submit it to the system. This terminal is equipped with an interface for detecting the user's emotions.

[0541] 2. Server

[0542] The server receives input data sent from the terminal, and simultaneously uses past contract data to generate a draft contract using AI. The server also has an emotion engine built in, which allows it to receive feedback based on the user's emotions.

[0543] 3. Generative AI and Emotion Engines

[0544] The generative AI generates a draft contract based on the given information and historical data from the database. During this process, the emotion engine analyzes the user's emotions in real time and feeds that information back to the generative AI. For example, if it detects that the user is frustrated with an overly long document, the generative AI will suggest a more concise expression.

[0545] 4. Presentation and modification functions

[0546] The draft generated by the server is sent to the user's terminal. The user reviews and modifies this draft. During this time, the emotion engine analyzes the user's facial expressions and tone of voice, continuously monitoring the user's satisfaction level. If the user expresses dissatisfaction, the engine analyzes the cause and makes further suggestions.

[0547] 5. Final Agreement and Learning Process

[0548] Once the user completes the final contract, it is saved again on the server. The final contract data is used as training material for the AI, contributing to improvements in the quality of future processes. Feedback through the emotion engine is also incorporated into the learning process, enabling more accurate adjustments.

[0549] Specific example

[0550] For example, consider a scenario where a user creates a sales contract between "Company C" and "Company D." The user inputs contract data on a terminal, and the server uses this data to extract information from past sales contracts and generates a draft using AI. If the user shows dissatisfaction with the proposed clauses through facial expressions or tone of voice, the emotion engine analyzes this and notifies the AI. The AI ​​then uses this feedback to revise the clauses and proposes a new one. Finally, a satisfactory contract is completed, saved on the server, and used as data to support future processes. In this way, the feedback loop utilizing the emotion engine ensures a highly satisfying contract creation process.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The user starts up the device and logs in by accessing a dedicated application or web interface. They enter necessary information such as the contracting company, contracting party, and contract type. The device verifies the format of the entered information before sending it to the server in a secure format.

[0554] Step 2:

[0555] The emotion engine installed in the device analyzes the user's facial expressions and voice in real time as they enter contract information, and temporarily stores this information on the device. When a change in emotion or a specific emotional state is detected, the information is sent to the server.

[0556] Step 3:

[0557] Based on the received contract-related information, the server searches its database of past contracts for similar data. Once a match is selected, the generation AI is activated and generates a draft contract using the stored data.

[0558] Step 4:

[0559] The server sends the generated draft to the user's terminal and simultaneously refers to feedback information from the emotion engine. Based on the emotion engine's analysis results, the generating AI may dynamically adjust the content of the draft.

[0560] Step 5:

[0561] Users can review a draft of the contract on their device and edit its contents as needed. While the user is reviewing the draft, the sentiment engine continues to monitor the user's reactions and notifies the server to make further adjustments if the user expresses dissatisfaction or confusion.

[0562] Step 6:

[0563] Once the user completes revisions to the draft, the final contract is uploaded to the server. This final version is stored in memory and used to retrain the generation AI. Sentiment data is also stored and used for future draft generation.

[0564] Step 7:

[0565] The server adjusts the generation AI and emotion engine based on the newly uploaded data to improve performance in the next process. This improves overall user satisfaction and generation accuracy.

[0566] (Example 2)

[0567] 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."

[0568] In the contract drafting process, it is difficult to make adjustments that reflect user sentiment in real time, and there is a lack of efficient means to generate contracts that increase user satisfaction. Furthermore, with conventional systems, there are limited ways for users to quickly review and revise contract contents within the organization.

[0569] 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.

[0570] In this invention, the server includes a terminal device that receives contract information from a user, an emotion detection means that analyzes the emotional state, a recording means that stores past contract data, an emotion recording means that holds the user's emotional data, a generation AI means that generates a draft contract based on the contract information and emotional data, and an adjustment means that performs adjustments taking the emotional data into consideration. This enables the efficient and highly satisfying generation of contracts based on the user's emotions, as well as rapid exchange of opinions and confirmation work within the organization.

[0571] A "terminal device" is a device used by users to input and transmit contract information, and it also has a function to detect emotional states.

[0572] "Emotion detection means" refers to technology that analyzes a user's facial expressions and voice to understand their emotional state in real time.

[0573] "Recording means" refers to a function that stores past contract data and emotional data, and retains information that serves as the basis for generating and adjusting contract drafts.

[0574] "Emotion recording means" refers to technology for storing emotional data obtained from users and utilizing it in subsequent processes.

[0575] "Generative AI means" refers to artificial intelligence technology used to create draft contracts based on recorded historical data.

[0576] A "modification mechanism" is a system that adjusts drafts generated while taking emotional data into consideration, in order to improve user satisfaction.

[0577] The "presentation method" refers to a function that provides users with a draft contract created by the generation AI method for review and revision.

[0578] A "correction suggestion method" is a technology that uses emotional feedback to show users the adjustments made by a generative AI.

[0579] "Learning method" refers to a function that saves the revised final document as retraining data and uses it to improve future contract generation processes.

[0580] A "communication function" is a system for sharing information with relevant departments within an organization, and it facilitates smooth exchange of opinions.

[0581] "Methods for exchanging opinions" refer to methods for sharing sentiment analysis results within an organization and improving cooperation among users and with related departments.

