system

Abstract

The system according to this embodiment aims to centrally manage contract information and analyze it using AI. [Solution] The system according to the embodiment comprises a reception unit, a management unit, an analysis unit, and a key point extraction unit. The reception unit receives contract information from users. The management unit centrally manages the contract information received by the reception unit. The analysis unit uses AI to analyze the contract information managed by the management unit. The key point extraction unit extracts the key points of the contract content analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the unified management and analysis of contract information have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to centrally manage contract information and analyze it by AI.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a management unit, an analysis unit, and a key point extraction unit. The reception unit receives contract information from a user. The management unit centrally manages the contract information received by the reception unit. The analysis unit analyzes the contract information managed by the management unit by AI. The key point extraction unit extracts key points of the contract content analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage contract information and analyze it using AI. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] 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.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

【

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The contract management system according to an embodiment of the present invention is a system for realizing a society where anyone can enter into contracts with peace of mind. In this contract management system, the user inputs contract information, the system centrally manages that information, and AI analyzes it to extract the key points of the contract content. For example, the user inputs contract information for scholarships, water, gas, internet services, subscription services, etc. This information is aggregated and centrally managed in the system. Next, the AI ​​analyzes the aggregated contract information and extracts the important points by analyzing the contract content. For example, it extracts the contract fee, payment deadline, contract period, special notes, etc. This allows the user to easily grasp the key points of the contract. Furthermore, if the user changes their contract information, the system notifies the change all at once. For example, if the user gets married and their surname changes, or moves, and it is necessary to notify the contracting party of the change, the system notifies them all at once. This saves the user time and allows companies to reliably grasp changes in customer information. The system also has a function that allows the AI ​​to translate long contract texts and summarize the key points concisely. For example, the AI ​​analyzes long terms of service or contracts, extracts the important points, and provides them to the user. This makes it easier for users to understand contract terms, and helps companies reduce customer misunderstandings. In today's contract-heavy society, this system allows users to enter into contracts with confidence, and enables companies to efficiently manage customer information. Ultimately, the goal is to create a system that stands at the heart of all contracts. This will enable the contract management system to efficiently manage, analyze, and extract key points from user contract information.

[0029] The contract management system according to this embodiment comprises a reception unit, a management unit, an analysis unit, and a key point extraction unit. The reception unit receives contract information from users. The reception unit can receive, for example, contract information such as scholarships, water, gas, internet services, and subscription services entered by users. The reception unit can receive contract information, for example, through web forms or mobile applications. The reception unit can also receive scanned data of contracts uploaded by users. For example, the reception unit receives contract information when a user scans and uploads a contract. The management unit centrally manages the contract information received by the reception unit. The management unit centrally manages the contract information using, for example, a database. The management unit can organize and efficiently manage the contract information by category. For example, the management unit manages contract information by dividing it into categories such as scholarships, water, gas, internet services, and subscription services. The analysis unit uses AI to analyze the contract information managed by the management unit. The analysis unit analyzes the contract information using, for example, a machine learning algorithm. The analysis unit can use natural language processing technology to extract the key points of the contract. For example, the analysis unit analyzes the text data of the contract and extracts the important points. The key point extraction unit extracts the key points of the contract analyzed by the analysis unit. The key point extraction unit extracts key points such as the contract fee, payment deadline, contract period, and special provisions. The key point extraction unit can display the extracted key points in a visually easy-to-understand format for the user. For example, the key point extraction unit displays the key points of the contract as graphs or charts. As a result, the contract management system according to this embodiment can efficiently manage, analyze, and extract key points from the user's contract information.

[0030] The reception department receives contract information from users. For example, it can receive contract information for scholarships, water, gas, internet services, and subscription services entered by users. Specifically, the reception department receives contract information through web forms and mobile applications. Web forms provide fields for users to enter contract information, allowing them to input details such as contract type, subscriber name, contract start date, contract end date, fees, and payment method. Mobile applications allow users to enter contract information using smartphones or tablets, and similar fields are provided. The reception department can also receive scanned data of contracts uploaded by users. For example, users can scan and upload contracts to receive contract information. The scanned data is saved as an image file and converted to text data in subsequent processing. Furthermore, the reception department can use OCR (Optical Character Recognition) technology to extract text information from the scanned data and import it as contract information. This allows users to digitize paper contracts and import them into the system. The reception department temporarily stores the received contract information and prepares to hand it over to the management department. This allows the reception department to efficiently receive contract information from users and play a role in ensuring a smooth data flow throughout the entire system.

[0031] The Management Department centrally manages contract information received by the Reception Department. For example, the Management Department uses a database to centrally manage contract information. Specifically, the Management Department can efficiently store, search, and update contract information using relational databases or NoSQL databases. Relational databases manage contract information in a table format and assign a unique ID to each contract. This makes searching and filtering contract information easy. NoSQL databases store contract information in a document format and have flexible schemas, allowing for efficient management of different types of contract information. The Management Department can organize and efficiently manage contract information by category. For example, the Management Department manages contract information by dividing it into categories such as scholarships, water, gas, internet services, and subscription services. Each category has specific fields and attributes, allowing for accurate recording of contract information details. Furthermore, the Management Department can implement version control of contract information, saving past contract information and change history. This allows for tracking the change history of contract information and reverting to previous states as needed. The Management Department implements access control and encryption to ensure the security of contract information. For example, access permissions can be set for each user, ensuring that only specific users can access specific contract information. Furthermore, contract information can be encrypted and stored to prevent unauthorized access and data leaks. This allows the management department to manage contract information securely and efficiently, improving the overall reliability of the system.

