system

Abstract

We provide the system. [Solution] Means of obtaining contract information, A means for analyzing the aforementioned contract information and extracting key contract elements, A means of evaluating the fairness and impartiality of a contract based on the extracted contract elements, A means for generating improvement proposals based on the aforementioned evaluation, A means of presenting the generated improvement proposals and supporting consensus building, A system that includes this.

Description

Technical Field

[0005]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] With the development of AI technology, the scenarios where AIs conclude contracts with each other are increasing. However, due to the huge amount of such contracts, it is realistically difficult for humans to directly audit and review them. In addition, there is a possibility that contracts lacking in appropriateness and fairness may be unconsciously established, which may cause legal troubles and ethical problems. The purpose of this invention is to solve these problems by ensuring appropriateness and fairness and enabling efficient agreement formation in the contract conclusion process between AIs.

Means for Solving the Problems

[0006] "Contract information" refers to a collection of documents or data containing details of the terms, obligations, rights, etc., agreed upon between AIs.

[0007] "Means of acquisition" refers to methods or devices for extracting and accessing necessary information from databases or communication networks.

[0008] "Means of analysis" refers to methods or processes that use natural language processing techniques to extract useful elements or patterns from complex information.

[0009] "Key contractual elements" refer to fundamental and important information that constitutes the content of a contract, such as its purpose, conditions, obligations, and rights.

[0010] "Means of evaluation" refers to methods or devices that determine the appropriateness or quality of information or processes based on specific criteria.

[0011] "Fairness" is a concept that indicates that the conditions and treatment provided are fair and unbiased for all parties involved.

[0012] "Means for generating improvement proposals" refers to methods or processes for creating new conditions or suggestions based on existing information.

[0013] "Means of supporting consensus building" refers to methods or devices that facilitate dialogue and negotiations among stakeholders to reach an agreement.

[0014] A "system" is a combination of numerous interrelated elements and means that work together to achieve a specific function or purpose. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention relates to a system for fairly and appropriately managing and guiding agreements between AIs in the conclusion of contracts. This system has the function of acquiring, analyzing, and evaluating various contract information, generating improvement proposals, and supporting agreement formation. Specific embodiments of this invention are shown below.

[0037] First, the server accesses a contract database between AIs to retrieve the necessary contract information. This information includes detailed details such as the purpose, terms, obligations, and rights of the contract. This information is then analyzed in detail in the next step.

[0038] Next, the terminal analyzes the acquired contract information using natural language processing technology. This analysis reveals the main components of the contract, namely its purpose, terms, and obligations. Based on the analysis, it evaluates whether there are any flaws or unfair elements in the contract.

[0039] The server then uses structured contract elements to perform an evaluation based on legal databases and ethical guidelines. It determines whether the contract is appropriate and whether any improvements are needed.

[0040] Based on the results of this evaluation, the device generates proposed improvements to the contract. This generation includes modifying contract clauses and re-evaluating conditions. The generated improvements are presented to the AI ​​of the other party to the contract, laying the groundwork for reaching an agreement.

[0041] Finally, the user participates by selecting or adjusting the proposed improvements. This facilitates smooth negotiations between the AIs until an agreement is reached. The final agreed-upon contract is saved in a database and used in future contract processes.

[0042] For example, in a contract for an AI-powered data analysis service, the server first retrieves the relevant contract. The terminal analyzes this contract and verifies whether the data usage conditions are excessively burdensome. If the server identifies inappropriate obligations on the data-providing AI as a result of the evaluation, the terminal automatically generates a new proposal, which the user reviews and sends revised versions to both AIs, thereby achieving agreement. In this way, the system enables fair and efficient contract negotiation between AIs.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server accesses a database containing contract information between AIs and retrieves the necessary contract data using a specific contract ID or condition. After retrieving the data, the server converts the contract information into a dedicated format for processing and sends it to the terminal.

[0046] Step 2:

[0047] The terminal inputs contract data obtained from the server into a natural language processing module and performs text analysis. This analysis extracts the main elements that constitute the purpose, conditions, obligations, and rights of the contract, and sets them up to prepare for the next step.

[0048] Step 3:

[0049] The server uses the key elements of the contract provided by the terminal to perform an evaluation based on legal databases and ethical guidelines. Specifically, it compares the contract content with standards of fairness and impartiality to determine whether these standards are met. In this process, it identifies elements that appear inappropriate or unfair and passes them on to the next processing step.

[0050] Step 4:

[0051] The device uses AI to generate improvement proposals for contract terms deemed unsuitable or unfair. These improvement proposals become new contract documents that modify necessary conditions and clauses. It also creates multiple alternatives and organizes their advantages and risks as additional information.

[0052] Step 5:

[0053] The user reviews the generated improvement proposals and selects the best one from a practical and unbiased perspective. The selected proposal is automatically presented to the contracting party's AI, and the agreement-building process begins.

[0054] Step 6:

[0055] The server and terminals verify whether the proposal has been accepted and finalize the agreed-upon contract terms. The agreement is stored in the record management system and used for future reference and audits.

[0056] (Example 1)

[0057] 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."

[0058] In modern contract processes, automated contract signing between AIs presents challenges in determining whether the contract terms are technically and ethically sound. This creates a risk that contracts may deviate from societal laws and ethical standards. Furthermore, there is a lack of mechanisms to quickly generate corrective measures when inappropriate contract terms exist. To address these issues, a system is needed that guarantees the fairness and impartiality of contracts and supports efficient consensus building.

[0059] 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.

[0060] In this invention, the server includes an information processing device for acquiring contract information, a data processing means for analyzing the contract information and extracting key components, and an evaluation means for evaluating the appropriateness and fairness of the contract based on the extracted components. This allows for verification of whether the contract content conforms to laws and ethical standards, and enables the rapid generation of improvement proposals if inappropriate contract conditions exist, thereby facilitating consensus building.

[0061] "Contract information" refers to a collection of data that includes details such as the purpose, terms, obligations, and rights of a contract.

[0062] An "information processing device" is a device that accesses a database and retrieves and stores necessary information.

[0063] A "data processing means" is a system that uses natural language processing technology to analyze contract information and extract necessary information.

[0064] An "evaluation tool" is a mechanism that evaluates whether the content of a contract conforms to laws and ethical standards based on the extracted components of the contract.

[0065] A "generative AI model" is an artificial intelligence technology that automatically generates new suggestions and options from existing information.

[0066] "Improvement generation means" refers to a function that includes the process of forming proposed revisions to contract terms and improvement measures based on evaluation results.

[0067] A "presentation method" refers to a method for clearly showing the generated improvement proposals to users and supporting consensus building.

[0068] "Legal information" refers to information contained in databases related to laws and regulations, and serves as a standard for evaluating the legality of a contract.

[0069] "Ethical guidelines" are guidelines that outline social and professional behavioral norms and serve as standards for evaluating the ethical appropriateness of contracts.

[0070] A "contracting party" is a participant whose purpose is to reach an agreement in a contract, and is usually the entity that accepts the terms of the contract.

[0071] The embodiments for carrying out this invention are shown below.

[0072] The server first accesses the database that manages contract information and retrieves it. This information retrieval uses a standard database management system and SQL queries to identify and retrieve the information. At this point, details such as the purpose, terms, obligations, and rights of the contract are collected.

[0073] Next, the terminal uses natural language processing software such as Python's NLTK library or spaCy to analyze the acquired contract information. During the analysis process, it extracts and classifies the main components of the contract. The analysis also provides a function to detect whether there are any deficiencies or unfairness in the contract clauses.

[0074] Subsequently, the server uses the extracted components to compare them against existing legal databases and ethical guidelines. This evaluation process includes a compliance check to ensure that the contract terms conform to various regulations and social standards.

[0075] Based on the evaluation results, the device automatically generates improvement proposals using a generative AI model. These proposals may include modifications to contract terms or the addition of new clauses. This process utilizes natural-sounding text generation by the AI ​​model. Specific examples of such proposals include "relaxing restrictions on data usage."

[0076] Users review the proposed improvements and make manual adjustments as needed. This allows for faster formation of joint agreements among the AIs. Users directly manipulate the improvements through a dedicated GUI and save them to the database after finalizing the agreement.

[0077] Examples of prompts include, "Please explain the process of a system that performs fair evaluations of contracts between AIs and generates improvement proposals when unfair elements are found." This system provides a solid foundation for more efficient and fair contracts between AIs.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server retrieves contract information. The input includes the ID of the required contract and related information. Based on this information, the server executes an SQL query to retrieve contract information from the contract database, including the purpose, terms, obligations, and rights of the relevant contract. The output is returned to the server in a structured data format containing the contract details.

[0081] Step 2:

[0082] The terminal analyzes the acquired contract information. The contract information acquired in step 1 is used as input. Natural language processing techniques are used to extract key contract elements from the contract information. Specifically, Python's NLTK library and spaCy are used to analyze each clause of the contract and tag the necessary parts. The output is a list of the extracted key contract elements.

[0083] Step 3:

[0084] The server evaluates the contract based on its structured contract elements. The input is the analysis results from Step 2. It compares the contract against existing legal databases and ethical guidelines to determine its legal compliance and ethical integrity. This evaluation identifies any problematic elements. The output is an evaluation report indicating the appropriateness of the contract and whether any improvements are necessary.

[0085] Step 4:

[0086] The terminal uses a generative AI model to create improvement proposals. The evaluation report from Step 3 is used as input. The AI ​​model automatically detects areas of the contract that need improvement and generates new clauses and conditions. This process utilizes a generative AI model such as GPT-3(registered trademark). The output presents improvement proposals and generates a new draft contract.

[0087] Step 5:

[0088] The user reviews and adjusts the suggested improvements from the terminal. The input provided is the suggested improvements generated in step 4. The user scrutinizes the suggestions on the GUI and makes manual modifications as needed. The output is the final confirmed contract details, which are used for the final agreement between the AIs.

[0089] In this way, a process is implemented through a series of processing steps to ensure that contracts between AIs are concluded fairly and equitably.

[0090] (Application Example 1)

[0091] 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."

[0092] Contracts concluded between AIs often face challenges regarding fairness and legality, and therefore require improvement. In particular, in situations requiring complex and rapid contract negotiations, such as electronic payment services, the analysis and refinement of contract content are often performed manually, which is inefficient. It is necessary to eliminate the resulting unfairness and inefficiency and to conclude contracts between AIs more quickly and fairly.

[0093] 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.

[0094] In this invention, the server includes means for receiving contract details, means for analyzing the contract details to detect key components, and means for evaluating the fairness and legal compliance of the contract based on the detected components. This makes it possible to automatically analyze contracts between AIs in detail and quickly present improvement suggestions. This system improves the efficiency of consensus building and the fairness of contract conclusion.

[0095] "Contract details" refers to all data related to agreements made between AIs.

[0096] "Analysis" involves examining the contents of a contract in detail, understanding its components and meaning, and gaining new insights.

[0097] "Components" refer to parts or elements that play an important role in a contract, and are the central information of the agreed-upon terms.

[0098] "Fairness" is a standard that indicates whether a contract is concluded fairly and without bias for both parties.

[0099] "Compliance with laws and regulations" refers to a state in which a contract complies with current laws and regulations.

[0100] An "alternative" is another option proposed to improve or modify the terms of an existing contract.

[0101] "Agreement building" refers to the process by which the parties to a contract understand and agree to its contents and formally approve them.

[0102] This invention is a system for efficiently and fairly concluding contracts between AIs. This system is realized through the collaboration of a server, terminals, and users.

[0103] The server first receives the contract details via the network. These details include the purpose, terms, and rights of the contract. Next, the server analyzes the contract details, identifying the key components in detail. The software used here is a natural language processing tool for contract analysis, utilizing several commercially available libraries.

[0104] The terminal evaluates the fairness and legal compliance of the contract based on the analysis results received from the server. This evaluation utilizes legal information sources and ethical guideline databases to confirm that the contract is fair and lawful. If the evaluation results indicate that a contract element is inappropriate, the terminal generates alternative proposals.

[0105] Once alternative proposals are generated, users can review them and use them to facilitate consensus building. Users can then adjust the contract terms as needed while reviewing the generated alternatives and finalize the agreement.

[0106] As a concrete example, consider a digital wallet contract between financial institutions. This system analyzes the contract terms proposed by the AIs of both parties, detects and corrects any unfair elements regarding data sharing, thereby achieving a fairer contract for both sides.

[0107] The following are specific examples of prompt statements for a generative AI model.

[0108] "Analyze the following contract data, identify any unfair elements, and propose improvements. Contract Data: Enter contract details in text format."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The server receives raw contract data over the network. The input is electronically represented contract content, usually in structured data format such as JSON or XML. This received data is converted into a format suitable for contract analysis. This conversion prepares the contract data for subsequent processing.

[0112] Step 2:

[0113] The server uses the converted contract data to analyze and extract the main components of the contract using natural language processing tools. The input is the contract data prepared in step 1. The output is a list of components such as the purpose, rights, and obligations of the contract. This is then analyzed using natural language processing techniques, including grammatical and semantic analysis, to extract the essence of the contract.

[0114] Step 3:

[0115] The server sends the extracted components to the terminal, where it evaluates the fairness and legal compliance of the contract. The input is a list of components. It compares them against legal databases and ethical guideline data to verify that the contract meets legal standards. The output is the evaluation result, identifying elements that are inadequate in terms of fairness or legal compliance.