[0582] This invention is a system that efficiently collects contract information from users, makes adjustments based on emotions, and generates contracts. It is primarily built using servers, terminals, and AI technology.

[0583] The terminal is a device that provides an interface for users to input contract information. This device incorporates emotion detection functions such as a camera and microphone, which analyze the user's emotional state in real time through their facial expressions and voice. This data is transmitted to the server along with the user's input.

[0584] The server receives contract information and emotional data transmitted from the terminal. Based on this data, the server uses a generative AI to create a draft contract, referencing recorded past contract data. The emotional data is also analyzed by an emotional detection engine and used as feedback to the generative AI, which adjusts the document according to the user's emotional state. This is particularly useful when making adjustments such as simplifying the writing style or using technical terms appropriately.

[0585] As a concrete example, consider a case where a user creates a sales contract. The user uses a terminal to input sales conditions and product information, and sends it to a server via the business network. The server searches a database of past cases and, using similar contracts as references, generates a new draft using a generation AI. If the user expresses dissatisfaction during this process, emotional feedback is immediately conveyed to the AI, and the contract is adjusted to be more easily understood.

[0586] As an example of a prompt, instructions can be given to the generating AI model in the form of, "What improvements can be made to this draft sales contract? Please make clearer and more concise suggestions, taking into account the user's feelings." This process allows users to quickly and effectively create contracts that reflect their feelings and opinions.

[0587] This system combines generative AI models with sentiment analysis technology to provide users with an intuitive and highly satisfying contract creation process.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The user enters contract information through the terminal and presses the submit button. The information entered includes the type of contract, the names of the parties involved, and the contract date. The terminal also simultaneously records the user's facial expressions and voice, acquiring them as emotional data. The output of this process is basic contract data and emotional data.

[0591] Step 2:

[0592] The server receives contract information and sentiment data sent from the terminal. Using this data as input, the server searches the database for relevant past contract data. The database stores usage history and templates, and extracts appropriate information depending on the type of contract. The output of this process is past contract data.

[0593] Step 3:

[0594] The server's generating AI creates a draft contract based on received contract information and past contract data. The generating AI analyzes the contract information and extracted data from the database, and constructs the document using prompt sentences. It also receives feedback from the emotion engine and performs specific actions to adjust the tone and length of the text. The output of this process is a draft contract proposed to the user.

[0595] Step 4:

[0596] The server sends the generated draft contract to the user's terminal. The user reviews the draft on the terminal and suggests revisions based on their emotions and opinions. The terminal then analyzes the facial expressions and voice again and sends the emotional data back to the emotion engine. The output of this process is the user's feedback data.

[0597] Step 5:

[0598] The generation AI receives feedback data from users and readjusts the contract based on instructions from the emotion engine. Specifically, it makes edits such as simplifying wording and reducing redundant items. This adjustment process continues until the user is satisfied. The output of this process is a more optimized contract.

[0599] Step 6:

[0600] Once the user approves the final version of the contract, the server saves it to a database. This saved data is also used as training material for the generating AI model, contributing to improvements in the next contract generation process. The output of this process is the final contract and the training data.

[0601] (Application Example 2)

[0602] 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."

[0603] In the contract creation process, contracts are often generated using a uniform interface without considering the user's feelings, resulting in a decline in the quality of the user experience. Furthermore, there are limited ways to obtain information regarding user discomfort and satisfaction during online payments, resulting in a lack of means to provide a better user experience. This can cause users to feel stressed, leading to a decrease in the final transaction completion rate. Another challenge is the inability to utilize user sentiment information obtained after a transaction for future transactions.

[0604] 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.

[0605] In this invention, the server includes an input device means for receiving contract-related information from a user, a storage device means for storing past document information, an artificial intelligence generation device means for generating a draft contract using past document information stored in the storage device based on the contract-related information received by the input device, a presentation device means for presenting the draft contract generated by the artificial intelligence generation device to the user, and an emotion recognition device means for detecting the user's emotions and adjusting the transaction process. This makes it possible to detect the user's emotions in real time, dynamically adjust the contract creation and settlement processes, and provide a more satisfying user experience.

[0606] An "input device" is a device used to receive contract-related information from users.

[0607] A "storage device" is a medium used to store and manage historical document information.

[0608] An "artificial intelligence generation device" is a device that creates a draft contract based on input contract-related information and stored past document information.

[0609] A "presentation device" is a device equipped with the function of displaying a draft contract created by an artificial intelligence generation device to the user.

[0610] An "emotion recognition device" is a device that has the function of detecting the user's emotions and adjusting the transaction process accordingly.

[0611] This invention realizes a system that provides a contract creation and payment experience that takes user emotions into consideration. The system mainly consists of an input device, a storage device, an artificial intelligence generation device, a presentation device, and an emotion recognition device.

[0612] The server receives contract-related information through the user's input device. This input device can be a smartphone or smart glasses. The received information, along with past document data stored in the storage device, is used by an artificial intelligence generation system to generate a draft contract. This AI generation system utilizes generation AI technology.

[0613] The generated draft is presented to the user via a display device, such as a monitor or display. As the user reviews the draft, an emotion recognition device detects their facial expressions and tone of voice to understand their emotional state. For emotion recognition, for example, Microsoft Azure's Emotion API is used. If the user expresses dissatisfaction or frustration, the emotion recognition device feeds this back to the artificial intelligence generation device, which then adjusts the content and interface of the contract.