[0032] The analysis unit uses AI to analyze contract information managed by the management unit. For example, the analysis unit analyzes contract information using machine learning algorithms. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the text data of contracts and extract key points. For instance, by tokenizing the text data of contracts and analyzing the meaning of each token, it identifies key points such as contract fees, payment deadlines, contract duration, and special provisions. The analysis unit extracts key points from the text data of contracts using a pre-trained model. This allows the analysis unit to quickly and accurately analyze the content of contracts and extract key points. Furthermore, the analysis unit can analyze patterns and trends in contract information to predict future risks and opportunities. For example, based on past contract information, it can predict how specific contract conditions will impact the future, which can be used for risk management and strategic planning. The analysis unit can also use anomaly detection algorithms to detect abnormal patterns and fraudulent data within contract information. This allows the analysis unit to improve the overall reliability and security of the system through the analysis of contract information.

[0033] The key points extraction unit extracts the key points of the contract content analyzed by the analysis unit. For example, the key points extraction unit extracts key points such as contract fees, payment deadlines, contract period, and special notes. Specifically, based on the key points information provided by the analysis unit, the key points extraction unit selects information important to the user and displays it in a visually easy-to-understand format. For example, the key points extraction unit displays the key points of the contract content as graphs and charts. By showing the trend of fees as a line graph or displaying payment deadlines in a calendar format, it enables users to understand intuitively. In addition, the key points extraction unit displays contract information in a dashboard format, allowing users to view multiple contract information at a glance. This makes it easier for users to grasp the overall picture of the contract information. Furthermore, the key points extraction unit provides functions for users to search and filter specific contract information. For example, it enables users to search for contract information based on specific contract conditions or periods and quickly obtain the necessary information. Based on user feedback, the key points extraction unit can improve its display format and functions to provide a more user-friendly interface. This allows the key points extraction unit to provide users with easily understandable summaries of contract information, thereby improving the efficiency of contract management.

[0034] The notification unit can notify users of changes to contract information in bulk. For example, the notification unit will send notifications in bulk when a user needs to notify their contracting party of a change, such as when a user gets married and their surname changes, or when they move. The notification unit can notify users of changes to contract information in bulk using methods such as email notifications, push notifications, and SMS notifications. The notification unit will notify users of changes to contract information according to the notification method specified by the user. For example, if the user requests email notifications, the notification unit will notify them of changes to contract information via email. The notification unit can also notify users of changes to contract information via push notifications if the user requests push notifications. This reduces the effort required of the user by notifying them of changes to contract information in bulk. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input changes to contract information into AI, which can select the optimal notification method and send the notifications.

[0035] The reception desk can analyze the user's past contract history and suggest the optimal input method. For example, the reception desk can automatically display contract information that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest contract information to be used during a specific time period based on the user's past contract history. In this way, by analyzing past contract history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past contract history data into a generating AI and have the generating AI suggest the optimal input method.

[0036] The reception unit can filter the user's current contract status and areas of interest when entering contract information. For example, the reception unit can automatically exclude services that the user already has a contract for and display only new contract information. For example, the reception unit can prioritize displaying relevant contract information based on the user's areas of interest. For example, the reception unit can analyze the user's current contract status and filter out duplicate contract information. This allows the reception unit to provide highly relevant contract information by filtering based on the user's current contract status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's contract status data into a generating AI and have the generating AI perform the filtering.

[0037] The reception unit can prioritize inputting highly relevant contract information by considering the user's geographical location when entering contract information. For example, the reception unit can prioritize displaying region-specific contract information based on the user's current location. For example, the reception unit can propose the optimal contract plan based on the user's geographical location. For example, the reception unit can automatically reflect region-specific contract conditions by considering the user's location. This allows for the provision of region-specific contract information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and cause the generating AI to prioritize the display of highly relevant contract information.

[0038] The reception desk can analyze the user's social media activity and input relevant contract information when entering contract information. For example, the reception desk can analyze the user's interests and preferences on social media and suggest relevant contract information. For example, the reception desk can suggest the optimal contract plan based on the user's social media activity history. For example, the reception desk can consider the user's social media friendships and suggest services that their friends are subscribed to. In this way, by analyzing social media activity, contract information based on the user's interests can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant contract information.

[0039] The management department can adjust the level of detail in managing contract information based on the importance of the contract. For example, the management department can manage important contract information in detail and update it regularly. For example, the management department can manage less important contract information in a simplified manner and update it as needed. For example, the management department can adjust the content displayed on the management screen according to the importance of the contract. This allows for detailed management of important contract information by adjusting the level of detail based on the importance of the contract. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input contract information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in management.

[0040] The management department can apply different management algorithms depending on the contract category when managing contract information. For example, the management department can manage subscription service contract information based on a regular payment schedule. For example, the management department can manage utility bill contract information based on payment deadlines and rate fluctuations. For example, the management department can manage internet service contract information based on usage and contract period. This enables efficient management by applying management algorithms according to the contract category. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input contract information category data into a generating AI and have the generating AI execute the application of management algorithms.

[0041] The management department can determine management priorities based on the contract submission date when managing contract information. For example, the management department can prioritize managing contract information with an approaching submission deadline. For example, the management department can manage contract information whose submission deadline has passed as an archive. For example, the management department can set a contract information update schedule based on the submission date. This enables deadline-based management by determining management priorities based on the contract submission date. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input contract information submission date data into a generating AI and have the generating AI perform the determination of management priorities.

[0042] The management department can adjust the order of management based on the relevance of contracts when managing contract information. For example, the management department can group and manage highly relevant contract information. For example, the management department can manage less relevant contract information individually. For example, the management department can adjust the display order of the management screen based on the relevance of contracts. This allows for efficient management of related information by adjusting the order of management based on the relevance of contracts. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the relevance data of contract information into a generating AI and have the generating AI perform the adjustment of the order of management.

