system

Abstract

The system according to this embodiment aims to streamline the creation of lease agreements and negotiation preparations associated with the installation of base stations. [Solution] The system according to the embodiment comprises a reception unit, an extraction unit, a collection unit, a generation unit, and a provision unit. The reception unit specifies the base station ID of the construction project. The extraction unit extracts necessary information from past contract documents based on the base station ID specified by the reception unit. The collection unit collects internal and external information based on the information extracted by the extraction unit. The generation unit analyzes the information collected by the collection unit and generates a draft contract. The provision unit provides the draft contract generated by the generation 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 a chatbot 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, there is a problem that it is difficult to collect information and acquire knowledge in the preparation of lease contracts and negotiations associated with base station installation, resulting in low productivity.

[0005] The system according to the embodiment aims to improve the efficiency of creating and negotiating lease contracts associated with base station installation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an extraction unit, a collection unit, a generation unit, and a provision unit. The reception unit specifies the base station ID for the construction project. The extraction unit extracts necessary information from past contract documents based on the base station ID specified by the reception unit. The collection unit collects internal and external information based on the information extracted by the extraction unit. The generation unit analyzes the information collected by the collection unit and generates a draft contract. The provision unit provides the draft contract generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the creation of lease agreements and negotiation preparations associated with the installation of base stations. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 system according to an embodiment of the present invention is a system that streamlines the creation and negotiation preparation of lease agreements associated with base station installation. This system comprises a reception unit for specifying the base station ID to be constructed, an extraction unit for extracting necessary information from past contract documents, a collection unit for collecting internal and external information, a generation unit for generating a draft contract, and a provision unit for providing the generated draft contract. For example, the user specifies the base station ID to be constructed. Next, necessary information is extracted from past contract documents using OCR technology. Furthermore, internal and external information is collected through API integration, and a RAG (Risk Assessment Guide) is created that is linked to the contract information management system and base station equipment information. This enables responses to the contract generation prompt. The generation AI automatically generates a draft contract and a draft contract explanation based on this information. For example, a contract that reflects the current contract details and equipment information after construction can be created in a short time. This system streamlines the contract negotiation preparation work, lowers the hurdle for securing personnel at base station construction companies, and indirectly contributes to the stable operation of base station equipment construction and the realization of area opening plans. As a result, the system can streamline the creation and negotiation preparation of lease agreements associated with base station installation.

[0029] The system according to this embodiment comprises a reception unit, an extraction unit, a collection unit, a generation unit, and a provision unit. The reception unit specifies the base station ID for which construction is planned. For example, the reception unit provides an interface for the user to input the base station ID for which construction is planned. The extraction unit extracts necessary information from past contract documents based on the base station ID specified by the reception unit. For example, the extraction unit uses OCR technology to extract information such as the contract period and rent from past contract documents. The collection unit collects internal and external information based on the information extracted by the extraction unit. For example, the collection unit collects information from a contract information management system, base station equipment information, and the Legal Affairs Bureau's registration database. The generation unit analyzes the information collected by the collection unit and generates a draft contract. For example, the generation unit uses generation AI to generate a draft contract and a draft contract explanation. The provision unit provides the draft contract generated by the generation unit. For example, the provision unit provides an interface for displaying the generated draft contract to the user. As a result, the system according to this embodiment can streamline the creation of lease agreements and preparation for negotiations related to base station installation.

[0030] The reception unit specifies the base station ID for the planned construction. For example, the reception unit provides an interface for the user to input the base station ID for the planned construction. Specifically, the reception unit designs an intuitive user interface so that the user can easily input the base station ID. The user interface includes text boxes and drop-down menus to enhance the convenience of the user when selecting or inputting the base station ID. The reception unit also has a validation function to verify the format and content of the entered base station ID in real time and prevent incorrect or invalid ID input. For example, it checks whether the entered ID exists in the existing database and displays an error message if it does not exist. Furthermore, the reception unit saves a history of base station IDs previously entered by the user and provides it as a reusable list, thereby streamlining the user's input work. This allows the reception unit to enable users to specify the base station ID for the planned construction quickly and accurately, improving the overall usability of the system.

[0031] The extraction unit extracts necessary information from past contract documents based on the base station ID specified by the reception unit. For example, the extraction unit uses OCR technology to extract information such as contract period and rent from past contract documents. Specifically, the extraction unit uses a high-precision OCR engine to extract text data from scanned contract document image data. The OCR engine has learned the format and layout of contract documents in advance to improve the accuracy of character recognition, and can handle contract documents of different formats. The extraction unit also analyzes the extracted text data and uses natural language processing technology to automatically identify specific information such as contract period and rent. For example, information regarding the contract period is extracted based on keywords indicating dates and periods, and information regarding rent is extracted based on amounts and currency symbols. Furthermore, the extraction unit performs cross-referencing between multiple contract documents to verify the accuracy of the extracted information and issues a warning if there is any mismatch. This allows the extraction unit to quickly and accurately extract necessary information from past contract documents and provide the data necessary for subsequent processing.

[0032] The data collection unit collects internal and external information based on the information extracted by the data extraction unit. For example, the data collection unit collects information from sources such as contract information management systems, base station equipment information, and the Legal Affairs Bureau's registration database. Specifically, the data collection unit accesses the internal contract information management system to search for past contract history and related contract documents to obtain necessary information. It also accesses the base station equipment information system to collect technical information such as the equipment status and maintenance history of designated base stations. Furthermore, it accesses the Legal Affairs Bureau's registration database to obtain registration information and owner information regarding the installation location of base stations. The data collection unit centrally manages the data collected from these sources and performs data cleansing processing to verify data integrity as needed. For example, if there are inconsistencies in data obtained from different sources, the data collection unit selects the most reliable information to maintain data consistency. The data collection unit also converts the collected information into an appropriate format and organizes the data so that it can be easily used by the subsequent generation unit. As a result, the data collection unit can efficiently collect necessary data from a variety of internal and external sources, improving the overall information accuracy and reliability of the system.

