system

Abstract

We provide the system. [Solution] Means of obtaining contract information for each organization, A means of analyzing contract terms using natural language processing and extracting relevant important information, A means of collecting historical transaction data and identifying standard usage patterns, A generation means for automatically generating additional transaction terms based on extracted contract terms and historical transaction data, A means for presenting the generated additional transaction terms to a visualization device, enabling confirmation and approval, Means for storing approved additional transaction terms in an information management device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, 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] In contract management operations in business-to-business transactions, there are problems such as a large amount of effort required for confirming complex contract terms and placing additional orders, and a high likelihood of errors due to responses under incorrect conditions. Sales representatives spend a lot of time and resources on confirming contract terms, which hinders efficient customer service. Therefore, it is necessary to improve business efficiency while reducing the risk of errors and improving customer satisfaction.

Means for Solving the Problems

[0005] This invention utilizes a technology that automatically extracts important relevant information by providing a means for acquiring contract information for each company and analyzing contract terms using natural language processing. Furthermore, it includes a generation means for automatically generating optimal additional order conditions based on contract terms and transaction information by collecting past transaction information and identifying common usage patterns. The generated conditions are presented to the user via a terminal, and the user can approve or modify them through an interactive interface. Approved conditions are stored in a database, and the contract status is automatically updated. These means simultaneously reduce labor and improve accuracy, alleviating the burden on sales representatives and streamlining contract management operations.

[0006] "Company-specific contract information" refers to information contained in documents related to contracts concluded by each company, including terms and conditions of transactions, obligations, and rights.

[0007] "Natural language processing" is a technology that enables computers to understand, analyze, and respond to human language, and it involves methods for extracting meaning and patterns from text data.

[0008] "Analysis" is the act of breaking down information or data and revealing its constituent elements and meaning.

[0009] "Past transaction information" refers to data that records the history and details of transactions that have taken place between companies in the past.

[0010] A "common usage pattern" refers to certain behaviors or characteristics that are repeatedly observed in past transaction data.

[0011] "Additional order conditions" are the contract terms and requirements that must be set out when placing a new additional order.

[0012] A "generation method" is a mechanism for automatically creating new conditions or content based on specific information or data.

[0013] "Generated conditions" refer to new order details or contract terms constructed by the generation method.

[0014] An "interactive interface" is a mechanism that allows users to interact with a system, exchange information, and perform operations interactively.

[0015] A "database" is a system for organizing and storing information, and managing it in a way that allows for access and retrieval as needed.

[0016] "Contract status" refers to information such as the current progress, validity period, and performance status of the contract. [Brief explanation of the drawing]

[0017] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] 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.

[0019] First, the language used in the following description will be described.

[0020] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0021] 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.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] The system of this invention aims to streamline contract management between companies and includes a function to analyze contract information and past transaction information to automatically generate optimal additional order conditions. Its configuration and operation are described below.

[0039] The system first uses a server to retrieve contract information for each company from a database. This contract information includes important contract terms such as transaction terms, fees, and duration. Next, the server uses natural language processing to analyze the contract and extract the necessary contract terms and relevant clauses.

[0040] Next, the server collects past transaction information with companies from the database. This allows the server to understand transaction history and usage patterns. Based on this data, the server identifies common usage patterns.

[0041] Based on this information, the server uses a generation mechanism to automatically generate optimal additional order conditions for each company. The generated conditions include the specifications and pricing plans required when conducting new transactions.

[0042] Next, the generated conditions are transferred to the terminal, allowing the user to review them through the interface. The user reviews the displayed conditions and approves them if there are no problems. They can also modify the conditions if necessary. After the user approves the conditions, the server records the approved conditions in the database and updates the contract status.

[0043] As a concrete example, consider a scenario where a major telecommunications company manages contracts with corporate clients. This company uses this system to analyze each client's contract and automatically proposes the optimal pricing plan based on data usage and communication service utilization. Sales representatives can approve the proposed plan with a single click and then focus on detailed communication with the client. This leads to increased efficiency in sales activities and improved customer satisfaction.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server connects to the database to retrieve contract information for each company. This contract information includes details such as terms, obligations, and fees.

[0047] Step 2:

[0048] The server analyzes the acquired contract information using natural language processing technology to extract important contract terms and relevant clauses. This clarifies the elements that should be emphasized in a particular contract.

[0049] Step 3:

[0050] The server collects historical transaction information from the database. This information includes past order history and details of transaction terms, and is used as foundational data for analysis.

[0051] Step 4:

[0052] The server analyzes collected historical transaction data to identify common usage patterns. This reveals a company's past behavior and trends, which can then be used for predictions.

[0053] Step 5:

[0054] The server automatically generates optimal additional order conditions using a generation mechanism based on contract terms and transaction patterns obtained through natural language processing. These generated conditions include specific details and plans to be used in the next transaction.

[0055] Step 6:

[0056] The server sends the generated additional order conditions to the terminal and presents them to the user. The user reviews the conditions via the terminal and checks them as appropriate.

[0057] Step 7:

[0058] Users can approve or modify the additional order conditions they have reviewed through the interface. If modifications are necessary, they can enter the information, and the system will recalculate.

[0059] Step 8:

[0060] When a user approves the terms, the server records the approved terms in the database and updates the company's contract status. This ensures that contract management is always based on the latest information.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] Improving the efficiency of inter-company contract management is a critical challenge for many organizations. Current methods rely on manual processes for analyzing contract terms and generating additional order conditions, which are time-consuming, costly, and prone to errors. Therefore, there is a need for systems that automate contract management tasks and provide optimal contract terms quickly and accurately.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for collecting past transaction information and identifying common usage patterns. This enables efficient collection and analysis of contract information, as well as the automatic generation and management of optimal additional order conditions.

[0066] "Means of obtaining contract information for each company" refers to a function for obtaining information about contracts exchanged between companies from storage devices such as databases.

[0067] "Methods for analyzing contract terms and extracting important relevant information using natural language processing" refers to technologies that automatically understand the text written in a contract and efficiently analyze and extract contract terms and related important information.

[0068] "Means of collecting past transaction information and identifying common usage patterns" refers to the process of collecting a company's transaction history from a database and analyzing that data to reveal similarities and trends.

[0069] "Methods for automatically generating additional order conditions using a generative AI model" refers to a process that utilizes artificial intelligence technology to automatically create optimal additional order conditions based on predictions and pattern analysis.

[0070] "A means of presenting the generated additional order conditions to an information processing device and enabling confirmation" refers to an interface that displays the generated order conditions on a device such as a terminal, allowing the user to confirm their contents.

[0071] "Means of providing an interactive interface that allows users to modify the conditions presented" refers to a function that provides an interactive operation screen designed to allow users to directly modify or edit the generated conditions.

[0072] "Means for storing approved additional order conditions in an information storage device" refers to a function that saves conditions approved by the user in data storage so that they can be referenced and managed later.

[0073] The system of this invention is built to streamline contract management. Specifically, it includes a series of processes in which servers, terminals, and users work together.

[0074] The server first accesses the database to retrieve contract information for each company. Because this process handles a large amount of contract data, a server capable of high-speed and accurate data processing is used. Furthermore, the server applies natural language processing technology to extract important conditions and relevant clauses from the retrieved contract information. This process utilizes natural language processing libraries and text analysis software.

[0075] Next, the server collects historical transaction information from the company's database and identifies transaction usage patterns based on this information. Machine learning algorithms come into play here, automatically analyzing patterns and trends within the data. Based on this analysis, the server utilizes a generative AI model to generate optimal additional order conditions. This AI model is designed to learn from large amounts of data and output optimized conditions.

[0076] The generated conditions are transferred to the terminal and presented to the user. The terminal visually displays the generated conditions via a user interface. The user can review the conditions on the interface and modify them as needed. The interface is interactively designed to intuitively support the user's operation.

[0077] Ultimately, once the user approves the terms, the server stores those terms in the database and updates the company's contract status in real time. This automated process significantly improves the accuracy and efficiency of contract management.

[0078] As a concrete example, consider a scenario where a telecommunications carrier manages contracts for corporate clients. This carrier can use its system to automatically propose the optimal pricing plan based on each customer's data usage and service history. Users can easily review and approve the proposed plan, saving time and allowing them to work efficiently.

[0079] Examples of prompts used in a generative AI model include specific instructions such as, "Based on the corporate customer's data usage over the past 12 months and contract terms, generate the optimal pricing plan to recommend for the next contract renewal." This allows the AI ​​to develop specific judgment criteria and recognition skills.

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

[0081] Step 1:

[0082] The server retrieves corporate contract information from a database. The input consists of company-specific contract information within the database. Using a specified company ID, the server quickly extracts contract data specific to that company. The output is a set of contract information including details such as contract terms, duration, and fees. The retrieved data is then structured to prepare it for text analysis.

[0083] Step 2:

[0084] The server applies natural language processing to the acquired contract information to analyze and extract important conditions and clauses. The input is the contract information obtained in step 1. The server tokenizes the text and processes the data using specific patterns to extract keywords. This process organizes the output into a list format of the main conditions of each contract. The analysis clarifies the specifications and restrictions that are intended to be used.

[0085] Step 3:

[0086] The server collects a company's past transaction history from a database to identify common usage patterns. The input is a company's past order records and transaction history. The server applies machine learning algorithms to reveal regularities and trends based on this data. The output provides an overview of the company's transaction usage patterns, which allows for analysis of the company's characteristics based on its transaction history.

[0087] Step 4:

[0088] The server uses a generative AI model to generate optimal additional order conditions based on analyzed contract terms and historical transaction information. The input is the data obtained in steps 2 and 3. The server prompts the AI ​​model to calculate order conditions that meet the company's needs. The output is a list of proposed pricing plans and specification requirements. This generation process designs a customized optimal plan for each company.

[0089] Step 5:

[0090] The server transfers the generated additional order conditions to the terminal and presents them to the user. The inputs are the conditions generated in step 4, and the server sends these to the terminal as visualized data. The terminal displays the suggested content in an intuitive and easy-to-understand format through the user interface. The output is a screen display of the conditions presented to the user. The user can quickly grasp the information they need.

[0091] Step 6:

[0092] The user operates the terminal, reviews the presented conditions, and modifies them if necessary. The input is the conditions presented in step 5. The user can review each item on the interface and adjust the plan and pricing details. The output is the final adjusted or unchanged conditions. The conditions approved by the user will be used for future contract renewals.

[0093] Step 7:

[0094] The server records the approved additional order conditions in the database and updates the company's contract status. The input consists of the conditions approved by the user in step 6. The server integrates these conditions with existing contract data and stores them while maintaining consistency. The output is a set of updated contract information. This ensures that the latest information is maintained and can be referenced in real time.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] Managing inter-company contracts and transaction terms is complex, requiring the creation and rapid confirmation of terms tailored to the specific needs of each organization. However, current methods require significant time and effort for analyzing contract information and optimizing terms, making efficiency improvements urgently needed. Furthermore, intuitive interfaces for reviewing and modifying terms are not adequately provided, often leading to cumbersome user approval processes.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for acquiring contract information for each organization, means for analyzing contract terms using natural language processing and extracting relevant important information, and means for collecting historical transaction data and identifying standard usage patterns. This enables the automatic generation of optimal contract terms for customers, rapid confirmation of terms using smart devices, and streamlining of the approval process.

[0100] "Means of obtaining contract information for each organization" refers to methods for accessing and obtaining contract-related information held by each organization.

[0101] "A means of analyzing contract terms and extracting relevant important information using natural language processing" refers to a method of analyzing information contained in a contract using natural language processing technology to find necessary contract terms and related information.

[0102] "Methods for collecting historical transaction data and identifying standard usage patterns" refers to methods for collecting data on past transactions and identifying common usage trends from that data.

[0103] A "generation method" is a method for automatically creating optimal transaction terms and contract terms based on extracted information and historical data.

[0104] A "visualization device" is a device that provides a visual interface to display generated transaction terms and contract terms in an easy-to-understand manner for the user.

[0105] An "interactive interface" is a two-way user interface designed to allow users to review, approve, and modify contract terms and transaction conditions.

[0106] A "management device" refers to a system or device used to update an organization's contract status based on approved transaction and contract terms.

[0107] An "information management device" is a database system for efficiently storing and managing contract information and transaction data.

[0108] The system that realizes this invention automates the process of generating, confirming, and approving optimal contract terms by exchanging information between servers, terminals, and users in order to efficiently manage contracts.

[0109] First, the server uses Google Cloud SQL to retrieve and store contract information for each organization from a database. This information includes contract terms and past transaction data. The server then uses Tensorflow to analyze the documents within the contracts and extract important contract terms using natural language processing algorithms.

[0110] Next, the server aggregates the past transaction data and performs analysis to identify common usage patterns. In this step, a generative AI model using PyTorch plays a key role, automatically generating optimal additional trading conditions based on the extracted conditions and usage patterns.

[0111] The server then transfers the generated conditions to a visualization device developed in Unity, allowing users to visually confirm the contract terms via their smart devices. The device provides an interactive interface for condition confirmation, enabling intuitive operation for users to approve or modify the conditions.

[0112] As a concrete example, a company can use this system to propose the optimal pricing plan based on past data when renewing a contract. Users can instantly review the presented terms using their smart devices and approve them on the spot if they are satisfied. As a result, contract renewals can be expedited, leading to improved operational efficiency.

[0113] As an example of a prompt, the instruction given to the generating AI model is as follows: "Based on past transaction history and contract terms, please propose the optimal pricing plan."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server retrieves organization-specific contract information from Google Cloud SQL. The input is the organization's identifier, and the output includes contract terms and historical transaction data. The server uses database queries to collect relevant information.