[0043] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of contracts during the analysis process. For example, the analysis unit can analyze the interrelationships of contracts and integrate and analyze the relevant contract information. For example, the analysis unit can eliminate redundant information by considering the interrelationships of contracts. For example, the analysis unit can apply the optimal analysis algorithm based on the interrelationships of contracts. This improves the accuracy of the analysis by considering the interrelationships of contracts. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input interrelationship data of contract information into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0044] The analysis unit can perform analysis while considering the attribute information of the contract submitter. For example, the analysis unit can perform analysis while considering the age and occupation of the contract submitter. For example, the analysis unit can perform analysis based on the past contract history of the contract submitter. For example, the analysis unit can perform analysis while considering the regional information of the contract submitter. This makes it possible to perform more accurate analysis by considering the attribute information of the contract submitter. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the attribute information data of the contract submitter into a generating AI and have the generating AI perform the analysis.

[0045] The analysis unit can perform analysis while considering the geographical distribution of contracts. For example, the analysis unit can analyze the geographical distribution of contracts and analyze contract trends for each region. For example, the analysis unit can perform analysis while considering region-specific contract conditions based on the geographical distribution. For example, the analysis unit can apply the optimal analysis algorithm while considering the geographical distribution. This makes region-specific analysis possible by considering the geographical distribution of contracts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input geographical distribution data of contract information into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant contract documents during the analysis process. For example, the analysis unit can improve the accuracy of the analysis results by referring to relevant contract documents. For example, the analysis unit can perform the analysis considering the background information of the contract based on the relevant documents. For example, the analysis unit can apply the optimal analysis algorithm by referring to relevant documents. This improves the accuracy of the analysis by referring to relevant documents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant document data of the contract information into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0047] The key point extraction unit can predict current key points by referring to past key point data during key point extraction. For example, the key point extraction unit predicts the key points of current contract information based on past key point data. For example, the key point extraction unit can extract important points by referring to past key point data. For example, the key point extraction unit can apply an optimal key point extraction algorithm based on past key point data. This allows for a more accurate prediction of current key points by referring to past key point data. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input past key point data into a generating AI and have the generating AI perform a prediction of current key points.

[0048] The key point extraction unit can apply different key point extraction methods to each contract category during key point extraction. For example, for subscription service contract information, the key point extraction unit can extract the price and payment deadline as key points. For example, for utility bill contract information, the key point extraction unit can extract the price fluctuation and payment deadline as key points. For example, for internet service contract information, the key point extraction unit can extract the usage status and contract period as key points. This makes it possible to extract more appropriate key points by applying a key point extraction method according to the contract category. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input contract information category data into a generating AI and have the generating AI execute the application of key point extraction methods.

[0049] The key point extraction unit can analyze changes in key points based on the contract submission date during key point extraction. For example, the key point extraction unit can prioritize extracting key points from contract information with an upcoming submission date. For example, the key point extraction unit can manage key points from contract information whose submission date has passed as an archive. For example, the key point extraction unit can analyze changes in key points based on the submission date and apply the optimal key point extraction algorithm. This makes it possible to extract key points more appropriately by analyzing changes in key points based on the contract submission date. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input contract information submission date data into a generating AI and have the generating AI perform an analysis of changes in key points.

[0050] The key point extraction unit can analyze key points by referring to relevant market data of the contract during key point extraction. For example, the key point extraction unit analyzes key points of contract information based on relevant market data. For example, the key point extraction unit can extract key points by referring to market data and considering the background information of the contract. For example, the key point extraction unit can apply an optimal key point extraction algorithm based on relevant market data. This improves the accuracy of key point analysis by referring to relevant market data. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input relevant market data of the contract information into a generating AI and have the generating AI perform the key point analysis.

[0051] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize providing notification methods that the user has preferred to use in the past. For example, the notification unit can suggest the optimal notification timing based on the user's past notification history. For example, the notification unit can analyze the user's past notification history and provide the optimal notification content. In this way, by referring to past notification history, the notification unit can provide the user with the most suitable notification method. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's notification history data into a generating AI and have the generating AI select the optimal notification method.

[0052] The notification unit can customize the notification method based on the user's current living situation when a notification is sent. For example, if the user is at work, the notification unit can provide a quiet notification method. For example, if the user is on vacation, the notification unit can provide a detailed notification method. For example, if the user is traveling, the notification unit can provide a concise notification method. By customizing the notification method according to the user's living situation, more appropriate notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user living situation data into a generating AI and have the generating AI perform the customization of the notification method.

[0053] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can provide the optimal notification method based on the user's current location. For example, the notification unit can suggest the optimal notification timing based on the user's geographical location information. For example, the notification unit can provide region-specific notification content, taking into account the user's location information. This allows for the provision of region-specific notification methods by considering the user's geographical location information. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal notification method.

[0054] The notification unit can analyze the user's social media activity and suggest notification methods when sending a notification. For example, the notification unit can suggest the optimal notification method based on the user's social media activity history. For example, the notification unit can analyze the user's interests and preferences on social media and provide relevant notification content. For example, the notification unit can consider the user's social media friendships and provide notification content that their friends might be interested in. In this way, by analyzing social media activity, the notification unit can provide the user with the most suitable notification method. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media data into a generating AI and have the generating AI suggest notification methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can improve input efficiency by referring to the user's past contract history when entering user contract information. For example, it can reduce input effort by automatically displaying previously entered contract information as suggestions. It can also prioritize suggesting input methods the user has used in the past (voice input, text input, etc.). Furthermore, it can predict and suggest contract information that the user will use at a specific time of day based on their past contract history. In this way, by utilizing past contract history, the system can provide users with the most suitable input method.

[0057] The management department can adjust the level of detail in managing contract information based on the importance of each contract. For example, important contract information can be managed in detail and updated regularly. Conversely, less important contract information can be managed in a simplified manner and updated as needed. Furthermore, the content displayed on the management screen can be adjusted according to the importance of each contract. This allows for detailed management of important contract information by adjusting the level of detail based on the importance of each contract.