[0116] Step 4:

[0117] The terminal automatically generates alternatives for contract elements deemed inappropriate based on the evaluation results. The input is the fairness evaluation result. By providing prompts to the generation AI model used, it generates improved proposals. The output is a set of alternatives. This results in new proposals that are fair and improved.

[0118] Step 5:

[0119] The user receives the alternative proposal generated on the terminal, reviews its contents, and makes revisions as needed. The input is the generated alternative proposal. The user reviews the improved contract and makes adjustments to reach an agreement. The output is the final agreed-upon contract proposal.

[0120] Step 6:

[0121] The terminal presents the final agreement to the AI ​​representing the other party, facilitating the formal agreement. The input is the agreed-upon contract proposal. Based on the presented proposal, the AI ​​representing the other party approves and modifies it to form the final agreement. The output is the mutually approved contract data.

[0122] Step 7:

[0123] The server stores the agreed-upon contract details in a database for use in future contract processes. The input is the final agreed-upon contract data. The data is saved and made available for external access and as a reference for future contracts. The output is the contract information recorded in the database.

[0124] 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.

[0125] This invention relates to a system that incorporates an emotion engine to recognize user emotions and support contract agreement formation when AIs enter into contracts with each other. This system has the function of accurately acquiring and analyzing contract information, and then monitoring the user's emotional state, enabling it to intervene in the agreement formation process with optimal suggestions and timing. Specific embodiments of this invention are shown below.

[0126] The server appropriately retrieves contract information between AIs from each database. The retrieved contract information is processed by the server and converted into a structured data format. This information is then sent to the terminal for contract analysis and evaluation.

[0127] The terminal analyzes the contract data received from the server using a natural language processing program. This analysis extracts the main elements of the contract, namely the terms, obligations, and rights. The terminal then evaluates the appropriateness of the contract by comparing these elements with laws and ethical guidelines, and generates improvement proposals as needed.

[0128] Furthermore, users are monitored by an emotion engine. The emotion engine analyzes emotional data obtained from the camera and microphone to understand the user's emotional state. This emotional data shows the user's reaction to proposed improvements and is used to support contract negotiations.

[0129] For example, in contract negotiations, if a user expresses anxiety or hesitation, the device adjusts the content and format of the proposal based on that emotional data, presenting a revised version that provides reassurance. This makes it possible to facilitate contract agreement formation based on the user's emotional state.

[0130] The server securely stores the newly agreed-upon contract information in a record database, making it available for future contract processes and reference. In this way, the present invention simultaneously achieves increased efficiency in AI-to-AI contract processes and improved user experience.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The server connects to the AI-to-AI contract database to retrieve the specified contract data. This contract data includes detailed information about the contract's purpose, terms, obligations, and rights. This data is then formatted and transmitted to the terminal.

[0134] Step 2:

[0135] The terminal analyzes contract data received from the server using natural language processing technology. During the analysis, key elements of the contract are extracted, and its purpose and conditions are identified. These elements are then organized and stored as structured data.

[0136] Step 3:

[0137] The server evaluates the fairness and impartiality of a contract by comparing the contract elements sent from the terminal with a legal database. During the evaluation, it utilizes current legal standards and ethical guidelines to verify compliance. If any deficiencies are found, the details are compiled into a report.

[0138] Step 4:

[0139] The terminal uses a generative AI to generate new improvement proposals for contract terms that need improvement based on the evaluation results. Multiple improvement proposals are created, and the merits and risks of each proposal are evaluated. This generation process may include changes to contract terms or the addition of new clauses.

[0140] Step 5:

[0141] The emotion engine continuously monitors the user's emotional state. The emotional data is analyzed, and if anxiety or misunderstanding is detected, the suggested improvements presented via the device are adjusted. This ensures that the optimal solution is presented based on the user's emotions.

[0142] Step 6:

[0143] The device presents the user with the most appropriate improvement plan based on the analysis results of the emotion engine. When presenting the plan, it selects the appropriate expression and timing, taking into account the user's emotional state. This facilitates the consensus-building process and improves the user's sense of satisfaction.

[0144] Step 7:

[0145] The server saves the final contract details, approved by the user, to the database and notifies each relevant system that the contract revision is complete. The newly agreed-upon contract is retained for future reference and can be used in other contract processes as needed.

[0146] (Example 2)

[0147] 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".

[0148] In modern contract negotiations, the complexity and ambiguity of contract terms often become obstacles to reaching an agreement. Furthermore, processes that disregard the feelings of the users involved can create unnecessary stress and misunderstandings before an agreement is reached. This situation leads to problems of inefficiency in contract negotiations and user dissatisfaction.

[0149] 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.

[0150] In this invention, the server includes means for acquiring contract information, means for analyzing the contract information and extracting key contract elements, and means for monitoring the user's emotions and adjusting the proposed content based on the emotional state. This enables rapid and smooth agreement formation that takes into account the user's emotional state while maintaining the fairness and impartiality of the contract.

[0151] "Contract information" refers to all data and documents related to a contract, including contract terms, obligations, and rights.

[0152] "Key contractual elements" refer to the fundamental elements of a contract, such as conditions, obligations, and rights, that form its core.

[0153] "Appropriateness" refers to demonstrating that a contract is structured fairly and justly in accordance with laws and ethics.

[0154] "Emotion monitoring" refers to the process of analyzing a user's emotions in real time from their facial expressions, voice, and other data to understand their emotional state.

[0155] "Adjusting the proposal" refers to modifying the wording, tone, and amount of information in the contract proposal according to the user's emotional state, thereby facilitating agreement.

[0156] A "recording medium" is a means of storing information for the safe and accurate preservation of contract information, and includes digital databases and the like.

[0157] "Agreement building" refers to the process by which all parties involved agree to the terms of the contract and formally conclude the agreement.

[0158] To implement this invention, a system is specifically constructed that supports contract conclusion between AIs while taking into account the user's emotions. The system mainly consists of three elements: a server, a terminal, and a user, and each element works together to support the contract process.

[0159] The server plays a central role in retrieving and processing contract information. It collects necessary contract information from the database and converts the retrieved data into a structured data format. In this process, the server accesses the database using the SQL language and converts the information into a list or JSON format. The structured data is then sent to the terminal, providing a foundation for further analysis.

[0160] The terminal analyzes the received contract information and extracts the key contract elements. The terminal has software libraries such as NLTK and spaCy installed for natural language processing. These libraries are used to extract and classify conditions, obligations, and rights from the contract document. The analyzed data is then compared against legal databases and ethical guidelines to verify the appropriateness of the contract content. Furthermore, a generative AI model is used to generate improvement suggestions if necessary and present them to the user.

[0161] The user is monitored by an emotion engine. The emotion engine utilizes facial recognition technology using OpenCV and voice emotion analysis technology using Praat to capture the user's facial expressions and voice tone through the camera and microphone. It then analyzes and digitizes this emotional state. If the user experiences anxiety or confusion, the device uses this data and a generative AI model to adjust the contract proposal.

[0162] For example, if a user expresses negative emotions, the device can modify the proposed contract terms or adjust the explanation to make it more approachable. An example of a prompt message would be, "Based on the user's emotional data, please adjust the contract proposal and generate a more reassuring version." This allows for smooth agreement building while respecting the user's emotions.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server connects to the database to retrieve contract information. The input is a request for contract information, using an SQL query. The server executes the query, extracts contract-related information from the database, and outputs unstructured contract data. This organizes the information necessary for the contract for subsequent processing.

[0166] Step 2:

[0167] The server converts the acquired contract information into structured data. The input is the contract data obtained in step 1. The server organizes the information into a list or JSON format, and applies tags and hierarchical structures. The output is standardized structured data. This format conversion makes the contract data suitable for subsequent analysis on the terminal.

[0168] Step 3:

[0169] The terminal analyzes the structured data sent from the server and extracts the key contract elements. The input is the structured data from step 2. The terminal uses a natural language processing program, such as NLTK or spaCy, to analyze elements such as contract terms, obligations, and rights, and extracts these elements as a list as output. This process clarifies the core of the contract.

[0170] Step 4:

[0171] The terminal uses the analyzed contract elements to evaluate their appropriateness. The input is the contract elements extracted in step 3. The terminal evaluates them against legal databases and ethical guidelines, and outputs the appropriateness evaluation results. This confirms whether the contract content is legally and ethically appropriate.

[0172] Step 5:

[0173] The device generates improvement suggestions as needed based on the suitability assessment. The input is the evaluation results from step 4. Using the generation AI model, the system identifies items that need improvement in the presented content and constructs detailed improvement proposals, resulting in improvement suggestions as output. These suggestions are prepared for presentation to the user.

[0174] Step 6:

[0175] The user's emotional state is monitored by an emotion engine. The input is real-time facial and voice data from the user. Emotion analysis software processes the data collected through the camera and microphone, quantifying or classifying the user's emotional state. The output is the user's most recent emotional data. This data serves as an indicator of how the contract proposal should be adjusted.

[0176] Step 7:

[0177] The device adjusts the proposal based on the user's sentiment data. The input is the sentiment data from step 6. A generative AI model is used to adjust the tone and clarity of the contract proposal, presenting it in a way that best suits the user's situation. The output is a revised contract proposal that is more acceptable to the user.

[0178] Step 8:

[0179] The server stores the agreed-upon final contract information on a storage medium. The input is the agreed-upon draft contract. The server securely registers this information in a database, making it accessible as needed. The output is the secure contract record that is stored, preparing it for future reference and audits.

[0180] (Application Example 2)

[0181] 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."

[0182] In the contract negotiation process between artificial intelligence entities, users may experience anxiety or confusion, and these emotions can hinder smooth agreement. Conventional systems often fail to adjust the presentation of contract information to the user's level of understanding or emotional state, which can impair the user experience. Therefore, there is a need to dynamically adjust the way contract information is presented according to the user's emotional state to support optimal agreement.

[0183] 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.

[0184] In this invention, the server includes means for acquiring contract information, means for analyzing the contract information and extracting key contract elements, means for evaluating the appropriateness and fairness of the contract based on the extracted contract elements, and means for analyzing the user's emotions and adjusting the method of presenting the contract information according to the user's emotional state. This makes it possible to optimally present contract information based on the user's level of understanding and emotional state.

[0185] "Contract information" refers to information regarding the terms, rights, and obligations agreed upon between the parties in a transaction.

[0186] "Analysis" refers to the process of examining acquired data in detail to identify its structure and elements.

[0187] "Contractual elements" are the constituent elements of important information in a contract, such as the terms, obligations, and rights.

[0188] "Appropriateness" is an evaluation criterion that indicates whether the terms of a contract are legally correct and fair.

[0189] "Fairness" is a standard used to evaluate whether the burdens and benefits of a contract are distributed equally among the parties involved.

[0190] An "improvement proposal" is a specific suggestion to make the current contract terms more appropriate and fair.

[0191] "Agreement building" refers to the process of obtaining consent between the contracting parties.

[0192] "Emotion" refers to the emotional state a user exhibits when entering into a contract.

[0193] "Presentation method" refers to the format in which contract information is displayed to the user.

[0194] The system of this invention mainly consists of three elements: a server, a terminal, and a user.

[0195] The server has a program built to retrieve and analyze contract information. The server collects the necessary contract information from multiple databases and processes it efficiently and accurately. Next, this information is used with natural language processing software (e.g., Google® Cloud Natural Language API) to extract key contract elements.

[0196] The terminal receives extracted contract data transmitted from the server and evaluates the fairness and impartiality of the contract using legal documents and ethical guidelines. Based on the evaluation results, it has the function to generate and present improvement proposals to the user to make the contract fair and impartial. In this process, it uses the camera and microphone of a smartphone or smart glasses (e.g., Amazon Rekognition, Google Cloud Speech-to-Text) to analyze the user's emotions and adjust the information presented according to their emotional state.

[0197] Users are supported in reaching agreements through improvement suggestions presented on the terminal. This system helps users understand purchase conditions and benefits with confidence while making electronic payments. As a result, for example, if a user feels uneasy about the contract details, the system is designed to provide information in a more easily understandable format. The generating AI model provides appropriate agreement support to the user by instructing, for example, "Analyze the contract details and optimize the information presentation method based on current user sentiment data."

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server retrieves contract information from the database. It receives a request containing contract information as input. It performs a database search and extracts the contract information. The output is the retrieved raw contract data.

[0201] Step 2:

[0202] The server analyzes the acquired contract information using natural language processing. The input is raw contract data. Using the Google Cloud Natural Language API, it extracts contract elements (terms, rights, obligations, etc.) and converts them into structured data. The output is structured contract element data.

[0203] Step 3:

[0204] The terminal receives structured data transmitted from the server and evaluates the fairness of the contract by comparing it with legal documents and ethical guidelines. The input is structured contract element data. Based on the evaluation algorithm, it determines legality and fairness. The output is the evaluation result and, if necessary, suggestions for improvement.

[0205] Step 4:

[0206] The device processes data collected from the camera and microphone to analyze the user's emotions. The input consists of data on the user's facial expressions and voice. Emotional states are identified using Amazon Rekognition or Google Cloud Speech-to-Text. The output is the user's emotional state information, which is the result of the analysis.

[0207] Step 5:

[0208] The terminal adjusts the way contract information is presented based on the user's emotional state. Inputs include emotional state information, evaluation results, and improvement suggestions. The system generates screens with diagrams and detailed explanations tailored to the user's level of understanding. The output is a contract information presentation screen optimized for the user.