[0033] The generation unit analyzes the information collected by the collection unit and generates a draft contract. For example, the generation unit uses generation AI to generate both a draft contract and a draft contract explanation. Specifically, the generation unit uses a template to automatically fill in each item of the contract based on the collected information. The generation AI inserts information in the appropriate context and generates natural-sounding sentences based on contract formats and wording that it has learned in advance. For example, information regarding the contract period and rent is automatically inserted into the corresponding parts of the template, ensuring the overall consistency of the contract. The generation unit also verifies the content of the generated draft contract and performs rule-based checks to ensure that it conforms to legal requirements and the company's internal regulations. Furthermore, in addition to the draft contract, the generation unit also generates draft explanations for each item of the contract to help users easily understand the contract content. As a result, the generation unit can quickly generate high-quality draft contracts based on the collected information, significantly streamlining the user's contract creation process.

[0034] The service provider provides the draft contract generated by the generation unit. For example, the service provider provides an interface to display the generated draft contract to the user. Specifically, the service provider designs an interface with editing functions that allow the user to review the generated draft contract and add revisions or comments as needed. The user interface displays each item of the contract in a visually clear manner, making it easy for the user to review the content. The service provider also provides a function to export the generated draft contract in common file formats such as PDF and Word, allowing the user to print the contract or share it with other parties. Furthermore, the service provider collects feedback from users and incorporates it into the generation unit to continuously improve the quality of the draft contract. For example, by analyzing comments and revisions added by users and using them as training data for the generation AI, the accuracy of future contract generation will be improved. In this way, the service provider can quickly provide users with high-quality draft contracts, achieving efficiency and quality improvement throughout the entire contract creation process.

[0035] The data collection unit can collect information from sources such as contract information management systems, base station equipment information, and the Legal Affairs Bureau's registration database. For example, the data collection unit can collect information from the contract information management system using its contract storage and search functions. It can also collect information such as the type of equipment, installation location, and operating status from base station equipment information. Furthermore, the data collection unit can collect registration information and owner information from the Legal Affairs Bureau's registration database. This allows the data collection unit to efficiently acquire the necessary information.

[0036] The generation unit can generate draft contracts and draft contract explanations using a generation AI. For example, the generation unit can generate draft contracts using the generation AI. The generation AI can generate draft contracts using natural language generation technology and machine learning algorithms. The generation unit can also generate draft contract explanations using the generation AI. The generation AI can generate draft contract explanations that include explanations of contract clauses and points to note. This allows the generation unit to streamline the contract creation process.

[0037] The generation unit can generate a contract that reflects the current contract details and the equipment information after construction using a generation AI. For example, the generation unit uses the generation AI to generate a contract that reflects the current contract details and the equipment information after construction. The generation AI can generate a contract based on the current contract details, such as the contract period, rent, and conditions, and the equipment information after construction, such as the type, location, and function of the newly installed equipment. This allows the generation unit to improve the accuracy of the contract.

[0038] The service provider can provide users with the generated draft contract. For example, the service provider can provide an interface for displaying the generated draft contract to the user. The service provider can provide the generated draft contract to users through web applications or mobile applications. Furthermore, the service provider can send the generated draft contract to users via email. This allows the service provider to streamline the contract negotiation preparation process.

[0039] The extraction unit can extract necessary information from past contract documents using OCR technology. For example, the extraction unit can extract information such as contract period and rent from past contract documents using OCR technology. OCR technology can extract textual information from contract documents using character recognition algorithms and image processing technology. This allows the extraction unit to improve the efficiency of information extraction.

[0040] The reception unit can analyze the user's past base station ID specification history and propose the optimal specification method. For example, the reception unit can automatically display base station IDs that the user has frequently specified in the past as candidates. The reception unit can also prioritize suggesting specification methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest base station IDs to be used during specific time periods based on the user's past specification history. This allows the reception unit to propose the optimal specification method.

[0041] The reception unit can filter base station IDs based on the user's current project status when the user specifies a base station ID. For example, the reception unit can prioritize displaying base station IDs related to the user's ongoing projects. It can also suggest appropriate base station IDs based on the user's project progress. Furthermore, the reception unit can filter base station ID requests according to the importance of the user's projects. This allows the reception unit to specify an appropriate base station ID.

[0042] The reception unit can prioritize specifying a base station ID that is highly relevant to the user's geographical location when the user specifies a base station ID. For example, the reception unit will prioritize displaying base station IDs that are close to the user's current location. The reception unit can also suggest a highly relevant base station ID based on the user's travel history. Furthermore, the reception unit can specify the optimal base station ID considering the user's geographical location. This allows the reception unit to improve user convenience.

[0043] The reception unit can analyze the user's social media activity when specifying a base station ID and suggest a relevant base station ID. For example, the reception unit can analyze the content of the user's social media posts and suggest a relevant base station ID. It can also suggest a highly relevant base station ID based on the user's social media friend relationships. Furthermore, the reception unit can analyze the user's social media activity history and suggest the optimal base station ID. This allows the reception unit to improve user convenience.

[0044] The extraction unit can adjust the level of detail of the information extracted from past contract documents based on its importance. For example, the extraction unit can prioritize the extraction of highly important information and provide detailed information. It can also simplify the extraction of less important information. Furthermore, the extraction unit can adjust the level of detail of the information extracted according to the content of the contract documents. This allows the extraction unit to efficiently obtain the necessary information.

[0045] The extraction unit can apply different extraction algorithms depending on the category of the contract document during extraction. For example, in the case of a real estate lease agreement, the extraction unit applies a specific extraction algorithm to extract information. Furthermore, in the case of a contract relating to base station equipment, the extraction unit can apply a different extraction algorithm to extract information. In addition, the extraction unit can select the optimal extraction algorithm depending on the category of the contract document. This allows the extraction unit to improve the accuracy of information extraction.