[0058] The key point extraction unit can apply different key point extraction methods depending on the contract category when extracting key points from contract information. For example, for subscription service contract information, the price and payment deadline can be extracted as key points. Similarly, for utility service contract information, the price fluctuation and payment deadline can be extracted as key points. Furthermore, for internet service contract information, the usage status and contract period can be extracted as key points. This allows for more appropriate key point extraction by applying a key point extraction method appropriate to the contract category.

[0059] The management department can prioritize contract management based on the submission date of each contract. For example, it can prioritize managing contracts with approaching submission deadlines. Contracts whose submission deadlines have passed can also be managed as archives. Furthermore, it can set a contract update schedule based on the submission date. This allows for deadline-based management by prioritizing contracts based on their submission date.

[0060] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between contracts. For example, it can analyze the interrelationships between contracts and integrate related contract information for analysis. It can also eliminate redundant information by considering the interrelationships between contracts. Furthermore, it can apply the optimal analysis algorithm based on the interrelationships between contracts. As a result, the accuracy of the analysis is improved by considering the interrelationships between contracts.

[0061] The notification unit can select the optimal notification method when sending notifications, taking into account the user's geographical location. For example, it can prioritize displaying region-specific contract information based on the user's current location. It can also suggest the optimal notification timing based on the user's geographical location. Furthermore, it can provide region-specific notification content, taking the user's location into consideration. In this way, by considering the user's geographical location, it is possible to provide region-specific notification methods.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives contract information from users. For example, it can receive contract information for scholarships, water, gas, internet services, subscription services, etc., entered by the user. The reception desk receives contract information through web forms and mobile applications. It can also receive scanned data of contracts uploaded by the user. Step 2: The management department centrally manages the contract information received by the reception department. For example, a database can be used to centrally manage contract information and organize it by category for efficient management. Contract information can be managed by dividing it into categories such as scholarships, water, gas, internet services, and subscription services. Step 3: The analysis unit uses AI to analyze contract information managed by the management unit. For example, it analyzes contract information using machine learning algorithms and analyzes the text data of contracts using natural language processing technology. Step 4: The key points extraction unit extracts the key points of the contract content analyzed by the analysis unit. For example, it can extract key points such as the contract fee, payment deadline, contract period, and special provisions, and display them in a visually easy-to-understand format. The key points extraction unit displays the key points of the contract content as graphs or charts.

[0064] (Example of form 2) The contract management system according to an embodiment of the present invention is a system for realizing a society where anyone can enter into contracts with peace of mind. In this contract management system, the user inputs contract information, the system centrally manages that information, and AI analyzes it to extract the key points of the contract content. For example, the user inputs contract information for scholarships, water, gas, internet services, subscription services, etc. This information is aggregated and centrally managed in the system. Next, the AI ​​analyzes the aggregated contract information and extracts the important points by analyzing the contract content. For example, it extracts the contract fee, payment deadline, contract period, special notes, etc. This allows the user to easily grasp the key points of the contract. Furthermore, if the user changes their contract information, the system notifies the change all at once. For example, if the user gets married and their surname changes, or moves, and it is necessary to notify the contracting party of the change, the system notifies them all at once. This saves the user time and allows companies to reliably grasp changes in customer information. The system also has a function that allows the AI ​​to translate long contract texts and summarize the key points concisely. For example, the AI ​​analyzes long terms of service or contracts, extracts the important points, and provides them to the user. This makes it easier for users to understand contract terms, and helps companies reduce customer misunderstandings. In today's contract-heavy society, this system allows users to enter into contracts with confidence, and enables companies to efficiently manage customer information. Ultimately, the goal is to create a system that stands at the heart of all contracts. This will enable the contract management system to efficiently manage, analyze, and extract key points from user contract information.

[0065] The contract management system according to this embodiment comprises a reception unit, a management unit, an analysis unit, and a key point extraction unit. The reception unit receives contract information from users. The reception unit can receive, for example, contract information such as scholarships, water, gas, internet services, and subscription services entered by users. The reception unit can receive contract information, for example, through web forms or mobile applications. The reception unit can also receive scanned data of contracts uploaded by users. For example, the reception unit receives contract information when a user scans and uploads a contract. The management unit centrally manages the contract information received by the reception unit. The management unit centrally manages the contract information using, for example, a database. The management unit can organize and efficiently manage the contract information by category. For example, the management unit manages contract information by dividing it into categories such as scholarships, water, gas, internet services, and subscription services. The analysis unit uses AI to analyze the contract information managed by the management unit. The analysis unit analyzes the contract information using, for example, a machine learning algorithm. The analysis unit can use natural language processing technology to extract the key points of the contract. For example, the analysis unit analyzes the text data of the contract and extracts the important points. The key point extraction unit extracts the key points of the contract analyzed by the analysis unit. The key point extraction unit extracts key points such as the contract fee, payment deadline, contract period, and special provisions. The key point extraction unit can display the extracted key points in a visually easy-to-understand format for the user. For example, the key point extraction unit displays the key points of the contract as graphs or charts. As a result, the contract management system according to this embodiment can efficiently manage, analyze, and extract key points from the user's contract information.

[0066] The reception department receives contract information from users. For example, it can receive contract information for scholarships, water, gas, internet services, and subscription services entered by users. Specifically, the reception department receives contract information through web forms and mobile applications. Web forms provide fields for users to enter contract information, allowing them to input details such as contract type, subscriber name, contract start date, contract end date, fees, and payment method. Mobile applications allow users to enter contract information using smartphones or tablets, and similar fields are provided. The reception department can also receive scanned data of contracts uploaded by users. For example, users can scan and upload contracts to receive contract information. The scanned data is saved as an image file and converted to text data in subsequent processing. Furthermore, the reception department can use OCR (Optical Character Recognition) technology to extract text information from the scanned data and import it as contract information. This allows users to digitize paper contracts and import them into the system. The reception department temporarily stores the received contract information and prepares to hand it over to the management department. This allows the reception department to efficiently receive contract information from users and play a role in ensuring a smooth data flow throughout the entire system.