[0209] Step 6:

[0210] The user makes decisions based on the information and improvement suggestions presented on the device and is supported in reaching a contract agreement. The input is an optimized contract information display screen. This allows the user to naturally proceed through a process that allows them to agree with confidence. The output is the user's final agreement or request for revisions.

[0211] Step 7:

[0212] The server securely stores the newly agreed-upon contract information in its record database. The input is the final agreed-upon data returned by the user. After going through the data storage process, the securely stored contract information is generated as output.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention relates to a system for fairly and appropriately managing and guiding agreements between AIs in the conclusion of contracts. This system has the function of acquiring, analyzing, and evaluating various contract information, generating improvement proposals, and supporting agreement formation. Specific embodiments of this invention are shown below.

[0230] First, the server accesses a contract database between AIs to retrieve the necessary contract information. This information includes detailed details such as the purpose, terms, obligations, and rights of the contract. This information is then analyzed in detail in the next step.

[0231] Next, the terminal analyzes the acquired contract information using natural language processing technology. This analysis reveals the main components of the contract, namely its purpose, terms, and obligations. Based on the analysis, it evaluates whether there are any flaws or unfair elements in the contract.

[0232] The server then uses structured contract elements to perform an evaluation based on legal databases and ethical guidelines. It determines whether the contract is appropriate and whether any improvements are needed.

[0233] Based on the results of this evaluation, the device generates proposed improvements to the contract. This generation includes modifying contract clauses and re-evaluating conditions. The generated improvements are presented to the AI ​​of the other party to the contract, laying the groundwork for reaching an agreement.

[0234] Finally, the user participates by selecting or adjusting the proposed improvements. This facilitates smooth negotiations between the AIs until an agreement is reached. The final agreed-upon contract is saved in a database and used in future contract processes.

[0235] For example, in a contract for an AI-powered data analysis service, the server first retrieves the relevant contract. The terminal analyzes this contract and verifies whether the data usage conditions are excessively burdensome. If the server identifies inappropriate obligations on the data-providing AI as a result of the evaluation, the terminal automatically generates a new proposal, which the user reviews and sends revised versions to both AIs, thereby achieving agreement. In this way, the system enables fair and efficient contract negotiation between AIs.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The server accesses a database containing contract information between AIs and retrieves the necessary contract data using a specific contract ID or condition. After retrieving the data, the server converts the contract information into a dedicated format for processing and sends it to the terminal.

[0239] Step 2:

[0240] The terminal inputs contract data obtained from the server into a natural language processing module and performs text analysis. This analysis extracts the main elements that constitute the purpose, conditions, obligations, and rights of the contract, and sets them up to prepare for the next step.

[0241] Step 3:

[0242] The server uses the key elements of the contract provided by the terminal to perform an evaluation based on legal databases and ethical guidelines. Specifically, it compares the contract content with standards of fairness and impartiality to determine whether these standards are met. In this process, it identifies elements that appear inappropriate or unfair and passes them on to the next processing step.

[0243] Step 4:

[0244] The device uses AI to generate improvement proposals for contract terms deemed unsuitable or unfair. These improvement proposals become new contract documents that modify necessary conditions and clauses. It also creates multiple alternatives and organizes their advantages and risks as additional information.

[0245] Step 5:

[0246] The user reviews the generated improvement proposals and selects the best one from a practical and unbiased perspective. The selected proposal is automatically presented to the contracting party's AI, and the agreement-building process begins.

[0247] Step 6:

[0248] The server and terminals verify whether the proposal has been accepted and finalize the agreed-upon contract terms. The agreement is stored in the record management system and used for future reference and audits.

[0249] (Example 1)

[0250] 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."

[0251] In modern contract processes, automated contract signing between AIs presents challenges in determining whether the contract terms are technically and ethically sound. This creates a risk that contracts may deviate from societal laws and ethical standards. Furthermore, there is a lack of mechanisms to quickly generate corrective measures when inappropriate contract terms exist. To address these issues, a system is needed that guarantees the fairness and impartiality of contracts and supports efficient consensus building.

[0252] 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.

[0253] In this invention, the server includes an information processing device for acquiring contract information, a data processing means for analyzing the contract information and extracting key components, and an evaluation means for evaluating the appropriateness and fairness of the contract based on the extracted components. This allows for verification of whether the contract content conforms to laws and ethical standards, and enables the rapid generation of improvement proposals if inappropriate contract conditions exist, thereby facilitating consensus building.

[0254] "Contract information" refers to a collection of data that includes details such as the purpose, terms, obligations, and rights of a contract.

[0255] An "information processing device" is a device that accesses a database and retrieves and stores necessary information.

[0256] A "data processing means" is a system that uses natural language processing technology to analyze contract information and extract necessary information.

[0257] An "evaluation tool" is a mechanism that evaluates whether the content of a contract conforms to laws and ethical standards based on the extracted components of the contract.

[0258] A "generative AI model" is an artificial intelligence technology that automatically generates new suggestions and options from existing information.

[0259] "Improvement generation means" refers to a function that includes the process of forming proposed revisions to contract terms and improvement measures based on evaluation results.

[0260] A "presentation method" refers to a method for clearly showing the generated improvement proposals to users and supporting consensus building.

[0261] "Legal information" refers to information contained in databases related to laws and regulations, and serves as a standard for evaluating the legality of a contract.

[0262] "Ethical guidelines" are guidelines that outline social and professional behavioral norms and serve as standards for evaluating the ethical appropriateness of contracts.

[0263] A "contracting party" is a participant whose purpose is to reach an agreement in a contract, and is usually the entity that accepts the terms of the contract.

[0264] The embodiments for carrying out this invention are shown below.

[0265] The server first accesses the database that manages contract information and retrieves it. This information retrieval uses a standard database management system and SQL queries to identify and retrieve the information. At this point, details such as the purpose, terms, obligations, and rights of the contract are collected.

[0266] Next, the terminal uses natural language processing software such as Python's NLTK library or spaCy to analyze the acquired contract information. During the analysis process, it extracts and classifies the main components of the contract. The analysis also provides a function to detect whether there are any deficiencies or unfairness in the contract clauses.

[0267] Subsequently, the server uses the extracted components to compare them against existing legal databases and ethical guidelines. This evaluation process includes a compliance check to ensure that the contract terms conform to various regulations and social standards.

[0268] Based on the evaluation results, the device automatically generates improvement proposals using a generative AI model. These proposals may include modifications to contract terms or the addition of new clauses. This process utilizes natural-sounding text generation by the AI ​​model. Specific examples of such proposals include "relaxing restrictions on data usage."

[0269] Users review the proposed improvements and make manual adjustments as needed. This allows for faster formation of joint agreements among the AIs. Users directly manipulate the improvements through a dedicated GUI and save them to the database after finalizing the agreement.

[0270] Examples of prompts include, "Please explain the process of a system that performs fair evaluations of contracts between AIs and generates improvement proposals when unfair elements are found." This system provides a solid foundation for more efficient and fair contracts between AIs.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The server retrieves contract information. The input includes the ID of the required contract and related information. Based on this information, the server executes an SQL query to retrieve contract information from the contract database, including the purpose, terms, obligations, and rights of the relevant contract. The output is returned to the server in a structured data format containing the contract details.

[0274] Step 2:

[0275] The terminal analyzes the acquired contract information. The contract information acquired in step 1 is used as input. Natural language processing techniques are used to extract key contract elements from the contract information. Specifically, Python's NLTK library and spaCy are used to analyze each clause of the contract and tag the necessary parts. The output is a list of the extracted key contract elements.

[0276] Step 3:

[0277] The server evaluates the contract based on its structured contract elements. The input is the analysis results from Step 2. It compares the contract against existing legal databases and ethical guidelines to determine its legal compliance and ethical integrity. This evaluation identifies any problematic elements. The output is an evaluation report indicating the appropriateness of the contract and whether any improvements are necessary.

[0278] Step 4:

[0279] The terminal uses a generative AI model to create improvement proposals. The evaluation report from Step 3 is used as input. The AI ​​model automatically detects areas of the contract that need improvement and generates new clauses and conditions. This process utilizes a generative AI model such as GPT-3. The output presents improvement proposals and generates a new draft contract.

[0280] Step 5:

[0281] The user checks and adjusts the improvement proposals from the terminal. As input, the improvement proposals generated in Step 4 are provided. The user examines the improvement proposals on the GUI and manually makes corrections if necessary. As output, the finally confirmed contract content is generated, which is used for the final agreement between the AIs.

[0282] In this way, through a series of processing steps, a process is implemented in which contracts between AIs are concluded properly and fairly.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] Contracts concluded between AIs often have issues regarding fairness and legality and thus need improvement. In particular, in scenarios where complex and rapid contract negotiations such as electronic payment services are required, the analysis and improvement of contract content are often carried out manually, which is not efficient. It is necessary to eliminate the resulting unfairness and inefficiency and conclude contracts between AIs more quickly and fairly.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for receiving contract content, means for analyzing the contract content to detect main components, and means for evaluating the fairness and compliance with laws of the contract based on the detected components. Thereby, it becomes possible to automatically and in detail analyze contracts between AIs and quickly present improvement proposals. With this system, the efficiency of agreement formation and the fairness of contract conclusion are improved.

[0288] "Contract content" refers to all data related to the agreed matters exchanged between AIs.

[0289] "Analysis" involves examining the contents of a contract in detail, understanding its components and meaning, and gaining new insights.

[0290] "Components" refer to parts or elements that play an important role in a contract, and are the central information of the agreed-upon terms.

[0291] "Fairness" is a standard that indicates whether a contract is concluded fairly and without bias for both parties.

[0292] "Compliance with laws and regulations" refers to a state in which a contract complies with current laws and regulations.

[0293] An "alternative" is another option proposed to improve or modify the terms of an existing contract.

[0294] "Agreement building" refers to the process by which the parties to a contract understand and agree to its contents and formally approve them.

[0295] This invention is a system for efficiently and fairly concluding contracts between AIs. This system is realized through the collaboration of a server, terminals, and users.

[0296] The server first receives the contract details via the network. These details include the purpose, terms, and rights of the contract. Next, the server analyzes the contract details, identifying the key components in detail. The software used here is a natural language processing tool for contract analysis, utilizing several commercially available libraries.

[0297] The terminal evaluates the fairness and legal compliance of the contract based on the analysis results received from the server. This evaluation utilizes legal information sources and ethical guideline databases to confirm that the contract is fair and lawful. If the evaluation results indicate that a contract element is inappropriate, the terminal generates alternative proposals.

[0298] Once an alternative is generated, the user can review it and use it to facilitate agreement formation. While viewing the generated alternative, the user can adjust the contract terms as needed and finalize the agreement.

[0299] As a specific example, assume a contract for digital wallets between financial institutions. This system analyzes the contract terms proposed by both AIs, detects and corrects unfair elements related to data sharing, and realizes a fairer contract for both parties.

[0300] Specific examples of prompt texts for the generation AI model are as follows.

[0301] "Analyze the following contract data, identify unfair elements, and propose improvement plans. Contract data: Enter the details of the contract in text format."

[0302] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0303] Step 1:

[0304] The server receives the raw contract data via the network. The input is the electronically represented contract content, usually structured data such as in JSON or XML format. This received data is converted into a form that can be analyzed for the contract. This conversion arranges the contract data into a form suitable for subsequent processing.

[0305] Step 2:

[0306] The server uses the converted contract data and uses natural language processing tools to analyze and extract the main components of the contract. The input is the contract data arranged in Step 1. The output is a list of components such as the purpose, rights, and obligations of the contract. This is based on natural language analysis techniques, performs syntactic and semantic analysis, and extracts the essence of the contract.

[0307] Step 3:

[0308] The server sends the extracted components to the terminal, where it evaluates the fairness and legal compliance of the contract. The input is a list of components. It compares them against legal databases and ethical guideline data to verify that the contract meets legal standards. The output is the evaluation result, identifying elements that are inadequate in terms of fairness or legal compliance.

[0309] Step 4:

[0310] The terminal automatically generates alternatives for contract elements deemed inappropriate based on the evaluation results. The input is the fairness evaluation result. By providing prompts to the generation AI model used, it generates improved proposals. The output is a set of alternatives. This results in new proposals that are fair and improved.

[0311] Step 5:

[0312] The user receives the alternative proposal generated on the terminal, reviews its contents, and makes revisions as needed. The input is the generated alternative proposal. The user reviews the improved contract and makes adjustments to reach an agreement. The output is the final agreed-upon contract proposal.

[0313] Step 6:

[0314] The terminal presents the final agreement to the AI ​​representing the other party, facilitating the formal agreement. The input is the agreed-upon contract proposal. Based on the presented proposal, the AI ​​representing the other party approves and modifies it to form the final agreement. The output is the mutually approved contract data.

[0315] Step 7:

[0316] The server stores the agreed-upon contract details in a database for use in future contract processes. The input is the final agreed-upon contract data. The data is saved and made available for external access and as a reference for future contracts. The output is the contract information recorded in the database.

[0317] 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.

[0318] This invention relates to a system that incorporates an emotion engine to recognize user emotions and support contract agreement formation when AIs enter into contracts with each other. This system has the function of accurately acquiring and analyzing contract information, and then monitoring the user's emotional state, enabling it to intervene in the agreement formation process with optimal suggestions and timing. Specific embodiments of this invention are shown below.

[0319] The server appropriately retrieves contract information between AIs from each database. The retrieved contract information is processed by the server and converted into a structured data format. This information is then sent to the terminal for contract analysis and evaluation.