[0614] A concrete example of this is a scenario involving electronic payments. When a user makes a payment to purchase goods, the system analyzes the user's facial expressions and voice using a camera and microphone, and an emotion recognition device then suggests the most suitable discount or simplifies the interface based on this analysis. This process improves user satisfaction and facilitates the smooth completion of transactions.

[0615] An example of a prompt would be, "Suggest strategies to provide a better experience when a user expresses dissatisfaction during online payment." Using this prompt, the generative AI model generates appropriate solutions tailored to the user's emotions.

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

[0617] Step 1:

[0618] The terminal receives contract-related information from the user as input. The input data is acquired through the interface of a smartphone or smart glasses, and the basic information necessary for creating the contract is sent to the server.

[0619] Step 2:

[0620] The server combines the received contract-related information with past document information stored in its memory, performing database searches and comparisons. Based on this data, an artificial intelligence generation device generates a draft contract. The output is an initial draft contract.

[0621] Step 3:

[0622] The server sends a draft of the generated contract to the terminal, which then presents it to the user. The terminal uses a monitor or display to allow the user to visually review the presented draft.

[0623] Step 4:

[0624] While the user reviews the presented draft contract, the emotion recognition device uses a camera and microphone to capture the user's facial expressions and voice as input. Based on this data, it performs emotion analysis and provides the emotion recognition device with the user's emotional state as output.

[0625] Step 5:

[0626] The server determines whether the user is expressing dissatisfaction or frustration based on the output of the emotion recognition device. This determination is fed back to the artificial intelligence generation device as input, and the contract is adjusted. The adjusted contract is then generated as output.

[0627] Step 6:

[0628] The device will present the revised contract to the user again. If the user does not express any negative feelings towards this proposal, the user will review the final version.

[0629] Step 7:

[0630] The server receives the final version of the contract reviewed by the user as input and saves it to its storage device. This data is then output as retraining data for future contract creation.

[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 is a system that streamlines the process from user input of contract-related information, through the generation of a draft contract using AI technology, to the confirmation and saving of the final contract. Specific embodiments are shown below.

[0649] System Configuration

[0650] 1. User terminal

[0651] Users utilize terminals equipped with a dedicated application or web interface for contract creation. Here, they can input necessary contract-related information such as the contracting party, contracting entity, and contract type. For security reasons, the user terminals also include a function to encrypt input data before sending it to the server.

[0652] 2. Server

[0653] The server receives contract-related information sent from user terminals. The server plays a central role in managing past contract data in conjunction with the database and running the generation AI that generates draft contracts.

[0654] 3. Generation AI

[0655] The AI ​​within the server uses the received contract-related information to compare it with stored past contract data and create a draft contract that reflects the specific wording and conditions unique to each company. This draft is quick, accurate, and carefully designed to align with the company's contract drafting practices.

[0656] 4. Presentation and revision of the draft.

[0657] The server returns the generated draft contract to the user's terminal, where the user reviews the draft. The user's terminal allows for editing of the draft's details and saving changes in real time. The user can then share this draft with relevant departments within the company for legal checks and necessary revisions.

[0658] 5. Saving the final version and AI training

[0659] The final version of the contract, after all revisions have been completed, will be uploaded to the server again. This final version's data will be used for new training by the generating AI, contributing to improved accuracy of future draft contracts.

[0660] Specific example

[0661] For example, consider a scenario where a user wants to enter into a service contract between "Company A" and "Company B." The user uses a terminal to input "Company A" as the contracting party, "Company B" as the contracting party, and "Service Contract" as the contract type. The server passes this information to the generating AI, which then references past service contract data to generate an optimal draft. The generated draft is suitable for the specific company's format and requirements and is presented to the user. This draft is then reviewed internally and revised as needed to finalize it. The final version is also saved on the server during this process and used for future AI training. This entire process saves time and allows for the creation of highly accurate contracts.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The user activates the terminal and accesses a dedicated application or web interface. Here, they enter contract-related information such as the contracting party, contracting entity, and contract type. The terminal verifies the entered data in real time and checks for errors in the input format.

[0665] Step 2:

[0666] The terminal encrypts verified contract-related information and sends it to the server using a secure communication protocol. Once the data transmission is complete, a notification of successful transmission is displayed to the user.

[0667] Step 3:

[0668] The server searches for past contract data based on the received data. It identifies appropriate datasets based on contract type and company name, and provides the data and input information to the generating AI.

[0669] Step 4:

[0670] The AI ​​generates a draft contract based on the input contract-related information and by referencing selected past contract data. This draft is created taking into account the company's specific format, expression, and contract terms.

[0671] Step 5:

[0672] The server sends the generated draft contract back to the terminal. The user receives the draft on the terminal and reviews its contents. If necessary, the user can edit the contents of the draft.

[0673] Step 6:

[0674] Users submit drafts to the company's legal department and other relevant departments, communicating to obtain confirmation and approval. The terminal records real-time comments and revision history from multiple users.

[0675] Step 7:

[0676] The revised final contract is uploaded from the user's device to the server. The final version of the contract is recorded as future training data.