[0046] The extraction unit can determine the extraction priority based on the submission date of the contract documents during the extraction process. For example, the extraction unit can prioritize the extraction of contract documents with a more recent submission date. It can also postpone the extraction of older contract documents. Furthermore, the extraction unit can adjust the extraction priority based on the submission date. This allows the extraction unit to efficiently obtain the necessary information.

[0047] The extraction unit can adjust the extraction order based on the relevance of the contract documents during the extraction process. For example, the extraction unit can prioritize extracting highly relevant contract documents. It can also postpone extracting less relevant contract documents. Furthermore, the extraction unit can adjust the extraction order based on the relevance of the contract documents. This allows the extraction unit to efficiently obtain the necessary information.

[0048] The data collection unit can select the optimal data collection method by referring to past data collection data during information gathering. For example, the data collection unit can select the optimal information collection method based on data collected in the past. Furthermore, the data collection unit can propose efficient data collection methods based on past data. In addition, the data collection unit can analyze past data to select the optimal data collection method. This allows the data collection unit to select the most suitable information collection method.

[0049] The data collection unit can apply different collection algorithms depending on the category of information being collected. For example, when collecting information related to real estate rentals, the unit applies a specific collection algorithm. Furthermore, when collecting information related to base station equipment, it can apply a different collection algorithm. In addition, the unit can select the optimal collection algorithm depending on the category of information being collected. This allows the unit to improve the accuracy of information collection.

[0050] The data collection unit can collect information while considering the geographical distribution of the target data. For example, the data collection unit can select the optimal data collection method based on the geographical distribution of the target data. Furthermore, the data collection unit can perform efficient data collection while considering the geographical distribution. In addition, the data collection unit can analyze the geographical distribution of the target data and select the optimal data collection method. This allows the data collection unit to improve the efficiency of data collection.

[0051] The data collection unit can improve the accuracy of its data collection by referring to relevant literature on the target of collection. For example, the data collection unit can select the optimal data collection method based on relevant literature on the target of collection. Furthermore, the data collection unit can improve the accuracy of its data collection by referring to relevant literature. In addition, the data collection unit can analyze relevant literature on the target of collection and select the optimal data collection method. This allows the data collection unit to improve the accuracy of its data collection.

[0052] The generation unit can select the optimal generation method by referring to past contract content when generating a draft contract. For example, the generation unit can generate the optimal draft contract based on past contract content. Furthermore, the generation unit can propose an efficient generation method based on past contract content. In addition, the generation unit can analyze past contract content and select the optimal generation method. As a result, the generation unit can generate the optimal draft contract.

[0053] The generation unit can apply different generation algorithms depending on the category of the contract content when generating draft contracts. For example, in the case of a real estate lease agreement, the generation unit applies a specific generation algorithm to generate the draft contract. Furthermore, in the case of a contract concerning base station equipment, the generation unit can apply a different generation algorithm to generate the draft contract. In addition, the generation unit can select the optimal generation algorithm depending on the category of the contract content. This allows the generation unit to improve the accuracy of the draft contracts.

[0054] The generation unit can determine the priority of contract drafts based on the submission date of the contract content. For example, the generation unit will prioritize generating contract content with a more recent submission date. It can also postpone generating older contract content. Furthermore, the generation unit can adjust the generation priority based on the submission date. This allows the generation unit to efficiently generate the necessary contract drafts.

[0055] The generation unit can adjust the generation order based on the relevance of the contract content when generating draft contracts. For example, the generation unit can prioritize generating contract content that is highly relevant. It can also postpone generating contract content that is less relevant. Furthermore, the generation unit can adjust the generation order based on the relevance of the contract content. This allows the generation unit to efficiently generate the necessary draft contracts.

[0056] The service provider can select the optimal delivery method when providing a draft contract by referring to the user's past operation history. For example, the service provider can select the optimal delivery method based on the user's past operation history. Furthermore, the service provider can propose an efficient delivery method based on past operation history. In addition, the service provider can analyze past operation history to select the optimal delivery method. This allows the service provider to select the most suitable delivery method.

[0057] The service provider can customize the delivery method based on the user's current project status when providing a draft contract. For example, the service provider can select the optimal delivery method based on the progress of the user's project. Furthermore, the service provider can customize the delivery method according to the importance of the project. In addition, the service provider can select the optimal delivery method considering the user's project status. This allows the service provider to improve user convenience.

[0058] The service provider can select the optimal delivery method when providing a draft contract, taking into account the user's geographical location information. For example, the service provider can select the optimal delivery method based on the user's current location. Furthermore, the service provider can propose an efficient delivery method considering geographical location information. In addition, the service provider can analyze the user's geographical location information and select the optimal delivery method. This allows the service provider to improve user convenience.

[0059] The service provider can analyze the user's social media activity and propose delivery methods when providing a draft contract. For example, the service provider can analyze the content of the user's social media posts and propose the most suitable delivery method. Furthermore, the service provider can propose efficient delivery methods based on the user's social media friend relationships. In addition, the service provider can analyze the user's social media activity history and propose the most suitable delivery method. This allows the service provider to improve user convenience.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze the user's past base station ID specification history and suggest the optimal specification method. For example, it can automatically display base station IDs that the user has frequently specified in the past as candidates. It can also prioritize suggesting specification methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest base station IDs to be used during specific time periods based on the user's past specification history. In this way, the reception desk can suggest the optimal specification method.

[0062] The reception unit can filter base station IDs based on the user's current project status when the user specifies a base station ID. For example, it can prioritize displaying base station IDs related to the user's ongoing projects. It can also suggest appropriate base station IDs based on the progress of the user's projects. Furthermore, it can filter base station ID specifications according to the importance of the user's projects. This allows the reception unit to specify the appropriate base station ID.

[0063] The reception unit can prioritize specifying base station IDs that are highly relevant to the user's geographical location when the user specifies a base station ID. For example, it can prioritize displaying base station IDs that are close to the user's current location. It can also suggest highly relevant base station IDs based on the user's travel history. Furthermore, it can specify the optimal base station ID considering the user's geographical location. This allows the reception unit to improve user convenience.