[0320] The terminal analyzes the contract data received from the server using a natural language processing program. This analysis extracts the main elements of the contract, namely the terms, obligations, and rights. The terminal then evaluates the appropriateness of the contract by comparing these elements with laws and ethical guidelines, and generates improvement proposals as needed.

[0321] Furthermore, users are monitored by an emotion engine. The emotion engine analyzes emotional data obtained from the camera and microphone to understand the user's emotional state. This emotional data shows the user's reaction to proposed improvements and is used to support contract negotiations.

[0322] For example, in contract negotiations, if a user expresses anxiety or hesitation, the device adjusts the content and format of the proposal based on that emotional data, presenting a revised version that provides reassurance. This makes it possible to facilitate contract agreement formation based on the user's emotional state.

[0323] The server securely stores the newly agreed-upon contract information in a record database, making it available for future contract processes and reference. In this way, the present invention simultaneously achieves increased efficiency in AI-to-AI contract processes and improved user experience.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The server connects to the AI-to-AI contract database to retrieve the specified contract data. This contract data includes detailed information about the contract's purpose, terms, obligations, and rights. This data is then formatted and transmitted to the terminal.

[0327] Step 2:

[0328] The terminal analyzes contract data received from the server using natural language processing technology. During the analysis, key elements of the contract are extracted, and its purpose and conditions are identified. These elements are then organized and stored as structured data.

[0329] Step 3:

[0330] The server evaluates the fairness and impartiality of a contract by comparing the contract elements sent from the terminal with a legal database. During the evaluation, it utilizes current legal standards and ethical guidelines to verify compliance. If any deficiencies are found, the details are compiled into a report.

[0331] Step 4:

[0332] The terminal uses a generative AI to generate new improvement proposals for contract terms that need improvement based on the evaluation results. Multiple improvement proposals are created, and the merits and risks of each proposal are evaluated. This generation process may include changes to contract terms or the addition of new clauses.

[0333] Step 5:

[0334] The emotion engine continuously monitors the user's emotional state. The emotional data is analyzed, and if anxiety or misunderstanding is detected, the suggested improvements presented via the device are adjusted. This ensures that the optimal solution is presented based on the user's emotions.

[0335] Step 6:

[0336] The device presents the user with the most appropriate improvement plan based on the analysis results of the emotion engine. When presenting the plan, it selects the appropriate expression and timing, taking into account the user's emotional state. This facilitates the consensus-building process and improves the user's sense of satisfaction.

[0337] Step 7:

[0338] The server saves the final contract details, approved by the user, to the database and notifies each relevant system that the contract revision is complete. The newly agreed-upon contract is retained for future reference and can be used in other contract processes as needed.

[0339] (Example 2)

[0340] 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".

[0341] In modern contract negotiations, the complexity and ambiguity of contract terms often become obstacles to reaching an agreement. Furthermore, processes that disregard the feelings of the users involved can create unnecessary stress and misunderstandings before an agreement is reached. This situation leads to problems of inefficiency in contract negotiations and user dissatisfaction.

[0342] 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.

[0343] In this invention, the server includes means for acquiring contract information, means for analyzing the contract information and extracting key contract elements, and means for monitoring the user's emotions and adjusting the proposed content based on the emotional state. This enables rapid and smooth agreement formation that takes into account the user's emotional state while maintaining the fairness and impartiality of the contract.

[0344] "Contract information" refers to all data and documents related to a contract, including contract terms, obligations, and rights.

[0345] "Key contractual elements" refer to the fundamental elements of a contract, such as conditions, obligations, and rights, that form its core.

[0346] "Appropriateness" refers to demonstrating that a contract is structured fairly and justly in accordance with laws and ethics.

[0347] "Emotion monitoring" refers to the process of analyzing a user's emotions in real time from their facial expressions, voice, and other data to understand their emotional state.

[0348] "Adjusting the proposal" refers to modifying the wording, tone, and amount of information in the contract proposal according to the user's emotional state, thereby facilitating agreement.

[0349] A "recording medium" is a means of storing information for the safe and accurate preservation of contract information, and includes digital databases and the like.

[0350] "Agreement building" refers to the process by which all parties involved agree to the terms of the contract and formally conclude the agreement.

[0351] To implement this invention, a system is specifically constructed that supports contract conclusion between AIs while taking into account the user's emotions. The system mainly consists of three elements: a server, a terminal, and a user, and each element works together to support the contract process.

[0352] The server plays a central role in retrieving and processing contract information. It collects necessary contract information from the database and converts the retrieved data into a structured data format. In this process, the server accesses the database using the SQL language and converts the information into a list or JSON format. The structured data is then sent to the terminal, providing a foundation for further analysis.

[0353] The terminal analyzes the received contract information and extracts the key contract elements. The terminal has software libraries such as NLTK and spaCy installed for natural language processing. These libraries are used to extract and classify conditions, obligations, and rights from the contract document. The analyzed data is then compared against legal databases and ethical guidelines to verify the appropriateness of the contract content. Furthermore, a generative AI model is used to generate improvement suggestions if necessary and present them to the user.

[0354] The user is monitored by an emotion engine. The emotion engine utilizes facial recognition technology using OpenCV and voice emotion analysis technology using Praat to capture the user's facial expressions and voice tone through the camera and microphone. It then analyzes and digitizes this emotional state. If the user experiences anxiety or confusion, the device uses this data and a generative AI model to adjust the contract proposal.

[0355] For example, if a user expresses negative emotions, the device can modify the proposed contract terms or adjust the explanation to make it more approachable. An example of a prompt message would be, "Based on the user's emotional data, please adjust the contract proposal and generate a more reassuring version." This allows for smooth agreement building while respecting the user's emotions.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The server connects to the database to retrieve contract information. The input is a request for contract information, using an SQL query. The server executes the query, extracts contract-related information from the database, and outputs unstructured contract data. This organizes the information necessary for the contract for subsequent processing.

[0359] Step 2:

[0360] The server converts the acquired contract information into structured data. The input is the contract data obtained in step 1. The server organizes the information into a list or JSON format, and applies tags and hierarchical structures. The output is standardized structured data. This format conversion makes the contract data suitable for subsequent analysis on the terminal.

[0361] Step 3:

[0362] The terminal analyzes the structured data sent from the server and extracts the key contract elements. The input is the structured data from step 2. The terminal uses a natural language processing program, such as NLTK or spaCy, to analyze elements such as contract terms, obligations, and rights, and extracts these elements as a list as output. This process clarifies the core of the contract.

[0363] Step 4:

[0364] The terminal uses the analyzed contract elements to evaluate their appropriateness. The input is the contract elements extracted in step 3. The terminal evaluates them against legal databases and ethical guidelines, and outputs the appropriateness evaluation results. This confirms whether the contract content is legally and ethically appropriate.

[0365] Step 5:

[0366] The device generates improvement suggestions as needed based on the suitability assessment. The input is the evaluation results from step 4. Using the generation AI model, the system identifies items that need improvement in the presented content and constructs detailed improvement proposals, resulting in improvement suggestions as output. These suggestions are prepared for presentation to the user.

[0367] Step 6:

[0368] The user's emotional state is monitored by an emotion engine. The input is real-time facial and voice data from the user. Emotion analysis software processes the data collected through the camera and microphone, quantifying or classifying the user's emotional state. The output is the user's most recent emotional data. This data serves as an indicator of how the contract proposal should be adjusted.

[0369] Step 7:

[0370] The device adjusts the proposal based on the user's sentiment data. The input is the sentiment data from step 6. A generative AI model is used to adjust the tone and clarity of the contract proposal, presenting it in a way that best suits the user's situation. The output is a revised contract proposal that is more acceptable to the user.

[0371] Step 8:

[0372] The server stores the agreed-upon final contract information on a storage medium. The input is the agreed-upon draft contract. The server securely registers this information in a database, making it accessible as needed. The output is the secure contract record that is stored, preparing it for future reference and audits.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0375] In the contract negotiation process between artificial intelligence entities, users may experience anxiety or confusion, and these emotions can hinder smooth agreement. Conventional systems often fail to adjust the presentation of contract information to the user's level of understanding or emotional state, which can impair the user experience. Therefore, there is a need to dynamically adjust the way contract information is presented according to the user's emotional state to support optimal agreement.

[0376] 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.

[0377] In this invention, the server includes means for acquiring contract information, means for analyzing the contract information and extracting key contract elements, means for evaluating the appropriateness and fairness of the contract based on the extracted contract elements, and means for analyzing the user's emotions and adjusting the method of presenting the contract information according to the user's emotional state. This makes it possible to optimally present contract information based on the user's level of understanding and emotional state.

[0378] "Contract information" refers to information regarding the terms, rights, and obligations agreed upon between the parties in a transaction.

[0379] "Analysis" refers to the process of examining acquired data in detail to identify its structure and elements.

[0380] "Contractual elements" are the constituent elements of important information in a contract, such as the terms, obligations, and rights.

[0381] "Appropriateness" is an evaluation criterion that indicates whether the terms of a contract are legally correct and fair.

[0382] "Fairness" is a standard used to evaluate whether the burdens and benefits of a contract are distributed equally among the parties involved.

[0383] An "improvement proposal" is a specific suggestion to make the current contract terms more appropriate and fair.

[0384] "Agreement building" refers to the process of obtaining consent between the contracting parties.

[0385] "Emotion" refers to the emotional state a user exhibits when entering into a contract.

[0386] "Presentation method" refers to the format in which contract information is displayed to the user.

[0387] The system of this invention mainly consists of three elements: a server, a terminal, and a user.

[0388] The server has a program built to retrieve and analyze contract information. The server collects necessary contract information from multiple databases and processes it efficiently and accurately. Next, this information is used with natural language processing software (e.g., Google Cloud Natural Language API) to extract key contract elements.

[0389] The terminal receives extracted contract data transmitted from the server and evaluates the fairness and impartiality of the contract using legal documents and ethical guidelines. Based on the evaluation results, it has the function to generate and present improvement proposals to the user to make the contract fair and impartial. In this process, it uses the camera and microphone of a smartphone or smart glasses (e.g., Amazon Rekognition, Google Cloud Speech-to-Text) to analyze the user's emotions and adjust the information presented according to their emotional state.

[0390] Users are supported in reaching agreements through improvement suggestions presented on the terminal. This system helps users understand purchase conditions and benefits with confidence while making electronic payments. As a result, for example, if a user feels uneasy about the contract details, the system is designed to provide information in a more easily understandable format. The generating AI model provides appropriate agreement support to the user by instructing, for example, "Analyze the contract details and optimize the information presentation method based on current user sentiment data."

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The server retrieves contract information from the database. It receives a request containing contract information as input. It performs a database search and extracts the contract information. The output is the retrieved raw contract data.

[0394] Step 2:

[0395] The server analyzes the acquired contract information using natural language processing. The input is raw contract data. Using the Google Cloud Natural Language API, it extracts contract elements (terms, rights, obligations, etc.) and converts them into structured data. The output is structured contract element data.

[0396] Step 3:

[0397] The terminal receives structured data transmitted from the server and evaluates the fairness of the contract by comparing it with legal documents and ethical guidelines. The input is structured contract element data. Based on the evaluation algorithm, it determines legality and fairness. The output is the evaluation result and, if necessary, suggestions for improvement.

[0398] Step 4:

[0399] The device processes data collected from the camera and microphone to analyze the user's emotions. The input consists of data on the user's facial expressions and voice. Emotional states are identified using Amazon Rekognition or Google Cloud Speech-to-Text. The output is the user's emotional state information, which is the result of the analysis.

[0400] Step 5:

[0401] The terminal adjusts the way contract information is presented based on the user's emotional state. Inputs include emotional state information, evaluation results, and improvement suggestions. The system generates screens with diagrams and detailed explanations tailored to the user's level of understanding. The output is a contract information presentation screen optimized for the user.

[0402] Step 6:

[0403] The user makes decisions based on the information and improvement suggestions presented on the device and is supported in reaching a contract agreement. The input is an optimized contract information display screen. This allows the user to naturally proceed through a process that allows them to agree with confidence. The output is the user's final agreement or request for revisions.

[0404] Step 7:

[0405] The server securely stores the newly agreed-upon contract information in its record database. The input is the final agreed-upon data returned by the user. After going through the data storage process, the securely stored contract information is generated as output.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] This invention relates to a system for fairly and appropriately managing and guiding agreements between AIs in the conclusion of contracts. This system has the function of acquiring, analyzing, and evaluating various contract information, generating improvement proposals, and supporting agreement formation. Specific embodiments of this invention are shown below.

[0423] First, the server accesses a contract database between AIs to retrieve the necessary contract information. This information includes detailed details such as the purpose, terms, obligations, and rights of the contract. This information is then analyzed in detail in the next step.

[0424] Next, the terminal analyzes the acquired contract information using natural language processing technology. This analysis reveals the main components of the contract, namely its purpose, terms, and obligations. Based on the analysis, it evaluates whether there are any flaws or unfair elements in the contract.

[0425] The server then uses structured contract elements to perform an evaluation based on legal databases and ethical guidelines. It determines whether the contract is appropriate and whether any improvements are needed.

[0426] Based on the results of this evaluation, the device generates proposed improvements to the contract. This generation includes modifying contract clauses and re-evaluating conditions. The generated improvements are presented to the AI ​​of the other party to the contract, laying the groundwork for reaching an agreement.

[0427] Finally, the user participates by selecting or adjusting the proposed improvements. This facilitates smooth negotiations between the AIs until an agreement is reached. The final agreed-upon contract is saved in a database and used in future contract processes.