[0677] Step 8:

[0678] The server uses newly uploaded contracts as training data for the generating AI, improving the accuracy of future contract drafts. The AI ​​retrains using past data, thereby improving the overall accuracy and efficiency of the system.

[0679] (Example 1)

[0680] 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".

[0681] In contract drafting, it is crucial to create contract documents quickly and accurately based on the diverse contract-related information held by the user. However, traditional methods require considerable effort and time for manual contract creation, and are prone to inconsistencies and errors. Furthermore, newly created contracts often do not reflect the organization's usage history. Therefore, there is a need for a system that can efficiently generate contracts and enable improvements based on past usage history.

[0682] 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.

[0683] In this invention, the server includes means for receiving information via a terminal device that accepts contract-related information from users, means for accessing a storage device that stores past document data and obtaining necessary data, and means for generating a draft contract document using a generative model based on the obtained data. This enables efficient generation of contracts and improved accuracy in document creation that reflects past usage history.

[0684] "User" refers to an individual or organization that is involved in the creation or modification of a contract and enters the necessary information.

[0685] "Terminal device" refers to a device used to input contract-related information or display draft contracts, and includes personal computers, tablets, and smartphones.

[0686] "Storage device" refers to a storage medium or system that stores past document data and usage history, and provides the data necessary for generating contracts.

[0687] A "generative model" refers to an artificial intelligence algorithm or system that automatically creates a draft contract document based on input contract-related information and data stored in a memory device.

[0688] A "display device" refers to a device equipped with a display that provides an interface for presenting a draft contract to the user and allowing them to make revisions or confirmations.

[0689] A "learning device" refers to a system equipped with a self-learning function that stores the final document modified by the user and utilizes that data to improve the performance of the generative model.

[0690] "Communication device" refers to a device or system equipped with network connectivity that a user uses to share and obtain confirmation of a draft contract with relevant departments within the company.

[0691] Modes for carrying out the invention

[0692] This invention is a system for efficiently generating contract documents by utilizing contract-related information. Specific embodiments thereof are described below.

[0693] To create a contract, users first use a terminal device equipped with a dedicated application or web interface. This terminal device may include a personal computer, tablet, or smartphone. Users input contract-related information, such as "contracting party," "contracting recipient," and "contract type." This information is encrypted for security purposes and transmitted to the server.

[0694] The server receives contract-related information transmitted from terminal devices and accesses storage devices for managing historical document data. These storage devices include cloud storage and databases on local servers. The server utilizes this historical contract data stored in these devices and automatically generates draft contract documents using a generative AI model. The generative AI model provides rapid and accurate drafts, taking into account the specific expressions and conditions of each company.

[0695] The generated draft contract is sent back from the server to the terminal device. The user can review the draft on the terminal device and edit its contents as needed. The user can then share the edited draft with relevant departments within the company and receive feedback. Once the final version of the contract document is completed, it is saved back to the server and used as training data for future AI-generated models.

[0696] For example, if a user wants to enter into a service contract between "Company A" and "Company B," they can enter the following prompt: "Please create a new service contract. Company A is the client, and Company B is the client." This enables the creation of efficient and accurate contract documents.

[0697] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0698] Step 1:

[0699] The user enters contract-related information using a terminal device. The user accesses an application or web interface and enters information such as "contracting party," "contracting recipient," and "contract type" into a form. After completion, the terminal encrypts this data and securely sends it to the server. The output indicates that the entered data has been sent to the server.

[0700] Step 2:

[0701] The server receives contract-related information transmitted from the terminal. It decrypts the received data and searches for and retrieves past contract data stored in its storage device. Through these operations, the server prepares past data related to the entered contract conditions and obtains input for the generating AI model. This data becomes the input for the generating AI model.

[0702] Step 3:

[0703] A generative AI model running on the server generates a draft contract based on the information received in the previous step. The generative AI model applies the company's specific format and terminology to generate the draft, drawing on insights gained from past history. The generated draft contract becomes the output, and the server prepares to send it to the next step.

[0704] Step 4:

[0705] The server sends the generated contract draft to the user's terminal device. The user receives this draft on their terminal and reviews its contents. If necessary, the user edits the draft and saves the revised content. The revised draft becomes the output, ready for internal sharing and review.

[0706] Step 5:

[0707] The user uploads the final version of the contract, after completing the revisions, to the server. The server saves the final document to its storage device and uses it as training data for subsequent AI models. As a result, the saved final data becomes the output, forming the basis for the continuous improvement of the AI ​​model.

[0708] (Application Example 1)

[0709] 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".

[0710] The contract drafting process is problematic because it is time-consuming and labor-intensive. This is especially true in fields like electronic payment services, where numerous contracts and terms of service need to be generated appropriately and processed efficiently. However, current systems make these processes cumbersome, and efficiency improvements are needed.

[0711] 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.

[0712] In this invention, the server includes a receiving means for receiving contract information from a user, a recording means for storing past data, and an AI generation means for generating a draft contract based on the contract information acquired by the receiving structure and using the past data stored in the recording structure. This makes it possible for users to easily input contract information from a mobile device such as a smartphone, and to quickly and accurately generate, modify, and electronically submit and save a draft contract.

[0713] The "reception structure" is an input method for receiving contract information from users, and it has the function of safely and efficiently acquiring contract-related data.

[0714] A "record structure" refers to a database or storage medium used to store past document data, making the accumulated information easily accessible.