[0064] The reception desk can analyze a user's social media activity when they specify a base station ID and suggest a relevant base station ID. For example, it can analyze the content of a user's social media posts and suggest a relevant base station ID. It can also suggest a highly relevant base station ID based on the user's social media friend relationships. Furthermore, it can analyze the user's social media activity history and suggest the optimal base station ID. This allows the reception desk to improve user convenience.

[0065] The extraction unit can adjust the level of detail of the information extracted from past contract documents based on its importance. For example, it can prioritize the extraction of highly important information and provide detailed information. It can also simplify the extraction of less important information. Furthermore, it can adjust the level of detail of the extracted information according to the content of the contract document. This allows the extraction unit to efficiently obtain the necessary information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception unit specifies the base station ID for the planned construction. For example, the reception unit provides an interface for the user to input the base station ID for the planned construction. Step 2: The extraction unit extracts the necessary information from past contract documents based on the base station ID specified by the reception unit. For example, the extraction unit uses OCR technology to extract information such as the contract period and rent from past contract documents. Step 3: The collection unit collects internal and external information based on the information extracted by the extraction unit. For example, the collection unit collects information from sources such as contract information management systems, base station equipment information, and the Legal Affairs Bureau's registration database. Step 4: The generation unit analyzes the information collected by the collection unit and generates a draft contract. For example, the generation unit uses a generation AI to generate a draft contract and a draft contract explanation. Step 5: The provider unit provides the draft contract generated by the generator unit. For example, the provider unit provides an interface to display the generated draft contract to the user.

[0068] (Example of form 2) The system according to an embodiment of the present invention is a system that streamlines the creation and negotiation preparation of lease agreements associated with base station installation. This system comprises a reception unit for specifying the base station ID to be constructed, an extraction unit for extracting necessary information from past contract documents, a collection unit for collecting internal and external information, a generation unit for generating a draft contract, and a provision unit for providing the generated draft contract. For example, the user specifies the base station ID to be constructed. Next, necessary information is extracted from past contract documents using OCR technology. Furthermore, internal and external information is collected through API integration, and a RAG (Risk Assessment Guide) is created that is linked to the contract information management system and base station equipment information. This enables responses to the contract generation prompt. The generation AI automatically generates a draft contract and a draft contract explanation based on this information. For example, a contract that reflects the current contract details and equipment information after construction can be created in a short time. This system streamlines the contract negotiation preparation work, lowers the hurdle for securing personnel at base station construction companies, and indirectly contributes to the stable operation of base station equipment construction and the realization of area opening plans. As a result, the system can streamline the creation and negotiation preparation of lease agreements associated with base station installation.

[0069] The system according to this embodiment comprises a reception unit, an extraction unit, a collection unit, a generation unit, and a provision unit. The reception unit specifies the base station ID for which construction is planned. For example, the reception unit provides an interface for the user to input the base station ID for which construction is planned. The extraction unit extracts necessary information from past contract documents based on the base station ID specified by the reception unit. For example, the extraction unit uses OCR technology to extract information such as the contract period and rent from past contract documents. The collection unit collects internal and external information based on the information extracted by the extraction unit. For example, the collection unit collects information from a contract information management system, base station equipment information, and the Legal Affairs Bureau's registration database. The generation unit analyzes the information collected by the collection unit and generates a draft contract. For example, the generation unit uses generation AI to generate a draft contract and a draft contract explanation. The provision unit provides the draft contract generated by the generation unit. For example, the provision unit provides an interface for displaying the generated draft contract to the user. As a result, the system according to this embodiment can streamline the creation of lease agreements and preparation for negotiations related to base station installation.

[0070] The reception unit specifies the base station ID for the planned construction. For example, the reception unit provides an interface for the user to input the base station ID for the planned construction. Specifically, the reception unit designs an intuitive user interface so that the user can easily input the base station ID. The user interface includes text boxes and drop-down menus to enhance the convenience of the user when selecting or inputting the base station ID. The reception unit also has a validation function to verify the format and content of the entered base station ID in real time and prevent incorrect or invalid ID input. For example, it checks whether the entered ID exists in the existing database and displays an error message if it does not exist. Furthermore, the reception unit saves a history of base station IDs previously entered by the user and provides it as a reusable list, thereby streamlining the user's input work. This allows the reception unit to enable users to specify the base station ID for the planned construction quickly and accurately, improving the overall usability of the system.

[0071] The extraction unit extracts necessary information from past contract documents based on the base station ID specified by the reception unit. For example, the extraction unit uses OCR technology to extract information such as contract period and rent from past contract documents. Specifically, the extraction unit uses a high-precision OCR engine to extract text data from scanned contract document image data. The OCR engine has learned the format and layout of contract documents in advance to improve the accuracy of character recognition, and can handle contract documents of different formats. The extraction unit also analyzes the extracted text data and uses natural language processing technology to automatically identify specific information such as contract period and rent. For example, information regarding the contract period is extracted based on keywords indicating dates and periods, and information regarding rent is extracted based on amounts and currency symbols. Furthermore, the extraction unit performs cross-referencing between multiple contract documents to verify the accuracy of the extracted information and issues a warning if there is any mismatch. This allows the extraction unit to quickly and accurately extract necessary information from past contract documents and provide the data necessary for subsequent processing.

[0072] The data collection unit collects internal and external information based on the information extracted by the data extraction unit. For example, the data collection unit collects information from sources such as contract information management systems, base station equipment information, and the Legal Affairs Bureau's registration database. Specifically, the data collection unit accesses the internal contract information management system to search for past contract history and related contract documents to obtain necessary information. It also accesses the base station equipment information system to collect technical information such as the equipment status and maintenance history of designated base stations. Furthermore, it accesses the Legal Affairs Bureau's registration database to obtain registration information and owner information regarding the installation location of base stations. The data collection unit centrally manages the data collected from these sources and performs data cleansing processing to verify data integrity as needed. For example, if there are inconsistencies in data obtained from different sources, the data collection unit selects the most reliable information to maintain data consistency. The data collection unit also converts the collected information into an appropriate format and organizes the data so that it can be easily used by the subsequent generation unit. As a result, the data collection unit can efficiently collect necessary data from a variety of internal and external sources, improving the overall information accuracy and reliability of the system.