[0428] For example, in a contract for an AI-powered data analysis service, the server first retrieves the relevant contract. The terminal analyzes this contract and verifies whether the data usage conditions are excessively burdensome. If the server identifies inappropriate obligations on the data-providing AI as a result of the evaluation, the terminal automatically generates a new proposal, which the user reviews and sends revised versions to both AIs, thereby achieving agreement. In this way, the system enables fair and efficient contract negotiation between AIs.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The server accesses a database containing contract information between AIs and retrieves the necessary contract data using a specific contract ID or condition. After retrieving the data, the server converts the contract information into a dedicated format for processing and sends it to the terminal.

[0432] Step 2:

[0433] The terminal inputs contract data obtained from the server into a natural language processing module and performs text analysis. This analysis extracts the main elements that constitute the purpose, conditions, obligations, and rights of the contract, and sets them up to prepare for the next step.

[0434] Step 3:

[0435] The server uses the key elements of the contract provided by the terminal to perform an evaluation based on legal databases and ethical guidelines. Specifically, it compares the contract content with standards of fairness and impartiality to determine whether these standards are met. In this process, it identifies elements that appear inappropriate or unfair and passes them on to the next processing step.

[0436] Step 4:

[0437] The device uses AI to generate improvement proposals for contract terms deemed unsuitable or unfair. These improvement proposals become new contract documents that modify necessary conditions and clauses. It also creates multiple alternatives and organizes their advantages and risks as additional information.

[0438] Step 5:

[0439] The user reviews the generated improvement proposals and selects the best one from a practical and unbiased perspective. The selected proposal is automatically presented to the contracting party's AI, and the agreement-building process begins.

[0440] Step 6:

[0441] The server and terminals verify whether the proposal has been accepted and finalize the agreed-upon contract terms. The agreement is stored in the record management system and used for future reference and audits.

[0442] (Example 1)

[0443] 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."

[0444] In modern contract processes, automated contract signing between AIs presents challenges in determining whether the contract terms are technically and ethically sound. This creates a risk that contracts may deviate from societal laws and ethical standards. Furthermore, there is a lack of mechanisms to quickly generate corrective measures when inappropriate contract terms exist. To address these issues, a system is needed that guarantees the fairness and impartiality of contracts and supports efficient consensus building.

[0445] 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.

[0446] In this invention, the server includes an information processing device for acquiring contract information, a data processing means for analyzing the contract information and extracting key components, and an evaluation means for evaluating the appropriateness and fairness of the contract based on the extracted components. This allows for verification of whether the contract content conforms to laws and ethical standards, and enables the rapid generation of improvement proposals if inappropriate contract conditions exist, thereby facilitating consensus building.

[0447] "Contract information" refers to a collection of data that includes details such as the purpose, terms, obligations, and rights of a contract.

[0448] An "information processing device" is a device that accesses a database and retrieves and stores necessary information.

[0449] A "data processing means" is a system that uses natural language processing technology to analyze contract information and extract necessary information.

[0450] An "evaluation tool" is a mechanism that evaluates whether the content of a contract conforms to laws and ethical standards based on the extracted components of the contract.

[0451] A "generative AI model" is an artificial intelligence technology that automatically generates new suggestions and options from existing information.

[0452] "Improvement generation means" refers to a function that includes the process of forming proposed revisions to contract terms and improvement measures based on evaluation results.

[0453] A "presentation method" refers to a method for clearly showing the generated improvement proposals to users and supporting consensus building.

[0454] "Legal information" refers to information contained in databases related to laws and regulations, and serves as a standard for evaluating the legality of a contract.

[0455] "Ethical guidelines" are guidelines that outline social and professional behavioral norms and serve as standards for evaluating the ethical appropriateness of contracts.

[0456] A "contracting party" is a participant whose purpose is to reach an agreement in a contract, and is usually the entity that accepts the terms of the contract.

[0457] The embodiments for carrying out this invention are shown below.

[0458] The server first accesses the database that manages contract information and retrieves it. This information retrieval uses a standard database management system and SQL queries to identify and retrieve the information. At this point, details such as the purpose, terms, obligations, and rights of the contract are collected.

[0459] Next, the terminal uses natural language processing software such as Python's NLTK library or spaCy to analyze the acquired contract information. During the analysis process, it extracts and classifies the main components of the contract. The analysis also provides a function to detect whether there are any deficiencies or unfairness in the contract clauses.

[0460] Subsequently, the server uses the extracted components to compare them against existing legal databases and ethical guidelines. This evaluation process includes a compliance check to ensure that the contract terms conform to various regulations and social standards.

[0461] Based on the evaluation results, the device automatically generates improvement proposals using a generative AI model. These proposals may include modifications to contract terms or the addition of new clauses. This process utilizes natural-sounding text generation by the AI ​​model. Specific examples of such proposals include "relaxing restrictions on data usage."

[0462] Users review the proposed improvements and make manual adjustments as needed. This allows for faster formation of joint agreements among the AIs. Users directly manipulate the improvements through a dedicated GUI and save them to the database after finalizing the agreement.

[0463] Examples of prompts include, "Please explain the process of a system that performs fair evaluations of contracts between AIs and generates improvement proposals when unfair elements are found." This system provides a solid foundation for more efficient and fair contracts between AIs.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The server retrieves contract information. The input includes the ID of the required contract and related information. Based on this information, the server executes an SQL query to retrieve contract information from the contract database, including the purpose, terms, obligations, and rights of the relevant contract. The output is returned to the server in a structured data format containing the contract details.

[0467] Step 2:

[0468] The terminal analyzes the acquired contract information. The contract information acquired in step 1 is used as input. Natural language processing techniques are used to extract key contract elements from the contract information. Specifically, Python's NLTK library and spaCy are used to analyze each clause of the contract and tag the necessary parts. The output is a list of the extracted key contract elements.

[0469] Step 3:

[0470] The server evaluates the contract based on its structured contract elements. The input is the analysis results from Step 2. It compares the contract against existing legal databases and ethical guidelines to determine its legal compliance and ethical integrity. This evaluation identifies any problematic elements. The output is an evaluation report indicating the appropriateness of the contract and whether any improvements are necessary.

[0471] Step 4:

[0472] The terminal uses a generative AI model to create improvement proposals. The evaluation report from Step 3 is used as input. The AI ​​model automatically detects areas of the contract that need improvement and generates new clauses and conditions. This process utilizes a generative AI model such as GPT-3. The output presents improvement proposals and generates a new draft contract.

[0473] Step 5:

[0474] The user reviews and adjusts the suggested improvements from the terminal. The input provided is the suggested improvements generated in step 4. The user scrutinizes the suggestions on the GUI and makes manual modifications as needed. The output is the final confirmed contract details, which are used for the final agreement between the AIs.

[0475] In this way, a process is implemented through a series of processing steps to ensure that contracts between AIs are concluded fairly and equitably.

[0476] (Application Example 1)

[0477] 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."

[0478] Contracts concluded between AIs often face challenges regarding fairness and legality, and therefore require improvement. In particular, in situations requiring complex and rapid contract negotiations, such as electronic payment services, the analysis and refinement of contract content are often performed manually, which is inefficient. It is necessary to eliminate the resulting unfairness and inefficiency and to conclude contracts between AIs more quickly and fairly.

[0479] 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.

[0480] In this invention, the server includes means for receiving contract details, means for analyzing the contract details to detect key components, and means for evaluating the fairness and legal compliance of the contract based on the detected components. This makes it possible to automatically analyze contracts between AIs in detail and quickly present improvement suggestions. This system improves the efficiency of consensus building and the fairness of contract conclusion.

[0481] "Contract details" refers to all data related to agreements made between AIs.

[0482] "Analysis" involves examining the contents of a contract in detail, understanding its components and meaning, and gaining new insights.

[0483] "Components" refer to parts or elements that play an important role in a contract, and are the central information of the agreed-upon terms.

[0484] "Fairness" is a standard that indicates whether a contract is concluded fairly and without bias for both parties.

[0485] "Compliance with laws and regulations" refers to a state in which a contract complies with current laws and regulations.

[0486] An "alternative" is another option proposed to improve or modify the terms of an existing contract.

[0487] "Agreement building" refers to the process by which the parties to a contract understand and agree to its contents and formally approve them.

[0488] This invention is a system for efficiently and fairly concluding contracts between AIs. This system is realized through the collaboration of a server, terminals, and users.

[0489] The server first receives the contract details via the network. These details include the purpose, terms, and rights of the contract. Next, the server analyzes the contract details, identifying the key components in detail. The software used here is a natural language processing tool for contract analysis, utilizing several commercially available libraries.

[0490] The terminal evaluates the fairness and legal compliance of the contract based on the analysis results received from the server. This evaluation utilizes legal information sources and ethical guideline databases to confirm that the contract is fair and lawful. If the evaluation results indicate that a contract element is inappropriate, the terminal generates alternative proposals.

[0491] Once alternative proposals are generated, users can review them and use them to facilitate consensus building. Users can then adjust the contract terms as needed while reviewing the generated alternatives and finalize the agreement.

[0492] As a concrete example, consider a digital wallet contract between financial institutions. This system analyzes the contract terms proposed by the AIs of both parties, detects and corrects any unfair elements regarding data sharing, thereby achieving a fairer contract for both sides.

[0493] The following are specific examples of prompt statements for a generative AI model.

[0494] "Analyze the following contract data, identify any unfair elements, and propose improvements. Contract Data: Enter contract details in text format."

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The server receives raw contract data over the network. The input is electronically represented contract content, usually in structured data format such as JSON or XML. This received data is converted into a format suitable for contract analysis. This conversion prepares the contract data for subsequent processing.

[0498] Step 2:

[0499] The server uses the converted contract data to analyze and extract the main components of the contract using natural language processing tools. The input is the contract data prepared in step 1. The output is a list of components such as the purpose, rights, and obligations of the contract. This is then analyzed using natural language processing techniques, including grammatical and semantic analysis, to extract the essence of the contract.

[0500] Step 3:

[0501] The server sends the extracted components to the terminal, where it evaluates the fairness and legal compliance of the contract. The input is a list of components. It compares them against legal databases and ethical guideline data to verify that the contract meets legal standards. The output is the evaluation result, identifying elements that are inadequate in terms of fairness or legal compliance.

[0502] Step 4:

[0503] The terminal automatically generates alternatives for contract elements deemed inappropriate based on the evaluation results. The input is the fairness evaluation result. By providing prompts to the generation AI model used, it generates improved proposals. The output is a set of alternatives. This results in new proposals that are fair and improved.

[0504] Step 5:

[0505] The user receives the alternative proposal generated on the terminal, reviews its contents, and makes revisions as needed. The input is the generated alternative proposal. The user reviews the improved contract and makes adjustments to reach an agreement. The output is the final agreed-upon contract proposal.

[0506] Step 6:

[0507] The terminal presents the final agreement to the AI ​​representing the other party, facilitating the formal agreement. The input is the agreed-upon contract proposal. Based on the presented proposal, the AI ​​representing the other party approves and modifies it to form the final agreement. The output is the mutually approved contract data.

[0508] Step 7:

[0509] The server stores the agreed-upon contract details in a database for use in future contract processes. The input is the final agreed-upon contract data. The data is saved and made available for external access and as a reference for future contracts. The output is the contract information recorded in the database.

[0510] 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.

[0511] This invention relates to a system that incorporates an emotion engine to recognize user emotions and support contract agreement formation when AIs enter into contracts with each other. This system has the function of accurately acquiring and analyzing contract information, and then monitoring the user's emotional state, enabling it to intervene in the agreement formation process with optimal suggestions and timing. Specific embodiments of this invention are shown below.

[0512] The server appropriately retrieves contract information between AIs from each database. The retrieved contract information is processed by the server and converted into a structured data format. This information is then sent to the terminal for contract analysis and evaluation.

[0513] The terminal analyzes the contract data received from the server using a natural language processing program. This analysis extracts the main elements of the contract, namely the terms, obligations, and rights. The terminal then evaluates the appropriateness of the contract by comparing these elements with laws and ethical guidelines, and generates improvement proposals as needed.

[0514] Furthermore, users are monitored by an emotion engine. The emotion engine analyzes emotional data obtained from the camera and microphone to understand the user's emotional state. This emotional data shows the user's reaction to proposed improvements and is used to support contract negotiations.

[0515] For example, in contract negotiations, if a user expresses anxiety or hesitation, the device adjusts the content and format of the proposal based on that emotional data, presenting a revised version that provides reassurance. This makes it possible to facilitate contract agreement formation based on the user's emotional state.

[0516] The server securely stores the newly agreed-upon contract information in a record database, making it available for future contract processes and reference. In this way, the present invention simultaneously achieves increased efficiency in AI-to-AI contract processes and improved user experience.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The server connects to the AI-to-AI contract database to retrieve the specified contract data. This contract data includes detailed information about the contract's purpose, terms, obligations, and rights. This data is then formatted and transmitted to the terminal.

[0520] Step 2:

[0521] The terminal analyzes contract data received from the server using natural language processing technology. During the analysis, key elements of the contract are extracted, and its purpose and conditions are identified. These elements are then organized and stored as structured data.

[0522] Step 3:

[0523] The server evaluates the fairness and impartiality of a contract by comparing the contract elements sent from the terminal with a legal database. During the evaluation, it utilizes current legal standards and ethical guidelines to verify compliance. If any deficiencies are found, the details are compiled into a report.