[0117] Step 2:

[0118] The server uses TensorFlow to analyze contract information obtained through natural language processing. The input is the document information of the contract, and the output is the extracted contract terms. The server applies natural language algorithms to identify important terms.

[0119] Step 3:

[0120] The server collects historical transaction data and identifies common usage patterns. The input is the historical transaction history, and the output is the result of identifying usage patterns. The server extracts patterns using data mining techniques.

[0121] Step 4:

[0122] The server is a generative AI model using PyTorch that generates optimal additional trading conditions based on extracted conditions and usage patterns. The input is contract conditions and usage patterns, and the output is the optimized trading conditions. The server uses a deep learning algorithm to optimize the conditions.

[0123] Step 5:

[0124] The server transfers the generated conditions to the terminal through a visualization device built with Unity. The input is optimized trading conditions, and the output is visual information displayed on the terminal. The server visualizes the data using UI components.

[0125] Step 6:

[0126] The user reviews the displayed transaction terms using the interactive interface provided on the terminal. Inputs are visual information, and outputs are instructions for approval or modification. The user interacts with the interface and selects the conditions.

[0127] Step 7:

[0128] The terminal sends approved conditions to the server based on user instructions. The input is user approval information, and the output is updated condition data. The terminal transmits information via network communication.

[0129] Step 8:

[0130] The server stores the approved conditions in the Google Cloud SQL information management device and updates the contract status. The input is the updated condition data, and the output is a notification that the update is complete. The server performs a write operation to the database.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] The system of the present invention highly streamlines contract management in inter-company transactions and, by recognizing user emotions, enables more appropriate and flexible customer service. Specific embodiments of this system are described below.

[0133] This system first uses a server to retrieve contract information for each company from a database. This information includes important details related to the contract, such as transaction terms, fees, and duration. The server then uses natural language processing technology to analyze this contract information and extract the necessary contract terms and important clauses.

[0134] In parallel, the server collects historical transaction information from companies. By analyzing the collected data, the server understands transaction history and usage patterns, and based on that, identifies common usage trends.

[0135] Based on these analysis results, the server automatically generates optimal additional order conditions for each company using a generation mechanism. These additional order conditions include the specifications and pricing plans required for the next transaction. The generated conditions are presented to the user via the terminal.

[0136] Here, the emotion engine, a key feature of this invention, plays a crucial role. While the user is reviewing additional order conditions, the terminal uses the emotion engine to recognize the user's emotions in real time. This emotion data is used to adjust the content and presentation method based on the user's response. For example, if the user expresses dissatisfaction, the details of the conditions can be displayed more clearly, or additional explanations can be provided.

[0137] Once a user reviews and approves the terms, the server records this information in a database and updates the contract status. Furthermore, user sentiment data is reflected in future offer presentations through a feedback function. For example, if a company providing communication services detects that a user is uneasy about the proposed plan, the system can suggest alternative plans and respond flexibly. This allows sales representatives to interact with customers more effectively and efficiently, leading to improved customer satisfaction.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The server connects to the database and retrieves contract information for each company. This information includes contract terms, fees, and obligation periods.

[0141] Step 2:

[0142] The server analyzes the acquired contract information using natural language processing technology. Through this analysis, it extracts important contract terms and relevant clauses.

[0143] Step 3:

[0144] The server collects past transaction information of companies from a database. The collected information includes order history and detailed transaction terms, which are then used for analysis.

[0145] Step 4:

[0146] The server analyzes the collected transaction information to identify common usage patterns. This analysis allows for an understanding of a company's past behavior and trends.

[0147] Step 5:

[0148] The server automatically generates optimal additional order conditions using a generation mechanism based on contract terms and transaction patterns obtained through natural language processing. These conditions include specific specifications and plans for conducting new transactions.

[0149] Step 6:

[0150] The generated additional order conditions are sent from the server to the terminal. The terminal presents these conditions to the user. The presented conditions are displayed in a format that the user can review.

[0151] Step 7:

[0152] The device uses an emotion engine to recognize the user's emotions in real time while the user is reviewing the conditions. The recognized emotions are used to adjust the presented content.

[0153] Step 8:

[0154] Users can review additional order conditions and approve or modify them based on sentiment-based feedback. If modifications are needed, users enter this information into their device.

[0155] Step 9:

[0156] Additional order conditions approved by the user are sent to the server. The server records this in its database and updates the company's contract status.

[0157] Step 10:

[0158] The server analyzes the user's emotional data, recognized by the emotion engine, using a feedback function and incorporates this into subsequent conditional suggestions. This feedback allows the system to provide more appropriate conditions.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] In business-to-business transactions, contract management becomes complex and inefficient. Furthermore, traditional systems struggle to respond flexibly to user emotions, limiting the potential for improving customer satisfaction.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for collecting past transaction data and identifying common usage patterns. This streamlines contract management and enables flexible customer service based on user sentiment.

[0164] A "corporation" is a legal entity or business group established for a specific purpose, and is an organization that pursues profit through commercial activities.

[0165] "Contract information" refers to a document that records the agreed-upon terms and conditions of a transaction, including the terms and conditions of the transaction, and clearly specifies the matters that both parties must comply with.

[0166] "Natural language processing" is a technology that enables computers to understand and process human language, and is a process used for extracting information and analyzing content within text.

[0167] "Transaction data" refers to historical information related to the buying and selling of goods and services, and is a collection of data that includes past sales status and purchase history.

[0168] A "generative model" is a technology that uses artificial intelligence to automatically generate specific information or conditions, and is an algorithm that learns patterns from large amounts of data.

[0169] An "information processing device" is an electronic device used for inputting, processing, and outputting data, and is a general-purpose computing device such as a computer or smartphone.

[0170] A "storage medium" is a physical medium used to store digital data for extended periods, and includes devices such as hard disk drives and SSDs.

[0171] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice, and it is a process that enables computers to understand and respond to human emotions.

[0172] An "operation screen" is a visual interface for users to interact with a computer system, and it is a screen where information is displayed and inputted.

[0173] The system of this invention streamlines contract management in inter-company transactions and enables responses based on user sentiment. This system is mainly composed of three elements: a server, terminals, and users.

[0174] The server retrieves contract information from a database for each company. This involves extracting necessary data by issuing SQL queries using conventional search techniques. This information includes transaction terms and contract periods. Subsequently, the server analyzes this contract information using natural language processing technology. Specifically, it uses text processing libraries (e.g., Python's NLTK or SpaCy) to extract contract terms and identify important clauses.

[0175] The server then collects historical transaction data and performs analysis to identify common usage patterns. This analysis uses data processing libraries (e.g., pandas and NumPy) to clarify the user's past behavior patterns. Based on these results, the server uses a generative AI model to automatically generate the additional conditions that will be needed next. In this process, prompts are input to the AI ​​model to obtain the optimal conditions. An example of a prompt might be, "What are the conditions for this company to make its next ideal transaction?"

[0176] The generated additional conditions are presented to the user via a terminal. The terminal provides a visual interface to allow the user to intuitively understand the presented conditions. For example, it might use a web browser to organize information in a dashboard format and display it using HTML and CSS for a designed presentation.

[0177] While the user is reviewing the conditions, the device uses emotion recognition technology to analyze the user's emotions in real time. The device uses a webcam and microphone, and leverages emotion recognition APIs (e.g., Microsoft® Azure® and Google’s Emotion AI) to analyze the user’s facial expressions and voice. Based on this emotion data, the device dynamically adjusts the content presented to ensure that the user receives the most relevant information.

[0178] In this way, collaboration between servers, terminals, and users streamlines corporate contract management and enables flexible and personalized customer service.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The server retrieves contract information for each company from the database. Specifically, the server uses SQL queries to extract relevant contract information based on the company ID. Based on this input company ID, it outputs contract information including transaction terms and contract period.

[0182] Step 2:

[0183] The server analyzes the contract information obtained using natural language processing technology. Specifically, the server utilizes the Python NLTK library to tokenize the text and extract important contract terms. Based on this input contract information, it outputs the analyzed terms and clauses.

[0184] Step 3:

[0185] The server collects and analyzes historical transaction data. It retrieves past transaction history from the database and processes the data using the Python pandas library. Using this historical data as input, it outputs trading trends and common usage patterns.

[0186] Step 4:

[0187] The server automatically generates additional conditions using a generative AI model based on the extracted contract terms and past transaction data. Here, the server inputs a prompt message into the AI ​​model: "Please tell me the conditions for this company to make its next ideal transaction." Based on this prompt message, the server outputs the optimal additional conditions.

[0188] Step 5:

[0189] The server sends the generated additional conditions to the terminal, which then presents them to the user. The terminal launches a web application and displays the information visualized using HTML and CSS on the screen. It then outputs the additional conditions received as input, formatted in a way that is easy for the user to understand.

[0190] Step 6:

[0191] The device uses emotion recognition technology to analyze user reactions in real time. The device captures the user's face and voice using its camera and microphone, and analyzes them using an emotion recognition API. Based on this input audio and image data, it outputs the user's emotional state.

[0192] Step 7:

[0193] The device dynamically adjusts the content and display methods presented to the user based on analyzed sentiment data. For example, if the user expresses dissatisfaction, the device will display additional supplementary information or different visuals. By outputting adjusted information according to the input sentiment data, the user experience is optimized.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] Traditional B2B transaction management systems often stifled complex and time-consuming contract management, making it difficult to respond flexibly to user emotions. Furthermore, in various business settings, including e-commerce sites, there is a growing need to recognize user emotions in real time and provide suggestions based on those emotions to improve the user's purchasing experience. A system is needed to address these challenges and enable more efficient and flexible contract management and customer service.

[0197] 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.

[0198] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for acquiring the user's emotions in real time using emotion recognition technology and adjusting the presented content. This improves the efficiency of information extraction in contract management and enables flexible suggestions based on the user's emotions.

[0199] "Means of obtaining contract information for each company" refers to a function that collects information such as contracts and transaction terms related to each company from a data system.

[0200] "Methods for analyzing contract terms using natural language processing and extracting important relevant information" refers to technologies that automatically extract important information such as conditions and clauses from contracts written in human language.

[0201] "Means of collecting past transaction information and identifying common usage patterns" refers to the process of gathering data on past transactions and finding similar patterns or regularities within that data.

[0202] The "generation means for automatically generating additional order conditions based on extracted contract conditions and past transaction information" refers to a function in which the system automatically constructs new order conditions based on the analyzed contract conditions and transaction history.

[0203] "A means of displaying generated additional order conditions on a terminal and enabling confirmation and approval" refers to an interface that displays the order conditions constructed by the system on a terminal operated by the user, allowing the user to confirm and approve their contents.

[0204] "Means for retaining approved additional order conditions in a data storage device" refers to a process for securely and efficiently recording and storing order conditions approved by the user in data storage.

[0205] "A means of acquiring user emotions in real time using emotion recognition technology and adjusting the content presented" refers to a function that grasps the user's emotional state in real time using sensors and analysis algorithms, and optimizes the information presented to the user based on that information.

[0206] The system for implementing this invention streamlines corporate contract management and customer service. The server retrieves relevant data from databases of contract information and transaction history, and analyzes contract terms using natural language processing technology. This makes it possible to accurately extract contract information for each company. It is recommended to use open-source natural language processing libraries on the server.

[0207] During program execution, emotion recognition technology will be integrated into the server to monitor user emotions in real time. This technology obtains input from terminal sensors, including cameras and microphones, and analyzes the user's emotions using a generative AI model. The analysis results will be a crucial element in adjusting the information and conditions presented to the user. Ideally, the terminal should be operated in conjunction with a cloud-based analytics platform.

[0208] As a concrete example, when a user is considering a new communication plan, the server analyzes the user's past usage patterns and automatically suggests the optimal plan. In this process, if the emotion recognition function detects the user's anxiety, the device will present an alternative communication plan, thereby improving user satisfaction.

[0209] An example of a prompt to input into the generating AI model might be, "I'm worried about whether this product is right for me. Please suggest alternative products based on my past purchase history." This implementation allows the system to operate dynamically and user-friendly, maximizing business synergies.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server retrieves contract information from the company's database. The input for this step is identifying information such as the company ID and contract period, and the output is the retrieved contract information as digital data. The server then performs data processing to organize this data into a structured format.

[0213] Step 2:

[0214] The server analyzes contract information using natural language processing technology. The input is the contract information obtained in step 1, and the output is the contract terms and important clauses extracted through the analysis. In this analysis process, summarization and keyword extraction are performed through linguistic analysis of the text data.

[0215] Step 3:

[0216] The server collects past transaction history from the database and identifies common usage patterns. The input for this step is the raw data about the customer's transaction history, and the output is the identified common usage patterns and trends. Pattern recognition and statistical analysis are used as data processing techniques.

[0217] Step 4:

[0218] The server automatically generates optimal additional order conditions based on extracted contract terms and usage pattern information. The input is the information obtained in steps 2 and 3, and the output is the generated additional order conditions. This generation is performed through data fusion and inference processing using an AI model.

[0219] Step 5:

[0220] The terminal presents the generated additional order conditions to the user. The input is the conditions sent from the server in step 4, and the output is the operation data that the user confirms and approves. Specific actions on the terminal include providing a visual display via the interface and offering preferred selection options.

[0221] Step 6:

[0222] The server uses emotion recognition technology to observe user responses in real time and adjust the presentation content accordingly. Input is sensor data from sensory devices connected to the terminal, and output is the optimized presentation content. Emotion analysis employs an algorithm trained on a generative AI model.