[0067] The Management Department centrally manages contract information received by the Reception Department. For example, the Management Department uses a database to centrally manage contract information. Specifically, the Management Department can efficiently store, search, and update contract information using relational databases or NoSQL databases. Relational databases manage contract information in a table format and assign a unique ID to each contract. This makes searching and filtering contract information easy. NoSQL databases store contract information in a document format and have flexible schemas, allowing for efficient management of different types of contract information. The Management Department can organize and efficiently manage contract information by category. For example, the Management Department manages contract information by dividing it into categories such as scholarships, water, gas, internet services, and subscription services. Each category has specific fields and attributes, allowing for accurate recording of contract information details. Furthermore, the Management Department can implement version control of contract information, saving past contract information and change history. This allows for tracking the change history of contract information and reverting to previous states as needed. The Management Department implements access control and encryption to ensure the security of contract information. For example, access permissions can be set for each user, ensuring that only specific users can access specific contract information. Furthermore, contract information can be encrypted and stored to prevent unauthorized access and data leaks. This allows the management department to manage contract information securely and efficiently, improving the overall reliability of the system.

[0068] The analysis unit uses AI to analyze contract information managed by the management unit. For example, the analysis unit analyzes contract information using machine learning algorithms. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the text data of contracts and extract key points. For instance, by tokenizing the text data of contracts and analyzing the meaning of each token, it identifies key points such as contract fees, payment deadlines, contract duration, and special provisions. The analysis unit extracts key points from the text data of contracts using a pre-trained model. This allows the analysis unit to quickly and accurately analyze the content of contracts and extract key points. Furthermore, the analysis unit can analyze patterns and trends in contract information to predict future risks and opportunities. For example, based on past contract information, it can predict how specific contract conditions will impact the future, which can be used for risk management and strategic planning. The analysis unit can also use anomaly detection algorithms to detect abnormal patterns and fraudulent data within contract information. This allows the analysis unit to improve the overall reliability and security of the system through the analysis of contract information.

[0069] The key points extraction unit extracts the key points of the contract content analyzed by the analysis unit. For example, the key points extraction unit extracts key points such as contract fees, payment deadlines, contract period, and special notes. Specifically, based on the key points information provided by the analysis unit, the key points extraction unit selects information important to the user and displays it in a visually easy-to-understand format. For example, the key points extraction unit displays the key points of the contract content as graphs and charts. By showing the trend of fees as a line graph or displaying payment deadlines in a calendar format, it enables users to understand intuitively. In addition, the key points extraction unit displays contract information in a dashboard format, allowing users to view multiple contract information at a glance. This makes it easier for users to grasp the overall picture of the contract information. Furthermore, the key points extraction unit provides functions for users to search and filter specific contract information. For example, it enables users to search for contract information based on specific contract conditions or periods and quickly obtain the necessary information. Based on user feedback, the key points extraction unit can improve its display format and functions to provide a more user-friendly interface. This allows the key points extraction unit to provide users with easily understandable summaries of contract information, thereby improving the efficiency of contract management.

[0070] The notification unit can notify users of changes to contract information in bulk. For example, the notification unit will send notifications in bulk when a user needs to notify their contracting party of a change, such as when a user gets married and their surname changes, or when they move. The notification unit can notify users of changes to contract information in bulk using methods such as email notifications, push notifications, and SMS notifications. The notification unit will notify users of changes to contract information according to the notification method specified by the user. For example, if the user requests email notifications, the notification unit will notify them of changes to contract information via email. The notification unit can also notify users of changes to contract information via push notifications if the user requests push notifications. This reduces the effort required of the user by notifying them of changes to contract information in bulk. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input changes to contract information into AI, which can select the optimal notification method and send the notifications.

[0071] The reception desk can estimate the user's emotions and adjust the input interface for contract information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of contract information. This provides a more comfortable input experience by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The reception desk can analyze the user's past contract history and suggest the optimal input method. For example, the reception desk can automatically display contract information that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest contract information to be used during a specific time period based on the user's past contract history. In this way, by analyzing past contract history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past contract history data into a generating AI and have the generating AI suggest the optimal input method.

[0073] The reception unit can filter the user's current contract status and areas of interest when entering contract information. For example, the reception unit can automatically exclude services that the user already has a contract for and display only new contract information. For example, the reception unit can prioritize displaying relevant contract information based on the user's areas of interest. For example, the reception unit can analyze the user's current contract status and filter out duplicate contract information. This allows the reception unit to provide highly relevant contract information by filtering based on the user's current contract status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's contract status data into a generating AI and have the generating AI perform the filtering.

[0074] The reception desk can estimate the user's emotions and determine the priority of contract information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize the input of important contract information. For example, if the user is relaxed, the reception desk may prompt the user to enter detailed contract information. For example, if the user is in a hurry, the reception desk may prompt the user to enter only the minimum necessary contract information. This ensures that important information is entered preferentially by prioritizing contract information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception unit can prioritize inputting highly relevant contract information by considering the user's geographical location when entering contract information. For example, the reception unit can prioritize displaying region-specific contract information based on the user's current location. For example, the reception unit can propose the optimal contract plan based on the user's geographical location. For example, the reception unit can automatically reflect region-specific contract conditions by considering the user's location. This allows for the provision of region-specific contract information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and cause the generating AI to prioritize the display of highly relevant contract information.

[0076] The reception desk can analyze the user's social media activity and input relevant contract information when entering contract information. For example, the reception desk can analyze the user's interests and preferences on social media and suggest relevant contract information. For example, the reception desk can suggest the optimal contract plan based on the user's social media activity history. For example, the reception desk can consider the user's social media friendships and suggest services that their friends are subscribed to. In this way, by analyzing social media activity, contract information based on the user's interests can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant contract information.