[0073] The generation unit analyzes the information collected by the collection unit and generates a draft contract. For example, the generation unit uses generation AI to generate both a draft contract and a draft contract explanation. Specifically, the generation unit uses a template to automatically fill in each item of the contract based on the collected information. The generation AI inserts information in the appropriate context and generates natural-sounding sentences based on contract formats and wording that it has learned in advance. For example, information regarding the contract period and rent is automatically inserted into the corresponding parts of the template, ensuring the overall consistency of the contract. The generation unit also verifies the content of the generated draft contract and performs rule-based checks to ensure that it conforms to legal requirements and the company's internal regulations. Furthermore, in addition to the draft contract, the generation unit also generates draft explanations for each item of the contract to help users easily understand the contract content. As a result, the generation unit can quickly generate high-quality draft contracts based on the collected information, significantly streamlining the user's contract creation process.

[0074] The service provider provides the draft contract generated by the generation unit. For example, the service provider provides an interface to display the generated draft contract to the user. Specifically, the service provider designs an interface with editing functions that allow the user to review the generated draft contract and add revisions or comments as needed. The user interface displays each item of the contract in a visually clear manner, making it easy for the user to review the content. The service provider also provides a function to export the generated draft contract in common file formats such as PDF and Word, allowing the user to print the contract or share it with other parties. Furthermore, the service provider collects feedback from users and incorporates it into the generation unit to continuously improve the quality of the draft contract. For example, by analyzing comments and revisions added by users and using them as training data for the generation AI, the accuracy of future contract generation will be improved. In this way, the service provider can quickly provide users with high-quality draft contracts, achieving efficiency and quality improvement throughout the entire contract creation process.

[0075] The data collection unit can collect information from sources such as contract information management systems, base station equipment information, and the Legal Affairs Bureau's registration database. For example, the data collection unit can collect information from the contract information management system using its contract storage and search functions. It can also collect information such as the type of equipment, installation location, and operating status from base station equipment information. Furthermore, the data collection unit can collect registration information and owner information from the Legal Affairs Bureau's registration database. This allows the data collection unit to efficiently acquire the necessary information.

[0076] The generation unit can generate draft contracts and draft contract explanations using a generation AI. For example, the generation unit can generate draft contracts using the generation AI. The generation AI can generate draft contracts using natural language generation technology and machine learning algorithms. The generation unit can also generate draft contract explanations using the generation AI. The generation AI can generate draft contract explanations that include explanations of contract clauses and points to note. This allows the generation unit to streamline the contract creation process.

[0077] The generation unit can generate a contract that reflects the current contract details and the equipment information after construction using a generation AI. For example, the generation unit uses the generation AI to generate a contract that reflects the current contract details and the equipment information after construction. The generation AI can generate a contract based on the current contract details, such as the contract period, rent, and conditions, and the equipment information after construction, such as the type, location, and function of the newly installed equipment. This allows the generation unit to improve the accuracy of the contract.

[0078] The service provider can provide users with the generated draft contract. For example, the service provider can provide an interface for displaying the generated draft contract to the user. The service provider can provide the generated draft contract to users through web applications or mobile applications. Furthermore, the service provider can send the generated draft contract to users via email. This allows the service provider to streamline the contract negotiation preparation process.

[0079] The extraction unit can extract necessary information from past contract documents using OCR technology. For example, the extraction unit can extract information such as contract period and rent from past contract documents using OCR technology. OCR technology can extract textual information from contract documents using character recognition algorithms and image processing technology. This allows the extraction unit to improve the efficiency of information extraction.

[0080] The reception unit can estimate the user's emotions and adjust the method of specifying the base station ID based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the steps for specifying the base station ID. If the user is relaxed, the reception unit can also provide detailed specification options and suggest a customizable specification method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick specification of the base station ID. This allows the reception unit to improve user convenience. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception unit can analyze the user's past base station ID specification history and propose the optimal specification method. For example, the reception unit can automatically display base station IDs that the user has frequently specified in the past as candidates. The reception unit can also prioritize suggesting specification methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest base station IDs to be used during specific time periods based on the user's past specification history. This allows the reception unit to propose the optimal specification method.

[0082] The reception unit can filter base station IDs based on the user's current project status when the user specifies a base station ID. For example, the reception unit can prioritize displaying base station IDs related to the user's ongoing projects. It can also suggest appropriate base station IDs based on the user's project progress. Furthermore, the reception unit can filter base station ID requests according to the importance of the user's projects. This allows the reception unit to specify an appropriate base station ID.

[0083] The reception unit can estimate the user's emotions and determine the priority of base station ID assignments based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize assigning base station IDs of higher importance. If the user is relaxed, the reception unit can also provide detailed assignment options and suggest customizable assignment methods. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying the most relevant base station IDs to allow for quick assignment. This allows the reception unit to improve user convenience. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception unit can prioritize specifying a base station ID that is highly relevant to the user's geographical location when the user specifies a base station ID. For example, the reception unit will prioritize displaying base station IDs that are close to the user's current location. The reception unit can also suggest a highly relevant base station ID based on the user's travel history. Furthermore, the reception unit can specify the optimal base station ID considering the user's geographical location. This allows the reception unit to improve user convenience.

[0085] The reception unit can analyze the user's social media activity when specifying a base station ID and suggest a relevant base station ID. For example, the reception unit can analyze the content of the user's social media posts and suggest a relevant base station ID. It can also suggest a highly relevant base station ID based on the user's social media friend relationships. Furthermore, the reception unit can analyze the user's social media activity history and suggest the optimal base station ID. This allows the reception unit to improve user convenience.