[0223] Step 7:

[0224] When a user approves additional order conditions, the server records those conditions in the data storage device. The input is the approval data from the user, and the output is the recorded new contract status. Specifically, the backend records the information and updates the contract status after the approval process.

[0225] 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.

[0226] Data generation model 58 is a type of 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] The system of this invention aims to streamline contract management between companies and includes a function to analyze contract information and past transaction information to automatically generate optimal additional order conditions. Its configuration and operation are described below.

[0242] The system first uses a server to retrieve contract information for each company from a database. This contract information includes important contract terms such as transaction terms, fees, and duration. Next, the server uses natural language processing to analyze the contract and extract the necessary contract terms and relevant clauses.

[0243] Next, the server collects past transaction information with companies from the database. This allows the server to understand transaction history and usage patterns. Based on this data, the server identifies common usage patterns.

[0244] Based on this information, the server uses a generation mechanism to automatically generate optimal additional order conditions for each company. The generated conditions include the specifications and pricing plans required when conducting new transactions.

[0245] Next, the generated conditions are transferred to the terminal, allowing the user to review them through the interface. The user reviews the displayed conditions and approves them if there are no problems. They can also modify the conditions if necessary. After the user approves the conditions, the server records the approved conditions in the database and updates the contract status.

[0246] As a concrete example, consider a scenario where a major telecommunications company manages contracts with corporate clients. This company uses this system to analyze each client's contract and automatically proposes the optimal pricing plan based on data usage and communication service utilization. Sales representatives can approve the proposed plan with a single click and then focus on detailed communication with the client. This leads to increased efficiency in sales activities and improved customer satisfaction.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The server connects to the database to retrieve contract information for each company. This contract information includes details such as terms, obligations, and fees.

[0250] Step 2:

[0251] The server analyzes the acquired contract information using natural language processing technology to extract important contract terms and relevant clauses. This clarifies the elements that should be emphasized in a particular contract.

[0252] Step 3:

[0253] The server collects historical transaction information from the database. This information includes past order history and details of transaction terms, and is used as foundational data for analysis.

[0254] Step 4:

[0255] The server analyzes collected historical transaction data to identify common usage patterns. This reveals a company's past behavior and trends, which can then be used for predictions.

[0256] Step 5:

[0257] The server automatically generates optimal additional order conditions using a generation mechanism based on contract terms and transaction patterns obtained through natural language processing. These generated conditions include specific details and plans to be used in the next transaction.

[0258] Step 6:

[0259] The server sends the generated additional order conditions to the terminal and presents them to the user. The user reviews the conditions via the terminal and checks them as appropriate.

[0260] Step 7:

[0261] Users can approve or modify the additional order conditions they have reviewed through the interface. If modifications are necessary, they can enter the information, and the system will recalculate.

[0262] Step 8:

[0263] When a user approves the terms, the server records the approved terms in the database and updates the company's contract status. This ensures that contract management is always based on the latest information.

[0264] (Example 1)

[0265] 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."

[0266] Improving the efficiency of inter-company contract management is a critical challenge for many organizations. Current methods rely on manual processes for analyzing contract terms and generating additional order conditions, which are time-consuming, costly, and prone to errors. Therefore, there is a need for systems that automate contract management tasks and provide optimal contract terms quickly and accurately.

[0267] 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.

[0268] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for collecting past transaction information and identifying common usage patterns. This enables efficient collection and analysis of contract information, as well as the automatic generation and management of optimal additional order conditions.

[0269] "Means of obtaining contract information for each company" refers to a function for obtaining information about contracts exchanged between companies from storage devices such as databases.

[0270] "Methods for analyzing contract terms and extracting important relevant information using natural language processing" refers to technologies that automatically understand the text written in a contract and efficiently analyze and extract contract terms and related important information.

[0271] "Means of collecting past transaction information and identifying common usage patterns" refers to the process of collecting a company's transaction history from a database and analyzing that data to reveal similarities and trends.

[0272] "Methods for automatically generating additional order conditions using a generative AI model" refers to a process that utilizes artificial intelligence technology to automatically create optimal additional order conditions based on predictions and pattern analysis.

[0273] "A means of presenting the generated additional order conditions to an information processing device and enabling confirmation" refers to an interface that displays the generated order conditions on a device such as a terminal, allowing the user to confirm their contents.

[0274] "Means of providing an interactive interface that allows users to modify the conditions presented" refers to a function that provides an interactive operation screen designed to allow users to directly modify or edit the generated conditions.

[0275] "Means for storing approved additional order conditions in an information storage device" refers to a function that saves conditions approved by the user in data storage so that they can be referenced and managed later.

[0276] The system of this invention is built to streamline contract management. Specifically, it includes a series of processes in which servers, terminals, and users work together.

[0277] The server first accesses the database to retrieve contract information for each company. Because this process handles a large amount of contract data, a server capable of high-speed and accurate data processing is used. Furthermore, the server applies natural language processing technology to extract important conditions and relevant clauses from the retrieved contract information. This process utilizes natural language processing libraries and text analysis software.

[0278] Subsequently, the server collects the enterprise's past transaction information from the database and identifies the usage patterns of the transactions based on it. Machine learning algorithms are useful here to automatically analyze the patterns and trends in the data. Based on the results of this analysis, the server utilizes a generative AI model to generate optimal reorder conditions. This AI model is designed to learn from large amounts of data and output optimized conditions.

[0279] The generated conditions are transferred to the terminal and presented to the user. The terminal visually displays the generated conditions via the user interface. The user can check the conditions on the interface and make modifications if necessary. The interface is designed interactively to intuitively assist the user's operations.

[0280] Finally, when the user approves the conditions, the server stores the conditions in the database and updates the enterprise's contract status in real time. This automated process significantly improves the accuracy and efficiency of contract management.

[0281] As a specific example, consider the scenario where a telecommunications operator manages contracts for corporate customers. This operator can utilize the system to automatically propose an optimal pricing plan based on each customer's data usage and service usage history. The user can easily check and approve the proposed plan, enabling efficient business operations while saving time.

[0282] As an example of the prompt text used in the generative AI model, it is possible to give specific instructions such as "Please generate the optimal pricing plan recommended for the next contract renewal based on the data usage and contract conditions of corporate customers in the past 12 months." to enable the AI to have specific judgment criteria and recognition.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The server retrieves the enterprise's contract information from the database. The input is the contract information for each enterprise in the database. Using the specified enterprise ID, the server quickly extracts the contract data dedicated to that enterprise. The output is a set of contract information including details such as contract terms, duration, and fees. The retrieved data is structured so that it is ready for text analysis.

[0286] Step 2:

[0287] The server applies natural language processing to the retrieved contract information to analyze and extract important terms and clauses. The input is the contract information obtained in Step 1. The server tokenizes the text and processes the data using specific patterns for keyword extraction. As a result of this process, the main terms of each contract are listed in an organized form as the output. The analysis clarifies the specifications and restrictions that are planned to be used.

[0288] Step 3:

[0289] The server collects the enterprise's past transaction history from the database to identify common usage patterns. The input is the enterprise's past order records and transaction history. The server applies machine learning algorithms to clarify the regularities and trends based on these data. As the output, an overview of the usage patterns related to the enterprise's transactions is obtained. Thereby, the characteristics of the enterprise based on the transaction history are analyzed.

[0290] Step 4:

[0291] The server uses a generative AI model to generate optimal additional order conditions based on the analyzed contract conditions and past transaction information. The input is the data obtained in Step 2 and Step 3. The server inputs a prompt sentence into the AI model to calculate order conditions that match the enterprise's needs. The output is a list of proposed fee plans and specification requirements. Through this generation process, an optimal plan customized for each enterprise is designed.

[0292] Step 5:

[0293] The server transfers the generated additional order conditions to the terminal and presents them to the user. The inputs are the conditions generated in step 4, and the server sends these to the terminal as visualized data. The terminal displays the suggested content in an intuitive and easy-to-understand format through the user interface. The output is a screen display of the conditions presented to the user. The user can quickly grasp the information they need.

[0294] Step 6:

[0295] The user operates the terminal, reviews the presented conditions, and modifies them if necessary. The input is the conditions presented in step 5. The user can review each item on the interface and adjust the plan and pricing details. The output is the final adjusted or unchanged conditions. The conditions approved by the user will be used for future contract renewals.

[0296] Step 7:

[0297] The server records the approved additional order conditions in the database and updates the company's contract status. The input consists of the conditions approved by the user in step 6. The server integrates these conditions with existing contract data and stores them while maintaining consistency. The output is a set of updated contract information. This ensures that the latest information is maintained and can be referenced in real time.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] Managing inter-company contracts and transaction terms is complex, requiring the creation and rapid confirmation of terms tailored to the specific needs of each organization. However, current methods require significant time and effort for analyzing contract information and optimizing terms, making efficiency improvements urgently needed. Furthermore, intuitive interfaces for reviewing and modifying terms are not adequately provided, often leading to cumbersome user approval processes.

[0301] 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.

[0302] In this invention, the server includes means for acquiring contract information for each organization, means for analyzing contract terms using natural language processing and extracting relevant important information, and means for collecting historical transaction data and identifying standard usage patterns. This enables the automatic generation of optimal contract terms for customers, rapid confirmation of terms using smart devices, and streamlining of the approval process.

[0303] "Means of obtaining contract information for each organization" refers to methods for accessing and obtaining contract-related information held by each organization.

[0304] "A means of analyzing contract terms and extracting relevant important information using natural language processing" refers to a method of analyzing information contained in a contract using natural language processing technology to find necessary contract terms and related information.

[0305] "Methods for collecting historical transaction data and identifying standard usage patterns" refers to methods for collecting data on past transactions and identifying common usage trends from that data.

[0306] A "generation method" is a method for automatically creating optimal transaction terms and contract terms based on extracted information and historical data.

[0307] A "visualization device" is a device that provides a visual interface for presenting the generated transaction conditions and contract terms in an understandable manner to the user.

[0308] A "dialogue interface" is a two-way operation screen designed to enable the user to view and approve or modify contract terms and transaction conditions.

[0309] A "management device" refers to a system or device for updating the contract status of an organization based on the approved transaction conditions and contract terms.

[0310] An "information management device" is a database system for efficiently storing and managing contract information and transaction data.

[0311] The system for implementing this invention automates the process of generating, verifying, and approving optimal contract terms by exchanging information among a server, terminals, and users in order to efficiently conduct contract management.

[0312] First, the server uses Google Cloud SQL to retrieve and store contract information for each organization from the database. This information includes contract terms and past transaction data. The server analyzes the documents in the contract using TensorFlow and extracts important contract terms using natural language processing algorithms.

[0313] Next, the server aggregates the past transaction data and analyzes it to identify common usage patterns. In this step, a generative AI model using PyTorch is used to automatically generate optimal additional transaction conditions based on the extracted conditions and usage patterns.

[0314] After that, the server transfers the generated conditions to a visualization device developed with Unity, enabling the user to visually confirm the contract terms through a smart device. The terminal provides a dialogue interface for condition verification, enabling intuitive operations for the user to approve or modify the conditions.

[0315] As a concrete example, a company can use this system to propose the optimal pricing plan based on past data when renewing a contract. Users can instantly review the presented terms using their smart devices and approve them on the spot if they are satisfied. As a result, contract renewals can be expedited, leading to improved operational efficiency.

[0316] As an example of a prompt, the instruction given to the generating AI model is as follows: "Based on past transaction history and contract terms, please propose the optimal pricing plan."

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

[0318] Step 1:

[0319] The server retrieves organization-specific contract information from Google Cloud SQL. The input is the organization's identifier, and the output includes contract terms and historical transaction data. The server uses database queries to collect relevant information.

[0320] Step 2:

[0321] The server uses TensorFlow to analyze contract information obtained through natural language processing. The input is the document information of the contract, and the output is the extracted contract terms. The server applies natural language algorithms to identify important terms.

[0322] Step 3:

[0323] The server collects historical transaction data and identifies common usage patterns. The input is the historical transaction history, and the output is the result of identifying usage patterns. The server extracts patterns using data mining techniques.

[0324] Step 4:

[0325] The server is a generative AI model using PyTorch that generates optimal additional trading conditions based on extracted conditions and usage patterns. The input is contract conditions and usage patterns, and the output is the optimized trading conditions. The server uses a deep learning algorithm to optimize the conditions.

[0326] Step 5:

[0327] The server transfers the generated conditions to the terminal through a visualization device built with Unity. The input is optimized trading conditions, and the output is visual information displayed on the terminal. The server visualizes the data using UI components.

[0328] Step 6:

[0329] The user reviews the displayed transaction terms using the interactive interface provided on the terminal. Inputs are visual information, and outputs are instructions for approval or modification. The user interacts with the interface and selects the conditions.

[0330] Step 7:

[0331] The terminal sends approved conditions to the server based on user instructions. The input is user approval information, and the output is updated condition data. The terminal transmits information via network communication.

[0332] Step 8:

[0333] The server stores the approved conditions in the Google Cloud SQL information management device and updates the contract status. The input is the updated condition data, and the output is a notification that the update is complete. The server performs a write operation to the database.

[0334] 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.

[0335] The system of the present invention highly streamlines contract management in inter-company transactions and, by recognizing user emotions, enables more appropriate and flexible customer service. Specific embodiments of this system are described below.

[0336] This system first uses a server to retrieve contract information for each company from a database. This information includes important details related to the contract, such as transaction terms, fees, and duration. The server then uses natural language processing technology to analyze this contract information and extract the necessary contract terms and important clauses.