[0077] The management unit can estimate the user's emotions and adjust how contract information is managed based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple management screen and simplify operations. For example, if the user is relaxed, the management unit can provide detailed management options and suggest a customizable management method. For example, if the user is in a hurry, the management unit can display only important contract information to allow for quick management. This provides a more comfortable management experience by adjusting the management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The management department can adjust the level of detail in managing contract information based on the importance of the contract. For example, the management department can manage important contract information in detail and update it regularly. For example, the management department can manage less important contract information in a simplified manner and update it as needed. For example, the management department can adjust the content displayed on the management screen according to the importance of the contract. This allows for detailed management of important contract information by adjusting the level of detail based on the importance of the contract. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input contract information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in management.

[0079] The management department can apply different management algorithms depending on the contract category when managing contract information. For example, the management department can manage subscription service contract information based on a regular payment schedule. For example, the management department can manage utility bill contract information based on payment deadlines and rate fluctuations. For example, the management department can manage internet service contract information based on usage and contract period. This enables efficient management by applying management algorithms according to the contract category. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input contract information category data into a generating AI and have the generating AI execute the application of management algorithms.

[0080] The management unit can estimate the user's emotions and determine the priority of contract information to manage based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize managing important contract information. For example, if the user is relaxed, the management unit can prioritize managing detailed contract information. For example, if the user is in a hurry, the management unit will manage only the minimum necessary contract information. This allows for the priority management of important information by determining the priority of contract information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The management department can determine management priorities based on the contract submission date when managing contract information. For example, the management department can prioritize managing contract information with an approaching submission deadline. For example, the management department can manage contract information whose submission deadline has passed as an archive. For example, the management department can set a contract information update schedule based on the submission date. This enables deadline-based management by determining management priorities based on the contract submission date. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input contract information submission date data into a generating AI and have the generating AI perform the determination of management priorities.

[0082] The management department can adjust the order of management based on the relevance of contracts when managing contract information. For example, the management department can group and manage highly relevant contract information. For example, the management department can manage less relevant contract information individually. For example, the management department can adjust the display order of the management screen based on the relevance of contracts. This allows for efficient management of related information by adjusting the order of management based on the relevance of contracts. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the relevance data of contract information into a generating AI and have the generating AI perform the adjustment of the order of management.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the analysis criteria according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of contracts during the analysis process. For example, the analysis unit can analyze the interrelationships of contracts and integrate and analyze the relevant contract information. For example, the analysis unit can eliminate redundant information by considering the interrelationships of contracts. For example, the analysis unit can apply the optimal analysis algorithm based on the interrelationships of contracts. This improves the accuracy of the analysis by considering the interrelationships of contracts. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input interrelationship data of contract information into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0085] The analysis unit can perform analysis while considering the attribute information of the contract submitter. For example, the analysis unit can perform analysis while considering the age and occupation of the contract submitter. For example, the analysis unit can perform analysis based on the past contract history of the contract submitter. For example, the analysis unit can perform analysis while considering the regional information of the contract submitter. This makes it possible to perform more accurate analysis by considering the attribute information of the contract submitter. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the attribute information data of the contract submitter into a generating AI and have the generating AI perform the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit can display detailed analysis results. For example, if the user is in a hurry, the analysis unit can prioritize displaying concise analysis results. In this way, by adjusting the display order of analysis results according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can perform analysis while considering the geographical distribution of contracts. For example, the analysis unit can analyze the geographical distribution of contracts and analyze contract trends for each region. For example, the analysis unit can perform analysis while considering region-specific contract conditions based on the geographical distribution. For example, the analysis unit can apply the optimal analysis algorithm while considering the geographical distribution. This makes region-specific analysis possible by considering the geographical distribution of contracts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input geographical distribution data of contract information into a generating AI and have the generating AI perform the analysis.

[0088] The analysis unit can improve the accuracy of its analysis by referring to relevant contract documents during the analysis process. For example, the analysis unit can improve the accuracy of the analysis results by referring to relevant contract documents. For example, the analysis unit can perform the analysis considering the background information of the contract based on the relevant documents. For example, the analysis unit can apply the optimal analysis algorithm by referring to relevant documents. This improves the accuracy of the analysis by referring to relevant documents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant document data of the contract information into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0089] The key point extraction unit can estimate the user's emotions and adjust the way the key points are displayed based on the estimated emotions. For example, if the user is stressed, the key point extraction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the key point extraction unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the key point extraction unit can provide a display method that gets straight to the point. By adjusting the way the key points are displayed according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0090] The key point extraction unit can predict current key points by referring to past key point data during key point extraction. For example, the key point extraction unit predicts the key points of current contract information based on past key point data. For example, the key point extraction unit can extract important points by referring to past key point data. For example, the key point extraction unit can apply an optimal key point extraction algorithm based on past key point data. This allows for a more accurate prediction of current key points by referring to past key point data. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input past key point data into a generating AI and have the generating AI perform a prediction of current key points.

[0091] The key point extraction unit can apply different key point extraction methods to each contract category during key point extraction. For example, for subscription service contract information, the key point extraction unit can extract the price and payment deadline as key points. For example, for utility bill contract information, the key point extraction unit can extract the price fluctuation and payment deadline as key points. For example, for internet service contract information, the key point extraction unit can extract the usage status and contract period as key points. This makes it possible to extract more appropriate key points by applying a key point extraction method according to the contract category. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input contract information category data into a generating AI and have the generating AI execute the application of key point extraction methods.