[0524] Step 4:

[0525] The terminal uses a generative AI to generate new improvement proposals for contract terms that need improvement based on the evaluation results. Multiple improvement proposals are created, and the merits and risks of each proposal are evaluated. This generation process may include changes to contract terms or the addition of new clauses.

[0526] Step 5:

[0527] The emotion engine continuously monitors the user's emotional state. The emotional data is analyzed, and if anxiety or misunderstanding is detected, the suggested improvements presented via the device are adjusted. This ensures that the optimal solution is presented based on the user's emotions.

[0528] Step 6:

[0529] The device presents the user with the most appropriate improvement plan based on the analysis results of the emotion engine. When presenting the plan, it selects the appropriate expression and timing, taking into account the user's emotional state. This facilitates the consensus-building process and improves the user's sense of satisfaction.

[0530] Step 7:

[0531] The server saves the final contract details, approved by the user, to the database and notifies each relevant system that the contract revision is complete. The newly agreed-upon contract is retained for future reference and can be used in other contract processes as needed.

[0532] (Example 2)

[0533] 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."

[0534] In modern contract negotiations, the complexity and ambiguity of contract terms often become obstacles to reaching an agreement. Furthermore, processes that disregard the feelings of the users involved can create unnecessary stress and misunderstandings before an agreement is reached. This situation leads to problems of inefficiency in contract negotiations and user dissatisfaction.

[0535] 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.

[0536] In this invention, the server includes means for acquiring contract information, means for analyzing the contract information and extracting key contract elements, and means for monitoring the user's emotions and adjusting the proposed content based on the emotional state. This enables rapid and smooth agreement formation that takes into account the user's emotional state while maintaining the fairness and impartiality of the contract.

[0537] "Contract information" refers to all data and documents related to a contract, including contract terms, obligations, and rights.

[0538] "Key contractual elements" refer to the fundamental elements of a contract, such as conditions, obligations, and rights, that form its core.

[0539] "Appropriateness" refers to demonstrating that a contract is structured fairly and justly in accordance with laws and ethics.

[0540] "Emotion monitoring" refers to the process of analyzing a user's emotions in real time from their facial expressions, voice, and other data to understand their emotional state.

[0541] "Adjusting the proposal" refers to modifying the wording, tone, and amount of information in the contract proposal according to the user's emotional state, thereby facilitating agreement.

[0542] A "recording medium" is a means of storing information for the safe and accurate preservation of contract information, and includes digital databases and the like.

[0543] "Agreement building" refers to the process by which all parties involved agree to the terms of the contract and formally conclude the agreement.

[0544] To implement this invention, a system is specifically constructed that supports contract conclusion between AIs while taking into account the user's emotions. The system mainly consists of three elements: a server, a terminal, and a user, and each element works together to support the contract process.

[0545] The server plays a central role in retrieving and processing contract information. It collects necessary contract information from the database and converts the retrieved data into a structured data format. In this process, the server accesses the database using the SQL language and converts the information into a list or JSON format. The structured data is then sent to the terminal, providing a foundation for further analysis.

[0546] The terminal analyzes the received contract information and extracts the key contract elements. The terminal has software libraries such as NLTK and spaCy installed for natural language processing. These libraries are used to extract and classify conditions, obligations, and rights from the contract document. The analyzed data is then compared against legal databases and ethical guidelines to verify the appropriateness of the contract content. Furthermore, a generative AI model is used to generate improvement suggestions if necessary and present them to the user.

[0547] The user is monitored by an emotion engine. The emotion engine utilizes facial recognition technology using OpenCV and voice emotion analysis technology using Praat to capture the user's facial expressions and voice tone through the camera and microphone. It then analyzes and digitizes this emotional state. If the user experiences anxiety or confusion, the device uses this data and a generative AI model to adjust the contract proposal.

[0548] For example, if a user expresses negative emotions, the device can modify the proposed contract terms or adjust the explanation to make it more approachable. An example of a prompt message would be, "Based on the user's emotional data, please adjust the contract proposal and generate a more reassuring version." This allows for smooth agreement building while respecting the user's emotions.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The server connects to the database to retrieve contract information. The input is a request for contract information, using an SQL query. The server executes the query, extracts contract-related information from the database, and outputs unstructured contract data. This organizes the information necessary for the contract for subsequent processing.

[0552] Step 2:

[0553] The server converts the acquired contract information into structured data. The input is the contract data obtained in step 1. The server organizes the information into a list or JSON format, and applies tags and hierarchical structures. The output is standardized structured data. This format conversion makes the contract data suitable for subsequent analysis on the terminal.

[0554] Step 3:

[0555] The terminal analyzes the structured data sent from the server and extracts the key contract elements. The input is the structured data from step 2. The terminal uses a natural language processing program, such as NLTK or spaCy, to analyze elements such as contract terms, obligations, and rights, and extracts these elements as a list as output. This process clarifies the core of the contract.

[0556] Step 4:

[0557] The terminal uses the analyzed contract elements to evaluate their appropriateness. The input is the contract elements extracted in step 3. The terminal evaluates them against legal databases and ethical guidelines, and outputs the appropriateness evaluation results. This confirms whether the contract content is legally and ethically appropriate.

[0558] Step 5:

[0559] The device generates improvement suggestions as needed based on the suitability assessment. The input is the evaluation results from step 4. Using the generation AI model, the system identifies items that need improvement in the presented content and constructs detailed improvement proposals, resulting in improvement suggestions as output. These suggestions are prepared for presentation to the user.

[0560] Step 6:

[0561] The user's emotional state is monitored by an emotion engine. The input is real-time facial and voice data from the user. Emotion analysis software processes the data collected through the camera and microphone, quantifying or classifying the user's emotional state. The output is the user's most recent emotional data. This data serves as an indicator of how the contract proposal should be adjusted.

[0562] Step 7:

[0563] The device adjusts the proposal based on the user's sentiment data. The input is the sentiment data from step 6. A generative AI model is used to adjust the tone and clarity of the contract proposal, presenting it in a way that best suits the user's situation. The output is a revised contract proposal that is more acceptable to the user.

[0564] Step 8:

[0565] The server stores the agreed-upon final contract information on a storage medium. The input is the agreed-upon draft contract. The server securely registers this information in a database, making it accessible as needed. The output is the secure contract record that is stored, preparing it for future reference and audits.

[0566] (Application Example 2)

[0567] 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."

[0568] In the contract negotiation process between artificial intelligence entities, users may experience anxiety or confusion, and these emotions can hinder smooth agreement. Conventional systems often fail to adjust the presentation of contract information to the user's level of understanding or emotional state, which can impair the user experience. Therefore, there is a need to dynamically adjust the way contract information is presented according to the user's emotional state to support optimal agreement.

[0569] 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.

[0570] In this invention, the server includes means for acquiring contract information, means for analyzing the contract information and extracting key contract elements, means for evaluating the appropriateness and fairness of the contract based on the extracted contract elements, and means for analyzing the user's emotions and adjusting the method of presenting the contract information according to the user's emotional state. This makes it possible to optimally present contract information based on the user's level of understanding and emotional state.

[0571] "Contract information" refers to information regarding the terms, rights, and obligations agreed upon between the parties in a transaction.

[0572] "Analysis" refers to the process of examining acquired data in detail to identify its structure and elements.

[0573] "Contractual elements" are the constituent elements of important information in a contract, such as the terms, obligations, and rights.

[0574] "Appropriateness" is an evaluation criterion that indicates whether the terms of a contract are legally correct and fair.

[0575] "Fairness" is a standard used to evaluate whether the burdens and benefits of a contract are distributed equally among the parties involved.

[0576] An "improvement proposal" is a specific suggestion to make the current contract terms more appropriate and fair.

[0577] "Agreement building" refers to the process of obtaining consent between the contracting parties.

[0578] "Emotion" refers to the emotional state a user exhibits when entering into a contract.

[0579] "Presentation method" refers to the format in which contract information is displayed to the user.

[0580] The system of this invention mainly consists of three elements: a server, a terminal, and a user.

[0581] The server has a program built to retrieve and analyze contract information. The server collects necessary contract information from multiple databases and processes it efficiently and accurately. Next, this information is used with natural language processing software (e.g., Google Cloud Natural Language API) to extract key contract elements.

[0582] The terminal receives extracted contract data transmitted from the server and evaluates the fairness and impartiality of the contract using legal documents and ethical guidelines. Based on the evaluation results, it has the function to generate and present improvement proposals to the user to make the contract fair and impartial. In this process, it uses the camera and microphone of a smartphone or smart glasses (e.g., Amazon Rekognition, Google Cloud Speech-to-Text) to analyze the user's emotions and adjust the information presented according to their emotional state.

[0583] Users are supported in reaching agreements through improvement suggestions presented on the terminal. This system helps users understand purchase conditions and benefits with confidence while making electronic payments. As a result, for example, if a user feels uneasy about the contract details, the system is designed to provide information in a more easily understandable format. The generating AI model provides appropriate agreement support to the user by instructing, for example, "Analyze the contract details and optimize the information presentation method based on current user sentiment data."

[0584] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0585] Step 1:

[0586] The server retrieves contract information from the database. It receives a request containing contract information as input. It performs a database search and extracts the contract information. The output is the retrieved raw contract data.

[0587] Step 2:

[0588] The server analyzes the acquired contract information using natural language processing. The input is raw contract data. Using the Google Cloud Natural Language API, it extracts contract elements (terms, rights, obligations, etc.) and converts them into structured data. The output is structured contract element data.

[0589] Step 3:

[0590] The terminal receives structured data transmitted from the server and evaluates the fairness of the contract by comparing it with legal documents and ethical guidelines. The input is structured contract element data. Based on the evaluation algorithm, it determines legality and fairness. The output is the evaluation result and, if necessary, suggestions for improvement.

[0591] Step 4:

[0592] The device processes data collected from the camera and microphone to analyze the user's emotions. The input consists of data on the user's facial expressions and voice. Emotional states are identified using Amazon Rekognition or Google Cloud Speech-to-Text. The output is the user's emotional state information, which is the result of the analysis.

[0593] Step 5:

[0594] The terminal adjusts the way contract information is presented based on the user's emotional state. Inputs include emotional state information, evaluation results, and improvement suggestions. The system generates screens with diagrams and detailed explanations tailored to the user's level of understanding. The output is a contract information presentation screen optimized for the user.

[0595] Step 6:

[0596] The user makes decisions based on the information and improvement suggestions presented on the device and is supported in reaching a contract agreement. The input is an optimized contract information display screen. This allows the user to naturally proceed through a process that allows them to agree with confidence. The output is the user's final agreement or request for revisions.

[0597] Step 7:

[0598] The server securely stores the newly agreed-upon contract information in its record database. The input is the final agreed-upon data returned by the user. After going through the data storage process, the securely stored contract information is generated as output.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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".

[0616] This invention relates to a system for fairly and appropriately managing and guiding agreements between AIs in the conclusion of contracts. This system has the function of acquiring, analyzing, and evaluating various contract information, generating improvement proposals, and supporting agreement formation. Specific embodiments of this invention are shown below.

[0617] First, the server accesses a contract database between AIs to retrieve the necessary contract information. This information includes detailed details such as the purpose, terms, obligations, and rights of the contract. This information is then analyzed in detail in the next step.

[0618] Next, the terminal analyzes the acquired contract information using natural language processing technology. This analysis reveals the main components of the contract, namely its purpose, terms, and obligations. Based on the analysis, it evaluates whether there are any flaws or unfair elements in the contract.

[0619] The server then uses structured contract elements to perform an evaluation based on legal databases and ethical guidelines. It determines whether the contract is appropriate and whether any improvements are needed.

[0620] Based on the results of this evaluation, the device generates proposed improvements to the contract. This generation includes modifying contract clauses and re-evaluating conditions. The generated improvements are presented to the AI ​​of the other party to the contract, laying the groundwork for reaching an agreement.

[0621] Finally, the user participates by selecting or adjusting the proposed improvements. This facilitates smooth negotiations between the AIs until an agreement is reached. The final agreed-upon contract is saved in a database and used in future contract processes.

[0622] For example, in a contract for an AI-powered data analysis service, the server first retrieves the relevant contract. The terminal analyzes this contract and verifies whether the data usage conditions are excessively burdensome. If the server identifies inappropriate obligations on the data-providing AI as a result of the evaluation, the terminal automatically generates a new proposal, which the user reviews and sends revised versions to both AIs, thereby achieving agreement. In this way, the system enables fair and efficient contract negotiation between AIs.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The server accesses a database containing contract information between AIs and retrieves the necessary contract data using a specific contract ID or condition. After retrieving the data, the server converts the contract information into a dedicated format for processing and sends it to the terminal.

[0626] Step 2:

[0627] The terminal inputs contract data obtained from the server into a natural language processing module and performs text analysis. This analysis extracts the main elements that constitute the purpose, conditions, obligations, and rights of the contract, and sets them up to prepare for the next step.

[0628] Step 3:

[0629] The server uses the key elements of the contract provided by the terminal to perform an evaluation based on legal databases and ethical guidelines. Specifically, it compares the contract content with standards of fairness and impartiality to determine whether these standards are met. In this process, it identifies elements that appear inappropriate or unfair and passes them on to the next processing step.

[0630] Step 4:

[0631] The device uses AI to generate improvement proposals for contract terms deemed unsuitable or unfair. These improvement proposals become new contract documents that modify necessary conditions and clauses. It also creates multiple alternatives and organizes their advantages and risks as additional information.