[0086] The extraction unit can estimate the user's emotions and adjust the timing of information extraction based on the estimated emotions. For example, if the user is relaxed, the extraction unit can adjust the timing of extracting detailed information. It can also quickly extract necessary information if the user is in a hurry. Furthermore, if the user is stressed, the extraction unit can prioritize extracting simple information. This allows the extraction unit to improve user convenience. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The extraction unit can adjust the level of detail of the information extracted from past contract documents based on its importance. For example, the extraction unit can prioritize the extraction of highly important information and provide detailed information. It can also simplify the extraction of less important information. Furthermore, the extraction unit can adjust the level of detail of the information extracted according to the content of the contract documents. This allows the extraction unit to efficiently obtain the necessary information.

[0088] The extraction unit can apply different extraction algorithms depending on the category of the contract document during extraction. For example, in the case of a real estate lease agreement, the extraction unit applies a specific extraction algorithm to extract information. Furthermore, in the case of a contract relating to base station equipment, the extraction unit can apply a different extraction algorithm to extract information. In addition, the extraction unit can select the optimal extraction algorithm depending on the category of the contract document. This allows the extraction unit to improve the accuracy of information extraction.

[0089] The extraction unit can estimate the user's emotions and determine the priority of information to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit will prioritize extracting information of high importance. If the user is relaxed, the extraction unit can also prioritize extracting detailed information. Furthermore, if the user is in a hurry, the extraction unit can quickly extract the necessary information. This allows the extraction unit to improve user convenience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The extraction unit can determine the extraction priority based on the submission date of the contract documents during the extraction process. For example, the extraction unit can prioritize the extraction of contract documents with a more recent submission date. It can also postpone the extraction of older contract documents. Furthermore, the extraction unit can adjust the extraction priority based on the submission date. This allows the extraction unit to efficiently obtain the necessary information.

[0091] The extraction unit can adjust the extraction order based on the relevance of the contract documents during the extraction process. For example, the extraction unit can prioritize extracting highly relevant contract documents. It can also postpone extracting less relevant contract documents. Furthermore, the extraction unit can adjust the extraction order based on the relevance of the contract documents. This allows the extraction unit to efficiently obtain the necessary information.

[0092] The data collection unit can estimate the user's emotions and adjust its information collection methods based on the estimated emotions. For example, if the user is relaxed, the data collection unit can select a method for collecting detailed information. If the user is in a hurry, the data collection unit can also select a method for collecting information quickly. Furthermore, if the user is stressed, the data collection unit can select a simpler method for collecting information. This allows the data collection unit to improve user convenience. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The data collection unit can select the optimal data collection method by referring to past data collection data during information gathering. For example, the data collection unit can select the optimal information collection method based on data collected in the past. Furthermore, the data collection unit can propose efficient data collection methods based on past data. In addition, the data collection unit can analyze past data to select the optimal data collection method. This allows the data collection unit to select the most suitable information collection method.

[0094] The data collection unit can apply different collection algorithms depending on the category of information being collected. For example, when collecting information related to real estate rentals, the unit applies a specific collection algorithm. Furthermore, when collecting information related to base station equipment, it can apply a different collection algorithm. In addition, the unit can select the optimal collection algorithm depending on the category of information being collected. This allows the unit to improve the accuracy of information collection.

[0095] The data collection unit can estimate the user's emotions and prioritize the information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority information. If the user is relaxed, the data collection unit can also prioritize collecting detailed information. Furthermore, if the user is in a hurry, the data collection unit can quickly collect necessary information. This allows the data collection unit to improve user convenience. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The data collection unit can collect information while considering the geographical distribution of the target data. For example, the data collection unit can select the optimal data collection method based on the geographical distribution of the target data. Furthermore, the data collection unit can perform efficient data collection while considering the geographical distribution. In addition, the data collection unit can analyze the geographical distribution of the target data and select the optimal data collection method. This allows the data collection unit to improve the efficiency of data collection.

[0097] The data collection unit can improve the accuracy of its data collection by referring to relevant literature on the target of collection. For example, the data collection unit can select the optimal data collection method based on relevant literature on the target of collection. Furthermore, the data collection unit can improve the accuracy of its data collection by referring to relevant literature. In addition, the data collection unit can analyze relevant literature on the target of collection and select the optimal data collection method. This allows the data collection unit to improve the accuracy of its data collection.

[0098] The generation unit can estimate the user's emotions and adjust the method of generating the draft contract based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a detailed draft contract. If the user is in a hurry, the generation unit can also generate a concise draft contract. Furthermore, if the user is stressed, the generation unit can generate a simple draft contract. This allows the generation unit to improve user convenience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The generation unit can select the optimal generation method by referring to past contract content when generating a draft contract. For example, the generation unit can generate the optimal draft contract based on past contract content. Furthermore, the generation unit can propose an efficient generation method based on past contract content. In addition, the generation unit can analyze past contract content and select the optimal generation method. As a result, the generation unit can generate the optimal draft contract.

[0100] The generation unit can apply different generation algorithms depending on the category of the contract content when generating draft contracts. For example, in the case of a real estate lease agreement, the generation unit applies a specific generation algorithm to generate the draft contract. Furthermore, in the case of a contract concerning base station equipment, the generation unit can apply a different generation algorithm to generate the draft contract. In addition, the generation unit can select the optimal generation algorithm depending on the category of the contract content. This allows the generation unit to improve the accuracy of the draft contracts.

[0101] The generation unit can estimate the user's emotions and prioritize contract drafts based on those emotions. For example, if the user is stressed, the generation unit will prioritize generating high-priority contract drafts. Conversely, if the user is relaxed, the generation unit can prioritize generating detailed contract drafts. Furthermore, if the user is in a hurry, the generation unit can quickly generate the necessary contract drafts. This allows the generation unit to improve user convenience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The generation unit can determine the priority of contract drafts based on the submission date of the contract content. For example, the generation unit will prioritize generating contract content with a more recent submission date. It can also postpone generating older contract content. Furthermore, the generation unit can adjust the generation priority based on the submission date. This allows the generation unit to efficiently generate the necessary contract drafts.