[0715] "AI-generated structure" refers to a method that uses AI technology to automatically generate draft contracts based on acquired contract information and utilizing past data.

[0716] The "display structure" refers to a means of presenting the generated draft contract to the user, allowing them to review its contents through a user interface.

[0717] "Revision structure" refers to a means of providing a function that allows users to edit and modify the generated draft contract.

[0718] A "archive structure" is a means of storing the final version of a contract, modified by the user, in a record structure and managing it so that it can be reused and referenced in the past.

[0719] An "AI learning structure" is a structure that uses the saved final document as retraining data and has the functionality to improve the AI's generation performance.

[0720] "Communication structure" refers to a function that includes communication means for users to share information with relevant departments and third parties, exchange opinions, and obtain confirmation.

[0721] An "electronic processing structure" is a structure that allows users to input contract information from a mobile device and generate, submit, and store contract documents electronically.

[0722] The system for implementing this invention mainly consists of a server, a user terminal, and related cloud AI services. The server has a reception structure for users to input contract information and a recording structure that records past document data as a cloud-based database. The AI ​​generation structure within the server automatically generates a draft contract based on the input information and past data.

[0723] The user terminal operates on a mobile device such as a smartphone or tablet and provides an interface for users to input contract information and view and modify the generated draft. This terminal also has a communication structure that allows it to send the revised final version of the contract to the server's storage structure and to share information with relevant departments.

[0724] The program uses Amazon Web Services' SageMaker and Google Cloud AI Platform as infrastructure for AI generation. Secure protocols are used for data encryption and transmission to ensure data security.

[0725] As a concrete example, consider a scenario where a user is preparing a contract with a new business partner. The user uses their smartphone to input the contracting party, the contracting party, and the contract terms. This information is encrypted and sent to the server. The server's AI generation structure generates a draft contract based on this information and similar past data, and presents it to the user. The user reviews the draft via the smartphone interface, makes revisions as needed, and saves the final version.

[0726] An example of a prompt message would be: "Generate a draft contract. Contracting party: 'Company X', Contracting party: 'Company Y', Contract type: 'Sales contract', and include the special condition 'Shipping costs are the responsibility of the buyer'."

[0727] In this way, it is possible to streamline the entire process of generating, modifying, and saving contracts, thereby reducing the workload on users.

[0728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0729] Step 1:

[0730] The user launches a dedicated app on their smartphone and enters contract information such as the contracting party, contracting entity, and contract terms. The entered information is collected through the input method and securely transmitted to the server using an encryption protocol. The input here is manual by the user, and the output is encrypted data.

[0731] Step 2:

[0732] The server decrypts the contract information received from the user and stores it in the reception structure. Based on this information, it activates the AI ​​generation structure and selects the necessary contract format and template based on the input data. The input is the decrypted contract information, and the output is template selection data for the contract draft.

[0733] Step 3:

[0734] The AI-generated structure searches for similar past contract data from its memory structure according to a selected template and generates a draft contract. This process uses a generative AI model, performs data calculations based on the input data, and produces an optimally tailored draft contract. The output is the generated draft contract.

[0735] Step 4:

[0736] The server sends the generated draft contract to the terminal and displays it to the user. The user reviews the draft on the terminal and makes revisions as needed. Revisions are made in real time, and change data based on the user's input is generated, forming a new draft. The output is the draft contract with the user's revisions.

[0737] Step 5:

[0738] The user reviews the final version of the contract after completing the revisions and sends the final document to the server. The server stores this final version in a storage structure and adds it to the library as retraining data using a record structure. The input here is the final version of the contract, and the output is the stored document data.

[0739] Step 6:

[0740] The server's AI learning structure trains the generation AI model using saved final documents to improve the accuracy of subsequent contract generation processes. During this learning process, data calculations are performed using past data, and the AI ​​model is adjusted as an output.

[0741] 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.

[0742] This invention provides a system for smoothly and efficiently carrying out a series of processes for drafting contracts. This system includes a function to recognize the user's emotions and make adjustments accordingly, providing a more intuitive and user-friendly interface.

[0743] System Configuration

[0744] 1. User terminal

[0745] Users access a contract creation application or web interface using a terminal. They enter basic contract information and submit it to the system. This terminal is equipped with an interface for detecting the user's emotions.

[0746] 2. Server

[0747] The server receives input data sent from the terminal, and simultaneously uses past contract data to generate a draft contract using AI. The server also has an emotion engine built in, which allows it to receive feedback based on the user's emotions.

[0748] 3. Generative AI and Emotion Engines

[0749] The generative AI generates a draft contract based on the given information and historical data from the database. During this process, the emotion engine analyzes the user's emotions in real time and feeds that information back to the generative AI. For example, if it detects that the user is frustrated with an overly long document, the generative AI will suggest a more concise expression.

[0750] 4. Presentation and modification functions

[0751] The draft generated by the server is sent to the user's terminal. The user reviews and modifies this draft. During this time, the emotion engine analyzes the user's facial expressions and tone of voice, continuously monitoring the user's satisfaction level. If the user expresses dissatisfaction, the engine analyzes the cause and makes further suggestions.

[0752] 5. Final Agreement and Learning Process

[0753] Once the user completes the final contract, it is saved again on the server. The final contract data is used as training material for the AI, contributing to improvements in the quality of future processes. Feedback through the emotion engine is also incorporated into the learning process, enabling more accurate adjustments.