[0632] Step 5:

[0633] The user reviews the generated improvement proposals and selects the best one from a practical and unbiased perspective. The selected proposal is automatically presented to the contracting party's AI, and the agreement-building process begins.

[0634] Step 6:

[0635] The server and terminals verify whether the proposal has been accepted and finalize the agreed-upon contract terms. The agreement is stored in the record management system and used for future reference and audits.

[0636] (Example 1)

[0637] 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".

[0638] In modern contract processes, automated contract signing between AIs presents challenges in determining whether the contract terms are technically and ethically sound. This creates a risk that contracts may deviate from societal laws and ethical standards. Furthermore, there is a lack of mechanisms to quickly generate corrective measures when inappropriate contract terms exist. To address these issues, a system is needed that guarantees the fairness and impartiality of contracts and supports efficient consensus building.

[0639] 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.

[0640] In this invention, the server includes an information processing device for acquiring contract information, a data processing means for analyzing the contract information and extracting key components, and an evaluation means for evaluating the appropriateness and fairness of the contract based on the extracted components. This allows for verification of whether the contract content conforms to laws and ethical standards, and enables the rapid generation of improvement proposals if inappropriate contract conditions exist, thereby facilitating consensus building.

[0641] "Contract information" refers to a collection of data that includes details such as the purpose, terms, obligations, and rights of a contract.

[0642] An "information processing device" is a device that accesses a database and retrieves and stores necessary information.

[0643] A "data processing means" is a system that uses natural language processing technology to analyze contract information and extract necessary information.

[0644] An "evaluation tool" is a mechanism that evaluates whether the content of a contract conforms to laws and ethical standards based on the extracted components of the contract.

[0645] A "generative AI model" is an artificial intelligence technology that automatically generates new suggestions and options from existing information.

[0646] "Improvement generation means" refers to a function that includes the process of forming proposed revisions to contract terms and improvement measures based on evaluation results.

[0647] A "presentation method" refers to a method for clearly showing the generated improvement proposals to users and supporting consensus building.

[0648] "Legal information" refers to information contained in databases related to laws and regulations, and serves as a standard for evaluating the legality of a contract.

[0649] "Ethical guidelines" are guidelines that outline social and professional behavioral norms and serve as standards for evaluating the ethical appropriateness of contracts.

[0650] A "contracting party" is a participant whose purpose is to reach an agreement in a contract, and is usually the entity that accepts the terms of the contract.

[0651] The embodiments for carrying out this invention are shown below.

[0652] The server first accesses the database that manages contract information and retrieves it. This information retrieval uses a standard database management system and SQL queries to identify and retrieve the information. At this point, details such as the purpose, terms, obligations, and rights of the contract are collected.

[0653] Next, the terminal uses natural language processing software such as Python's NLTK library or spaCy to analyze the acquired contract information. During the analysis process, it extracts and classifies the main components of the contract. The analysis also provides a function to detect whether there are any deficiencies or unfairness in the contract clauses.

[0654] Subsequently, the server uses the extracted components to compare them against existing legal databases and ethical guidelines. This evaluation process includes a compliance check to ensure that the contract terms conform to various regulations and social standards.

[0655] Based on the evaluation results, the device automatically generates improvement proposals using a generative AI model. These proposals may include modifications to contract terms or the addition of new clauses. This process utilizes natural-sounding text generation by the AI ​​model. Specific examples of such proposals include "relaxing restrictions on data usage."

[0656] Users review the proposed improvements and make manual adjustments as needed. This allows for faster formation of joint agreements among the AIs. Users directly manipulate the improvements through a dedicated GUI and save them to the database after finalizing the agreement.

[0657] Examples of prompts include, "Please explain the process of a system that performs fair evaluations of contracts between AIs and generates improvement proposals when unfair elements are found." This system provides a solid foundation for more efficient and fair contracts between AIs.

[0658] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0659] Step 1:

[0660] The server retrieves contract information. The input includes the ID of the required contract and related information. Based on this information, the server executes an SQL query to retrieve contract information from the contract database, including the purpose, terms, obligations, and rights of the relevant contract. The output is returned to the server in a structured data format containing the contract details.

[0661] Step 2:

[0662] The terminal analyzes the acquired contract information. The contract information acquired in step 1 is used as input. Natural language processing techniques are used to extract key contract elements from the contract information. Specifically, Python's NLTK library and spaCy are used to analyze each clause of the contract and tag the necessary parts. The output is a list of the extracted key contract elements.

[0663] Step 3:

[0664] The server evaluates the contract based on its structured contract elements. The input is the analysis results from Step 2. It compares the contract against existing legal databases and ethical guidelines to determine its legal compliance and ethical integrity. This evaluation identifies any problematic elements. The output is an evaluation report indicating the appropriateness of the contract and whether any improvements are necessary.

[0665] Step 4:

[0666] The terminal uses a generative AI model to create improvement proposals. The evaluation report from Step 3 is used as input. The AI ​​model automatically detects areas of the contract that need improvement and generates new clauses and conditions. This process utilizes a generative AI model such as GPT-3. The output presents improvement proposals and generates a new draft contract.

[0667] Step 5:

[0668] The user reviews and adjusts the suggested improvements from the terminal. The input provided is the suggested improvements generated in step 4. The user scrutinizes the suggestions on the GUI and makes manual modifications as needed. The output is the final confirmed contract details, which are used for the final agreement between the AIs.

[0669] In this way, a process is implemented through a series of processing steps to ensure that contracts between AIs are concluded fairly and equitably.

[0670] (Application Example 1)

[0671] 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".

[0672] Contracts concluded between AIs often face challenges regarding fairness and legality, and therefore require improvement. In particular, in situations requiring complex and rapid contract negotiations, such as electronic payment services, the analysis and refinement of contract content are often performed manually, which is inefficient. It is necessary to eliminate the resulting unfairness and inefficiency and to conclude contracts between AIs more quickly and fairly.

[0673] 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.

[0674] In this invention, the server includes means for receiving contract details, means for analyzing the contract details to detect key components, and means for evaluating the fairness and legal compliance of the contract based on the detected components. This makes it possible to automatically analyze contracts between AIs in detail and quickly present improvement suggestions. This system improves the efficiency of consensus building and the fairness of contract conclusion.

[0675] "Contract details" refers to all data related to agreements made between AIs.

[0676] "Analysis" involves examining the contents of a contract in detail, understanding its components and meaning, and gaining new insights.

[0677] "Components" refer to parts or elements that play an important role in a contract, and are the central information of the agreed-upon terms.

[0678] "Fairness" is a standard that indicates whether a contract is concluded fairly and without bias for both parties.

[0679] "Compliance with laws and regulations" refers to a state in which a contract complies with current laws and regulations.

[0680] An "alternative" is another option proposed to improve or modify the terms of an existing contract.

[0681] "Agreement building" refers to the process by which the parties to a contract understand and agree to its contents and formally approve them.

[0682] This invention is a system for efficiently and fairly concluding contracts between AIs. This system is realized through the collaboration of a server, terminals, and users.

[0683] The server first receives the contract details via the network. These details include the purpose, terms, and rights of the contract. Next, the server analyzes the contract details, identifying the key components in detail. The software used here is a natural language processing tool for contract analysis, utilizing several commercially available libraries.

[0684] The terminal evaluates the fairness and legal compliance of the contract based on the analysis results received from the server. This evaluation utilizes legal information sources and ethical guideline databases to confirm that the contract is fair and lawful. If the evaluation results indicate that a contract element is inappropriate, the terminal generates alternative proposals.

[0685] Once alternative proposals are generated, users can review them and use them to facilitate consensus building. Users can then adjust the contract terms as needed while reviewing the generated alternatives and finalize the agreement.

[0686] As a concrete example, consider a digital wallet contract between financial institutions. This system analyzes the contract terms proposed by the AIs of both parties, detects and corrects any unfair elements regarding data sharing, thereby achieving a fairer contract for both sides.

[0687] The following are specific examples of prompt statements for a generative AI model.

[0688] "Analyze the following contract data, identify any unfair elements, and propose improvements. Contract Data: Enter contract details in text format."

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The server receives raw contract data over the network. The input is electronically represented contract content, usually in structured data format such as JSON or XML. This received data is converted into a format suitable for contract analysis. This conversion prepares the contract data for subsequent processing.

[0692] Step 2:

[0693] The server uses the converted contract data to analyze and extract the main components of the contract using natural language processing tools. The input is the contract data prepared in step 1. The output is a list of components such as the purpose, rights, and obligations of the contract. This is then analyzed using natural language processing techniques, including grammatical and semantic analysis, to extract the essence of the contract.

[0694] Step 3:

[0695] The server sends the extracted components to the terminal, where it evaluates the fairness and legal compliance of the contract. The input is a list of components. It compares them against legal databases and ethical guideline data to verify that the contract meets legal standards. The output is the evaluation result, identifying elements that are inadequate in terms of fairness or legal compliance.

[0696] Step 4:

[0697] The terminal automatically generates alternatives for contract elements deemed inappropriate based on the evaluation results. The input is the fairness evaluation result. By providing prompts to the generation AI model used, it generates improved proposals. The output is a set of alternatives. This results in new proposals that are fair and improved.

[0698] Step 5:

[0699] The user receives the alternative proposal generated on the terminal, reviews its contents, and makes revisions as needed. The input is the generated alternative proposal. The user reviews the improved contract and makes adjustments to reach an agreement. The output is the final agreed-upon contract proposal.

[0700] Step 6:

[0701] The terminal presents the final agreement to the AI ​​representing the other party, facilitating the formal agreement. The input is the agreed-upon contract proposal. Based on the presented proposal, the AI ​​representing the other party approves and modifies it to form the final agreement. The output is the mutually approved contract data.

[0702] Step 7:

[0703] The server stores the agreed-upon contract details in a database for use in future contract processes. The input is the final agreed-upon contract data. The data is saved and made available for external access and as a reference for future contracts. The output is the contract information recorded in the database.

[0704] 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.

[0705] This invention relates to a system that incorporates an emotion engine to recognize user emotions and support contract agreement formation when AIs enter into contracts with each other. This system has the function of accurately acquiring and analyzing contract information, and then monitoring the user's emotional state, enabling it to intervene in the agreement formation process with optimal suggestions and timing. Specific embodiments of this invention are shown below.

[0706] The server appropriately retrieves contract information between AIs from each database. The retrieved contract information is processed by the server and converted into a structured data format. This information is then sent to the terminal for contract analysis and evaluation.

[0707] The terminal analyzes the contract data received from the server using a natural language processing program. This analysis extracts the main elements of the contract, namely the terms, obligations, and rights. The terminal then evaluates the appropriateness of the contract by comparing these elements with laws and ethical guidelines, and generates improvement proposals as needed.

[0708] Furthermore, users are monitored by an emotion engine. The emotion engine analyzes emotional data obtained from the camera and microphone to understand the user's emotional state. This emotional data shows the user's reaction to proposed improvements and is used to support contract negotiations.

[0709] For example, in contract negotiations, if a user expresses anxiety or hesitation, the device adjusts the content and format of the proposal based on that emotional data, presenting a revised version that provides reassurance. This makes it possible to facilitate contract agreement formation based on the user's emotional state.

[0710] The server securely stores the newly agreed-upon contract information in a record database, making it available for future contract processes and reference. In this way, the present invention simultaneously achieves increased efficiency in AI-to-AI contract processes and improved user experience.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The server connects to the AI-to-AI contract database to retrieve the specified contract data. This contract data includes detailed information about the contract's purpose, terms, obligations, and rights. This data is then formatted and transmitted to the terminal.

[0714] Step 2:

[0715] The terminal analyzes contract data received from the server using natural language processing technology. During the analysis, key elements of the contract are extracted, and its purpose and conditions are identified. These elements are then organized and stored as structured data.

[0716] Step 3:

[0717] The server evaluates the fairness and impartiality of a contract by comparing the contract elements sent from the terminal with a legal database. During the evaluation, it utilizes current legal standards and ethical guidelines to verify compliance. If any deficiencies are found, the details are compiled into a report.

[0718] Step 4:

[0719] The terminal uses a generative AI to generate new improvement proposals for contract terms that need improvement based on the evaluation results. Multiple improvement proposals are created, and the merits and risks of each proposal are evaluated. This generation process may include changes to contract terms or the addition of new clauses.

[0720] Step 5:

[0721] The emotion engine continuously monitors the user's emotional state. The emotional data is analyzed, and if anxiety or misunderstanding is detected, the suggested improvements presented via the device are adjusted. This ensures that the optimal solution is presented based on the user's emotions.

[0722] Step 6:

[0723] The device presents the user with the most appropriate improvement plan based on the analysis results of the emotion engine. When presenting the plan, it selects the appropriate expression and timing, taking into account the user's emotional state. This facilitates the consensus-building process and improves the user's sense of satisfaction.

[0724] Step 7:

[0725] The server saves the final contract details, approved by the user, to the database and notifies each relevant system that the contract revision is complete. The newly agreed-upon contract is retained for future reference and can be used in other contract processes as needed.

[0726] (Example 2)

[0727] 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".

[0728] In modern contract negotiations, the complexity and ambiguity of contract terms often become obstacles to reaching an agreement. Furthermore, processes that disregard the feelings of the users involved can create unnecessary stress and misunderstandings before an agreement is reached. This situation leads to problems of inefficiency in contract negotiations and user dissatisfaction.

[0729] 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.