[0337] In parallel, the server collects historical transaction information from companies. By analyzing the collected data, the server understands transaction history and usage patterns, and based on that, identifies common usage trends.

[0338] Based on these analysis results, the server automatically generates optimal additional order conditions for each company using a generation mechanism. These additional order conditions include the specifications and pricing plans required for the next transaction. The generated conditions are presented to the user via the terminal.

[0339] Here, the emotion engine, a key feature of this invention, plays a crucial role. While the user is reviewing additional order conditions, the terminal uses the emotion engine to recognize the user's emotions in real time. This emotion data is used to adjust the content and presentation method based on the user's response. For example, if the user expresses dissatisfaction, the details of the conditions can be displayed more clearly, or additional explanations can be provided.

[0340] Once a user reviews and approves the terms, the server records this information in a database and updates the contract status. Furthermore, user sentiment data is reflected in future offer presentations through a feedback function. For example, if a company providing communication services detects that a user is uneasy about the proposed plan, the system can suggest alternative plans and respond flexibly. This allows sales representatives to interact with customers more effectively and efficiently, leading to improved customer satisfaction.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The server connects to the database and retrieves contract information for each company. This information includes contract terms, fees, and obligation periods.

[0344] Step 2:

[0345] The server analyzes the acquired contract information using natural language processing technology. Through this analysis, it extracts important contract terms and relevant clauses.

[0346] Step 3:

[0347] The server collects past transaction information of companies from a database. The collected information includes order history and detailed transaction terms, which are then used for analysis.

[0348] Step 4:

[0349] The server analyzes the collected transaction information to identify common usage patterns. This analysis allows for an understanding of a company's past behavior and trends.

[0350] Step 5:

[0351] The server automatically generates optimal additional order conditions using a generation mechanism based on contract terms and transaction patterns obtained through natural language processing. These conditions include specific specifications and plans for conducting new transactions.

[0352] Step 6:

[0353] The generated additional order conditions are sent from the server to the terminal. The terminal presents these conditions to the user. The presented conditions are displayed in a format that the user can review.

[0354] Step 7:

[0355] The device uses an emotion engine to recognize the user's emotions in real time while the user is reviewing the conditions. The recognized emotions are used to adjust the presented content.

[0356] Step 8:

[0357] Users can review additional order conditions and approve or modify them based on sentiment-based feedback. If modifications are needed, users enter this information into their device.

[0358] Step 9:

[0359] Additional order conditions approved by the user are sent to the server. The server records this in its database and updates the company's contract status.

[0360] Step 10:

[0361] The server analyzes the user's emotional data, recognized by the emotion engine, using a feedback function and incorporates this into subsequent conditional suggestions. This feedback allows the system to provide more appropriate conditions.

[0362] (Example 2)

[0363] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0364] In business-to-business transactions, contract management becomes complex and inefficient. Furthermore, traditional systems struggle to respond flexibly to user emotions, limiting the potential for improving customer satisfaction.

[0365] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0366] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for collecting past transaction data and identifying common usage patterns. This streamlines contract management and enables flexible customer service based on user sentiment.

[0367] A "corporation" is a legal entity or business group established for a specific purpose, and is an organization that pursues profit through commercial activities.

[0368] "Contract information" refers to a document that records the agreed-upon terms and conditions of a transaction, including the terms and conditions of the transaction, and clearly specifies the matters that both parties must comply with.

[0369] "Natural language processing" is a technology that enables computers to understand and process human language, and is a process used for extracting information and analyzing content within text.

[0370] "Transaction data" refers to historical information related to the buying and selling of goods and services, and is a collection of data that includes past sales status and purchase history.

[0371] A "generative model" is a technology that uses artificial intelligence to automatically generate specific information or conditions, and is an algorithm that learns patterns from large amounts of data.

[0372] An "information processing device" is an electronic device used for inputting, processing, and outputting data, and is a general-purpose computing device such as a computer or smartphone.

[0373] A "storage medium" is a physical medium used to store digital data for extended periods, and includes devices such as hard disk drives and SSDs.

[0374] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice, and it is a process that enables computers to understand and respond to human emotions.

[0375] An "operation screen" is a visual interface for users to interact with a computer system, and it is a screen where information is displayed and inputted.

[0376] The system of this invention streamlines contract management in inter-company transactions and enables responses based on user sentiment. This system is mainly composed of three elements: a server, terminals, and users.

[0377] The server retrieves contract information from a database for each company. This involves extracting necessary data by issuing SQL queries using conventional search techniques. This information includes transaction terms and contract periods. Subsequently, the server analyzes this contract information using natural language processing technology. Specifically, it uses text processing libraries (e.g., Python's NLTK or SpaCy) to extract contract terms and identify important clauses.

[0378] The server then collects historical transaction data and performs analysis to identify common usage patterns. This analysis uses data processing libraries (e.g., pandas and NumPy) to clarify the user's past behavior patterns. Based on these results, the server uses a generative AI model to automatically generate the additional conditions that will be needed next. In this process, prompts are input to the AI ​​model to obtain the optimal conditions. An example of a prompt might be, "What are the conditions for this company to make its next ideal transaction?"

[0379] The generated additional conditions are presented to the user via a terminal. The terminal provides a visual interface to allow the user to intuitively understand the presented conditions. For example, it might use a web browser to organize information in a dashboard format and display it using HTML and CSS for a designed presentation.

[0380] While the user is reviewing the conditions, the device uses emotion recognition technology to analyze the user's emotions in real time. The device uses a webcam and microphone, and leverages emotion recognition APIs (e.g., Microsoft Azure or Google's Emotion AI) to analyze the user's facial expressions and voice. Based on this emotion data, the device dynamically adjusts the content presented to ensure that the user receives the most relevant information.

[0381] In this way, collaboration between servers, terminals, and users streamlines corporate contract management and enables flexible and personalized customer service.

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

[0383] Step 1:

[0384] The server retrieves contract information for each company from the database. Specifically, the server uses SQL queries to extract relevant contract information based on the company ID. Based on this input company ID, it outputs contract information including transaction terms and contract period.

[0385] Step 2:

[0386] The server analyzes the contract information obtained using natural language processing technology. Specifically, the server utilizes the Python NLTK library to tokenize the text and extract important contract terms. Based on this input contract information, it outputs the analyzed terms and clauses.

[0387] Step 3:

[0388] The server collects and analyzes historical transaction data. It retrieves past transaction history from the database and processes the data using the Python pandas library. Using this historical data as input, it outputs trading trends and common usage patterns.

[0389] Step 4:

[0390] The server automatically generates additional conditions using a generative AI model based on the extracted contract terms and past transaction data. Here, the server inputs a prompt message into the AI ​​model: "Please tell me the conditions for this company to make its next ideal transaction." Based on this prompt message, the server outputs the optimal additional conditions.

[0391] Step 5:

[0392] The server sends the generated additional conditions to the terminal, which then presents them to the user. The terminal launches a web application and displays the information visualized using HTML and CSS on the screen. It then outputs the additional conditions received as input, formatted in a way that is easy for the user to understand.

[0393] Step 6:

[0394] The device uses emotion recognition technology to analyze user reactions in real time. The device captures the user's face and voice using its camera and microphone, and analyzes them using an emotion recognition API. Based on this input audio and image data, it outputs the user's emotional state.

[0395] Step 7:

[0396] The device dynamically adjusts the content and display methods presented to the user based on analyzed sentiment data. For example, if the user expresses dissatisfaction, the device will display additional supplementary information or different visuals. By outputting adjusted information according to the input sentiment data, the user experience is optimized.

[0397] (Application Example 2)

[0398] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0399] Traditional B2B transaction management systems often stifled complex and time-consuming contract management, making it difficult to respond flexibly to user emotions. Furthermore, in various business settings, including e-commerce sites, there is a growing need to recognize user emotions in real time and provide suggestions based on those emotions to improve the user's purchasing experience. A system is needed to address these challenges and enable more efficient and flexible contract management and customer service.

[0400] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0401] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for acquiring the user's emotions in real time using emotion recognition technology and adjusting the presented content. This improves the efficiency of information extraction in contract management and enables flexible suggestions based on the user's emotions.

[0402] "Means of obtaining contract information for each company" refers to a function that collects information such as contracts and transaction terms related to each company from a data system.

[0403] "Methods for analyzing contract terms using natural language processing and extracting important relevant information" refers to technologies that automatically extract important information such as conditions and clauses from contracts written in human language.

[0404] "Means of collecting past transaction information and identifying common usage patterns" refers to the process of gathering data on past transactions and finding similar patterns or regularities within that data.

[0405] The "generation means for automatically generating additional order conditions based on extracted contract conditions and past transaction information" refers to a function in which the system automatically constructs new order conditions based on the analyzed contract conditions and transaction history.

[0406] "A means of displaying generated additional order conditions on a terminal and enabling confirmation and approval" refers to an interface that displays the order conditions constructed by the system on a terminal operated by the user, allowing the user to confirm and approve their contents.

[0407] "Means for retaining approved additional order conditions in a data storage device" refers to a process for securely and efficiently recording and storing order conditions approved by the user in data storage.

[0408] "A means of acquiring user emotions in real time using emotion recognition technology and adjusting the content presented" refers to a function that grasps the user's emotional state in real time using sensors and analysis algorithms, and optimizes the information presented to the user based on that information.

[0409] The system for implementing this invention streamlines corporate contract management and customer service. The server retrieves relevant data from databases of contract information and transaction history, and analyzes contract terms using natural language processing technology. This makes it possible to accurately extract contract information for each company. It is recommended to use open-source natural language processing libraries on the server.

[0410] During program execution, emotion recognition technology will be integrated into the server to monitor user emotions in real time. This technology obtains input from terminal sensors, including cameras and microphones, and analyzes the user's emotions using a generative AI model. The analysis results will be a crucial element in adjusting the information and conditions presented to the user. Ideally, the terminal should be operated in conjunction with a cloud-based analytics platform.

[0411] As a concrete example, when a user is considering a new communication plan, the server analyzes the user's past usage patterns and automatically suggests the optimal plan. In this process, if the emotion recognition function detects the user's anxiety, the device will present an alternative communication plan, thereby improving user satisfaction.

[0412] An example of a prompt to input into the generating AI model might be, "I'm worried about whether this product is right for me. Please suggest alternative products based on my past purchase history." This implementation allows the system to operate dynamically and user-friendly, maximizing business synergies.

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] The server retrieves contract information from the company's database. The input for this step is identifying information such as the company ID and contract period, and the output is the retrieved contract information as digital data. The server then performs data processing to organize this data into a structured format.

[0416] Step 2:

[0417] The server analyzes contract information using natural language processing technology. The input is the contract information obtained in step 1, and the output is the contract terms and important clauses extracted through the analysis. In this analysis process, summarization and keyword extraction are performed through linguistic analysis of the text data.

[0418] Step 3:

[0419] The server collects past transaction history from the database and identifies common usage patterns. The input for this step is the raw data about the customer's transaction history, and the output is the identified common usage patterns and trends. Pattern recognition and statistical analysis are used as data processing techniques.

[0420] Step 4:

[0421] The server automatically generates optimal additional order conditions based on extracted contract terms and usage pattern information. The input is the information obtained in steps 2 and 3, and the output is the generated additional order conditions. This generation is performed through data fusion and inference processing using an AI model.

[0422] Step 5:

[0423] The terminal presents the generated additional order conditions to the user. The input is the conditions sent from the server in step 4, and the output is the operation data that the user confirms and approves. Specific actions on the terminal include providing a visual display via the interface and offering preferred selection options.

[0424] Step 6:

[0425] The server uses emotion recognition technology to observe user responses in real time and adjust the presentation content accordingly. Input is sensor data from sensory devices connected to the terminal, and output is the optimized presentation content. Emotion analysis employs an algorithm trained on a generative AI model.

[0426] Step 7:

[0427] When a user approves additional order conditions, the server records those conditions in the data storage device. The input is the approval data from the user, and the output is the recorded new contract status. Specifically, the backend records the information and updates the contract status after the approval process.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] [Third Embodiment]

[0432] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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".

[0444] The system of this invention aims to streamline contract management between companies and includes a function to analyze contract information and past transaction information to automatically generate optimal additional order conditions. Its configuration and operation are described below.

[0445] The system first uses a server to retrieve contract information for each company from a database. This contract information includes important contract terms such as transaction terms, fees, and duration. Next, the server uses natural language processing to analyze the contract and extract the necessary contract terms and relevant clauses.

[0446] Next, the server collects past transaction information with companies from the database. This allows the server to understand transaction history and usage patterns. Based on this data, the server identifies common usage patterns.

[0447] Based on this information, the server uses a generation mechanism to automatically generate optimal additional order conditions for each company. The generated conditions include the specifications and pricing plans required when conducting new transactions.

[0448] Next, the generated conditions are transferred to the terminal, allowing the user to review them through the interface. The user reviews the displayed conditions and approves them if there are no problems. They can also modify the conditions if necessary. After the user approves the conditions, the server records the approved conditions in the database and updates the contract status.

[0449] As a concrete example, consider a scenario where a major telecommunications company manages contracts with corporate clients. This company uses this system to analyze each client's contract and automatically proposes the optimal pricing plan based on data usage and communication service utilization. Sales representatives can approve the proposed plan with a single click and then focus on detailed communication with the client. This leads to increased efficiency in sales activities and improved customer satisfaction.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] The server connects to the database to retrieve contract information for each company. This contract information includes details such as terms, obligations, and fees.