[0754] Specific example

[0755] For example, consider a scenario where a user creates a sales contract between "Company C" and "Company D." The user inputs contract data on a terminal, and the server uses this data to extract information from past sales contracts and generates a draft using AI. If the user shows dissatisfaction with the proposed clauses through facial expressions or tone of voice, the emotion engine analyzes this and notifies the AI. The AI ​​then uses this feedback to revise the clauses and proposes a new one. Finally, a satisfactory contract is completed, saved on the server, and used as data to support future processes. In this way, the feedback loop utilizing the emotion engine ensures a highly satisfying contract creation process.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] The user starts up the device and logs in by accessing a dedicated application or web interface. They enter necessary information such as the contracting company, contracting party, and contract type. The device verifies the format of the entered information before sending it to the server in a secure format.

[0759] Step 2:

[0760] The emotion engine installed in the device analyzes the user's facial expressions and voice in real time as they enter contract information, and temporarily stores this information on the device. When a change in emotion or a specific emotional state is detected, the information is sent to the server.

[0761] Step 3:

[0762] Based on the received contract-related information, the server searches its database of past contracts for similar data. Once a match is selected, the generation AI is activated and generates a draft contract using the stored data.

[0763] Step 4:

[0764] The server sends the generated draft to the user's terminal and simultaneously refers to feedback information from the emotion engine. Based on the emotion engine's analysis results, the generating AI may dynamically adjust the content of the draft.

[0765] Step 5:

[0766] Users can review a draft of the contract on their device and edit its contents as needed. While the user is reviewing the draft, the sentiment engine continues to monitor the user's reactions and notifies the server to make further adjustments if the user expresses dissatisfaction or confusion.

[0767] Step 6:

[0768] Once the user completes revisions to the draft, the final contract is uploaded to the server. This final version is stored in memory and used to retrain the generation AI. Sentiment data is also stored and used for future draft generation.

[0769] Step 7:

[0770] The server adjusts the generation AI and emotion engine based on the newly uploaded data to improve performance in the next process. This improves overall user satisfaction and generation accuracy.

[0771] (Example 2)

[0772] 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".

[0773] In the contract drafting process, it is difficult to make adjustments that reflect user sentiment in real time, and there is a lack of efficient means to generate contracts that increase user satisfaction. Furthermore, with conventional systems, there are limited ways for users to quickly review and revise contract contents within the organization.

[0774] 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.

[0775] In this invention, the server includes a terminal device that receives contract information from a user, an emotion detection means that analyzes the emotional state, a recording means that stores past contract data, an emotion recording means that holds the user's emotional data, a generation AI means that generates a draft contract based on the contract information and emotional data, and an adjustment means that performs adjustments taking the emotional data into consideration. This enables the efficient and highly satisfying generation of contracts based on the user's emotions, as well as rapid exchange of opinions and confirmation work within the organization.

[0776] A "terminal device" is a device used by users to input and transmit contract information, and it also has a function to detect emotional states.

[0777] "Emotion detection means" refers to technology that analyzes a user's facial expressions and voice to understand their emotional state in real time.

[0778] "Recording means" refers to a function that stores past contract data and emotional data, and retains information that serves as the basis for generating and adjusting contract drafts.

[0779] "Emotion recording means" refers to technology for storing emotional data obtained from users and utilizing it in subsequent processes.

[0780] "Generative AI means" refers to artificial intelligence technology used to create draft contracts based on recorded historical data.

[0781] A "modification mechanism" is a system that adjusts drafts generated while taking emotional data into consideration, in order to improve user satisfaction.

[0782] The "presentation method" refers to a function that provides users with a draft contract created by the generation AI method for review and revision.

[0783] A "correction suggestion method" is a technology that uses emotional feedback to show users the adjustments made by a generative AI.

[0784] "Learning method" refers to a function that saves the revised final document as retraining data and uses it to improve future contract generation processes.

[0785] A "communication function" is a system for sharing information with relevant departments within an organization, and it facilitates smooth exchange of opinions.

[0786] "Methods for exchanging opinions" refer to methods for sharing sentiment analysis results within an organization and improving cooperation among users and with related departments.

[0787] This invention is a system that efficiently collects contract information from users, makes adjustments based on emotions, and generates contracts. It is primarily built using servers, terminals, and AI technology.

[0788] The terminal is a device that provides an interface for users to input contract information. This device incorporates emotion detection functions such as a camera and microphone, which analyze the user's emotional state in real time through their facial expressions and voice. This data is transmitted to the server along with the user's input.

[0789] The server receives contract information and emotional data transmitted from the terminal. Based on this data, the server uses a generative AI to create a draft contract, referencing recorded past contract data. The emotional data is also analyzed by an emotional detection engine and used as feedback to the generative AI, which adjusts the document according to the user's emotional state. This is particularly useful when making adjustments such as simplifying the writing style or using technical terms appropriately.

[0790] As a concrete example, consider a case where a user creates a sales contract. The user uses a terminal to input sales conditions and product information, and sends it to a server via the business network. The server searches a database of past cases and, using similar contracts as references, generates a new draft using a generation AI. If the user expresses dissatisfaction during this process, emotional feedback is immediately conveyed to the AI, and the contract is adjusted to be more easily understood.