[0092] The key point extraction unit can estimate the user's emotions and adjust the importance of key points based on the estimated emotions. For example, if the user is stressed, the key point extraction unit will prioritize displaying important key points. For example, if the user is relaxed, the key point extraction unit can display detailed key points. For example, if the user is in a hurry, the key point extraction unit will display key points concisely. In this way, by adjusting the importance of key points according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The key point extraction unit can analyze changes in key points based on the contract submission date during key point extraction. For example, the key point extraction unit can prioritize extracting key points from contract information with an upcoming submission date. For example, the key point extraction unit can manage key points from contract information whose submission date has passed as an archive. For example, the key point extraction unit can analyze changes in key points based on the submission date and apply the optimal key point extraction algorithm. This makes it possible to extract key points more appropriately by analyzing changes in key points based on the contract submission date. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input contract information submission date data into a generating AI and have the generating AI perform an analysis of changes in key points.

[0094] The key point extraction unit can analyze key points by referring to relevant market data of the contract during key point extraction. For example, the key point extraction unit analyzes key points of contract information based on relevant market data. For example, the key point extraction unit can extract key points by referring to market data and considering the background information of the contract. For example, the key point extraction unit can apply an optimal key point extraction algorithm based on relevant market data. This improves the accuracy of key point analysis by referring to relevant market data. Some or all of the above processing in the key point extraction unit may be performed using AI, for example, or without AI. For example, the key point extraction unit can input relevant market data of the contract information into a generating AI and have the generating AI perform the key point analysis.

[0095] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple notification method. For example, if the user is relaxed, the notification unit can provide a detailed notification method. For example, if the user is in a hurry, the notification unit can provide a rapid notification method. By adjusting the notification method according to the user's emotions, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize providing notification methods that the user has preferred to use in the past. For example, the notification unit can suggest the optimal notification timing based on the user's past notification history. For example, the notification unit can analyze the user's past notification history and provide the optimal notification content. In this way, by referring to past notification history, the notification unit can provide the user with the most suitable notification method. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's notification history data into a generating AI and have the generating AI select the optimal notification method.

[0097] The notification unit can customize the notification method based on the user's current living situation when a notification is sent. For example, if the user is at work, the notification unit can provide a quiet notification method. For example, if the user is on vacation, the notification unit can provide a detailed notification method. For example, if the user is traveling, the notification unit can provide a concise notification method. By customizing the notification method according to the user's living situation, more appropriate notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user living situation data into a generating AI and have the generating AI perform the customization of the notification method.

[0098] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. For example, if the user is relaxed, the notification unit can provide detailed notifications. For example, if the user is in a hurry, the notification unit will provide concise notifications. In this way, by determining the priority of notifications according to the user's emotions, important information can be delivered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can provide the optimal notification method based on the user's current location. For example, the notification unit can suggest the optimal notification timing based on the user's geographical location information. For example, the notification unit can provide region-specific notification content, taking into account the user's location information. This allows for the provision of region-specific notification methods by considering the user's geographical location information. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal notification method.

[0100] The notification unit can analyze the user's social media activity and suggest notification methods when sending a notification. For example, the notification unit can suggest the optimal notification method based on the user's social media activity history. For example, the notification unit can analyze the user's interests and preferences on social media and provide relevant notification content. For example, the notification unit can consider the user's social media friendships and provide notification content that their friends might be interested in. In this way, by analyzing social media activity, the notification unit can provide the user with the most suitable notification method. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media data into a generating AI and have the generating AI suggest notification methods.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can improve input efficiency by referring to the user's past contract history when entering user contract information. For example, it can reduce input effort by automatically displaying previously entered contract information as suggestions. It can also prioritize suggesting input methods the user has used in the past (voice input, text input, etc.). Furthermore, it can predict and suggest contract information that the user will use at a specific time of day based on their past contract history. In this way, by utilizing past contract history, the system can provide users with the most suitable input method.

[0103] The notification unit can estimate the user's emotions and adjust the content and timing of notifications based on those estimates. For example, if the user is stressed, important notifications can be prioritized and detailed explanations can be omitted. Conversely, if the user is relaxed, detailed notifications can be provided to ensure the user fully understands them. Furthermore, if the user is in a hurry, concise notifications can be provided to quickly convey information. This enables appropriate notifications tailored to the user's emotions.

[0104] The management department can adjust the level of detail in managing contract information based on the importance of each contract. For example, important contract information can be managed in detail and updated regularly. Conversely, less important contract information can be managed in a simplified manner and updated as needed. Furthermore, the content displayed on the management screen can be adjusted according to the importance of each contract. This allows for detailed management of important contract information by adjusting the level of detail based on the importance of each contract.

[0105] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on those emotions. For example, if the user is stressed, it can provide a simple analysis result. If the user is relaxed, it can provide a more detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result to quickly convey information. This allows for the provision of appropriate analysis results tailored to the user's emotions.

[0106] The key point extraction unit can apply different key point extraction methods depending on the contract category when extracting key points from contract information. For example, for subscription service contract information, the price and payment deadline can be extracted as key points. Similarly, for utility service contract information, the price fluctuation and payment deadline can be extracted as key points. Furthermore, for internet service contract information, the usage status and contract period can be extracted as key points. This allows for more appropriate key point extraction by applying a key point extraction method appropriate to the contract category.

[0107] The reception desk can estimate the user's emotions and prioritize the contract information to be entered based on those emotions. For example, if the user is stressed, it can prioritize entering important contract information. If the user is relaxed, it can prompt them to enter detailed contract information. Furthermore, if the user is in a hurry, it can only enter the minimum necessary contract information. In this way, by prioritizing contract information according to the user's emotions, important information can be entered first.

[0108] The management department can prioritize contract management based on the submission date of each contract. For example, it can prioritize managing contracts with approaching submission deadlines. Contracts whose submission deadlines have passed can also be managed as archives. Furthermore, it can set a contract update schedule based on the submission date. This allows for deadline-based management by prioritizing contracts based on their submission date.