[0453] Step 2:

[0454] The server analyzes the acquired contract information using natural language processing technology to extract important contract terms and relevant clauses. This clarifies the elements that should be emphasized in a particular contract.

[0455] Step 3:

[0456] The server collects historical transaction information from the database. This information includes past order history and details of transaction terms, and is used as foundational data for analysis.

[0457] Step 4:

[0458] The server analyzes collected historical transaction data to identify common usage patterns. This reveals a company's past behavior and trends, which can then be used for predictions.

[0459] Step 5:

[0460] The server automatically generates optimal additional order conditions using a generation mechanism based on contract terms and transaction patterns obtained through natural language processing. These generated conditions include specific details and plans to be used in the next transaction.

[0461] Step 6:

[0462] The server sends the generated additional order conditions to the terminal and presents them to the user. The user reviews the conditions via the terminal and checks them as appropriate.

[0463] Step 7:

[0464] Users can approve or modify the additional order conditions they have reviewed through the interface. If modifications are necessary, they can enter the information, and the system will recalculate.

[0465] Step 8:

[0466] When a user approves the terms, the server records the approved terms in the database and updates the company's contract status. This ensures that contract management is always based on the latest information.

[0467] (Example 1)

[0468] 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."

[0469] Improving the efficiency of inter-company contract management is a critical challenge for many organizations. Current methods rely on manual processes for analyzing contract terms and generating additional order conditions, which are time-consuming, costly, and prone to errors. Therefore, there is a need for systems that automate contract management tasks and provide optimal contract terms quickly and accurately.

[0470] 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.

[0471] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for collecting past transaction information and identifying common usage patterns. This enables efficient collection and analysis of contract information, as well as the automatic generation and management of optimal additional order conditions.

[0472] "Means of obtaining contract information for each company" refers to a function for obtaining information about contracts exchanged between companies from storage devices such as databases.

[0473] "Methods for analyzing contract terms and extracting important relevant information using natural language processing" refers to technologies that automatically understand the text written in a contract and efficiently analyze and extract contract terms and related important information.

[0474] "Means of collecting past transaction information and identifying common usage patterns" refers to the process of collecting a company's transaction history from a database and analyzing that data to reveal similarities and trends.

[0475] "Methods for automatically generating additional order conditions using a generative AI model" refers to a process that utilizes artificial intelligence technology to automatically create optimal additional order conditions based on predictions and pattern analysis.

[0476] "A means of presenting the generated additional order conditions to an information processing device and enabling confirmation" refers to an interface that displays the generated order conditions on a device such as a terminal, allowing the user to confirm their contents.

[0477] "Means of providing an interactive interface that allows users to modify the conditions presented" refers to a function that provides an interactive operation screen designed to allow users to directly modify or edit the generated conditions.

[0478] "Means for storing approved additional order conditions in an information storage device" refers to a function that saves conditions approved by the user in data storage so that they can be referenced and managed later.

[0479] The system of this invention is built to streamline contract management. Specifically, it includes a series of processes in which servers, terminals, and users work together.

[0480] The server first accesses the database to retrieve contract information for each company. Because this process handles a large amount of contract data, a server capable of high-speed and accurate data processing is used. Furthermore, the server applies natural language processing technology to extract important conditions and relevant clauses from the retrieved contract information. This process utilizes natural language processing libraries and text analysis software.

[0481] Next, the server collects historical transaction information from the company's database and identifies transaction usage patterns based on this information. Machine learning algorithms come into play here, automatically analyzing patterns and trends within the data. Based on this analysis, the server utilizes a generative AI model to generate optimal additional order conditions. This AI model is designed to learn from large amounts of data and output optimized conditions.

[0482] The generated conditions are transferred to the terminal and presented to the user. The terminal visually displays the generated conditions via a user interface. The user can review the conditions on the interface and modify them as needed. The interface is interactively designed to intuitively support the user's operation.

[0483] Ultimately, once the user approves the terms, the server stores those terms in the database and updates the company's contract status in real time. This automated process significantly improves the accuracy and efficiency of contract management.

[0484] As a concrete example, consider a scenario where a telecommunications carrier manages contracts for corporate clients. This carrier can use its system to automatically propose the optimal pricing plan based on each customer's data usage and service history. Users can easily review and approve the proposed plan, saving time and allowing them to work efficiently.

[0485] Examples of prompts used in a generative AI model include specific instructions such as, "Based on the corporate customer's data usage over the past 12 months and contract terms, generate the optimal pricing plan to recommend for the next contract renewal." This allows the AI ​​to develop specific judgment criteria and recognition skills.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The server retrieves corporate contract information from a database. The input consists of company-specific contract information within the database. Using a specified company ID, the server quickly extracts contract data specific to that company. The output is a set of contract information including details such as contract terms, duration, and fees. The retrieved data is then structured to prepare it for text analysis.

[0489] Step 2:

[0490] The server applies natural language processing to the acquired contract information to analyze and extract important conditions and clauses. The input is the contract information obtained in step 1. The server tokenizes the text and processes the data using specific patterns to extract keywords. This process organizes the output into a list format of the main conditions of each contract. The analysis clarifies the specifications and restrictions that are intended to be used.

[0491] Step 3:

[0492] The server collects a company's past transaction history from a database to identify common usage patterns. The input is a company's past order records and transaction history. The server applies machine learning algorithms to reveal regularities and trends based on this data. The output provides an overview of the company's transaction usage patterns, which allows for analysis of the company's characteristics based on its transaction history.

[0493] Step 4:

[0494] The server uses a generative AI model to generate optimal additional order conditions based on analyzed contract terms and historical transaction information. The input is the data obtained in steps 2 and 3. The server prompts the AI ​​model to calculate order conditions that meet the company's needs. The output is a list of proposed pricing plans and specification requirements. This generation process designs a customized optimal plan for each company.

[0495] Step 5:

[0496] The server transfers the generated additional order conditions to the terminal and presents them to the user. The inputs are the conditions generated in step 4, and the server sends these to the terminal as visualized data. The terminal displays the suggested content in an intuitive and easy-to-understand format through the user interface. The output is a screen display of the conditions presented to the user. The user can quickly grasp the information they need.

[0497] Step 6:

[0498] The user operates the terminal, reviews the presented conditions, and modifies them if necessary. The input is the conditions presented in step 5. The user can review each item on the interface and adjust the plan and pricing details. The output is the final adjusted or unchanged conditions. The conditions approved by the user will be used for future contract renewals.

[0499] Step 7:

[0500] The server records the approved additional order conditions in the database and updates the company's contract status. The input consists of the conditions approved by the user in step 6. The server integrates these conditions with existing contract data and stores them while maintaining consistency. The output is a set of updated contract information. This ensures that the latest information is maintained and can be referenced in real time.

[0501] (Application Example 1)

[0502] 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."

[0503] Managing inter-company contracts and transaction terms is complex, requiring the creation and rapid confirmation of terms tailored to the specific needs of each organization. However, current methods require significant time and effort for analyzing contract information and optimizing terms, making efficiency improvements urgently needed. Furthermore, intuitive interfaces for reviewing and modifying terms are not adequately provided, often leading to cumbersome user approval processes.

[0504] 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.

[0505] In this invention, the server includes means for acquiring contract information for each organization, means for analyzing contract terms using natural language processing and extracting relevant important information, and means for collecting historical transaction data and identifying standard usage patterns. This enables the automatic generation of optimal contract terms for customers, rapid confirmation of terms using smart devices, and streamlining of the approval process.

[0506] "Means of obtaining contract information for each organization" refers to methods for accessing and obtaining contract-related information held by each organization.

[0507] "A means of analyzing contract terms and extracting relevant important information using natural language processing" refers to a method of analyzing information contained in a contract using natural language processing technology to find necessary contract terms and related information.

[0508] "Methods for collecting historical transaction data and identifying standard usage patterns" refers to methods for collecting data on past transactions and identifying common usage trends from that data.

[0509] A "generation method" is a method for automatically creating optimal transaction terms and contract terms based on extracted information and historical data.

[0510] A "visualization device" is a device that provides a visual interface to display generated transaction terms and contract terms in an easy-to-understand manner for the user.

[0511] An "interactive interface" is a two-way user interface designed to allow users to review, approve, and modify contract terms and transaction conditions.

[0512] A "management device" refers to a system or device used to update an organization's contract status based on approved transaction and contract terms.

[0513] An "information management device" is a database system for efficiently storing and managing contract information and transaction data.

[0514] The system that realizes this invention automates the process of generating, confirming, and approving optimal contract terms by exchanging information between servers, terminals, and users in order to efficiently manage contracts.

[0515] First, the server uses Google Cloud SQL to retrieve and store contract information for each organization from the database. This information includes contract terms and historical transaction data. The server then uses TensorFlow to analyze the documents within the contracts and extract important contract terms using natural language processing algorithms.

[0516] Next, the server aggregates the past transaction data and performs analysis to identify common usage patterns. In this step, a generative AI model using PyTorch plays a key role, automatically generating optimal additional trading conditions based on the extracted conditions and usage patterns.

[0517] The server then transfers the generated conditions to a visualization device developed in Unity, allowing users to visually confirm the contract terms via their smart devices. The device provides an interactive interface for condition confirmation, enabling intuitive operation for users to approve or modify the conditions.

[0518] As a concrete example, a company can use this system to propose the optimal pricing plan based on past data when renewing a contract. Users can instantly review the presented terms using their smart devices and approve them on the spot if they are satisfied. As a result, contract renewals can be expedited, leading to improved operational efficiency.

[0519] As an example of a prompt, the instruction given to the generating AI model is as follows: "Based on past transaction history and contract terms, please propose the optimal pricing plan."

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Step 1:

[0522] The server retrieves organization-specific contract information from Google Cloud SQL. The input is the organization's identifier, and the output includes contract terms and historical transaction data. The server uses database queries to collect relevant information.

[0523] Step 2:

[0524] The server uses TensorFlow to analyze contract information obtained through natural language processing. The input is the document information of the contract, and the output is the extracted contract terms. The server applies natural language algorithms to identify important terms.

[0525] Step 3:

[0526] The server collects historical transaction data and identifies common usage patterns. The input is the historical transaction history, and the output is the result of identifying usage patterns. The server extracts patterns using data mining techniques.

[0527] Step 4:

[0528] The server is a generative AI model using PyTorch that generates optimal additional trading conditions based on extracted conditions and usage patterns. The input is contract conditions and usage patterns, and the output is the optimized trading conditions. The server uses a deep learning algorithm to optimize the conditions.

[0529] Step 5:

[0530] The server transfers the generated conditions to the terminal through a visualization device built with Unity. The input is optimized trading conditions, and the output is visual information displayed on the terminal. The server visualizes the data using UI components.

[0531] Step 6:

[0532] The user reviews the displayed transaction terms using the interactive interface provided on the terminal. Inputs are visual information, and outputs are instructions for approval or modification. The user interacts with the interface and selects the conditions.

[0533] Step 7:

[0534] The terminal sends approved conditions to the server based on user instructions. The input is user approval information, and the output is updated condition data. The terminal transmits information via network communication.

[0535] Step 8:

[0536] The server stores the approved conditions in the Google Cloud SQL information management device and updates the contract status. The input is the updated condition data, and the output is a notification that the update is complete. The server performs a write operation to the database.

[0537] 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.

[0538] The system of the present invention highly streamlines contract management in inter-company transactions and, by recognizing user emotions, enables more appropriate and flexible customer service. Specific embodiments of this system are described below.

[0539] This system first uses a server to retrieve contract information for each company from a database. This information includes important details related to the contract, such as transaction terms, fees, and duration. The server then uses natural language processing technology to analyze this contract information and extract the necessary contract terms and important clauses.

[0540] In parallel, the server collects historical transaction information from companies. By analyzing the collected data, the server understands transaction history and usage patterns, and based on that, identifies common usage trends.

[0541] Based on these analysis results, the server automatically generates optimal additional order conditions for each company using a generation mechanism. These additional order conditions include the specifications and pricing plans required for the next transaction. The generated conditions are presented to the user via the terminal.

[0542] Here, the emotion engine, a key feature of this invention, plays a crucial role. While the user is reviewing additional order conditions, the terminal uses the emotion engine to recognize the user's emotions in real time. This emotion data is used to adjust the content and presentation method based on the user's response. For example, if the user expresses dissatisfaction, the details of the conditions can be displayed more clearly, or additional explanations can be provided.

[0543] Once a user reviews and approves the terms, the server records this information in a database and updates the contract status. Furthermore, user sentiment data is reflected in future offer presentations through a feedback function. For example, if a company providing communication services detects that a user is uneasy about the proposed plan, the system can suggest alternative plans and respond flexibly. This allows sales representatives to interact with customers more effectively and efficiently, leading to improved customer satisfaction.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The server connects to the database and retrieves contract information for each company. This information includes contract terms, fees, and obligation periods.

[0547] Step 2:

[0548] The server analyzes the acquired contract information using natural language processing technology. Through this analysis, it extracts important contract terms and relevant clauses.

[0549] Step 3:

[0550] The server collects past transaction information of companies from a database. The collected information includes order history and detailed transaction terms, which are then used for analysis.

[0551] Step 4:

[0552] The server analyzes the collected transaction information to identify common usage patterns. This analysis allows for an understanding of a company's past behavior and trends.

[0553] Step 5:

[0554] The server automatically generates optimal additional order conditions using a generation mechanism based on contract terms and transaction patterns obtained through natural language processing. These conditions include specific specifications and plans for conducting new transactions.