[0791] As an example of a prompt, instructions can be given to the generating AI model in the form of, "What improvements can be made to this draft sales contract? Please make clearer and more concise suggestions, taking into account the user's feelings." This process allows users to quickly and effectively create contracts that reflect their feelings and opinions.

[0792] This system combines generative AI models with sentiment analysis technology to provide users with an intuitive and highly satisfying contract creation process.

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] The user enters contract information through the terminal and presses the submit button. The information entered includes the type of contract, the names of the parties involved, and the contract date. The terminal also simultaneously records the user's facial expressions and voice, acquiring them as emotional data. The output of this process is basic contract data and emotional data.

[0796] Step 2:

[0797] The server receives contract information and sentiment data sent from the terminal. Using this data as input, the server searches the database for relevant past contract data. The database stores usage history and templates, and extracts appropriate information depending on the type of contract. The output of this process is past contract data.

[0798] Step 3:

[0799] The server's generating AI creates a draft contract based on received contract information and past contract data. The generating AI analyzes the contract information and extracted data from the database, and constructs the document using prompt sentences. It also receives feedback from the emotion engine and performs specific actions to adjust the tone and length of the text. The output of this process is a draft contract proposed to the user.

[0800] Step 4:

[0801] The server sends the generated draft contract to the user's terminal. The user reviews the draft on the terminal and suggests revisions based on their emotions and opinions. The terminal then analyzes the facial expressions and voice again and sends the emotional data back to the emotion engine. The output of this process is the user's feedback data.

[0802] Step 5:

[0803] The generation AI receives feedback data from users and readjusts the contract based on instructions from the emotion engine. Specifically, it makes edits such as simplifying wording and reducing redundant items. This adjustment process continues until the user is satisfied. The output of this process is a more optimized contract.

[0804] Step 6:

[0805] Once the user approves the final version of the contract, the server saves it to a database. This saved data is also used as training material for the generating AI model, contributing to improvements in the next contract generation process. The output of this process is the final contract and the training data.

[0806] (Application Example 2)

[0807] 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".

[0808] In the contract creation process, contracts are often generated using a uniform interface without considering the user's feelings, resulting in a decline in the quality of the user experience. Furthermore, there are limited ways to obtain information regarding user discomfort and satisfaction during online payments, resulting in a lack of means to provide a better user experience. This can cause users to feel stressed, leading to a decrease in the final transaction completion rate. Another challenge is the inability to utilize user sentiment information obtained after a transaction for future transactions.

[0809] 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.

[0810] In this invention, the server includes an input device means for receiving contract-related information from a user, a storage device means for storing past document information, an artificial intelligence generation device means for generating a draft contract using past document information stored in the storage device based on the contract-related information received by the input device, a presentation device means for presenting the draft contract generated by the artificial intelligence generation device to the user, and an emotion recognition device means for detecting the user's emotions and adjusting the transaction process. This makes it possible to detect the user's emotions in real time, dynamically adjust the contract creation and settlement processes, and provide a more satisfying user experience.

[0811] An "input device" is a device used to receive contract-related information from users.

[0812] A "storage device" is a medium used to store and manage historical document information.

[0813] An "artificial intelligence generation device" is a device that creates a draft contract based on input contract-related information and stored past document information.

[0814] A "presentation device" is a device equipped with the function of displaying a draft contract created by an artificial intelligence generation device to the user.

[0815] An "emotion recognition device" is a device that has the function of detecting the user's emotions and adjusting the transaction process accordingly.

[0816] This invention realizes a system that provides a contract creation and payment experience that takes user emotions into consideration. The system mainly consists of an input device, a storage device, an artificial intelligence generation device, a presentation device, and an emotion recognition device.

[0817] The server receives contract-related information through the user's input device. This input device can be a smartphone or smart glasses. The received information, along with past document data stored in the storage device, is used by an artificial intelligence generation system to generate a draft contract. This AI generation system utilizes generation AI technology.

[0818] The generated draft is presented to the user via a display device, such as a monitor or display. As the user reviews the draft, an emotion recognition device detects their facial expressions and tone of voice to understand their emotional state. For emotion recognition, for example, Microsoft Azure's Emotion API is used. If the user expresses dissatisfaction or frustration, the emotion recognition device feeds this back to the artificial intelligence generation device, which then adjusts the content and interface of the contract.

[0819] A concrete example of this is a scenario involving electronic payments. When a user makes a payment to purchase goods, the system analyzes the user's facial expressions and voice using a camera and microphone, and an emotion recognition device then suggests the most suitable discount or simplifies the interface based on this analysis. This process improves user satisfaction and facilitates the smooth completion of transactions.

[0820] An example of a prompt would be, "Suggest strategies to provide a better experience when a user expresses dissatisfaction during online payment." Using this prompt, the generative AI model generates appropriate solutions tailored to the user's emotions.

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

[0822] Step 1:

[0823] The terminal receives contract-related information from the user as input. The input data is acquired through the interface of a smartphone or smart glasses, and the basic information necessary for creating the contract is sent to the server.

[0824] Step 2:

[0825] The server combines the received contract-related information with past document information stored in its memory, performing database searches and comparisons. Based on this data, an artificial intelligence generation device generates a draft contract. The output is an initial draft contract.