[0109] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between contracts. For example, it can analyze the interrelationships between contracts and integrate related contract information for analysis. It can also eliminate redundant information by considering the interrelationships between contracts. Furthermore, it can apply the optimal analysis algorithm based on the interrelationships between contracts. As a result, the accuracy of the analysis is improved by considering the interrelationships between contracts.

[0110] The key point extraction unit can estimate the user's emotions and adjust the way the key points are displayed based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display method to quickly convey the information. This enables the provision of appropriate information tailored to the user's emotions.

[0111] The notification unit can select the optimal notification method when sending notifications, taking into account the user's geographical location. For example, it can prioritize displaying region-specific contract information based on the user's current location. It can also suggest the optimal notification timing based on the user's geographical location. Furthermore, it can provide region-specific notification content, taking the user's location into consideration. In this way, by considering the user's geographical location, it is possible to provide region-specific notification methods.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives contract information from users. For example, it can receive contract information for scholarships, water, gas, internet services, subscription services, etc., entered by the user. The reception desk receives contract information through web forms and mobile applications. It can also receive scanned data of contracts uploaded by the user. Step 2: The management department centrally manages the contract information received by the reception department. For example, a database can be used to centrally manage contract information and organize it by category for efficient management. Contract information can be managed by dividing it into categories such as scholarships, water, gas, internet services, and subscription services. Step 3: The analysis unit uses AI to analyze contract information managed by the management unit. For example, it analyzes contract information using machine learning algorithms and analyzes the text data of contracts using natural language processing technology. Step 4: The key points extraction unit extracts the key points of the contract content analyzed by the analysis unit. For example, it can extract key points such as the contract fee, payment deadline, contract period, and special provisions, and display them in a visually easy-to-understand format. The key points extraction unit displays the key points of the contract content as graphs or charts.

[0114] 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.

[0115] Data generation model 58 is a form 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, management unit, analysis unit, key point extraction unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives contract information entered by the user. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and centrally manages the contract information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contract information using AI. The key point extraction unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts the key points of the contract content. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies users of changes to the contract information in a batch. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0121] 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.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0123] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] 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.

[0125] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, management unit, analysis unit, key point extraction unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives contract information entered by the user. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and centrally manages the contract information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the contract information using AI. The key point extraction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and extracts the key points of the contract content. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies users of changes to the contract information all at once. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0137] 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.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0139] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] 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.

[0141] 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.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, management unit, analysis unit, key point extraction unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives contract information entered by the user. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the contract information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the contract information using AI. The key point extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and extracts the key points of the contract content. The notification unit is implemented by, for example, the control unit 46A of the headset terminal 314 and notifies users of changes to the contract information all at once. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0153] 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.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0155] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] 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.

[0157] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] 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.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, management unit, analysis unit, key point extraction unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives contract information entered by the user. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the contract information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the contract information using AI. The key point extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and extracts the key points of the contract content. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and notifies users of changes to the contract information all at once. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] 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.

[0168] Figure 9 shows the 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.

[0169] 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.

[0170] 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.

[0171] 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, and motorcycles, 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 based, for example, 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.

[0172] 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."

[0173] 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.

[0174] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] 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 other things 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.

[0184] 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.

[0185] (Note 1) The reception department receives contract information from users, The management department centrally manages the contract information received by the aforementioned reception department, The aforementioned management department has an analysis department in which AI analyzes contract information, The system includes a key point extraction unit that extracts key points of the contract content analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) It includes a notification unit that notifies users of changes to contract information in bulk. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It estimates the user's emotions and adjusts the contract information input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is We analyze the user's past contract history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When entering contract information, filtering is performed based on the user's current contract status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the contract information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering contract information, the system prioritizes inputting highly relevant contract information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering contract information, the system analyzes the user's social media activity and enters relevant contract information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, The system estimates user sentiment and adjusts how contract information is managed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, When managing contract information, adjust the level of detail based on the importance of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned management department, When managing contract information, different management algorithms are applied depending on the contract category. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned management department, It estimates user sentiment and prioritizes contract information to be managed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, When managing contract information, prioritize management based on when the contract was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, When managing contract information, adjust the order of management based on the relevance of the contracts. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, consider the interrelationships between contracts to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the attribute information of the contract submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the order in which the analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the geographical distribution of contracts will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, we refer to relevant contract literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned key point extraction unit, It estimates the user's emotions and adjusts how key points are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned key point extraction unit, When extracting key points, we refer to past key point data to predict the current key points. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned key point extraction unit, When extracting key points, different key point extraction methods are applied to each contract category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned key point extraction unit, It estimates the user's emotions and adjusts the importance of key points based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned key point extraction unit, When extracting key points, analyze how those points change based on the contract submission date. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned key point extraction unit, When extracting key points, analyze those points by referring to relevant market data for the contract. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current life situation. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and suggest notification methods. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception department receives contract information from users, The management department centrally manages the contract information received by the aforementioned reception department, The aforementioned management unit has an analysis unit in which AI analyzes contract information managed by the management unit, The system includes a key point extraction unit that extracts key points of the contract content analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. It includes a notification unit that notifies users of changes to contract information in bulk. The system according to feature 1.

3. The aforementioned reception unit is It estimates the user's emotions and adjusts the contract information input interface based on the estimated user emotions. The system according to feature 1.

4. The aforementioned reception unit is We analyze the user's past contract history and suggest the optimal input method. The system according to feature 1.

5. The aforementioned reception unit is When entering contract information, filtering is performed based on the user's current contract status and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the contract information to be entered based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is When entering contract information, the system prioritizes inputting highly relevant contract information, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When entering contract information, the system analyzes the user's social media activity and enters relevant contract information. The system according to feature 1.

9. The aforementioned management department, The system estimates user sentiment and adjusts how contract information is managed based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned management department, When managing contract information, adjust the level of detail based on the importance of the contract. The system according to feature 1.

Images