[0555] Step 6:

[0556] The generated additional order conditions are sent from the server to the terminal. The terminal presents these conditions to the user. The presented conditions are displayed in a format that the user can review.

[0557] Step 7:

[0558] The device uses an emotion engine to recognize the user's emotions in real time while the user is reviewing the conditions. The recognized emotions are used to adjust the presented content.

[0559] Step 8:

[0560] Users can review additional order conditions and approve or modify them based on sentiment-based feedback. If modifications are needed, users enter this information into their device.

[0561] Step 9:

[0562] Additional order conditions approved by the user are sent to the server. The server records this in its database and updates the company's contract status.

[0563] Step 10:

[0564] The server analyzes the user's emotional data, recognized by the emotion engine, using a feedback function and incorporates this into subsequent conditional suggestions. This feedback allows the system to provide more appropriate conditions.

[0565] (Example 2)

[0566] 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."

[0567] In business-to-business transactions, contract management becomes complex and inefficient. Furthermore, traditional systems struggle to respond flexibly to user emotions, limiting the potential for improving customer satisfaction.

[0568] 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.

[0569] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for collecting past transaction data and identifying common usage patterns. This streamlines contract management and enables flexible customer service based on user sentiment.

[0570] A "corporation" is a legal entity or business group established for a specific purpose, and is an organization that pursues profit through commercial activities.

[0571] "Contract information" refers to a document that records the agreed-upon terms and conditions of a transaction, including the terms and conditions of the transaction, and clearly specifies the matters that both parties must comply with.

[0572] "Natural language processing" is a technology that enables computers to understand and process human language, and is a process used for extracting information and analyzing content within text.

[0573] "Transaction data" refers to historical information related to the buying and selling of goods and services, and is a collection of data that includes past sales status and purchase history.

[0574] A "generative model" is a technology that uses artificial intelligence to automatically generate specific information or conditions, and is an algorithm that learns patterns from large amounts of data.

[0575] An "information processing device" is an electronic device used for inputting, processing, and outputting data, and is a general-purpose computing device such as a computer or smartphone.

[0576] A "storage medium" is a physical medium used to store digital data for extended periods, and includes devices such as hard disk drives and SSDs.

[0577] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice, and it is a process that enables computers to understand and respond to human emotions.

[0578] An "operation screen" is a visual interface for users to interact with a computer system, and it is a screen where information is displayed and inputted.

[0579] The system of this invention streamlines contract management in inter-company transactions and enables responses based on user sentiment. This system is mainly composed of three elements: a server, terminals, and users.

[0580] The server retrieves contract information from a database for each company. This involves extracting necessary data by issuing SQL queries using conventional search techniques. This information includes transaction terms and contract periods. Subsequently, the server analyzes this contract information using natural language processing technology. Specifically, it uses text processing libraries (e.g., Python's NLTK or SpaCy) to extract contract terms and identify important clauses.

[0581] The server then collects historical transaction data and performs analysis to identify common usage patterns. This analysis uses data processing libraries (e.g., pandas and NumPy) to clarify the user's past behavior patterns. Based on these results, the server uses a generative AI model to automatically generate the additional conditions that will be needed next. In this process, prompts are input to the AI ​​model to obtain the optimal conditions. An example of a prompt might be, "What are the conditions for this company to make its next ideal transaction?"

[0582] The generated additional conditions are presented to the user via a terminal. The terminal provides a visual interface to allow the user to intuitively understand the presented conditions. For example, it might use a web browser to organize information in a dashboard format and display it using HTML and CSS for a designed presentation.

[0583] While the user is reviewing the conditions, the device uses emotion recognition technology to analyze the user's emotions in real time. The device uses a webcam and microphone, and leverages emotion recognition APIs (e.g., Microsoft Azure or Google's Emotion AI) to analyze the user's facial expressions and voice. Based on this emotion data, the device dynamically adjusts the content presented to ensure that the user receives the most relevant information.

[0584] In this way, collaboration between servers, terminals, and users streamlines corporate contract management and enables flexible and personalized customer service.

[0585] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0586] Step 1:

[0587] The server retrieves contract information for each company from the database. Specifically, the server uses SQL queries to extract relevant contract information based on the company ID. Based on this input company ID, it outputs contract information including transaction terms and contract period.

[0588] Step 2:

[0589] The server analyzes the contract information obtained using natural language processing technology. Specifically, the server utilizes the Python NLTK library to tokenize the text and extract important contract terms. Based on this input contract information, it outputs the analyzed terms and clauses.

[0590] Step 3:

[0591] The server collects and analyzes historical transaction data. It retrieves past transaction history from the database and processes the data using the Python pandas library. Using this historical data as input, it outputs trading trends and common usage patterns.

[0592] Step 4:

[0593] The server automatically generates additional conditions using a generative AI model based on the extracted contract terms and past transaction data. Here, the server inputs a prompt message into the AI ​​model: "Please tell me the conditions for this company to make its next ideal transaction." Based on this prompt message, the server outputs the optimal additional conditions.

[0594] Step 5:

[0595] The server sends the generated additional conditions to the terminal, which then presents them to the user. The terminal launches a web application and displays the information visualized using HTML and CSS on the screen. It then outputs the additional conditions received as input, formatted in a way that is easy for the user to understand.

[0596] Step 6:

[0597] The device uses emotion recognition technology to analyze user reactions in real time. The device captures the user's face and voice using its camera and microphone, and analyzes them using an emotion recognition API. Based on this input audio and image data, it outputs the user's emotional state.

[0598] Step 7:

[0599] The device dynamically adjusts the content and display methods presented to the user based on analyzed sentiment data. For example, if the user expresses dissatisfaction, the device will display additional supplementary information or different visuals. By outputting adjusted information according to the input sentiment data, the user experience is optimized.

[0600] (Application Example 2)

[0601] 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."

[0602] Traditional B2B transaction management systems often stifled complex and time-consuming contract management, making it difficult to respond flexibly to user emotions. Furthermore, in various business settings, including e-commerce sites, there is a growing need to recognize user emotions in real time and provide suggestions based on those emotions to improve the user's purchasing experience. A system is needed to address these challenges and enable more efficient and flexible contract management and customer service.

[0603] 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.

[0604] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for acquiring the user's emotions in real time using emotion recognition technology and adjusting the presented content. This improves the efficiency of information extraction in contract management and enables flexible suggestions based on the user's emotions.

[0605] "Means of obtaining contract information for each company" refers to a function that collects information such as contracts and transaction terms related to each company from a data system.

[0606] "Methods for analyzing contract terms using natural language processing and extracting important relevant information" refers to technologies that automatically extract important information such as conditions and clauses from contracts written in human language.

[0607] "Means of collecting past transaction information and identifying common usage patterns" refers to the process of gathering data on past transactions and finding similar patterns or regularities within that data.

[0608] The "generation means for automatically generating additional order conditions based on extracted contract conditions and past transaction information" refers to a function in which the system automatically constructs new order conditions based on the analyzed contract conditions and transaction history.

[0609] "A means of displaying generated additional order conditions on a terminal and enabling confirmation and approval" refers to an interface that displays the order conditions constructed by the system on a terminal operated by the user, allowing the user to confirm and approve their contents.

[0610] "Means for retaining approved additional order conditions in a data storage device" refers to a process for securely and efficiently recording and storing order conditions approved by the user in data storage.

[0611] "A means of acquiring user emotions in real time using emotion recognition technology and adjusting the content presented" refers to a function that grasps the user's emotional state in real time using sensors and analysis algorithms, and optimizes the information presented to the user based on that information.

[0612] The system for implementing this invention streamlines corporate contract management and customer service. The server retrieves relevant data from databases of contract information and transaction history, and analyzes contract terms using natural language processing technology. This makes it possible to accurately extract contract information for each company. It is recommended to use open-source natural language processing libraries on the server.

[0613] During program execution, emotion recognition technology will be integrated into the server to monitor user emotions in real time. This technology obtains input from terminal sensors, including cameras and microphones, and analyzes the user's emotions using a generative AI model. The analysis results will be a crucial element in adjusting the information and conditions presented to the user. Ideally, the terminal should be operated in conjunction with a cloud-based analytics platform.

[0614] As a concrete example, when a user is considering a new communication plan, the server analyzes the user's past usage patterns and automatically suggests the optimal plan. In this process, if the emotion recognition function detects the user's anxiety, the device will present an alternative communication plan, thereby improving user satisfaction.

[0615] An example of a prompt to input into the generating AI model might be, "I'm worried about whether this product is right for me. Please suggest alternative products based on my past purchase history." This implementation allows the system to operate dynamically and user-friendly, maximizing business synergies.

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

[0617] Step 1:

[0618] The server retrieves contract information from the company's database. The input for this step is identifying information such as the company ID and contract period, and the output is the retrieved contract information as digital data. The server then performs data processing to organize this data into a structured format.

[0619] Step 2:

[0620] The server analyzes contract information using natural language processing technology. The input is the contract information obtained in step 1, and the output is the contract terms and important clauses extracted through the analysis. In this analysis process, summarization and keyword extraction are performed through linguistic analysis of the text data.

[0621] Step 3:

[0622] The server collects past transaction history from the database and identifies common usage patterns. The input for this step is the raw data about the customer's transaction history, and the output is the identified common usage patterns and trends. Pattern recognition and statistical analysis are used as data processing techniques.

[0623] Step 4:

[0624] The server automatically generates optimal additional order conditions based on extracted contract terms and usage pattern information. The input is the information obtained in steps 2 and 3, and the output is the generated additional order conditions. This generation is performed through data fusion and inference processing using an AI model.

[0625] Step 5:

[0626] The terminal presents the generated additional order conditions to the user. The input is the conditions sent from the server in step 4, and the output is the operation data that the user confirms and approves. Specific actions on the terminal include providing a visual display via the interface and offering preferred selection options.

[0627] Step 6:

[0628] The server uses emotion recognition technology to observe user responses in real time and adjust the presentation content accordingly. Input is sensor data from sensory devices connected to the terminal, and output is the optimized presentation content. Emotion analysis employs an algorithm trained on a generative AI model.

[0629] Step 7:

[0630] When a user approves additional order conditions, the server records those conditions in the data storage device. The input is the approval data from the user, and the output is the recorded new contract status. Specifically, the backend records the information and updates the contract status after the approval process.

[0631] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0632] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0633] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0634] [Fourth Embodiment]

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

[0636] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0637] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0638] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0639] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0640] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0641] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0642] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0643] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0644] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0645] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0646] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0647] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0648] The system of this invention aims to streamline contract management between companies and includes a function to analyze contract information and past transaction information to automatically generate optimal additional order conditions. Its configuration and operation are described below.

[0649] The system first uses a server to retrieve contract information for each company from a database. This contract information includes important contract terms such as transaction terms, fees, and duration. Next, the server uses natural language processing to analyze the contract and extract the necessary contract terms and relevant clauses.

[0650] Next, the server collects past transaction information with companies from the database. This allows the server to understand transaction history and usage patterns. Based on this data, the server identifies common usage patterns.

[0651] Based on this information, the server uses a generation mechanism to automatically generate optimal additional order conditions for each company. The generated conditions include the specifications and pricing plans required when conducting new transactions.

[0652] Next, the generated conditions are transferred to the terminal, allowing the user to review them through the interface. The user reviews the displayed conditions and approves them if there are no problems. They can also modify the conditions if necessary. After the user approves the conditions, the server records the approved conditions in the database and updates the contract status.

[0653] As a concrete example, consider a scenario where a major telecommunications company manages contracts with corporate clients. This company uses this system to analyze each client's contract and automatically proposes the optimal pricing plan based on data usage and communication service utilization. Sales representatives can approve the proposed plan with a single click and then focus on detailed communication with the client. This leads to increased efficiency in sales activities and improved customer satisfaction.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The server connects to the database to retrieve contract information for each company. This contract information includes details such as terms, obligations, and fees.

[0657] Step 2:

[0658] The server analyzes the acquired contract information using natural language processing technology to extract important contract terms and relevant clauses. This clarifies the elements that should be emphasized in a particular contract.

[0659] Step 3:

[0660] The server collects historical transaction information from the database. This information includes past order history and details of transaction terms, and is used as foundational data for analysis.

[0661] Step 4:

[0662] The server analyzes collected historical transaction data to identify common usage patterns. This reveals a company's past behavior and trends, which can then be used for predictions.

[0663] Step 5:

[0664] The server automatically generates optimal additional order conditions using a generation mechanism based on contract terms and transaction patterns obtained through natural language processing. These generated conditions include specific details and plans to be used in the next transaction.

[0665] Step 6:

[0666] The server sends the generated additional order conditions to the terminal and presents them to the user. The user reviews the conditions via the terminal and checks them as appropriate.

[0667] Step 7:

[0668] Users can approve or modify the additional order conditions they have reviewed through the interface. If modifications are necessary, they can enter the information, and the system will recalculate.

[0669] Step 8:

[0670] When a user approves the terms, the server records the approved terms in the database and updates the company's contract status. This ensures that contract management is always based on the latest information.

[0671] (Example 1)

[0672] 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".

[0673] Improving the efficiency of inter-company contract management is a critical challenge for many organizations. Current methods rely on manual processes for analyzing contract terms and generating additional order conditions, which are time-consuming, costly, and prone to errors. Therefore, there is a need for systems that automate contract management tasks and provide optimal contract terms quickly and accurately.

[0674] 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.

[0675] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for collecting past transaction information and identifying common usage patterns. This enables efficient collection and analysis of contract information, as well as the automatic generation and management of optimal additional order conditions.