[0826] Step 3:

[0827] The server sends a draft of the generated contract to the terminal, which then presents it to the user. The terminal uses a monitor or display to allow the user to visually review the presented draft.

[0828] Step 4:

[0829] While the user reviews the presented draft contract, the emotion recognition device uses a camera and microphone to capture the user's facial expressions and voice as input. Based on this data, it performs emotion analysis and provides the emotion recognition device with the user's emotional state as output.

[0830] Step 5:

[0831] The server determines whether the user is expressing dissatisfaction or frustration based on the output of the emotion recognition device. This determination is fed back to the artificial intelligence generation device as input, and the contract is adjusted. The adjusted contract is then generated as output.

[0832] Step 6:

[0833] The device will present the revised contract to the user again. If the user does not express any negative feelings towards this proposal, the user will review the final version.

[0834] Step 7:

[0835] The server receives the final version of the contract reviewed by the user as input and saves it to its storage device. This data is then output as retraining data for future contract creation.

[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 to be incorporated by reference.

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

[0858] (Claim 1)

[0859] An input method for receiving contract-related information from users,

[0860] A storage means for storing past document data,

[0861] An AI generation means that generates a draft contract based on contract-related information received by the input means and using past document data stored in the storage means,

[0862] A presentation means for presenting a draft contract generated by the AI ​​generation means to the user,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, further comprising a learning means for storing a final document modified by a user based on a draft generated by the AI ​​generation means and using it as retraining data.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising communication means for the user to share and confirm information with relevant departments within the company based on the draft presented by the presentation means.

[0868] "Example 1"

[0869] (Claim 1)

[0870] A terminal device and means for receiving contract-related information from users,

[0871] A storage device and means for saving past document data,

[0872] A generation model and means for generating a draft contract document using past document data recorded in the storage device, based on contract-related information received by the terminal device.

[0873] A display device and means for presenting a draft contract document generated by the aforementioned generation model to a user,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising a learning device that stores the final document modified by the user based on the draft generated by the generation model in the storage device and uses it as data for retraining.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising a communication device for users to share and confirm information with relevant departments within the company based on the draft presented by the display device.

[0879] "Application Example 1"

[0880] (Claim 1)

[0881] A means of receiving contract information from users,

[0882] A recording means for storing past data,

[0883] An AI generation means that generates a draft contract based on contract information obtained by the reception structure and using past data stored in the record structure,

[0884] A display means for presenting a draft contract generated by the aforementioned AI generation structure to the user,

[0885] A means of modification that allows users to revise the generated draft contract,

[0886] A storage means for saving the revised final document in a record structure,

[0887] An AI learning method that uses the aforementioned saved final document as retraining data,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising a communication structure for users to share information with relevant departments and obtain confirmation based on the draft presented by the display structure.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising an electronic processing structure for a user to input contract information from a mobile device and to electronically submit and save the contract.

[0893] "Example 2 of combining an emotion engine"

[0894] (Claim 1)

[0895] A terminal device that receives contract information from the user, and an emotion detection means that analyzes the emotional state,

[0896] A recording means for storing past contract data, and an emotional recording means for storing user emotional data,

[0897] A generation AI means that generates a draft contract using past contract data stored in a recording means based on contract information and emotional data received from the terminal device, and an adjustment means that performs adjustments taking emotional data into consideration.

[0898] A presentation means that provides the user with a draft contract generated by the aforementioned generation AI means, and a revision presentation means that presents revised proposals based on emotional feedback,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, further comprising a learning means for storing the final document modified by the user based on the draft generated by the AI ​​means in a recording means and using it as data for future process improvement, and a retraining function using sentiment data.

[0902] (Claim 3)

[0903] The system according to claim 1, further comprising a communication function for users to share information with relevant departments within the organization based on the draft presented by the presentation means, and including an opinion exchange means for sharing sentiment analysis results.

[0904] "Application example 2 when combining with an emotional engine"

[0905] (Claim 1)

[0906] An input device and means for receiving contract-related information from users,

[0907] A memory device and means for storing past document information,

[0908] An artificial intelligence generation device and means for generating a draft contract based on contract-related information received by the input device and using past document information stored in the storage device,

[0909] A presentation device and means for presenting a draft contract generated by the aforementioned artificial intelligence generation device to a user,

[0910] An emotion recognition device and means for detecting user emotions and adjusting the transaction process,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, further comprising a learning device that stores a final document modified by a user based on a draft generated by the artificial intelligence generation device in the storage device and uses it as retraining information.

[0914] (Claim 3)

[0915] The system according to claim 1, further comprising a communication device for users to share and confirm information with relevant departments within the organization based on the draft presented by the presentation device. [Explanation of Symbols]

[0916] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving contract information from users, A recording means for storing past data, An AI generation means that generates a draft contract based on contract information obtained by the reception structure and using past data stored in the record structure, A display means for presenting a draft contract generated by the aforementioned AI generation structure to the user, A means of modification that allows users to revise the generated draft contract, A storage means for saving the revised final document in a record structure, An AI learning method that uses the aforementioned saved final document as retraining data, A system that includes this.

2. The system according to claim 1, further comprising a communication structure for users to share information with relevant departments and obtain confirmation based on the draft presented by the display means.

3. The system according to claim 1, further comprising an electronic processing structure for a user to input contract information from a mobile device and to electronically submit and save the contract.

Images