[0103] The generation unit can adjust the generation order based on the relevance of the contract content when generating draft contracts. For example, the generation unit can prioritize generating contract content that is highly relevant. It can also postpone generating contract content that is less relevant. Furthermore, the generation unit can adjust the generation order based on the relevance of the contract content. This allows the generation unit to efficiently generate the necessary draft contracts.

[0104] The service provider can estimate the user's emotions and adjust the method of delivering the draft contract based on those emotions. For example, if the user is relaxed, the service provider may select a delivery method that includes detailed explanations. If the user is in a hurry, the service provider may select a delivery method that includes concise explanations. Furthermore, if the user is stressed, the service provider may select a simple delivery method. This allows the service provider to improve user convenience. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The service provider can select the optimal delivery method when providing a draft contract by referring to the user's past operation history. For example, the service provider can select the optimal delivery method based on the user's past operation history. Furthermore, the service provider can propose an efficient delivery method based on past operation history. In addition, the service provider can analyze past operation history to select the optimal delivery method. This allows the service provider to select the most suitable delivery method.

[0106] The service provider can customize the delivery method based on the user's current project status when providing a draft contract. For example, the service provider can select the optimal delivery method based on the progress of the user's project. Furthermore, the service provider can customize the delivery method according to the importance of the project. In addition, the service provider can select the optimal delivery method considering the user's project status. This allows the service provider to improve user convenience.

[0107] The service provider can estimate the user's emotions and determine the priority of providing draft contracts based on those emotions. For example, if the user is stressed, the service provider will prioritize providing high-priority draft contracts. If the user is relaxed, the service provider can also prioritize providing detailed draft contracts. Furthermore, if the user is in a hurry, the service provider can quickly provide the necessary draft contracts. This allows the service provider to improve user convenience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The service provider can select the optimal delivery method when providing a draft contract, taking into account the user's geographical location information. For example, the service provider can select the optimal delivery method based on the user's current location. Furthermore, the service provider can propose an efficient delivery method considering geographical location information. In addition, the service provider can analyze the user's geographical location information and select the optimal delivery method. This allows the service provider to improve user convenience.

[0109] The service provider can analyze the user's social media activity and propose delivery methods when providing a draft contract. For example, the service provider can analyze the content of the user's social media posts and propose the most suitable delivery method. Furthermore, the service provider can propose efficient delivery methods based on the user's social media friend relationships. In addition, the service provider can analyze the user's social media activity history and propose the most suitable delivery method. This allows the service provider to improve user convenience.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can estimate the user's emotions and adjust the method of specifying the base station ID based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, and the procedure for specifying the base station ID can be minimized. If the user is relaxed, detailed specification options can be provided, and a customizable specification method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick specification of the base station ID. In this way, the reception desk can improve user convenience. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The extraction unit can estimate the user's emotions and adjust the timing of information extraction based on the estimated emotions. For example, if the user is relaxed, the timing of extracting detailed information can be adjusted. If the user is in a hurry, the necessary information can be extracted quickly. Furthermore, if the user is stressed, simple information can be prioritized. In this way, the extraction unit can improve user convenience. 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.

[0113] The data collection unit can estimate the user's emotions and adjust its information collection methods based on the estimated emotions. For example, if the user is relaxed, it can select a method for collecting detailed information. If the user is in a hurry, it can select a method for collecting information quickly. Furthermore, if the user is stressed, it can select a simpler information collection method. This allows the data collection unit to improve user convenience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The generation unit can estimate the user's emotions and adjust the method of generating the draft contract based on the estimated emotions. For example, if the user is relaxed, it can generate a detailed draft contract. If the user is in a hurry, it can generate a concise draft contract. Furthermore, if the user is stressed, it can generate a simple draft contract. In this way, the generation unit can improve user convenience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] The delivery unit can estimate the user's emotions and adjust the delivery method of the draft contract based on the estimated emotions. For example, if the user is relaxed, it can select a delivery method that includes detailed explanations. If the user is in a hurry, it can select a delivery method that includes concise explanations. Furthermore, if the user is stressed, it can select a simple delivery method. This allows the delivery unit to improve user convenience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The reception desk can analyze the user's past base station ID specification history and suggest the optimal specification method. For example, it can automatically display base station IDs that the user has frequently specified in the past as candidates. It can also prioritize suggesting specification methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest base station IDs to be used during specific time periods based on the user's past specification history. In this way, the reception desk can suggest the optimal specification method.

[0117] The reception unit can filter base station IDs based on the user's current project status when the user specifies a base station ID. For example, it can prioritize displaying base station IDs related to the user's ongoing projects. It can also suggest appropriate base station IDs based on the progress of the user's projects. Furthermore, it can filter base station ID specifications according to the importance of the user's projects. This allows the reception unit to specify the appropriate base station ID.

[0118] The reception unit can prioritize specifying base station IDs that are highly relevant to the user's geographical location when the user specifies a base station ID. For example, it can prioritize displaying base station IDs that are close to the user's current location. It can also suggest highly relevant base station IDs based on the user's travel history. Furthermore, it can specify the optimal base station ID considering the user's geographical location. This allows the reception unit to improve user convenience.

[0119] The reception desk can analyze a user's social media activity when they specify a base station ID and suggest a relevant base station ID. For example, it can analyze the content of a user's social media posts and suggest a relevant base station ID. It can also suggest a highly relevant base station ID based on the user's social media friend relationships. Furthermore, it can analyze the user's social media activity history and suggest the optimal base station ID. This allows the reception desk to improve user convenience.