[0676] "Means of obtaining contract information for each company" refers to a function for obtaining information about contracts exchanged between companies from storage devices such as databases.

[0677] "Methods for analyzing contract terms and extracting important relevant information using natural language processing" refers to technologies that automatically understand the text written in a contract and efficiently analyze and extract contract terms and related important information.

[0678] "Means of collecting past transaction information and identifying common usage patterns" refers to the process of collecting a company's transaction history from a database and analyzing that data to reveal similarities and trends.

[0679] "Methods for automatically generating additional order conditions using a generative AI model" refers to a process that utilizes artificial intelligence technology to automatically create optimal additional order conditions based on predictions and pattern analysis.

[0680] "A means of presenting the generated additional order conditions to an information processing device and enabling confirmation" refers to an interface that displays the generated order conditions on a device such as a terminal, allowing the user to confirm their contents.

[0681] "Means of providing an interactive interface that allows users to modify the conditions presented" refers to a function that provides an interactive operation screen designed to allow users to directly modify or edit the generated conditions.

[0682] "Means for storing approved additional order conditions in an information storage device" refers to a function that saves conditions approved by the user in data storage so that they can be referenced and managed later.

[0683] The system of this invention is built to streamline contract management. Specifically, it includes a series of processes in which servers, terminals, and users work together.

[0684] The server first accesses the database to retrieve contract information for each company. Because this process handles a large amount of contract data, a server capable of high-speed and accurate data processing is used. Furthermore, the server applies natural language processing technology to extract important conditions and relevant clauses from the retrieved contract information. This process utilizes natural language processing libraries and text analysis software.

[0685] Next, the server collects historical transaction information from the company's database and identifies transaction usage patterns based on this information. Machine learning algorithms come into play here, automatically analyzing patterns and trends within the data. Based on this analysis, the server utilizes a generative AI model to generate optimal additional order conditions. This AI model is designed to learn from large amounts of data and output optimized conditions.

[0686] The generated conditions are transferred to the terminal and presented to the user. The terminal visually displays the generated conditions via a user interface. The user can review the conditions on the interface and modify them as needed. The interface is interactively designed to intuitively support the user's operation.

[0687] Ultimately, once the user approves the terms, the server stores those terms in the database and updates the company's contract status in real time. This automated process significantly improves the accuracy and efficiency of contract management.

[0688] As a concrete example, consider a scenario where a telecommunications carrier manages contracts for corporate clients. This carrier can use its system to automatically propose the optimal pricing plan based on each customer's data usage and service history. Users can easily review and approve the proposed plan, saving time and allowing them to work efficiently.

[0689] Examples of prompts used in a generative AI model include specific instructions such as, "Based on the corporate customer's data usage over the past 12 months and contract terms, generate the optimal pricing plan to recommend for the next contract renewal." This allows the AI ​​to develop specific judgment criteria and recognition skills.

[0690] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0691] Step 1:

[0692] The server retrieves corporate contract information from a database. The input consists of company-specific contract information within the database. Using a specified company ID, the server quickly extracts contract data specific to that company. The output is a set of contract information including details such as contract terms, duration, and fees. The retrieved data is then structured to prepare it for text analysis.

[0693] Step 2:

[0694] The server applies natural language processing to the acquired contract information to analyze and extract important conditions and clauses. The input is the contract information obtained in step 1. The server tokenizes the text and processes the data using specific patterns to extract keywords. This process organizes the output into a list format of the main conditions of each contract. The analysis clarifies the specifications and restrictions that are intended to be used.

[0695] Step 3:

[0696] The server collects a company's past transaction history from a database to identify common usage patterns. The input is a company's past order records and transaction history. The server applies machine learning algorithms to reveal regularities and trends based on this data. The output provides an overview of the company's transaction usage patterns, which allows for analysis of the company's characteristics based on its transaction history.

[0697] Step 4:

[0698] The server uses a generative AI model to generate optimal additional order conditions based on analyzed contract terms and historical transaction information. The input is the data obtained in steps 2 and 3. The server prompts the AI ​​model to calculate order conditions that meet the company's needs. The output is a list of proposed pricing plans and specification requirements. This generation process designs a customized optimal plan for each company.

[0699] Step 5:

[0700] The server transfers the generated additional order conditions to the terminal and presents them to the user. The inputs are the conditions generated in step 4, and the server sends these to the terminal as visualized data. The terminal displays the suggested content in an intuitive and easy-to-understand format through the user interface. The output is a screen display of the conditions presented to the user. The user can quickly grasp the information they need.

[0701] Step 6:

[0702] The user operates the terminal, reviews the presented conditions, and modifies them if necessary. The input is the conditions presented in step 5. The user can review each item on the interface and adjust the plan and pricing details. The output is the final adjusted or unchanged conditions. The conditions approved by the user will be used for future contract renewals.

[0703] Step 7:

[0704] The server records the approved additional order conditions in the database and updates the company's contract status. The input consists of the conditions approved by the user in step 6. The server integrates these conditions with existing contract data and stores them while maintaining consistency. The output is a set of updated contract information. This ensures that the latest information is maintained and can be referenced in real time.

[0705] (Application Example 1)

[0706] 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".

[0707] Managing inter-company contracts and transaction terms is complex, requiring the creation and rapid confirmation of terms tailored to the specific needs of each organization. However, current methods require significant time and effort for analyzing contract information and optimizing terms, making efficiency improvements urgently needed. Furthermore, intuitive interfaces for reviewing and modifying terms are not adequately provided, often leading to cumbersome user approval processes.

[0708] 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.

[0709] In this invention, the server includes means for acquiring contract information for each organization, means for analyzing contract terms using natural language processing and extracting relevant important information, and means for collecting historical transaction data and identifying standard usage patterns. This enables the automatic generation of optimal contract terms for customers, rapid confirmation of terms using smart devices, and streamlining of the approval process.

[0710] "Means of obtaining contract information for each organization" refers to methods for accessing and obtaining contract-related information held by each organization.

[0711] "A means of analyzing contract terms and extracting relevant important information using natural language processing" refers to a method of analyzing information contained in a contract using natural language processing technology to find necessary contract terms and related information.

[0712] "Methods for collecting historical transaction data and identifying standard usage patterns" refers to methods for collecting data on past transactions and identifying common usage trends from that data.

[0713] A "generation method" is a method for automatically creating optimal transaction terms and contract terms based on extracted information and historical data.

[0714] A "visualization device" is a device that provides a visual interface to display generated transaction terms and contract terms in an easy-to-understand manner for the user.

[0715] An "interactive interface" is a two-way user interface designed to allow users to review, approve, and modify contract terms and transaction conditions.

[0716] A "management device" refers to a system or device used to update an organization's contract status based on approved transaction and contract terms.

[0717] An "information management device" is a database system for efficiently storing and managing contract information and transaction data.

[0718] The system that realizes this invention automates the process of generating, confirming, and approving optimal contract terms by exchanging information between servers, terminals, and users in order to efficiently manage contracts.

[0719] First, the server uses Google Cloud SQL to retrieve and store contract information for each organization from the database. This information includes contract terms and historical transaction data. The server then uses TensorFlow to analyze the documents within the contracts and extract important contract terms using natural language processing algorithms.

[0720] Next, the server aggregates the past transaction data and performs analysis to identify common usage patterns. In this step, a generative AI model using PyTorch plays a key role, automatically generating optimal additional trading conditions based on the extracted conditions and usage patterns.

[0721] The server then transfers the generated conditions to a visualization device developed in Unity, allowing users to visually confirm the contract terms via their smart devices. The device provides an interactive interface for condition confirmation, enabling intuitive operation for users to approve or modify the conditions.

[0722] As a concrete example, a company can use this system to propose the optimal pricing plan based on past data when renewing a contract. Users can instantly review the presented terms using their smart devices and approve them on the spot if they are satisfied. As a result, contract renewals can be expedited, leading to improved operational efficiency.

[0723] As an example of a prompt, the instruction given to the generating AI model is as follows: "Based on past transaction history and contract terms, please propose the optimal pricing plan."

[0724] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0725] Step 1:

[0726] The server retrieves organization-specific contract information from Google Cloud SQL. The input is the organization's identifier, and the output includes contract terms and historical transaction data. The server uses database queries to collect relevant information.

[0727] Step 2:

[0728] The server uses TensorFlow to analyze contract information obtained through natural language processing. The input is the document information of the contract, and the output is the extracted contract terms. The server applies natural language algorithms to identify important terms.

[0729] Step 3:

[0730] The server collects historical transaction data and identifies common usage patterns. The input is the historical transaction history, and the output is the result of identifying usage patterns. The server extracts patterns using data mining techniques.

[0731] Step 4:

[0732] The server is a generative AI model using PyTorch that generates optimal additional trading conditions based on extracted conditions and usage patterns. The input is contract conditions and usage patterns, and the output is the optimized trading conditions. The server uses a deep learning algorithm to optimize the conditions.

[0733] Step 5:

[0734] The server transfers the generated conditions to the terminal through a visualization device built with Unity. The input is optimized trading conditions, and the output is visual information displayed on the terminal. The server visualizes the data using UI components.

[0735] Step 6:

[0736] The user reviews the displayed transaction terms using the interactive interface provided on the terminal. Inputs are visual information, and outputs are instructions for approval or modification. The user interacts with the interface and selects the conditions.

[0737] Step 7:

[0738] The terminal sends approved conditions to the server based on user instructions. The input is user approval information, and the output is updated condition data. The terminal transmits information via network communication.

[0739] Step 8:

[0740] The server stores the approved conditions in the Google Cloud SQL information management device and updates the contract status. The input is the updated condition data, and the output is a notification that the update is complete. The server performs a write operation to the database.

[0741] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0742] The system of the present invention highly streamlines contract management in inter-company transactions and, by recognizing user emotions, enables more appropriate and flexible customer service. Specific embodiments of this system are described below.

[0743] This system first uses a server to retrieve contract information for each company from a database. This information includes important details related to the contract, such as transaction terms, fees, and duration. The server then uses natural language processing technology to analyze this contract information and extract the necessary contract terms and important clauses.

[0744] In parallel, the server collects historical transaction information from companies. By analyzing the collected data, the server understands transaction history and usage patterns, and based on that, identifies common usage trends.

[0745] Based on these analysis results, the server automatically generates optimal additional order conditions for each company using a generation mechanism. These additional order conditions include the specifications and pricing plans required for the next transaction. The generated conditions are presented to the user via the terminal.

[0746] Here, the emotion engine, a key feature of this invention, plays a crucial role. While the user is reviewing additional order conditions, the terminal uses the emotion engine to recognize the user's emotions in real time. This emotion data is used to adjust the content and presentation method based on the user's response. For example, if the user expresses dissatisfaction, the details of the conditions can be displayed more clearly, or additional explanations can be provided.

[0747] Once a user reviews and approves the terms, the server records this information in a database and updates the contract status. Furthermore, user sentiment data is reflected in future offer presentations through a feedback function. For example, if a company providing communication services detects that a user is uneasy about the proposed plan, the system can suggest alternative plans and respond flexibly. This allows sales representatives to interact with customers more effectively and efficiently, leading to improved customer satisfaction.

[0748] The following describes the processing flow.

[0749] Step 1:

[0750] The server connects to the database and retrieves contract information for each company. This information includes contract terms, fees, and obligation periods.

[0751] Step 2:

[0752] The server analyzes the acquired contract information using natural language processing technology. Through this analysis, it extracts important contract terms and relevant clauses.

[0753] Step 3:

[0754] The server collects past transaction information of companies from a database. The collected information includes order history and detailed transaction terms, which are then used for analysis.

[0755] Step 4:

[0756] The server analyzes the collected transaction information to identify common usage patterns. This analysis allows for an understanding of a company's past behavior and trends.

[0757] Step 5:

[0758] The server automatically generates optimal additional order conditions using a generation mechanism based on contract terms and transaction patterns obtained through natural language processing. These conditions include specific specifications and plans for conducting new transactions.

[0759] Step 6:

[0760] The generated additional order conditions are sent from the server to the terminal. The terminal presents these conditions to the user. The presented conditions are displayed in a format that the user can review.

[0761] Step 7:

[0762] The device uses an emotion engine to recognize the user's emotions in real time while the user is reviewing the conditions. The recognized emotions are used to adjust the presented content.

[0763] Step 8:

[0764] Users can review additional order conditions and approve or modify them based on sentiment-based feedback. If modifications are needed, users enter this information into their device.

[0765] Step 9:

[0766] Additional order conditions approved by the user are sent to the server. The server records this in its database and updates the company's contract status.

[0767] Step 10:

[0768] The server analyzes the user's emotional data, recognized by the emotion engine, using a feedback function and incorporates this into subsequent conditional suggestions. This feedback allows the system to provide more appropriate conditions.

[0769] (Example 2)

[0770] 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".

[0771] In business-to-business transactions, contract management becomes complex and inefficient. Furthermore, traditional systems struggle to respond flexibly to user emotions, limiting the potential for improving customer satisfaction.

[0772] 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.

[0773] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for collecting past transaction data and identifying common usage patterns. This streamlines contract management and enables flexible customer service based on user sentiment.

[0774] A "corporation" is a legal entity or business group established for a specific purpose, and is an organization that pursues profit through commercial activities.

[0775] "Contract information" refers to a document that records the agreed-upon terms and conditions of a transaction, including the terms and conditions of the transaction, and clearly specifies the matters that both parties must comply with.

[0776] "Natural language processing" is a technology that enables computers to understand and process human language, and is a process used for extracting information and analyzing content within text.