[0730] In this invention, the server includes means for acquiring contract information, means for analyzing the contract information and extracting key contract elements, and means for monitoring the user's emotions and adjusting the proposed content based on the emotional state. This enables rapid and smooth agreement formation that takes into account the user's emotional state while maintaining the fairness and impartiality of the contract.

[0731] "Contract information" refers to all data and documents related to a contract, including contract terms, obligations, and rights.

[0732] "Key contractual elements" refer to the fundamental elements of a contract, such as conditions, obligations, and rights, that form its core.

[0733] "Appropriateness" refers to demonstrating that a contract is structured fairly and justly in accordance with laws and ethics.

[0734] "Emotion monitoring" refers to the process of analyzing a user's emotions in real time from their facial expressions, voice, and other data to understand their emotional state.

[0735] "Adjusting the proposal" refers to modifying the wording, tone, and amount of information in the contract proposal according to the user's emotional state, thereby facilitating agreement.

[0736] A "recording medium" is a means of storing information for the safe and accurate preservation of contract information, and includes digital databases and the like.

[0737] "Agreement building" refers to the process by which all parties involved agree to the terms of the contract and formally conclude the agreement.

[0738] To implement this invention, a system is specifically constructed that supports contract conclusion between AIs while taking into account the user's emotions. The system mainly consists of three elements: a server, a terminal, and a user, and each element works together to support the contract process.

[0739] The server plays a central role in retrieving and processing contract information. It collects necessary contract information from the database and converts the retrieved data into a structured data format. In this process, the server accesses the database using the SQL language and converts the information into a list or JSON format. The structured data is then sent to the terminal, providing a foundation for further analysis.

[0740] The terminal analyzes the received contract information and extracts the key contract elements. The terminal has software libraries such as NLTK and spaCy installed for natural language processing. These libraries are used to extract and classify conditions, obligations, and rights from the contract document. The analyzed data is then compared against legal databases and ethical guidelines to verify the appropriateness of the contract content. Furthermore, a generative AI model is used to generate improvement suggestions if necessary and present them to the user.

[0741] The user is monitored by an emotion engine. The emotion engine utilizes facial recognition technology using OpenCV and voice emotion analysis technology using Praat to capture the user's facial expressions and voice tone through the camera and microphone. It then analyzes and digitizes this emotional state. If the user experiences anxiety or confusion, the device uses this data and a generative AI model to adjust the contract proposal.

[0742] For example, if a user expresses negative emotions, the device can modify the proposed contract terms or adjust the explanation to make it more approachable. An example of a prompt message would be, "Based on the user's emotional data, please adjust the contract proposal and generate a more reassuring version." This allows for smooth agreement building while respecting the user's emotions.

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The server connects to the database to retrieve contract information. The input is a request for contract information, using an SQL query. The server executes the query, extracts contract-related information from the database, and outputs unstructured contract data. This organizes the information necessary for the contract for subsequent processing.

[0746] Step 2:

[0747] The server converts the acquired contract information into structured data. The input is the contract data obtained in step 1. The server organizes the information into a list or JSON format, and applies tags and hierarchical structures. The output is standardized structured data. This format conversion makes the contract data suitable for subsequent analysis on the terminal.

[0748] Step 3:

[0749] The terminal analyzes the structured data sent from the server and extracts the key contract elements. The input is the structured data from step 2. The terminal uses a natural language processing program, such as NLTK or spaCy, to analyze elements such as contract terms, obligations, and rights, and extracts these elements as a list as output. This process clarifies the core of the contract.

[0750] Step 4:

[0751] The terminal uses the analyzed contract elements to evaluate their appropriateness. The input is the contract elements extracted in step 3. The terminal evaluates them against legal databases and ethical guidelines, and outputs the appropriateness evaluation results. This confirms whether the contract content is legally and ethically appropriate.

[0752] Step 5:

[0753] The device generates improvement suggestions as needed based on the suitability assessment. The input is the evaluation results from step 4. Using the generation AI model, the system identifies items that need improvement in the presented content and constructs detailed improvement proposals, resulting in improvement suggestions as output. These suggestions are prepared for presentation to the user.

[0754] Step 6:

[0755] The user's emotional state is monitored by an emotion engine. The input is real-time facial and voice data from the user. Emotion analysis software processes the data collected through the camera and microphone, quantifying or classifying the user's emotional state. The output is the user's most recent emotional data. This data serves as an indicator of how the contract proposal should be adjusted.

[0756] Step 7:

[0757] The device adjusts the proposal based on the user's sentiment data. The input is the sentiment data from step 6. A generative AI model is used to adjust the tone and clarity of the contract proposal, presenting it in a way that best suits the user's situation. The output is a revised contract proposal that is more acceptable to the user.

[0758] Step 8:

[0759] The server stores the agreed-upon final contract information on a storage medium. The input is the agreed-upon draft contract. The server securely registers this information in a database, making it accessible as needed. The output is the secure contract record that is stored, preparing it for future reference and audits.

[0760] (Application Example 2)

[0761] 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".

[0762] In the contract negotiation process between artificial intelligence entities, users may experience anxiety or confusion, and these emotions can hinder smooth agreement. Conventional systems often fail to adjust the presentation of contract information to the user's level of understanding or emotional state, which can impair the user experience. Therefore, there is a need to dynamically adjust the way contract information is presented according to the user's emotional state to support optimal agreement.

[0763] 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.

[0764] In this invention, the server includes means for acquiring contract information, means for analyzing the contract information and extracting key contract elements, means for evaluating the appropriateness and fairness of the contract based on the extracted contract elements, and means for analyzing the user's emotions and adjusting the method of presenting the contract information according to the user's emotional state. This makes it possible to optimally present contract information based on the user's level of understanding and emotional state.

[0765] "Contract information" refers to information regarding the terms, rights, and obligations agreed upon between the parties in a transaction.

[0766] "Analysis" refers to the process of examining acquired data in detail to identify its structure and elements.

[0767] "Contractual elements" are the constituent elements of important information in a contract, such as the terms, obligations, and rights.

[0768] "Appropriateness" is an evaluation criterion that indicates whether the terms of a contract are legally correct and fair.

[0769] "Fairness" is a standard used to evaluate whether the burdens and benefits of a contract are distributed equally among the parties involved.

[0770] An "improvement proposal" is a specific suggestion to make the current contract terms more appropriate and fair.

[0771] "Agreement building" refers to the process of obtaining consent between the contracting parties.

[0772] "Emotion" refers to the emotional state a user exhibits when entering into a contract.

[0773] "Presentation method" refers to the format in which contract information is displayed to the user.

[0774] The system of this invention mainly consists of three elements: a server, a terminal, and a user.

[0775] The server has a program built to retrieve and analyze contract information. The server collects necessary contract information from multiple databases and processes it efficiently and accurately. Next, this information is used with natural language processing software (e.g., Google Cloud Natural Language API) to extract key contract elements.

[0776] The terminal receives extracted contract data transmitted from the server and evaluates the fairness and impartiality of the contract using legal documents and ethical guidelines. Based on the evaluation results, it has the function to generate and present improvement proposals to the user to make the contract fair and impartial. In this process, it uses the camera and microphone of a smartphone or smart glasses (e.g., Amazon Rekognition, Google Cloud Speech-to-Text) to analyze the user's emotions and adjust the information presented according to their emotional state.

[0777] Users are supported in reaching agreements through improvement suggestions presented on the terminal. This system helps users understand purchase conditions and benefits with confidence while making electronic payments. As a result, for example, if a user feels uneasy about the contract details, the system is designed to provide information in a more easily understandable format. The generating AI model provides appropriate agreement support to the user by instructing, for example, "Analyze the contract details and optimize the information presentation method based on current user sentiment data."

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The server retrieves contract information from the database. It receives a request containing contract information as input. It performs a database search and extracts the contract information. The output is the retrieved raw contract data.

[0781] Step 2:

[0782] The server analyzes the acquired contract information using natural language processing. The input is raw contract data. Using the Google Cloud Natural Language API, it extracts contract elements (terms, rights, obligations, etc.) and converts them into structured data. The output is structured contract element data.

[0783] Step 3:

[0784] The terminal receives structured data transmitted from the server and evaluates the fairness of the contract by comparing it with legal documents and ethical guidelines. The input is structured contract element data. Based on the evaluation algorithm, it determines legality and fairness. The output is the evaluation result and, if necessary, suggestions for improvement.

[0785] Step 4:

[0786] The device processes data collected from the camera and microphone to analyze the user's emotions. The input consists of data on the user's facial expressions and voice. Emotional states are identified using Amazon Rekognition or Google Cloud Speech-to-Text. The output is the user's emotional state information, which is the result of the analysis.

[0787] Step 5:

[0788] The terminal adjusts the way contract information is presented based on the user's emotional state. Inputs include emotional state information, evaluation results, and improvement suggestions. The system generates screens with diagrams and detailed explanations tailored to the user's level of understanding. The output is a contract information presentation screen optimized for the user.

[0789] Step 6:

[0790] The user makes decisions based on the information and improvement suggestions presented on the device and is supported in reaching a contract agreement. The input is an optimized contract information display screen. This allows the user to naturally proceed through a process that allows them to agree with confidence. The output is the user's final agreement or request for revisions.

[0791] Step 7:

[0792] The server securely stores the newly agreed-upon contract information in its record database. The input is the final agreed-upon data returned by the user. After going through the data storage process, the securely stored contract information is generated as output.

[0793] 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.

[0794] 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.

[0795] 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 robot 414.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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."

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0814] The following is further disclosed regarding the embodiments described above.

[0815] (Claim 1)

[0816] Means of obtaining contract information,

[0817] A means for analyzing the aforementioned contract information and extracting key contract elements,

[0818] A means of evaluating the fairness and impartiality of a contract based on the extracted contract elements,

[0819] A means for generating improvement proposals based on the aforementioned evaluation,

[0820] A means of presenting the generated improvement proposals and supporting consensus building,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the evaluation means includes means for determining the appropriateness of a contract based on a legal database and ethical guidelines.

[0824] (Claim 3)

[0825] The system according to claim 1, wherein the agreement-building means includes means for presenting a plurality of generated improvement proposals to the contracting party and supporting negotiations.

[0826] "Example 1"

[0827] (Claim 1)

[0828] An information processing device for acquiring contract information,

[0829] A data processing means for analyzing the aforementioned contract information and extracting the main components,

[0830] An evaluation method for assessing the fairness and impartiality of a contract based on extracted components,

[0831] An improvement generation means that generates improvement proposals using a generated AI model based on the aforementioned evaluation,

[0832] A presentation method that presents the generated improvement proposals to the user and supports consensus building,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the evaluation means includes a recognition function that determines the appropriateness of a contract based on legal information and ethical guidelines.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the presentation means includes a function to present a plurality of generated improvement proposals to the contracting parties and to support the progress of negotiations.

[0838] "Application Example 1"

[0839] (Claim 1)

[0840] Means of receiving contract details,

[0841] A means for analyzing the contents of the aforementioned contract and detecting its main components,

[0842] A means of evaluating the fairness and legal compliance of a contract based on the detected components,

[0843] Means for generating alternatives based on the aforementioned evaluation,

[0844] A means of presenting generated alternatives and facilitating consensus building,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the evaluation means includes means for determining the fairness of a contract based on legal information sources and ethical guidelines.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the agreement-building means includes means for presenting a plurality of generated alternatives to the contracting party and assisting in decision-making.

[0850] "Example 2 of combining an emotion engine"

[0851] (Claim 1)

[0852] Means of obtaining contract information,

[0853] A means for analyzing the aforementioned contract information and extracting key contract elements,

[0854] A means of evaluating the fairness and impartiality of a contract based on the extracted contract elements,

[0855] A means for generating improvement proposals based on the aforementioned evaluation,

[0856] A means of monitoring user emotions and adjusting suggestions based on their emotional state,

[0857] A means of presenting the generated improvement proposals and supporting consensus building,

[0858] A means of storing newly agreed contract information on a recording medium,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein the evaluation means includes means for determining the appropriateness of a contract based on a standard database and ethical guidelines.

[0862] (Claim 3)

[0863] The system according to claim 1, wherein the agreement-building means includes means for presenting a plurality of generated improvement proposals to the contracting party and supporting negotiations.

[0864] "Application example 2 of combining emotional engines"

[0865] (Claim 1)

[0866] Means of obtaining contract information,

[0867] A means for analyzing the aforementioned contract information and extracting key contract elements,

[0868] A means of evaluating the fairness and impartiality of a contract based on the extracted contract elements,

[0869] A means for generating improvement proposals based on the aforementioned evaluation,

[0870] A means of presenting the generated improvement proposals and supporting consensus building,

[0871] A means for analyzing the user's emotions and adjusting the method of presenting contract information according to the user's emotional state,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein the evaluation means includes means for determining the appropriateness of a contract based on legal documents and ethical guidelines.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein the agreement-building means includes means for presenting a plurality of generated improvement proposals to the contracting party and supporting negotiations. [Explanation of Symbols]

[0877] 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. Means of obtaining contract information, A means for analyzing the aforementioned contract information and extracting key contract elements, A means of evaluating the fairness and impartiality of a contract based on the extracted contract elements, A means for generating improvement proposals based on the aforementioned evaluation, A means of presenting the generated improvement proposals and supporting consensus building, A system that includes this.

2. The system according to claim 1, wherein the evaluation means includes means for determining the appropriateness of a contract based on a legal database and ethical guidelines.

3. The system according to claim 1, wherein the agreement-building means includes means for presenting a plurality of generated improvement proposals to the contracting party and supporting negotiations.

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