[0120] The extraction unit can adjust the level of detail of the information extracted from past contract documents based on its importance. For example, it can prioritize the extraction of highly important information and provide detailed information. It can also simplify the extraction of less important information. Furthermore, it can adjust the level of detail of the extracted information according to the content of the contract document. This allows the extraction unit to efficiently obtain the necessary information.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception unit specifies the base station ID for the planned construction. For example, the reception unit provides an interface for the user to input the base station ID for the planned construction. Step 2: The extraction unit extracts the necessary information from past contract documents based on the base station ID specified by the reception unit. For example, the extraction unit uses OCR technology to extract information such as the contract period and rent from past contract documents. Step 3: The collection unit collects internal and external information based on the information extracted by the extraction unit. For example, the collection unit collects information from sources such as contract information management systems, base station equipment information, and the Legal Affairs Bureau's registration database. Step 4: The generation unit analyzes the information collected by the collection unit and generates a draft contract. For example, the generation unit uses a generation AI to generate a draft contract and a draft contract explanation. Step 5: The provider unit provides the draft contract generated by the generator unit. For example, the provider unit provides an interface to display the generated draft contract to the user.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, extraction unit, collection unit, generation unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input the base station ID for construction. The extraction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and extracts necessary information from past contract documents using OCR technology. The collection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and collects information from the contract information management system, base station equipment information, the Legal Affairs Bureau's registration database, etc. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates a draft contract document and a draft contract explanation using generation AI. The provision unit is implemented by, for example, the output device 40 of the smart device 14 and provides an interface for displaying the generated draft contract document to the user. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, extraction unit, collection unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to voice input the base station ID for construction. The extraction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and extracts necessary information from past contract documents using OCR technology. The collection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and collects information from the contract information management system, base station equipment information, the Legal Affairs Bureau's registration database, etc. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates a draft contract and a draft contract explanation using generation AI. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated draft contract to the user by voice. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, extraction unit, collection unit, generation unit, and provision 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 microphone 238 of the headset terminal 314 and provides an interface for the user to voice input the base station ID for construction. The extraction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and extracts necessary information from past contract documents using OCR technology. The collection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and collects information from the contract information management system, base station equipment information, the Legal Affairs Bureau's registration database, etc. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates a draft contract document and a draft contract explanation using generation AI. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides an interface for displaying the generated draft contract document to the user. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, extraction unit, collection unit, generation unit, and provision 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 microphone 238 of the robot 414 and provides an interface for the user to voice input the base station ID for construction. The extraction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and extracts necessary information from past contract documents using OCR technology. The collection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and collects information from the contract information management system, base station equipment information, the Legal Affairs Bureau's registration database, etc. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates a draft contract document and a draft contract explanation using generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated draft contract document to the user by voice. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception desk where you specify the base station ID for the construction project, An extraction unit that extracts necessary information from past contract documents based on the base station ID specified by the reception unit, A collection unit collects internal and external information based on the information extracted by the extraction unit, A generation unit analyzes the information collected by the collection unit and generates a draft contract document, The system includes a providing unit that provides a draft contract document generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Information is collected from sources such as contract information management systems, base station equipment information, and the Legal Affairs Bureau's registration database. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates draft contracts and contract explanations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates a contract that reflects the current contract details and the equipment information after construction. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated draft contract to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The extraction unit is Use OCR technology to extract necessary information from past contract documents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of assigning base station IDs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past base station ID specification history and propose the optimal specification method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When specifying a base station ID, filtering is performed based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of base station ID assignments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When specifying a base station ID, the system prioritizes the selection of the most relevant base station ID, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When specifying a base station ID, the system analyzes the user's social media activity and suggests relevant base station IDs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The extraction unit is It estimates the user's emotions and adjusts the timing of information extraction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The extraction unit is Adjust the level of detail of the extraction based on the importance of the information extracted from past contract documents. The system described in Appendix 1, characterized by the features described herein. (Note 15) The extraction unit is During extraction, different extraction algorithms are applied depending on the category of the contract document. The system described in Appendix 1, characterized by the features described herein. (Note 16) The extraction unit is It estimates the user's emotions and determines the priority of information to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The extraction unit is During the selection process, the priority of the selection will be determined based on the submission date of the contract documents. The system described in Appendix 1, characterized by the features described herein. (Note 18) The extraction unit is During extraction, adjust the extraction order based on the relevance of the contract documents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is It estimates the user's emotions and adjusts the information gathering method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When gathering information, refer to past data to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is When collecting information, different collection algorithms are applied depending on the category of information being collected. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When collecting information, take into consideration the geographical distribution of the target data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When gathering information, refer to relevant literature to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is The system estimates the user's emotions and adjusts the contract draft generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating a draft contract, the optimal generation method is selected by referring to past contract content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating draft contracts, different generation algorithms are applied depending on the category of the contract content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is The system estimates user sentiment and prioritizes draft contracts based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating draft contracts, the priority of generation is determined based on the timing of the submission of the contract details. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating draft contracts, the generation order is adjusted based on the relevance of the contract content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the user's emotions and adjust the method of providing the draft contract based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing a draft contract, the optimal delivery method is selected by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing a draft contract, the method of delivery will be customized based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of providing draft contracts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing a draft contract, the optimal delivery method will be selected, taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing a draft contract, we analyze the user's social media activity and propose a method of delivery. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. A reception desk where you specify the base station ID for the construction project, An extraction unit that extracts necessary information from past contract documents based on the base station ID specified by the reception unit, A collection unit collects internal and external information based on the information extracted by the extraction unit, A generation unit analyzes the information collected by the collection unit and generates a draft contract document, The system includes a providing unit that provides a draft contract document generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Information is collected from sources such as contract information management systems, base station equipment information, and the Legal Affairs Bureau's registration database. The system according to feature 1.

3. The generating unit is The AI ​​generates draft contracts and contract explanations. The system according to feature 1.

4. The generating unit is The AI ​​generates a contract that reflects the current contract details and the equipment information after construction. The system according to feature 1.

5. The aforementioned supply unit is, Provide the user with a generated draft contract. The system according to feature 1.

6. The extraction unit is Use OCR technology to extract necessary information from past contract documents. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of assigning base station IDs based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past base station ID specification history and propose the optimal specification method. The system according to feature 1.

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