[0777] "Transaction data" refers to historical information related to the buying and selling of goods and services, and is a collection of data that includes past sales status and purchase history.

[0778] A "generative model" is a technology that uses artificial intelligence to automatically generate specific information or conditions, and is an algorithm that learns patterns from large amounts of data.

[0779] An "information processing device" is an electronic device used for inputting, processing, and outputting data, and is a general-purpose computing device such as a computer or smartphone.

[0780] A "storage medium" is a physical medium used to store digital data for extended periods, and includes devices such as hard disk drives and SSDs.

[0781] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice, and it is a process that enables computers to understand and respond to human emotions.

[0782] An "operation screen" is a visual interface for users to interact with a computer system, and it is a screen where information is displayed and inputted.

[0783] The system of this invention streamlines contract management in inter-company transactions and enables responses based on user sentiment. This system is mainly composed of three elements: a server, terminals, and users.

[0784] The server retrieves contract information from a database for each company. This involves extracting necessary data by issuing SQL queries using conventional search techniques. This information includes transaction terms and contract periods. Subsequently, the server analyzes this contract information using natural language processing technology. Specifically, it uses text processing libraries (e.g., Python's NLTK or SpaCy) to extract contract terms and identify important clauses.

[0785] The server then collects historical transaction data and performs analysis to identify common usage patterns. This analysis uses data processing libraries (e.g., pandas and NumPy) to clarify the user's past behavior patterns. Based on these results, the server uses a generative AI model to automatically generate the additional conditions that will be needed next. In this process, prompts are input to the AI ​​model to obtain the optimal conditions. An example of a prompt might be, "What are the conditions for this company to make its next ideal transaction?"

[0786] The generated additional conditions are presented to the user via a terminal. The terminal provides a visual interface to allow the user to intuitively understand the presented conditions. For example, it might use a web browser to organize information in a dashboard format and display it using HTML and CSS for a designed presentation.

[0787] While the user is reviewing the conditions, the device uses emotion recognition technology to analyze the user's emotions in real time. The device uses a webcam and microphone, and leverages emotion recognition APIs (e.g., Microsoft Azure or Google's Emotion AI) to analyze the user's facial expressions and voice. Based on this emotion data, the device dynamically adjusts the content presented to ensure that the user receives the most relevant information.

[0788] In this way, collaboration between servers, terminals, and users streamlines corporate contract management and enables flexible and personalized customer service.

[0789] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0790] Step 1:

[0791] The server retrieves contract information for each company from the database. Specifically, the server uses SQL queries to extract relevant contract information based on the company ID. Based on this input company ID, it outputs contract information including transaction terms and contract period.

[0792] Step 2:

[0793] The server analyzes the contract information obtained using natural language processing technology. Specifically, the server utilizes the Python NLTK library to tokenize the text and extract important contract terms. Based on this input contract information, it outputs the analyzed terms and clauses.

[0794] Step 3:

[0795] The server collects and analyzes historical transaction data. It retrieves past transaction history from the database and processes the data using the Python pandas library. Using this historical data as input, it outputs trading trends and common usage patterns.

[0796] Step 4:

[0797] The server automatically generates additional conditions using a generative AI model based on the extracted contract terms and past transaction data. Here, the server inputs a prompt message into the AI ​​model: "Please tell me the conditions for this company to make its next ideal transaction." Based on this prompt message, the server outputs the optimal additional conditions.

[0798] Step 5:

[0799] The server sends the generated additional conditions to the terminal, which then presents them to the user. The terminal launches a web application and displays the information visualized using HTML and CSS on the screen. It then outputs the additional conditions received as input, formatted in a way that is easy for the user to understand.

[0800] Step 6:

[0801] The device uses emotion recognition technology to analyze user reactions in real time. The device captures the user's face and voice using its camera and microphone, and analyzes them using an emotion recognition API. Based on this input audio and image data, it outputs the user's emotional state.

[0802] Step 7:

[0803] The device dynamically adjusts the content and display methods presented to the user based on analyzed sentiment data. For example, if the user expresses dissatisfaction, the device will display additional supplementary information or different visuals. By outputting adjusted information according to the input sentiment data, the user experience is optimized.

[0804] (Application Example 2)

[0805] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0806] Traditional B2B transaction management systems often stifled complex and time-consuming contract management, making it difficult to respond flexibly to user emotions. Furthermore, in various business settings, including e-commerce sites, there is a growing need to recognize user emotions in real time and provide suggestions based on those emotions to improve the user's purchasing experience. A system is needed to address these challenges and enable more efficient and flexible contract management and customer service.

[0807] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0808] In this invention, the server includes means for acquiring contract information for each company, means for analyzing contract terms using natural language processing and extracting important relevant information, and means for acquiring the user's emotions in real time using emotion recognition technology and adjusting the presented content. This improves the efficiency of information extraction in contract management and enables flexible suggestions based on the user's emotions.

[0809] "Means of obtaining contract information for each company" refers to a function that collects information such as contracts and transaction terms related to each company from a data system.

[0810] "Methods for analyzing contract terms using natural language processing and extracting important relevant information" refers to technologies that automatically extract important information such as conditions and clauses from contracts written in human language.

[0811] "Means of collecting past transaction information and identifying common usage patterns" refers to the process of gathering data on past transactions and finding similar patterns or regularities within that data.

[0812] The "generation means for automatically generating additional order conditions based on extracted contract conditions and past transaction information" refers to a function in which the system automatically constructs new order conditions based on the analyzed contract conditions and transaction history.

[0813] "A means of displaying generated additional order conditions on a terminal and enabling confirmation and approval" refers to an interface that displays the order conditions constructed by the system on a terminal operated by the user, allowing the user to confirm and approve their contents.

[0814] "Means for retaining approved additional order conditions in a data storage device" refers to a process for securely and efficiently recording and storing order conditions approved by the user in data storage.

[0815] "A means of acquiring user emotions in real time using emotion recognition technology and adjusting the content presented" refers to a function that grasps the user's emotional state in real time using sensors and analysis algorithms, and optimizes the information presented to the user based on that information.

[0816] The system for implementing this invention streamlines corporate contract management and customer service. The server retrieves relevant data from databases of contract information and transaction history, and analyzes contract terms using natural language processing technology. This makes it possible to accurately extract contract information for each company. It is recommended to use open-source natural language processing libraries on the server.

[0817] During program execution, emotion recognition technology will be integrated into the server to monitor user emotions in real time. This technology obtains input from terminal sensors, including cameras and microphones, and analyzes the user's emotions using a generative AI model. The analysis results will be a crucial element in adjusting the information and conditions presented to the user. Ideally, the terminal should be operated in conjunction with a cloud-based analytics platform.

[0818] As a concrete example, when a user is considering a new communication plan, the server analyzes the user's past usage patterns and automatically suggests the optimal plan. In this process, if the emotion recognition function detects the user's anxiety, the device will present an alternative communication plan, thereby improving user satisfaction.

[0819] An example of a prompt to input into the generating AI model might be, "I'm worried about whether this product is right for me. Please suggest alternative products based on my past purchase history." This implementation allows the system to operate dynamically and user-friendly, maximizing business synergies.

[0820] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0821] Step 1:

[0822] The server retrieves contract information from the company's database. The input for this step is identifying information such as the company ID and contract period, and the output is the retrieved contract information as digital data. The server then performs data processing to organize this data into a structured format.

[0823] Step 2:

[0824] The server analyzes contract information using natural language processing technology. The input is the contract information obtained in step 1, and the output is the contract terms and important clauses extracted through the analysis. In this analysis process, summarization and keyword extraction are performed through linguistic analysis of the text data.

[0825] Step 3:

[0826] The server collects past transaction history from the database and identifies common usage patterns. The input for this step is the raw data about the customer's transaction history, and the output is the identified common usage patterns and trends. Pattern recognition and statistical analysis are used as data processing techniques.

[0827] Step 4:

[0828] The server automatically generates optimal additional order conditions based on extracted contract terms and usage pattern information. The input is the information obtained in steps 2 and 3, and the output is the generated additional order conditions. This generation is performed through data fusion and inference processing using an AI model.

[0829] Step 5:

[0830] The terminal presents the generated additional order conditions to the user. The input is the conditions sent from the server in step 4, and the output is the operation data that the user confirms and approves. Specific actions on the terminal include providing a visual display via the interface and offering preferred selection options.

[0831] Step 6:

[0832] The server uses emotion recognition technology to observe user responses in real time and adjust the presentation content accordingly. Input is sensor data from sensory devices connected to the terminal, and output is the optimized presentation content. Emotion analysis employs an algorithm trained on a generative AI model.

[0833] Step 7:

[0834] When a user approves additional order conditions, the server records those conditions in the data storage device. The input is the approval data from the user, and the output is the recorded new contract status. Specifically, the backend records the information and updates the contract status after the approval process.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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."

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0856] The following is further disclosed regarding the embodiments described above.

[0857] (Claim 1)

[0858] Means of obtaining contract information for each company,

[0859] A means of analyzing contract terms using natural language processing and extracting important relevant information,

[0860] A means of collecting past transaction information and identifying common usage patterns,

[0861] A generation means that automatically generates additional order conditions based on extracted contract terms and past transaction information,

[0862] A means to present the generated additional order conditions to the terminal and enable confirmation and approval,

[0863] A means of storing approved additional order conditions in a database,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising means for providing an interactive interface for the user to approve or modify the generated additional order conditions when presenting the conditions to the user.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising a management means for automatically updating the company's contract status using additional order conditions after the user has approved those conditions.

[0869] "Example 1"

[0870] (Claim 1)

[0871] Means of obtaining contract information for each company,

[0872] A means of analyzing contract terms using natural language processing and extracting important relevant information,

[0873] A means of collecting past transaction information and identifying common usage patterns,

[0874] A means for automatically generating additional order conditions using a generation AI model based on extracted contract terms and past transaction information,

[0875] A means for presenting the generated additional order conditions to an information processing device and enabling confirmation,

[0876] A means of providing an interactive interface that allows the user to modify the conditions presented,

[0877] Means for storing approved additional order conditions in an information storage device,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, further comprising means for automatically updating the company's contract status using the generated additional order conditions when the user approves those conditions, after presenting the conditions to the user.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for presenting the generated additional order conditions to the user through a user interface and enabling approval with a single click.

[0883] "Application Example 1"

[0884] (Claim 1)

[0885] Means of obtaining contract information for each organization,

[0886] A means of analyzing contract terms using natural language processing and extracting relevant important information,

[0887] A means of collecting historical transaction data and identifying standard usage patterns,

[0888] A generation means for automatically generating additional transaction terms based on extracted contract terms and historical transaction data,

[0889] A means for presenting the generated additional transaction terms to a visualization device, enabling confirmation and approval,

[0890] Means for storing approved additional transaction terms in an information management device,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, further comprising means for providing an interactive interface for the user to approve or modify the terms when presenting the generated additional transaction terms to the user.

[0894] (Claim 3)

[0895] The system according to claim 1, further comprising a management device that automatically updates the organization's contract status using additional transaction terms after the user has approved those terms.

[0896] "Example 2 of combining an emotion engine"

[0897] (Claim 1)

[0898] Means of obtaining contract information for each company,

[0899] A means of analyzing contract terms using natural language processing and extracting important relevant information,

[0900] A means of collecting historical transaction data and identifying common usage patterns,

[0901] A means for automatically generating additional conditions using a generative model based on extracted contract terms and historical transaction data,

[0902] A means for presenting the generated additional conditions to an information processing device and enabling confirmation and approval,

[0903] Means for storing approved additional conditions on a storage medium,

[0904] A means of recognizing the user's emotions and dynamically adjusting the content presented based on those emotions,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, further comprising means for providing an interactive screen for the user to approve or modify the generated additional conditions when presenting the conditions to the user.

[0908] (Claim 3)

[0909] The system according to claim 1, further comprising a management means for automatically updating the company's contract status using additional conditions after the user has approved those conditions.

[0910] "Application example 2 when combining with an emotional engine"

[0911] (Claim 1)

[0912] Means of obtaining contract information for each company,

[0913] A means of analyzing contract terms using natural language processing and extracting important relevant information,

[0914] A means of collecting past transaction information and identifying common usage patterns,

[0915] A generation means that automatically generates additional order conditions based on extracted contract terms and past transaction information,

[0916] A means to present the generated additional order conditions to the terminal and enable confirmation and approval,

[0917] Means for storing approved additional order conditions in a data storage device,

[0918] A means of acquiring the user's emotions in real time using emotion recognition technology and adjusting the presented content accordingly,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, further comprising means for providing an interactive user interface for the user to approve or modify the generated additional order conditions when presenting the conditions to the user.

[0922] (Claim 3)

[0923] The system according to claim 1, further comprising a status management means for automatically updating the company's contract status using additional order conditions after the user has approved those conditions. [Explanation of Symbols]

[0924] 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 for each organization, A means of analyzing contract terms using natural language processing and extracting relevant important information, A means of collecting historical transaction data and identifying standard usage patterns, A generation means for automatically generating additional transaction terms based on extracted contract terms and historical transaction data, A means for presenting the generated additional transaction terms to a visualization device, enabling confirmation and approval, Means for storing approved additional transaction terms in an information management device, A system that includes this.

2. The system according to claim 1, further comprising means for providing an interactive interface for the user to approve or modify the generated additional transaction terms when presenting them to the user.

3. The system according to claim 1, further comprising a management device that automatically updates the organization's contract status using additional transaction terms after the user has approved those terms.

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